1. APAC data ROI compared to US and European standards?

2. Changes in the cross-border and regulatory regime

3. Alpha opportunities in APAC investing, leveraging the latest data and technology

Link to the Agenda: https://webflow2.neudata.co/agenda/hong-kong-data-summit-2026

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of 19 April 2026

Thursday, April 23

9:10 – 9:25 | The Harbour Stage – Grand Ballroom

Welcome & opening remarks

Speaker: Helena Yu, Head of Asia Research, Neudata

As the alternative data market matures globally, Asia Pacific’s data ecosystem demands its own lens. This session draws on Neudata’s latest research to reassess the APAC alternative data universe — surfacing what has changed, which new dataset categories are gaining traction across the APAC region, and what structural and regulatory forces are driving the shift.

Question: Which APAC data categories have reached the quality and coverage threshold expected by US and European data buyers and which data categories are worth another look because they are getting closer to expectations?

9:25 – 9:45 | The Harbour Stage – Grand Ballroom

Data versus journalism: Two views on reality

Speaker: Robin Harding, Asia Editor, Financial Times

Data is systematic and objective. Journalism is anecdotal and human. But from surveys that turn out to be fabricated to market-making scoops that exist only inside the brains of certain individuals, Robin will give you a new perspective on how journalism can complement data to produce a clearer picture of the world as it really is.

Question: How do you decide when alternative data should be included in an article as supporting evidence of what’s happening and when to leave the data point out?

9:45 – 10:15 | The Harbour Stage – Grand Ballroom

China’s AI path and investment applications

Speakers:

• Laura Wang, Chief China Equity Strategist and Asia Chief Investment Strategist, PWM Asia Managing Director, Morgan Stanley Research Asia

• Gary Yu, Head of Asia Telecoms, China Internet and Media Research, Morgan Stanley

Question: Across the large internet platforms and their telecoms and semiconductor suppliers, which companies are best positioned to convert AI spending into operating leverage on the 2026 to 2027 horizon? Which datasets are you watching to potentially change your view or increase your conviction in the view?

10:15 – 10:50 | The Harbour Stage – Grand Ballroom

The rise of APAC data champions: Sourcing, scale and cross-border strategy

Speakers:

• James Eason, Independent Consultant (Moderator)

• Xiaoquan (Ace) Liu, Programme Director, BBA in Finance, HKCHC and Offshore Fund Director, Shanghai AJ Group

• Emily Liu, APAC Head of Data Strategy, Jump Trading

• Jay Shen, Director, Data Sourcing and Strategy (APAC), Balyasny Asset Management

APAC’s alternative data ecosystem is entering a new phase as regional data providers scale beyond domestic markets and adapt to increasingly complex regulatory and operational demands. This session explores how cross-border expansion is reshaping data sourcing, partnerships, and product design, and what this means for global investors. The panelists will examine regional aggregation models, market-specific sourcing challenges, and how buyside firms evaluate and manage APAC data relationships in a fragmented, fast-evolving landscape.

Question: Looking at the APAC vendors you have each added to your data stack in the past twelve months, what did those vendors get right that distinguished them from the regional providers you evaluated and passed on?

11:20 – 11:50 | The Harbour Stage – Grand Ballroom

Using high frequency alternative macro economic data to inform investment decisions: State Street PriceStats inflation indices

Speakers:

• Jamie Stewart, Managing Director – Data Intelligence Team, State Street Data Intelligence

• Yimou (Andrew) Li, Head of State Street Associates APAC, State Street

State Street PriceStats tracks billions of prices sold online by retailers globally to generate daily inflation indices, helping investors anticipate the impact of inflation and implications for monetary policy. Our methodology is built upon a robust academic foundation through the Billion Prices Project initiative, established by Harvard Business School professor Alberto Carvallo. This session will highlight how State Street PriceStats provides daily inflation indices for 27 countries to offer timely insights to help investors get ahead of major shifts in inflation trends. We will also introduce new indicators we have developed to provide more granular insights into inflation impacts at the sector, subsector and category level.

Macro track sponsored by State Street.

Question: As you introduce the new sector, subsector, and category-level inflation indicators, which APAC-specific use cases are you seeing gain the most traction with institutional clients, and how does the post-acquisition platform change what is possible in product velocity and regional coverage relative to the standalone PriceStats offering?

11:50 – 12:10 | The Harbour Stage – Grand Ballroom

Chinese data dynamics under state-driven movements

Speaker: Bobby Wang, Research Analyst, Neudata

In the global race for digital supremacy, China is pivoting away from a “Wild West” era of unregulated data accumulation and moving towards a state-driven framework that is rapidly, if unevenly, maturing. The state has transitioned from a passive observer to an active market maker. This shift is characterised by a dual-track strategy: the rigid protection of sensitive information at the cross-border frontier to bolster national sovereignty, and the exploratory assetisation of public data to fuel the next generation of “New Quality Productive Forces”. As such, we seek to clarify and discuss the State’s movements in shaping the data industry and the indication for data providers and investors.

Macro track sponsored by State Street.

Question: Which state-sanctioned data categories have produced the most real institutional demand from outside the mainland, and which ones remain primarily domestic despite the policy infrastructure being in place?

12:10 – 12:30 | The Harbour Stage – Grand Ballroom

Tracking the commodity boom with alternative data

Speakers:

• Lucy Gao, Research Analyst, Neudata (Moderator)

• Andre Kwok, Director, Rainmaker Ventures

• Kian Han, Director, Overseas Business Department, Mysteel

The emerging metals and energy supercycle is being driven by distinct but overlapping forces, from AI-fueled power demand to escalating geopolitical risk. This panel explores how investors are using alternative datasets and analytics to monitor supply–demand dynamics, forecast price movements, and identify winners across key commodities, including critical materials for clean tech and battery production.

Macro track sponsored by State Street.

Question: Of the alternative datasets your teams track on steel, iron ore, copper, battery materials, oil, and natural gas, which ones have given the earliest and most reliable signals?

12:30 – 1:05 | The Harbour Stage – Grand Ballroom

Cross-border data transfer: What’s changed in the data and compliance landscape

Speakers:

• Diana Parusheva-Lowery, Managing Director – Public Policy and Sustainable Finance, ASIFMA (Moderator)

• Alex Roberts, Partner, Linklaters

• Zhiyi Ren, Partner, Fangda Partners

• Helina Lo, Head of Risk, Compliance and Control, Protiviti

Asia’s regulatory environment for data, technology, and cross-border data flows is shifting from broad principles to more targeted, operational compliance requirements. Changes such as China’s 2025 data export negative lists and certification requirements to tightening privacy, data governance, and AI accountability are complicating cross-border data transfers across the region. This panel examines what’s new, what’s changed, and what matters now for firms navigating global disruptions, including regional use cases to exemplify successes and challenges.

Macro track sponsored by State Street.

Question: Looking forward to 2030, do you think the regulatory environment for cross-border data flow will become stricter or more flexible and what do you think the drivers of that tightening or loosening will be?

2:05 – 2:25 | The Harbour Stage – Grand Ballroom

Compute as an asset class: Pricing GPUs, tokens, and memory in real time

Speaker: Carmen Li, Founder & CEO, Silicon Data

AI has turned compute into one of the most critical and least transparent inputs of the global economy. GPUs, memory, and tokens now function like commodities, yet pricing remains fragmented, opaque, and wildly inefficient.

This session will cover: • How GPU rental and spot pricing indices (including H100, A100, and B200) are built and distributed across public platforms and Bloomberg • Alternative datasets powering GPU pricing: rental markets, transactions, utilisation signals, and non-retail sources • Why token pricing and token transaction volumes matter as a new proxy for compute demand • RAM indices and RAM futures, and why memory may be the next bottleneck market • What institutional investors, data teams, and AI operators should watch as compute markets mature

Question: One of the key debates about the data center expenses and capex is the rate of depreciation, with bears suggesting the depreciation rate is slower than the actual obsolescence of the GPU chips. How could one using GPU cloud prices be used to test this thesis? What would you look for in the data to prove or disprove the thesis?

2:25 – 2:45 | The Harbour Stage – Grand Ballroom

China-specific alternative data: From local signals to earnings forecasts

Speaker: Joken Liu, Director of Data Products, Aurora Mobile – MoonFox Data

As China’s economic landscape shifts, traditional metrics often fail to capture real-time inflections. This session leverages MoonFox Alternative Data to unveil exclusive insights from our “2025-2026 China Cross-Sector Annual Report.” Attendees will learn how to bridge the information gap using high-frequency digital and offline signals to identify emerging sector winners and nowcast the earnings performance of key China Tickers (ADRs & A-shares). Participants will gain actionable intelligence to refine investment strategies and capture alpha within the “New Normal” of Chinese consumption.

Question: Heading into 2026, which sub-sectors are producing the strongest signals in your composite model, and what does the data suggest about the persistence of current consumer and industrial trends into the second half of the year?

2:45 – 3:20 | The Harbour Stage – Grand Ballroom

AI at work: Re-engineering research and alternative data workflows

Speakers:

• Nathaniel Rushforth, Senior Counsel, Shan Zhang International Law Firm (Moderator)

• Min Fu, Head of Asia Data Research, Point72

• Leo Ma, Chief Scientist APAC, Votee AI

• Kevin Sung, Head of ETF Portfolio Management, Mirae Asset Global Investments (Hong Kong) Limited

As GenAI and agentic systems move from “novelty” to “necessity,” investment teams are fundamentally re-wiring their research DNA. This session dives into the practicalities of the AI-augmented workflow, focusing on how firms are extracting alpha from alternative datasets without the overhead of building proprietary models.

Question: Which specific research or investment workflows have you moved fully into production with measurable lift over the prior process, and where has the adoption ceiling been higher or lower than you expected?

3:50 – 4:25 | The Harbour Stage – Grand Ballroom

Prediction markets: The missing link between alternative data and decision-making?

Speakers:

• Leon Liang, Director of Business Development, ICE (Moderator)

• Yan Xiong, Associate Professor, HKU Business School

• Victor Lai, Managing Director, Cai Fu Ju Asset Management Limited

• Yubo Tao, Assistant Professor of Economics and Finance, University of Macau

Prediction markets are evolving beyond simple event outcomes into more sophisticated tools for forecasting, risk management, and decision-making. This fireside chat explores how the petition market is being looked at, touching on the role of latent signals, deeper liquidity pools, and the emergence of new hedging and structuring mechanisms. Panelists will discuss how these markets may intersect with alternative data, quantitative strategies, and institutional workflows, and what needs to change for broader adoption.

Question: What is the most useful way for fund managers to treat prediction market data in their process today, and what would need to change in APAC regulation or market infrastructure for a comparably deep venue to emerge in the region?

4:25 – 5:00 | The Harbour Stage – Grand Ballroom

AI meets alpha: The next generation of quant signals

Speakers:

• Robert Morse, Head of Data Strategy and Sourcing, PDT Partners (Moderator)

• April Fu, Quantitative Portfolio Manager, Ping An of China Asset Management Hong Kong

• Gavin Feng, Associate Professor of Finance and Statistics, City University of Hong Kong

• Marcus Kim, Founder & Chairman, Qraft Technologies

• Andrew Tong, Responsible Officer, Executive Director, BlackWing Asset Management

Artificial intelligence is transforming how quantitative teams discover, classify, and extract signals from data and Asia is at the forefront of this evolution. This panel explores the latest trends in quant research powered by large language models (LLMs), including chatbots, tagging, classification, and insight extraction. We’ll examine the implications of these models for local data suppliers, the opportunities they create for generating new signals, and how they are reshaping traditional quant workflows.

Question: Where is the line today between using AI as a data-processing tool for parsing and classification versus using AI as a direct alpha-generation engine?

What questions do you have for the panelists? Leave a comment below

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Know someone attending or presenting at the conference? Feel free to forward this to them.

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- Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel.

There’s a catch to this version of top questions. I’m running a test of ChatGPT (5.3) and Claude’s (Sonnet 4.6) base models to see how well they follow instructions to create questions in the style of The Data Score. I had both models:

• Copy the agenda accurately from the Neudata website and format it into the standard Data Score Top Questions format.

• Research the topic in context of the speakers’ backgrounds and recent shared content

• Generate thoughtful questions following a specific set of instructions and an important list of things to avoid.

I’ll provide thoughts on which question is better or add my own question if both missed the mark.

• I would note that Claude crushed it on the rote tasks like gathering and formatting the agenda and completing research on the topics and speakers. By contrast, ChatGPT got the timeline order mixed up and completely missed a panel topic.

• However, the quality of the questions was mixed depending on the scenario. Sometimes Claude or ChatGPT was better. Sometimes both questions were good and other times both offered low-quality questions.

There are 3 key themes to the questions in this article:

1. Who captures the value from AI adoption

2. Production-grade controls for AI and data

3. Macro and geopolitical use cases for data

Link to conference agenda: Neudata London Data Summit

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Share

Subscribe now

Note: Agenda as of 22 March, 2026

8:50 – 9:05 Welcome & Neudata Update

Room: Westminster Stage

Speaker: Daryl Smith, Head of Research, Neudata

The latest Neudata analysis estimates that investment managers spent approximately $2.8bn on alternative data in 2025. This opening session will set the stage for the day, showcasing insights from a new report that draws on Neudata platform data and buyer survey findings to assess how spending is evolving across dataset types, geographies, and use cases, and examine whether the rise of AI is translating into real revenue growth for data vendors.

🤖Claude Question: Your February 2026 report found that 66% of investment firms are now using AI primarily for internal productivity, while only 31% say they are using it to optimise investment strategies directly, and vendors who added AI features did not see above-expected spending uplift as a result. Does this suggest that AI is commoditising the data consumption layer, making it easier for buy-side firms to extract more value from datasets they already own, rather than driving demand for new vendor products?

🤖ChatGPT Question: As alternative data spend continues to grow, where are you seeing the clearest evidence that increased investment, particularly in AI-driven use cases, is translating into measurable ROI rather than simply expanding experimentation budgets?

My take: both questions are effectively a mirror of the summary of the description from the agenda focused on: “Examine whether the rise of AI is translating into real revenue growth for data vendors," though Claude does it in more words. I’m not sure how Claude got the stats; there’s a captcha on the Neudata website to get access to the report, which includes entering personal info, including contact info. I just tried to get access to verify the stats, and I’m waiting for the access to be granted.

My Question: What is Neudata’s advice to its data clients on how to evolve their product features, go-to-market strategy, and pricing to benefit from the rise of AI?

9:05 – 9:35 Adapting Investment Research to an AI World

Room: Westminster Stage

Speakers:

• Sophie Beland, Executive Director, EMEA Head of Systematic Advisory Sales, Morgan Stanley (Moderator)

• Paul Walsh, EMEA Head of Research Product, Morgan Stanley

As investment professionals face information overload, growing pressure for differentiated insight, and accelerating advances in AI, the role of research is changing fast. Paul Walsh and Sophie Beland at Morgan Stanley will discuss the biggest debates in investment research today, covering how research products are becoming more structured and data-oriented, as well as what an AI-enabled future means for both research providers and consumers.

🤖Claude Question: As Morgan Stanley restructures its research products to be more structured and machine-readable, is the primary driver buy-side demand for content that AI systems can consume directly, or is it systematic investor demand for data that goes into a model without analyst intermediation, and does the answer change depending on whether you are serving a discretionary long-only or a quantitative hedge fund?

🤖ChatGPT Question: As research products become more structured and AI-enabled, how are you deciding what elements of the research process should be standardized and automated versus where human judgment remains a source of differentiation for clients?

My take: I like both questions because the questions try to think about the next logical question based on the information stated in the description that’s going to be shared. Claude focused on the clients, and ChatGPT focused on the analysts. Both are good questions.

9:35 – 9:55 An Interview with a Founder and Entrepreneur

Room: Westminster Stage

Speakers:

• Rado Lipuš, Founder & CEO, Neudata (Moderator)

• Mark O’Hare, Founder and Former CEO, Preqin

Mark O’Hare, the founder and former CEO of Preqin, joins Neudata’s CEO Rado Lipuš for an exclusive fireside chat. Mark will share the story of how Preqin grew from a niche London team into a premier global provider of alternative assets data, from lessons learned to opportunities taken. Preqin was acquired by BlackRock in March 2025; Mark served as Vice Chair of BlackRock through February 2026 to support integration into Aladdin.

🤖Claude Question: Preqin was built on the thesis that private markets were deeply opaque and that a neutral, independent data layer would become essential infrastructure, yet the company was ultimately acquired by the world’s largest asset manager and integrated into the technology platform that BlackRock sells to its competitors. At what point in building Preqin did you recognise that the most logical acquirer might also be the one that creates the most complicated relationship with the clients you had spent 20 years serving?

🤖ChatGPT Question: Looking back at Preqin’s evolution and today’s alternative data landscape, what capabilities or decisions were most critical in turning data into a scalable, defensible business, and how have those requirements changed for new entrants today?

My take: Geez… Claude. Why is that so combative? Meanwhile, ChatGPT has the most generic question.

My question: I would take the opposite side of the ChatGPT question via a blameless retro construct: With the benefit of hindsight but also acknowledging that everyone at Preqin did the best they could with the information they had at the time, what would you have done differently knowing what you know now?

9:55 – 10:15 Future of Export Controls: From Chips to Critical Minerals

Room: Westminster Stage

Speaker: Dr Maria Shagina, Senior Research Fellow for Economic Sanctions, Standards and Strategy, IISS

Export controls have evolved from a niche compliance function into a defining feature of global competition. The US-China technology conflict is reshaping rules around advanced computing, semiconductors, and enabling technologies, while China’s tightening export restrictions and licensing regimes for critical minerals are turning upstream inputs into leverage. This keynote maps the emerging export-control environment and the key shifts businesses must prepare for, connecting export controls to the rise of state capitalism and how Washington and Beijing are using industrial policy, stockpiles, offtake agreements, and strategic finance to restructure markets.

🤖Claude Question: Your 2026 IISS research on US critical minerals diplomacy maps how Washington is using offtake agreements and strategic finance to restructure supply chains in response to China’s leverage over critical mineral exports. For an institutional investor trying to position around these dynamics, how much of the risk is already observable in publicly available trade flows and commodity prices, and where does it remain genuinely invisible to markets until a policy decision is announced?

🤖ChatGPT Question: As export controls increasingly shape supply chains and market structure, what types of alternative data have you found most effective in detecting early signals of policy impact before they are reflected in traditional economic indicators?

My take: I like Claude’s question, which gets into the practical application of data in the context of a key investment question. ChatGPT’s is a bit generic given the research available about speaker’s w’s work.

10:10 – 10:50 Workshop: From Data Chaos to Deal Flow: How Identity Resolution Unlocks Value for Providers and Buyers

Room: The Soho Studio

Speakers:

• Gurpinder Dhillon, Head of Data Partner Strategy & Ecosystem, Senzing

• Jamie Foss, Head of Data Partner GTM, Senzing

Whether selling or buying alternative data, the biggest barrier to value is identity chaos that prevents quick integration and confident decision-making. This workshop covers how modern Entity Resolution removes friction on both sides of the alternative data transaction, why identity problems stall vendor sales cycles and delay alpha extraction, and a live demonstration of real-time entity resolution across ticker symbols, corporate hierarchies, and people records using Senzing.

🤖Claude Question: Identifier crosswalk tables have been available to institutional investors for years, yet entity resolution remains one of the most commonly cited reasons that promising data trials fail to reach production. Is the core problem that alternative data entities, private companies, people records, and non-standard security identifiers, are genuinely harder to resolve than the financial identifiers that existing crosswalk infrastructure was built for, or is the problem that the integration workflow itself has not kept pace with the rate at which new datasets are being evaluated?

🤖ChatGPT Question: In practice, how much of the time-to-value for new datasets is still driven by entity resolution challenges, and what approaches have proven most effective in reducing integration friction without introducing mapping errors?

My take: Again Claude is being combative… there’s probably a decent question in there that tees up why Senzing’s solution succeeds where others have struggled. ChatGPT’s question is a bit generic, but it’s a somewhat interesting context to try to quantify the size of the problem being addressed. Maybe someone who hasn’t tried to join two datasets with the same actual companies but not actually joinable data features wouldn’t know it’s a big pain point.

10:15 – 10:50 Then and Now: A Decade of Alternative Data and the Future of Alpha

Room: Westminster Stage

Speakers:

• Daryl Smith, Head of Research, Neudata (Moderator)

• Rado Lipuš, Founder & CEO, Neudata

• Marc Noët, Co-Founder and CEO, Dataprovider.com

• Tony Berkman, Alternative Data Pioneer (formerly Managing Director, Two Sigma; founder, Majestic Research / M-Science)

• Tjeerd van Cappelle, Founder & Managing Director, aiLiftoff

Marking Neudata’s 10-year anniversary, this panel explores how alternative data has evolved from a niche experiment to a mainstream market input, and what it’s taken to build the vendor ecosystem behind it. Panellists will reflect on the realities of running alternative data businesses, from hiring and funding cycles to building enterprise-grade companies, as well as how market awareness and adoption have shifted. The discussion will also examine how AI, real-time data, and regulation are reshaping both vendors and the buy side, why banks are becoming data providers, and where the next edge may come from.

🤖Claude Question: Ten years ago the scarcity of alternative data was itself a source of alpha, and Tony Berkman’s work at Majestic Research was built on the premise that proprietary sourcing was a durable competitive advantage. Now that web-scraped and transactional datasets account for the largest share of the market and supply has grown faster than demand, has the locus of alpha shifted from data acquisition to data processing, or is there still a meaningful first-mover advantage in finding and licensing a dataset before it becomes widely known?

🤖ChatGPT Question: Across your experiences building and scaling data businesses, where do you see the next durable edge emerging, in proprietary data creation, faster delivery, or tighter integration into investment workflows, as AI accelerates convergence across the industry?

My take: Claude is trying too hard to sound smart in its wording, but there’s a good question in there.

11:10 – 11:40 Using High-Frequency Alternative Macroeconomic Data to Inform Investment Decisions: State Street PriceStats Inflation Indices

Room: Westminster Stage

Speakers:

• Jamie Stewart, Managing Director, Data Intelligence, State Street

• Rajeev Bhargava, Managing Director and Head of State Street PriceStats

• Michael Metcalfe, Senior Vice President and Head of Macro Strategy, State Street Global Markets

State Street PriceStats tracks billions of prices sold online by retailers globally to generate daily inflation indices, helping investors anticipate the impact of inflation and implications for monetary policy. The methodology builds on the Billion Prices Project established by Harvard Business School professor Alberto Carvallo. This session covers daily inflation indices for 27 countries and introduces new indicators providing more granular insights at the sector, subsector, and category level.

🤖Claude Question: PriceStats has demonstrated a correlation with BLS CPI measures above 80% over five years, and in September 2025 the annual rate came within one basis point of the delayed BLS reading. As you introduce new sector and subsector indicators, is the investment case primarily that granularity produces signals not yet embedded in official data, or is the core value still speed, in that investors are trading the same information as CPI but arriving three weeks earlier?

🤖ChatGPT Question: As high-frequency inflation data becomes more widely available, how are you validating that these signals provide incremental predictive power over traditional indicators, particularly during periods of structural economic change?

My take: Claude’s question is the better question

11:10 – 11:50 Workshop: AI for Fund Managers

Room: The Soho Studio

Speakers:

• Kelly Koscuiszka, Partner, Schulte Roth & Zabel

• Greg Worsfold, Founding Partner, Regulation & Compliance, Optima Partners

This interactive workshop is designed for private fund managers navigating the evolving AI landscape. Topics include crafting AI policies and governance frameworks, updating fund documentation, leveraging transcription and analysis tools, and preparing for regulatory scrutiny.

🤖Claude Question: As AI moves from pilots to production at investment firms, the regulatory examination question is shifting from “do you have an AI policy?” to “show me your controls in practice.” For a fund manager preparing for that scrutiny today, what is the difference between governance documentation that satisfies an examiner and governance that actually reduces model risk, and are there specific control gaps that regulators have already flagged in examinations that firms are not yet taking seriously enough?

🤖ChatGPT Question: None generated; it didn’t collect this agenda item at all, so it didn’t research speakers and generate a question.

11:40 – 12:00 A Comparative and Forward-Looking View of US vs European Transaction Data

Room: Westminster Stage

Speaker: Kaitlyn Majkoski, Research Analyst, Neudata

Transaction data is one of the most popular types of alternative data used by hedge funds. This presentation explores how different transaction data types can track macroeconomic trends and how retailers have responded to tighter wallets across the US and Europe, with attention to the structural differences between the US single market and Europe’s fragmented market structure.

🤖Claude Question: US transaction data benefits from a relatively homogeneous card infrastructure and a single regulatory regime, while European coverage has to navigate more than 27 distinct national payment systems, privacy frameworks, and consumer behaviors. Which retail categories show the most divergent macro signals between the US and Europe right now, and are there sectors where European transaction data is actually more informative for a discretionary macro investor than its US equivalent?

🤖ChatGPT Question: Given the structural differences between US and European transaction data, where are you seeing the most meaningful gaps in coverage or quality that still create opportunities for differentiated insight?

My take: I think both questions are good

12:00 – 12:30 Quantifying Global Trade Disruption: Real-Time Data for Macro Signals

Room: Westminster Stage

Speakers:

• Greta Farina, Macro Research Analyst, LGIM (Moderator)

• Mabrouk Chetouane, Head of Global Market Strategy, Natixis Investment Managers

• Lasse de la Porte Simonsen, Director of Systems & Advanced Analytics, Macrosynergy

• Didier Borowski, Head of Macro Policy Research, Amundi Investment Institute

Tariffs, sanctions, and geopolitical shocks are constantly altering global supply chains, yet many investment models still rely on static assumptions. This panel explores how alternative data provides continuous, dynamic trade signals and how investment teams integrate them into systematic frameworks to capture actionable insights faster than traditional sources.

🤖Claude Question: Official trade statistics arrive with a three-to-six-month lag, while vessel tracking, freight cost indices, and customs scrapes can detect supply chain shifts in near real time. In practice, are these real-time trade signals being integrated into systematic investment frameworks at firms like Natixis, Macrosynergy, and Amundi, or are they still used primarily as qualitative context that informs a discretionary view rather than entering a model directly?

🤖 ChatGPT Question: As real-time trade data becomes more accessible, how are you integrating these signals into macro frameworks in a way that captures regime shifts without overreacting to short-term noise?

My take: I think both questions are good

12:30 – 1:00 The Power Crunch: What Investors Need to Know Now

Room: Westminster Stage

Speakers:

• Alex Fidgeon-Keeler, Associate, Neudata (Moderator)

• Dr Paul Bilokon, Visiting Lecturer in Quantitative Finance, Imperial College London

• Kona Haque, Former Head of Research, Macro & Commodity Specialist, ED&F Man

• Mario Dell’Era, Senior Quantitative Market Risk Manager, EnBW

Power market dynamics are changing as continued demand from data centres strains capacity, while ageing infrastructure and growing interconnection queue times create less stable power systems. This session covers how investors are using data to gain insights into changing power dynamics, from tracking and measuring the impact of new data centres to analysing trends in distributed energy resources.

🤖Claude Question: Hyperscaler capex guidance and utility load forecasts have both proven unreliable as leading indicators of actual AI power demand, with interconnection queues distorted by speculative filings and DeepSeek R1 demonstrating that algorithmic efficiency can compress demand projections overnight. From the perspectives of a quantitative finance researcher, a commodity market specialist, and an energy company risk manager, what combination of alternative signals, whether satellite imagery of construction activity, power futures curves, or grid interconnection data, would you trust to monitor AI-driven power demand without relying on company guidance?

🤖 ChatGPT Question: As power markets become more constrained and complex, what datasets or indicators have proven most effective in linking infrastructure dynamics to investable signals across commodities and equities?

My take: They both asked similar questions about what datasets should be leveraged (Claude with so many words).

1:10 – 1:50 Lunch & Learn: Compute as an Asset Class: Pricing GPUs, Tokens, and Memory in Real Time

Room: The Soho Studio

Speaker: Carmen Li, Founder & CEO, Silicon Data (also CEO, Compute Exchange)

AI has turned compute into one of the most critical and least transparent inputs of the global economy. This workshop covers how GPU rental and spot pricing indices (H100, A100, B200) are built and distributed, including via Bloomberg; alternative datasets powering GPU pricing; why token pricing and transaction volumes matter as a proxy for compute demand; and RAM indices and RAM futures as the next bottleneck market.

🤖Claude Question: Silicon Data’s H100 Rental Index normalizes GPU rental prices across subtypes, geographies, and platform conditions using 3.5 million daily data points, and the index captured a 23% price decline from the September 2024 peak as hyperscaler supply expanded. For an institutional investor using GPU pricing as an investment signal rather than a procurement tool, what is the lead-lag relationship between spot rental price movements and the forward-looking signals that matter at the portfolio level, whether hyperscaler capex cycles, AI company revenue growth, or grid-level power demand?

🤖ChatGPT Question: As compute markets evolve into a tradable and measurable asset class, which pricing signals have proven most reliable in capturing real demand dynamics rather than transient capacity constraints?

My take: They both asked similar questions, though Claude’s is more practical and ChatGPT’s more theoretical.

2:00 – 2:40 Providers & Product Spotlight

Room: Westminster Stage

Speakers:

• Abdulaziz Alobaid, Chief Revenue Officer, Argaam Investments

• Mark Aron Szulyovszky, Co-Founder, Unravel

• Shannon Lal, Chief Technology Officer, BCA Research

• Usman Khan, Founder & CEO, APEX:E3

In this showcase session, hear from a selection of the industry’s data providers as they share the datasets and product launches set to impact the world of alternative data.

🤖Claude Question: Good luck to all the showcase participants. For each presenter, the questions I most want answered are: what is the strongest investment use case where your data has demonstrably moved the needle for a buy-side client, what does your sourcing methodology look like under compliance review, and what is the specific characteristic that differentiates your dataset from the nearest competing product?

🤖ChatGPT Question: As data buyers become more selective, what specific product decisions have most improved conversion from initial interest to production usage?

My take: It looks like Claude saw the pattern in my questions for vendor showcase agenda items in other Top Questions articles and copied it almost exactly how I’ve written it, which is what I would have wanted. ChatGPT totally missed the mark.

2:00 – 2:20 Integrating AI into Investment Decision-Making: Is the Industry Ready?

Room: Canary Wharf Stage

Speaker: Takaya Sekine, Founder & CEO, TAIM Consult and Professor of AI for Decision Making, HEC Paris

This presentation distinguishes between AI applied to productivity (well understood, widely adopted) and AI applied to decision-making (still in flux). It walks through the empirical use of a language model to emulate expert knowledge in investment decision-making contexts.

🤖Claude Question: Your October 2025 Journal of Portfolio Management paper demonstrated that lightweight question-answering pipelines can replicate the synthesis of specialist knowledge bases with high accuracy. When you move from that replication result to the harder question of whether the output actually improves investment decisions, what does your empirical evidence show about the conditions under which an LLM’s synthesis of expert knowledge adds value versus simply reflecting the consensus that an experienced analyst would have reached anyway?

🤖ChatGPT Question: When applying AI directly to decision-making rather than productivity, what evidence have you seen that models can reliably replicate or augment expert judgment without introducing new forms of bias?

My take: Both are good questions, which overlap a lot, but the last bit of nuance about avoiding consensus and introducing new biases are good additions from each LLM.

2:20 – 2:40 Small Models, Big Alpha

Room: Canary Wharf Stage

Speaker: Aditya Sharma, Head of Textual Data and Quantitative Signals, S&P Global Market Intelligence

This presentation explores quantitative drivers in 2025 and how fine-tuned LLMs provided an efficient way to boost alpha for quantitative strategies.

🤖Claude Question: When fine-tuned smaller models outperformed larger frontier models on quantitative financial text tasks in 2025, how much of the alpha improvement was attributable to the model architecture itself, how much to the proprietary financial text used in fine-tuning, and how much to the feature engineering choices that converted model output into a usable signal, and which of those three levers offers the most durable advantage for a buy-side firm trying to replicate the approach?

🤖ChatGPT Question: In what scenarios have smaller, fine-tuned models outperformed larger general-purpose models, and what does that imply for how investment teams should allocate resources across model development?

My take: Claude’s question reads like it’s a “bad cop” interrogating a witness, while ChatGPT is acting as the “good cop” asking open-ended questions. I must resist the urge to have Gemini generate this image and just move on with the article…

2:40 – 3:00 AI Agents in Production

Room: Westminster Stage

Speakers:

• Tavis Lochhead, Co-Founder, Kadoa (Moderator)

• Mark Thompson, Head of Primary Research, Norges Bank Investment Management

• Zuzana Manhart, Portfolio Manager, Data and AI, CBPE

This panel explores how AI is transforming investment research and the impact on both data and investment teams. Panellists will reflect on what works, what doesn’t, and the lessons learned from implementing AI across workflows. The discussion also examines the organisational implications, from changing team structures and skill requirements to workflow adaptation and governance challenges, with a forward-looking outlook on where firms may gain a competitive edge.

🤖Claude Question: NBIM’s 2025 responsible investment report disclosed that its LLM-based screening system flagged ESG risks in new portfolio companies within 24 hours of investment, and in multiple instances the fund sold positions before the broader market reacted to those risks. For Mark Thompson in his primary research role, what did it take to move from that kind of defensive risk-screening application to a point where AI is actually informing the offensive, alpha-generating side of the research process, and what governance structure separates the two?

🤖ChatGPT Question: For teams deploying AI agents in production, what have been the most important lessons in balancing automation with oversight, particularly when outputs directly inform investment decisions?

My take: I appreciate the angle Claude took based on the research it found on NBIM’s approach. It’s the most interesting angle because most asset managers aren’t sharing how they are using AI. NBIM’s transparency is worth exploring further.

2:40 – 3:20 Optimising Textual Inputs for Quantitative Investment Strategies

Room: Canary Wharf Stage

Speakers:

• Finn Cousins, Senior Research Analyst, Neudata (Moderator)

• Lauren Stagnol, Cross Asset Quantitative Researcher, Amundi Investment Institute

• Jahmal Nicholson, Senior Data Scientist, Discretionary Data Product Lead, Man Group

• Felix Pagden-Ratcliffe, Vice President and AI Product Manager, JP Morgan Chase Asset Management

• Aditya Sharma, Head of Textual Data and Quantitative Signals, S&P Global Market Intelligence

This panel considers how textual data is sourced, processed, and deployed in quantitative investment strategies. Panellists will discuss core data needs, differentiated sources, compliance considerations, and the evolving role of social media and retail investor platforms, as well as the gaps in the market and what the “dream” textual datasets look like.

🤖Claude Question: Lauren Stagnol’s published work at Amundi has used NLP to construct political risk scores from news and to assess SDG alignment in corporate disclosures, while Jahmal Nicholson’s role at Man Group focuses on making data science outputs usable by discretionary analysts rather than only systematic strategies. Where does the panel draw the compliance boundary on social media data for investment use, and is the constraint primarily legal, potential MNPI exposure, or methodological, in that retail investor platform signals have not proven durable across multiple market regimes?

🤖ChatGPT Question: As textual data sources proliferate, how are you identifying which inputs provide genuinely differentiated signals, and what gaps still exist between available datasets and the “ideal” textual data for alpha generation?

My take: Happy with both questions… just wish Claude was less wordy. It’s in the instructions to not provide a paragraph of context before the question.

3:00 – 3:20 Navigating the Storm: Using Emerging Data to Enhance Return and Innovate Strategies in a World of Exponential and Interconnected Risk

Room: Westminster Stage

Speakers:

• Steve Kidd, Senior Director and Head of Asset Management Specialists, Moody’s

• David Hamilton, Managing Director, Head of Capital Markets Research, Moody’s

This session explores how data and analytics, from credit risk to other emerging datasets, can drive value across a range of trading strategies. With coverage of an ever-expanding investment universe, a more complex risk environment translates into more exciting opportunities to develop new and innovative solutions.

🤖Claude Question: Credit risk data has historically been most useful for fixed income investors, but in an environment where private credit has expanded rapidly and public equity valuations increasingly reflect the credit quality of corporate balance sheets, what emerging signals from Moody’s Analytics are asset managers now applying cross-asset, and at what horizon does credit data provide a genuinely non-redundant leading indicator relative to what equity market data already captures?

🤖ChatGPT Question: As risk signals become more interconnected, how are you determining which emerging datasets genuinely improve risk-adjusted returns versus those that simply add complexity to models?

My take: It feels like both of these questions are mirrors of the premise of the agenda item that credit risk can be used across a range of trading strategies. In a lot of words the models basically said to the premise of the agenda: “for real?” I think neither could find research on the topic (claude said so in its research document provided to me).

3:20 – 4:00 Data Sourcing and Vendor Engagement: Trends and Best Practices in 2026

Room: Westminster Stage

Speakers:

• Mark Fleming-Williams, Head of Data Sourcing, Capital Fund Management (Moderator)

• Abigail McInnes, Principal Analyst, Data Sourcing and Strategy Manager, Man Group

• Ben Cohen, Managing Director, Data Strategy, Final (formerly Global Head of Data Strategy, WorldQuant)

• Abhijeet Gaikwad, Founder and Chief Investment Officer, Agami Capital

• Eugene Miculet, Global Head of Data Strategy & Sourcing, WorldQuant

Against a backdrop of rapid provider proliferation and increasing buyer sophistication, this panel addresses key pain points and priorities across the sourcing lifecycle. As data buyers grapple with discovery fatigue, inconsistent standards, and due diligence burdens, how can vendors prioritise clarity, trust, and data quality to build lasting relationships?

🤖Claude Question: Ben Cohen spent a decade building WorldQuant’s data sourcing function, and Eugene Miculet now runs that same function at a firm with over a thousand quantitative researchers globally, while Abhijeet Gaikwad is building sourcing infrastructure at Agami Capital from scratch with AI-native tools. Across the full evaluation lifecycle, which stage, whether discovery, compliance review, trial design, or production onboarding, has improved the least despite all the tooling investment of the past five years?

🤖ChatGPT Question: Where in the sourcing and onboarding process are you still seeing the greatest friction, and what changes in tooling or vendor behavior have had the most impact on accelerating time from discovery to deployment?

My take: Both asked about frictions… why is Claude trying so hard to sound smart rather than just ask the thoughtful question? Wait… is it trying to copy how I write questions? Is this what I sound like?

3:20 – 4:00 AI at Work: Transforming Research Workflows

Room: Canary Wharf Stage

Speakers:

• Takaya Sekine, Founder & CEO, TAIM Consult and Professor of AI for Decision Making, HEC Paris (Moderator)

• Matthew Bell, Senior Data Scientist, Man Group

• Dr Ana Armstrong, Founder & CEO, AIM Cube; Senior Lecturer in FinTech, University of Greenwich; Advisory Board Member, AIDA Lab

• Timothée Consigny, Chief Technology Officer & Head of GenAI Innovation, H20 Asset Management

• Petr Merkuryev, Founder, Medusa Investment Partners

As AI tools like LLMs and agentic systems become commonplace, this panel explores how research teams are integrating AI into daily workflows without building models from scratch. Discussions centre on how existing tools are changing decision-making, which vendors are enabling or limiting access to AI-enhanced datasets, and practical examples of AI in action.

🤖Claude Question: Ana Armstrong’s research on AI governance for investment management and H20 Asset Management’s track record as an early and aggressive adopter of machine learning represent two very different vantage points on where AI in investment research actually stands. Which research tasks have moved fully into AI automation with measurable and sustained results, and where has the industry consistently overestimated how much human judgment can be replaced, specifically in ways that only became apparent after a workflow was deployed at scale rather than in a pilot?

🤖ChatGPT Question: As AI tools become embedded in daily workflows, what changes have had the greatest impact on actual investment outcomes rather than just productivity gains?

My take: I like Claude’s question considering the role of human judgment as rote tasks are removed. Sort of like how I don’t need to spend a lot of time extracting the agenda and formatting it into this article because Claude did all that for me so I can spend more time thinking about what the panel is about and what I’d want to learn from the panel… and provide snarky comments about AI writing style.

4:20 – 4:40 Trading on Trends

🤖Claude Question: Measuring macro sentiment and tracking retail investor positioning from social media are relatively well-established use cases, but monitoring consumer boycotts driven by social platforms, where a brand’s political positioning generates measurable commercial impact, requires linking social engagement to actual purchasing behavior. How much of the boycott signal is present in social data alone, and at what point does the investment thesis require transaction data to confirm that online sentiment has actually moved spending?

🤖ChatGPT Question: When using social media data for investment signals, how do you distinguish between transient noise and shifts in sentiment that have durable implications for markets?

My take: I like ChatGPTs question here which is more about the integration of the signal in context of the investment debate. Claude’s is an overly wordy mirror of the agenda descirption and then basically undermines it all by basically asking “why not just use another dataset?” The ideal choice without constraints is always “yes and” for combining datasets rather than just choosing one dataset because the mosaic fills in gaps of each dataset and analytics approach.

4:40 – 5:10 Using Consumer Data to Anticipate Demand

🤖 Claude Question: This panel brings together a macro allocator at Lombard Odier, a data scientist applying consumer signals in private equity due diligence at Advent International, and a corporate strategist at H&M Group using the same category of data for competitive intelligence. When each of you looks at a shift in basket composition or spending frequency in a consumer category, is there a use case where the same transaction signal that is directionally useful for an equity investor is actively misleading for a corporate strategist, or vice versa?

🤖ChatGPT Question: As consumer datasets become more central to investment research, what analytical approaches have proven most effective in separating structural demand shifts from short-term volatility in spending behavior?

My take: I don’t think either question is good. I think the LLMs aren’t sure how to connect the dots between the panelists.

My question: Market commentators have often sited consumer transaction data as crowded and hard to generate alpha: what investment questions and analytic approaches are still generating alpha and what investment questions and analytic approaches are effectively establishing the consensus view because of crowding.

What questions do you have for the panelists? Leave a comment below.

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Know someone attending or presenting at the conference? Feel free to forward this to them.

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-Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. The gap between what works in research and what works in production.

2. Who controls the path from data to decision, and how is that changing?

3. Practical frameworks for sizing, timing, and allocating in the current environment.

Link to Agenda: https://www.rebellionresearch.com/quantvision-2026-fordhams-quantitative-conference

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of March 15, 2026

Day 1—Thursday, March 19

8:50am Opening Address from the Chairs

Speakers:

• Qing Sheng, Director MSQF Fordham University;

• Faruque Khan, Conference Chair;

• Alexander Fleiss, CEO Rebellion Research

Question: Can you discuss the breadth of the conference agenda and the implications for where quantitative finance is moving in the next three to five years?

8:55am Keynote

One of the largest sovereign wealth funds & Cornell University Professor

Speaker:

• Marcos M. Lopez de Prado, Global Head of Quantitative R&D, Abu Dhabi Investment Authority (ADIA);

• Professor, Cornell University

Question: As AI tools accelerate hypothesis generation across the industry, what does good experimental discipline look like for a quant team that wants to avoid being fooled by patterns that don’t hold up in live markets (i.e., get ahead of that shift to causal factors instead of correlative factors)?

9:40am Panel: Future of Machine Learning in Quantitative Finance

Machine learning has moved from an experimental tool to a core component of quantitative finance, but the field is still evolving at a rapid pace. This panel will examine where ML is heading in the next five years, with a focus on architectures, interpretability, and integration into live trading environments. Topics will include advances in transformer and graph-based models, the blending of traditional statistical methods with deep learning, and the challenge of maintaining model robustness in shifting market regimes.

Speakers:

• Moderator: Samson Qian, Citadel

• Matt Rowe, Managing Director Man Group;

• Mike Tiano, Deputy Head of Systematic Strategies Schonfeld;

• Gordon Ritter, Hedge Fund Manager;

• Ed Mitby, Portfolio Manager TCW Group;

• Natascha Hey, Adjunct Professor Columbia University;

• Arun Verma, Head of Quantitative Research Solutions Bloomberg;

• Arkin Gupta, Citadel

Question: How has the path from research prototype to production deployment changed over the last 5 years, and where has the transition created the most value for the investment process?

10:20am Panel: Can LTCM Happen in 2026?

This panel examines whether a modern version of Long-Term Capital Management could emerge in today’s markets. Using the collapse of Long-Term Capital Management as a historical anchor, the discussion will explore how leverage, crowded trades, model risk, and liquidity mismatches manifest in a world of systematic strategies, AI-driven portfolios, and faster capital flows. Panelists will debate whether advances in risk management, regulation, and transparency truly reduce systemic risk, or whether new forms of correlation, hidden leverage, and feedback loops make a future LTCM-style event not only possible but inevitable. The session will focus on where fragility may be building today, how it might unwind, and what investors should watch before the next crisis reveals itself.

Speakers:

• Moderator: Jacob Bowers, Lead Financial Engineer, BlackRock

• Mike Soss, Chief Investment Officer, Millburn Hedge Fund

• Kevin Gahwhyler, Managing Director, Meteora Capital Hedge Fund

• Abhi Khane, Managing Director, 400 Capital Management

• Patrick Hop, Chief Investment Officer, Draco Ova Holdings

• Photios Harmantzis, Hedge Fund Manager

• Gabriel Sciannella, Head of Risk, Marex

Question: As a thought experiment, if a stress event forced several funds to unwind at the same time, where would the panelists expect the pressure to show up first, and what should risk teams be watching for today that might not appear in their standard dashboards?

11:05am Fireside Chat: Future of Investment Data

Speakers:

• Sameer Gupta, Managing Director & Chief Data and Analytics Officer, Apollo Global Management; Former Head of Data, Point72

• Evan Schnidman, Head of Fidelity Labs

Question: Given your experience building successful data teams and data products, what are the biggest lessons about building a data-driven investment organization that leaders consistently underestimate?

11:50am Keynote Fireside Chat: AI vs Data

Speakers:

• Moderator: Christina Qi, CEO, Databento

• Claudia Perlich, Managing Director, Two Sigma

• Lisa Huang, Head of AI Investment Management & Planning, Fidelity

Questions: Looking at the past few years, has the bigger contributor been better AI models or better underlying data? How should funds think about where to invest next?

12:10pm Panel: Capturing Alpha in 2026 Markets & ETFs

As market structures evolve, liquidity fragments, and AI-driven trading becomes the norm, traditional alpha sources are shrinking while new ones emerge in unconventional places. This panel will explore the most promising alpha-generation approaches for 2026 and beyond, from integrating alternative datasets and multimodal signals to deploying cutting-edge machine learning architectures and causal inference methods. In addition, a look at the future of ETF investing in today’s market.

Speakers:

• Moderator: Caio Natividade, Deutsche Bank, Global Head of Quantitative Investment Solutions Research

• Sid Ghatak, CEO, Increase Alpha

• Neal Berger, Founder & CIO, Eagle’s View Capital Management

• Zachary Squire, Managing Partner, Tekmerion Capital Management

• Macrae Sykes, Portfolio Manager, Gabelli Financial Opportunities ETF (NYSE: GABF)

• Ilia Bouchouev, Pentathlon Investments; former President, Koch Global Partners; NYU/Oxford

• Jess Stauth, CIO Systematic Equity, Fidelity

Question: Given the wide range of investment approaches on the panel, it would be interesting to learn their view on the past three years: what new data sources, tools, or analytical approaches have made the biggest practical difference in how each panelist generates investment ideas or manages risk?

12:50pm Keynote: Entering the Age of Cognitive Markets

Finance has entered an exponential era, where progress accelerates faster than institutions can adapt through linear thinking. This keynote explores what it means to operate in the age of cognitive markets, an era in which intelligence becomes a scalable resource, workflows turn cognitive rather than procedural, and markets co-evolve with autonomous reasoning systems. We examine how agentic AI is reshaping discovery, risk, research, and market structure, and why firms must rearchitect around intelligence rather than merely implement AI.

Speaker:

• Armando Gonzalez, Founder & CEO, RavenPack

Question: For asset managers who are earlier in the journey to leveraging an agentic framework to cognitive workflows that scale, what does the first meaningful step look like in practice, and where have RavenPack’s clients seen the clearest gains so far?

2:00pm Panel: Multimodal Alpha Feeds

From text to images, audio, and geospatial signals, quant teams are now fusing multimodal data streams into unified predictive pipelines. This session will examine the technical, computational, and data-governance challenges of extracting alpha from disparate, high-dimensional sources.

Moderator: Dr. Harvey Stein, Two Sigma

Speakers:

• Paul Krueger, Millenium Quant Trader

• Rahul Gupta, DRW Quant Researcher & Trader

• Dr. Gregory Pelts, Director, ScotiaBank

• Dr. Andy Li, Bank of America Director & Fordham Professor

• Nancy Davis, Quadratic Capital Management, Founder

• Chris Kantos, Head of Quant Research, Alexandria Technology

Question: The idea of combining different types of data (numeric, text, images, audio, and satellite imagery) into a single investment signal is compelling, but the practical challenges of doing this well are significant. For the practitioners on this panel, which combination of data types has produced the most useful investment insights so far, and where has the effort to combine multiple data sources turned out to be more trouble than it was worth?

2:40pm Panel: Alternative Data’s Next Frontier: From Novelty to Necessity

As alternative data cements its role in institutional investing, the challenge shifts from discovery to integration. This panel will explore how hedge funds and asset managers are operationalizing diverse datasets, geolocation, satellite imagery, supply chain signals, and unstructured web data, while balancing costs, compliance, and scalability. Discussion will cover regulatory scrutiny, vendor transparency, model interpretability, and whether AI-driven data synthesis will unlock the next wave of alpha or overwhelm firms with noise.

Speakers:

• Qaisar Hasan, CEO, Maiden Century

• Oleg Nusinzon, Senior Portfolio Manager, Columbia Threadneedle

• Dimitri Bianco, Founder, Fancy Quant

• Tony Berkman, Fmr Two Sigma Head of Data, Garden Leave currently

Question: In a world where each investor can spin up their own dashboard and analytics with a few prompts, what are the key areas that data companies should focus on to adapt? And what advice do you have for centralized data teams within asset managers in this scenario of mass self-service?

Data Sourcers Panel: How Quant Funds Source, Evaluate, and Discard Alternative Data

(Sometime before 3:30pm, no time provided in agenda

In this candid practitioners panel, three of the industry’s most experienced data sourcing leaders pull back the curtain on how top quant funds actually evaluate, acquire, and, just as importantly, walk away from data. From navigating vendor relationships and build vs. buy decisions, to the commoditization of once-proprietary signals and the emerging frontier of AI-generated data, this conversation covers the full lifecycle of a dataset inside a modern quant trading firm. Whether you’re a data vendor, an allocator, or a quant researcher, you’ll leave with a clearer picture of what separates data that drives decisions from data that collects dust.

Speakers:

• Natalya Dmitriyeva, Fmr Global Head of Enterprise Data, Schonfeld & Data Sourcing, Two Sigma

• Dorothy Ruderman, Data Strategy at Verition Fund Management

• Mark Fleming-Williams, Head of Data Sourcing, Capital Fund Management

Questions: How should teams decide when a dataset that once provided value has become too widely used to be worth keeping? Has the availability of AI-generated data changed that calculus?

Day 2—Friday, March 20

8:50am Panel: State-of-the-Art ML Architectures in Quant Finance

Transformers, diffusion models, graph neural networks, what’s hype, what’s working, and what’s next in cutting-edge ML for trading and risk? Experts will break down real-world case studies and discuss scaling these architectures without losing interpretability or control.

Speakers:

• Moderator*:** Petros Zerfos, Principal Research Scientist & Manager at IBM Research*

• Manish Aurora, Hedge Fund Manager, Rational Investments

• Michael Sotiropoulos, Princeton Professor (visiting), NYU Professor (visiting), Fordham Professor (visiting) & MD, Imperative Execution

• Mehul Mehta, Lead Quant, OCC

• Revant Nayer, Fund Manager

• Konstantin Fominykh, CFA, TenViz CEO, Fmr Chief Equity & Cross-Asset Strategist, JPMorgan

• Venkat Vedam, Head of Generative AI, Manulife

Question: Can the panel share a concrete example where the various ML approaches worked well in practice or where a simpler method turned out to be just as effective?

9:30am Keynote: Dr. Dhagash Mehta

Speaker: Dr. Dhagash Mehta, Head of Applied Artificial Intelligence Research for Investment Management, BlackRock

Question: As AI moves from research experiments into tools that influence real portfolio decisions, how does BlackRock ensure that the people using these tools can understand and trust what the AI is telling them, and what has been the most important governance step in that process?

10:10am Equity Nowcasting from Macro Data for Quants

Speaker: Dr. Ajit Agrawal, CEO, AKAnomics

Question: Which areas of the economy has AKAnomics found the strongest connection between its macro signals and equity market performance?

11:00am Panel: Is AI Taking Over Alternative Data?

The explosion of machine learning, multimodal embeddings, and foundation models is transforming how quant investors source, process, and monetize alternative data. Once a niche differentiator, alternative data is increasingly dominated by AI-driven extraction, pattern detection, and signal generation. This session will debate whether AI is enhancing the alt-data ecosystem or fundamentally replacing traditional research heuristics. The panel will explore how automated pipelines are reshaping vendor landscapes, how models ingest unstructured streams, and whether true alpha now depends more on model architecture than dataset exclusivity.

Speakers:

• Moderator: Haoxue Wang, Quant, Millenium

• Dr. Iro Tasitsiomi, Head of Investments Data Science, T. Rowe Price

• Sahana Athreya, 30 Under 30, Millenium

• Eren Kurshan, Princeton University

• Peter Cotton, Stealth Hedge Fund

• Peter Hafez, Chief Data Scientist, RavenPack

Question: From the buy-side perspective, when a data vendor uses AI to extract patterns or generate signals from raw data, does that make the product more useful to the investment team, or does it actually reduce the team’s ability to find something unique?

11:40am CFA Institute: AI Developments in Asset Management

Speakers:

• Moderator: Rhodri Preece

• Agathe Sadeghi, Professor, Stevens Institute

• Igor Halperin, Quant, Fidelity

• Gueorgui Konstantinov, Portfolio Manager

• Jim Liew, JHU Professor

• Alireza Yazdani, Citi Quant & Northeastern Professor

• Leo Schmidt, CIO, River Eddy Capital Management

Question: Which aspect of AI governance, whether it is explainability, bias testing, documentation, or responsible use, has had the most practical impact on how teams actually build and deploy their tools today?

12:20pm Keynote: Jon Najarian

Speaker: Jon Najarian, Market Rebellion Co-Founder

Question: Algorithmic trading and machine learning-driven hedging strategies have added a lot of new activity to the options market. Has that made it easier or harder to identify the trades that reflect genuine conviction, and how has Market Rebellion adapted its approach?

12:55pm Keynote: Andrew Left

Speaker: Andrew Left, Citron Research Editor

Question: I could probably come up with a clever question about the implications of market regulation on short selling strategies, but I think I’m just going to go with… Will there be popcorn?

2:15pm Keynote: Data Structure in the Age of AI Where Everything is a Trade

Speaker: Kirk McKeown, Co-Founder, Carbon Arc & Point72’s Fmr Head of Proprietary Research

Question: How do you see the relationship between structured, entity-level data and the growing prediction market ecosystem extending use cases beyond entertainment, such as economics, corporate activity, and politics? Note: Carbon Arc recently used its structured data to predict that Bruno Mars would finish February as the most-listened-to artist on Spotify, projecting 124 to 131 million listeners. He ended the month at 127.8 million. On the same day as the prediction, an event contract for that outcome was available on Kalshi with a 5% return and would have cost less than $20 to access the insight on Carbon Arc.

2:35pm Panel: Bitcoin vs Crypto & Blockchain

This panel will examine the ongoing tension between Bitcoin as the original decentralized digital asset and the broader cryptocurrency ecosystem encompassing altcoins, stablecoins, and blockchain-based tokens. Panelists will explore whether Bitcoin’s brand dominance, network security, and adoption trajectory can maintain its leadership, or whether other crypto innovations, DeFi protocols, and Web3-native assets will capture the future of digital value.

Speakers:

• Moderator: Logan Beirne, Strive Asset Management Board Member, Yale Law School

• Professor Jim Liew, Johns Hopkins University

• Kelly Ye, Deputy CIO, Avenir Group

• Brooks Dudley, Head of Digital Asset Sales, Marex Capital Markets

• Kathryn Zhao (2025 Quant of the Year), Head Quant & Head of Trading, Cantor Fitzgerald

• Samir Shah, Managing Partner, Alpha Research and Management

• Thomas G. Sy, Head of Multi-Asset Solutions and Digital Asset Strategy Team, New York Life Investments

• Duke University Professor & Director of Duke FinTech, Jimmie Lenz

Question: Considering institutional investors that have not yet made a meaningful allocation into the asset class, what is the most important development, whether regulatory, operational, or related to the asset class itself, that could move them from watching to participating?

2:55pm Keynote: When More Data Hurts: Navigating the Nonstationarity-Complexity Tradeoff in Return Prediction

Speaker: Professor Agostino Capponi, Columbia University

Question: More complex models need more training data, but using longer histories introduces the risk that older data no longer reflects how markets behave today. For quant teams trying to decide how much history to use and how complex their models should be, what is the most actionable takeaway from your work?

3:35pm Keynote: Multivariate Kelly Criterion, Leverage and Regime-Switching Universal Portfolios

Speaker: Professor Mikhail Smirnov, Columbia University

Question: What are the most important practical takeaways from combining the multivariate Kelly criterion with regime-switching portfolios, and how sensitive are the results to getting the regime identification wrong? Note: Ed Thorp, who pioneered both card counting in blackjack and the Kelly criterion in investing, showed that the key to winning is knowing both how much to bet and when the odds are in your favor. Smirnov’s research brings these two ideas together for portfolio management: the Kelly paper addresses optimal sizing and leverage, while the regime-switching paper addresses when to be invested and when to step aside. Here are two papers worth reading to prep for the session:

• https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5341539

• https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6060414

3:55pm Keynote: Understanding Where Bitcoin & Crypto Will Go in 2026

Speaker: Jimmie Lenz, Duke University Professor & Director of Duke FinTech

Question: What could have the most lasting impact on how institutional portfolios will think about digital assets considering regulatory changes, technology changes, and market structure changes?

4:15pm The Evolution of Financial Data Infrastructure: OpenBB x Snowflake

Speakers:

• Tom Gray, Snowflake;

• Ihsan Erman Saracgil, OpenBB

Question: For small to mid-sized funds trying to assemble their data infrastructure without a large technology budget, how do open-source platforms change the build-vs-buy decision?

4:35pm Keynote: Trial, Error, and Outcome: Development of Alternative Data Research Agents

Speaker: Charlie Marin, CEO, Quanted

Question: What part of the research workflow turned out to be harder to automate than the team initially expected, and where did the agents produce results that genuinely surprised the people building them?

4:55pm Concluding Keynote

Speaker: Sudip Gupta, Johns Hopkins Professor of Practice

Question: Which theme from this conference will have the most lasting impact on how investment teams are built, staffed, and resourced over the next few years?

What questions do you have for the panelists? Leave a comment below.

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Proof standards: what counts as sufficient evidence

2. Durability: how long does an advantage hold

3. The production gap: what breaks after adoption

Link to conference agenda: https://a-teaminsight.com/events/alternative-data-conference-new-york-city/?section=agenda

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of March 14, 2026

8:50am Opening and Welcome

Speaker: Andrew Delaney, President & Chief Content Officer, A-Team Group

Question: A-Team Group now sits across trading technology, data management, and alternative data, which gives you a useful cross-market vantage point. What capability or operating habit that is standard in trading technology still feels underdeveloped in alternative data, and why does it matter now?

9:00am Keynote Address: The NextGen Multimodal Economist: A Demo

Speaker: Apurv Jain, Founder and CEO, MacroX

MacroX’s multimodal AI engine measures the world 1-3 months faster, with lower point-in-time errors, is interpretable, can be easily chatted with, and is substantially cheaper than a human expert. Now available broadly after a successful live 2 year track record at a $1T+ AUM manager, the live demo will step through 4 case studies:

• measuring GDP with lower PIT vs. Atlanta Fed GDPNow;

• faster inflation measurement beyond online prices;

• trading initial jobless claims (IJC);

• analyzing NYC and SF city economies.

Question: Across the four case studies in today’s demo, which use case has produced the most actionable signal for portfolio managers so far, and what does the investment workflow look like when a MacroX nowcast diverges sharply from consensus?

9:20am Panel: How to Use AI to Build Better Data Products

• Entity resolution, normalization, and enrichment at scale;

• Productization, transforming raw sources into investable signals;

• Quality controls, monitoring drift, breakages, and methodology changes.

Speakers:

• Andrew Delaney, President & Chief Content Officer, A-Team Group (Moderator)

• Chris Kelliher, Senior Quantitative Researcher and Portfolio Manager, Global Asset Allocation, Fidelity Investments

• Sid Ghatak, CEO & Founder, Increase Alpha

Question: Data vendors are using AI in very different ways: some to enrich and productize raw data, others to deliver signal directly, and others as an interface to access insights. From the buy-side perspective, what evidence threshold should a team require before trusting AI-generated output, and how do you assess whether that output provides a genuine edge that a base foundational model couldn’t replicate?

9:50am Fireside Chat: Mapping Taiwan Retail Market Dynamics: Order Flow, Transactions, and Retail Sentiment

Speaker: Brendan Furlong, Chief Data Advisory Officer, Eagle Alpha, in conversation with Jack Yeh, COO, CMoney

Question: CMoney serves both retail investors through a consumer platform with over 10 million users and institutional clients through financial data services, which means the same underlying behavioral signals can be interpreted very differently depending on who is using them. How do you help institutional investors understand and trust data that originates from a retail community, and what investment use case has been the strongest proof point so far?

10:05am Fresh Features from New to Market and Leading Data Vendors

Speakers: Various data vendors

Question: For each presentation, what is the clearest investment use case with the strongest evidence of value, and what should buyers understand about sourcing, compliance, and ongoing data quality before taking the dataset seriously?

10:55am Fireside Chat: Private Market Pricing Data: Transparency, Trends & Opportunity

Speakers: Nick Fusco, Founder & CEO, PM Insights, in conversation with Julia Bardmesser, Data Expert

Question: Daily market-based pricing for private companies changes what is possible compared to quarterly valuations, particularly for risk management and portfolio monitoring between funding rounds. Which investment or operational decisions have clients found most improved by the move from periodic to daily pricing, and where does the data still leave gaps that buyers need to account for?

1:20pm STREAM A (OASIS SUITE): Leaders Panel: The State of Alt Data in 2026, What’s Changed That Actually Matters

• What’s durable vs what’s hype in AI, private markets, regulation, and budgets;

• Where teams are genuinely reallocating spend and effort;

• The new “must-have” capabilities for a modern data program.

Speakers:

• Christos Koutsoyannis, Chief Investment Officer, Atlas Ridge Capital (Moderator)

• Norman Niemer, Head of Investment Research and Data Science, Real Estate, UBS Asset Management

• Eliza Raphael, Head of Data Strategy, Jump Trading

• David X Martin, Chairman and CEO, Arctium Capital Management

• Andrew Sprague, VP, Investor Sales, Sensor Tower

• Apurv Jain, Founder and CEO, MacroX

Question: Across the panel’s wide range of perspectives and focus areas, which capability that felt advanced two years ago is now standard infrastructure for a competitive data program, and what is the next capability moving onto that path?

1:20pm STREAM B (BAMBOO SUITE): Data Quality Lab

• How different teams define “quality” and why the definitions clash;

• Practical approaches to improving quality including QC checks, anomaly detection, and drift;

• Short tactical takeaways, the checks teams run before trusting a dataset.

Speakers:

• Brendan Furlong, Chief Data Advisory Officer, Eagle Alpha (Moderator)

• Mike Soss, Chief Investment Officer, Millburn

• Enrico Dallavecchia, President and COO, Arctium Capital Management

• Samantha Mait, Data Science Operations Lead, Balyasny Asset Management L.P.

• Isabel Ahsler, Team Manager, Strategic Accounts, Sensor Tower

Question: Most quality checks focus on what happens before a dataset is trusted, but failures that emerge after a dataset is already in production can be more consequential. What does a robust ongoing monitoring process look like for a live strategy, and which signals tend to give the earliest warning that something in the data has shifted?

2:00pm STREAM A: Panel: Synthetic Data: When It Helps, When It’s a Trap, and How to Validate It

• Legitimate use cases vs red flags;

• Methodologies, how synthetic data is generated and what that implies for risk, dealing with AI scraping;

• Buy vs. build, should this come from vendors or be generated in-house;

• New opportunities.

Speakers:

• Chris Kelliher, Senior Quantitative Researcher and Portfolio Manager, Global Asset Allocation, Fidelity Investments (Moderator)

• Jeffrey Maron, Managing Director, 7RIDGE

• Sarkis Agaian, Senior Quantitative Developer, Laurion Capital Management

• Iro Tasitsiomi, Head of Investments Data Science, T. Rowe Price

• Yifang Cao, Senior Quant Researcher, Jupiter Research Capital

Question: The hardest problem with synthetic data is not generating it but validating it, since a process that looks rigorous in backtesting can still fail to improve live performance. What does a validation framework need to include to give a quant team genuine confidence that synthetic data is adding signal rather than adding noise?

2:00pm STREAM B: Panel: How Funds Decide to License a Dataset: Trials, Pitfalls, and What Breaks Deals

• What really happens during trials on both buyer and vendor sides;

• Common failure modes including unclear value, integration friction, and weak documentation;

• The latest compliance concerns to be aware of.

Speakers:

• Eric Duncan, Head of Business Development, Databento (Moderator)

• Carrie Anton, Director, Data and Research Management, Jain Global

• Don D’Amico, Founder & CEO, Glacier Network

• Mark Fleming Williams, Head of Data Sourcing, CFM

Question: Trials are often structured around speed, but the real decision is whether a dataset can survive operational, analytical, and compliance scrutiny over time. If you could design the ideal trial from scratch, what would it include, and where does current practice still fall short? Bonus question for Mark: Does the introduction of GenAI-generated outputs, which include a variable cost for token use as a cost to the vendor, finally justify charging for a trial, or does your view on free trials still hold?

4:10pm Closing Discussion: Democratization of Alternative Data, When Everyone Has LLMs, What Still Creates Edge?

• Does easier access compress alpha or shift it to engineering and process advantages;

• “Off-the-shelf” vs proprietary, where differentiation still survives;

• Is alpha created via the analysis or the underlying dataset, can the same dataset yield very different alpha based on the analysis and resulting trading implementation;

• What this means for strategy, resourcing, and vendor selection.

Speakers:

• Kathryn Zhao, Adjunct Professor, Cornell University (Moderator)

• Gordon Ritter, Chief Investment Officer, Ritter Alpha

• Tony Berkman, Former Managing Director, Two Sigma

• Evan Reich, Former Head of Data, Verition Fund Management

• Duncan Robinson, Director of AI & Optimization — Principal Research Scientist, Franklin Templeton

• Ellison Kandler, SVP and Head of Equity Solutions and Classification, Syntax

Question: Looking back at where a durable edge has actually come from, is the advantage more often in data that competitors could not access or in what your team did with data that was widely available?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

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- Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. What the buy side2 actually needs from AI and data vendors in 2026

2. How alternative datasets continue to reveal alpha3 generating insights vs consensus views, despite wide adoption over the past decade

3. The infrastructure and operational challenges: from vendor IP protection and compliance frameworks to API design and integration, which must be solved for AI to scale at asset managers

Agenda: https://www.battlefin.com/miami-2026#agenda

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Share

Subscribe now

Note: Agenda as of Jan 16, 2026

Wednesday, January 21

Opening Remarks & Alt Data & AI Trends for 2026

4:00 PM - 4:10 PM

Speaker: Tim Harrington, CEO at BattleFin

Results of our buy side survey to 100 funds

Question: Based on the survey results and BattleFin and Exabel’s work with investors, what will unlock adoption of alternative data across the long tail of asset managers? For those newer to the space looking for additional context for this question, I wrote about the long-tail of asset managers in a very early Data Score article:

So much data, so little time. Quantifying dataset additivity

4:10 PM - 4:20 PM

Speaker: Harrison Perl, Alt Data Strategist at Exabel

Question: When evaluating dataset impact on incremental insight, alpha and returns, how do you balance quantitative measures of incremental signal with qualitative judgments about whether a dataset enables fundamentally new questions?

Finally a Benchmark for Alt Data....

4:20 PM - 4:30 PM

Speakers:

• Tim Harrington, CEO at BattleFin

• Gene Gallagher, Director of Research at Exabel

Question: While the specifics of what this benchmark tracks aren’t yet public, the industry has long needed a way to measure alternative data performance, whether that’s alpha generation, signal consistency, data quality, ease of use, or compliance approved ready. Which aspects of dataset performance deserve to be benchmarked, and how could standardized measurement of these KPIs reshape relationships between data buyers and vendors?

The Folly of Trading on Aggregate Data

4:30 PM - 4:50 PM

Speaker: Jonathan Kay, Founder & CEO, Apptopia

Why disaggregated data is now table stakes, with examples in AI, Fintech and more

Question: As investors accept data broken down into granular components as table stakes in analytics, what comes next? Is the edge in combining multiple layers of granularity, or in applying new analytical frameworks to extract signal?

Data Mosaic - Healthcare, Medical Devices, Industrials and Materials (B2B Billed Businesses)

4:50 PM - 5:20 PM

Speakers:

• Rayne Gaisford, Chief Risk Officer & Chief Data Strategy Officer at RTW

• Mark Holmquist, Vice President, Head of MarketPulse at Sg2

• Thomas Paulson, Head of Market Insights at Advan

Joint replacement market

• Procedure demand and GLP-1 effects

• Site of care and infrastructure

Medical devices landscape

• M&A and Deal Activity

• Reimbursement and value based care

Industrials and materials

• B2B billed business

• Med Devices is a B2B billed business

Question: As GLP-1 treatments reshape procedure demand and care shifts toward outpatient settings, how can investors compliantly combine procurement signals, foot-traffic data, and claims trends to identify which medical device supply chains are positioned to win or lose?

Real-time ECM Insights

5:20 PM - 5:30 PM

Speaker: Zachary Schwartz, Head of Buyside Sales, CMG

Transforming Capital Markets Through Data and Connectivity

Question: As Equity Capital Markets( ECM) activity rebounds, which real-time demand signals from CMG’s platform have most helped buy-side firms move beyond static syndicate updates (information distributed by investment banks during capital raises regarding demand, pricing, and allocations) to influence pricing or allocations?

Supply chains, Semi’s, Data Centers & Power

5:30 PM - 5:45 PM

Speaker: Jonathan Liggett, Head of Data Partnerships, Rushmore Labs

• AI Infrastructure capex surge.Power demand and grid constraints.Semis market growth and architecture shifts.

• Data Center Physical Infrastructure, Energy gen colo and cooling.

Question: As hyperscalers race to build AI infrastructure, How can investors use building permits, shipment data, and power-grid analytics to forecast not just semiconductor demand, but the physical bottlenecks (like power, cooling, and construction) that may constrain the ecosystem?

Alt Data Industry Influencers - Hot Takes on the Data Industry

5:45 PM - 6:15 PM

Speakers:

• Jason DeRise, Senior Director at LMI

• Dan Entrup at It’s Pronounced Data, Co-Founder AggKnoweldge

• Michael Watson, CEO & Founder at Hedgineer.io

Question: You’ll have to be there to hear our most non-consensus views on how the use of data and AI will evolve in practice. I recently published predictions for 2026 (AI will accelerate back to office trends?! I think Dan and Michael each bring perspectives that challenge prevailing narratives and are grounded in real-world experience. Have a beverage ready to cool yourself from these hot takes :)

Ahead of the conference, let us know your controversial views about how data and AI will be used in investing in 2026 in the comments below!

Leave a comment

Thursday, January 22

Sell-Side Signals & Supply Chains: What’s Worked & Looking Ahead

9:00 AM - 9:20 AM

Speaker: Dan Sandberg, Global Head of Quantitative Research & Solutions at S&P Global

• Separating signal from noise in a rapidly shifting macro and cost environment.

• Sell-side expectations and supply-chain relationships can be combined for clearer earnings risk and resilience.

• How analyst estimate revisions evolve through earnings season

• How cost and margin pressures propagate through customer-supplier networks.

• Monitoring Signals in real time using integrated datasets.

Question: Given S&P Global’s “Margin Math” research on tariff-related cost shocks potentially reaching $1.2 trillion, which combinations of sell-side revision patterns and supply-chain network data have proven most reliable at flagging margin compression before it appears in quarterly earnings?

Data Provider Showcase: Increase Alpha; Turning AI & Data into Uncorrelated Returns

9:20 AM - 9:30 AM

Speaker: Sid Ghatak

• A new source of uncorrelated alpha,

• Long range fundamental predictions

Question: Your approach specifically avoids LLMs4 in favor of purpose-built deep learning for markets, and you emphasize using only public compliance-grade data. Can you walk us through what types of public data inputs drive your predictions, and how your architecture differs from others in this space?

From Agents to Alpha: How & Why Funds Can Deploy AI in 2026

9:30 AM - 9:40 AM

Moderated by: Mark Thompson, Head of Primary Research, Norges Bank Investment Management

• The gap in AI evolution and adoption

• Winners in the next gen AI world

• LLMs, autonomous research agents, moving from experimentation to production.

• Data vendor due diligence: Challenges and Opportunities in the AI driven world.

Question:What are the most important steps large asset owners like NBIM must take in 2026 to turn AI agents5 from pilots into production tools, and how should they evaluate vendor claims about data governance6?

Decoding the Noise: When AI Meets MESSY Data

9:55 AM - 10:15 AM

Moderator: Rob Morse, Head of Data Strategy and Sourcing, PDT Partners

Speaker: Or Zabludowski, Co-Founder & CEO at Flexor

How AI can filter, harmonize, and extract signal from complex, unstructured MESSY data

Question: When turning unstructured text into structured features, how does Flexor balance the trade-off between extraction accuracy and explainability7? Many in this space have been able to generate precision and recall8 scores in the 90%, but leaves the user to check the output to find the 10% that isn’t right. What will it take to get to 99.99% precision or useful indicators about which 10% of the output is likely incorrect?

Building Deterministic Web Data Pipelines with AI: What Actually Works at Scale

10:15 AM - 10:30 AM

Speaker: Sarah McKenna, CEO at Sequentum

AI-augmented agent creation, human-in-the-loop web scraping with intelligent unblockers and atomic-level customization to deliver the precision required for mission-critical decisions

Question: In practice, where does human-in-the-loop9 oversight add the most value in AI-driven web scraping, and where has automation become reliable enough to stand on its own?

Data Mosaic - Consumer Retail

10:50 AM - 11:30 AM

Moderator: Gene Gallagher, Director of Research at Exabel

Speakers:

• Jharonne Martis, Director of Consumer Research at LSEG

• Jeremy Scott, Director of Research, Data Purl

• Anthony Bartolacci, Chief Strategy Officer, Sensor Tower

• K-Shaped consumer divergence

• Consumer spending outlook

• AI and Agentic Commerce

• Omnichannel as competitive requirement

• M&A and Portfolio Reshaping

• Return rates and profitability pressure

Question: As consumer spending bifurcates between higher- and lower-income households in a K-shaped economy10, which combinations of alternative signals are proving most effective at identifying which retailers’ omnichannel strategies will deliver profitable growth versus which are masking structural deterioration?

Data Provider Showcase: The Next Frontier in Web Scraping

11:30 AM - 11:40 AM

Speaker: Tavis Lochhead, Co-Founder at Kadoa

• PMs and analysts building their own datasets in minutes

• How AI codes and maintains scrapers that don’t break

• Data you can trust, trace, and audit

• Automated compliance controls built-in

• Live demo: From source to dataset in 60 seconds

Question: In your experience, what design choices make the biggest difference between web data pipelines that keep working quietly in the background and those that degrade as sources change?

Data Provider Showcase

11:40 AM - 12:00 PM

• Providers were qualified and scored on multiple parameters

• Selected providers will provide a brief intro to their product

Question: Good luck to the showcase participants. My advice is to highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets.

Friday, January 23

Opening Remarks

9:00 AM - 9:10 AM

Speaker: Tim Harrington, CEO at BattleFin

Question: What emerging themes from the first two days suggest the biggest shifts in how buy-side firms will approach data sourcing and AI deployment in 2026?

Compliance in the Age of Intelligent Machines

9:10 AM - 9:40 AM

Moderator: Jason Koulouras, Research and Data Ranger, Bridgewater Associates

Speakers:

• Boris Liberman, Partner at Lowenstein Sandler

• George Danenhauer, Counsel at Lowenstein Sandler

• Adapting to global AI regulation, transparency, explainability requirements, and market oversight (including the EU AI Act).

• Tools and workflows for SEC and global compliance, as well as insights into future legal trends impacting data and AI

Question: With different regulatory frameworks emerging globally, what practical compliance foundations should firms put in place now so they can adopt AI more confidently as expectations evolve?

Earnings Expectations Analysis, using unique signals to generate Alpha

9:40 AM - 10:10 AM

Speaker: Arabella Graves, Senior Behavioural Analyst, Norges Bank Investment Management

KPI analysis to confirm variant perceptions, linguistic fingerprints

Question: What does the investment workflow look like when deviations from baseline speaking patterns are detected? Is it a signal to dig in deeper on the fundamental research or are there some signals so strong that trading activity begins right away?

Data Provider Showcase

10:10 AM - 10:20 AM

Speaker: Anju Marempudi, CEO at EventVestor

Question: Having a dataset of potential fundamental catalysts is a key component of a systematic model that triggers there could outside factors weighing on the security price compared to the modeled factors. What additional traditional and alternative datasets pair well with your product to systematically understand what the new signal is in real time?

Data Mosaic TMT

10:20 AM - 10:50 AM

Moderator: Joss Tricks, Investment Associate, Bridgewater

Speakers:

• Melissa Otto, Head of Visible Alpha Research at S&P Global

• Adam Little, VP of Data Operations at RepVue

Question: When sell-side consensus models project strong growth for a software or telecom company, what patterns in salesforce compensation, quota attainment, and hiring activity serve as the most reliable confirmation or contradiction of those forecasts, and how quickly do these workforce signals typically lead earnings revisions?

Evolution of the Data Strategies in an AI World

10:50 AM - 11:20 AM

Moderator: Ish Pander, CSO, BattleFin

Speakers:

• Ben Cohen, Managing Director, Data Strategy, Final

• Gregory Kurzman, Data Sourcing, Balyasny

• Rayne Gaisford, Chief Risk Officer & Chief Data Strategy Officer at RTW Investments

Question: Many data vendors are concerned about losing control of their IP when their datasets are consumed by Gen AI instances fully contained within asset managers’ tech environments, such as enterprise accounts that meet strict asset manager compliance and data control requirements. What advice would you give vendors to help them get comfortable with buy-side use of their data products in enterprise Gen AI applications?

Inside an Alpha Machine. From Signals to Scaling

11:20 AM - 11:50 AM

Moderator: Tim Harrington, CEO at BattleFin

Speaker: David Rukshin, CTO at WorldQuant LLC

How are LLMs and Agents affecting workflows and other aspects of the business. What do quants really want from data vendors

Question: As WorldQuant integrates LLMs and multi-agent systems into production workflows, what new requirements are emerging for data vendors around documentation, APIs, and integration support? More fundamentally, are you finding that most vendors’ delivery models were built for human consumption rather than AI agents, and if so, what needs to change?

What questions would you ask these panelists? Leave a comment below.

Leave a comment

Know someone attending or presenting? Feel free to forward this to them.

Share

- Jason DeRise, CFA

8

Precision and Recall: Precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of the total number of relevant instances that were actually retrieved. Put another way, precision is the percentage of the predictions that were right. Recall is the percentage of the actual answer that was correctly predicted. Both are used to measure the quality of a machine learning model.

More info can be found on Wikipedia: https://en.wikipedia.org/wiki/Precision_and_recall

There are 4 themes in my questions

1. Model and strategy governance

2. Data quality, reliability and compliance risk

3. AI infrastructure and workflow transformation

4. Market and commercial dynamics in data ecosystems

https://www.neudata.co/events/new-york-winter-data-summit-2025

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Share

Subscribe now

Note: Agenda as of November 30, 2025.

Opening remarks

8:45 - 8:55 Empire Stage

• Ian Webster, Chief Revenue Officer, Neudata

Question: What signals from data buyers and vendors over the past year most strongly suggest where the alternative data market is heading in 2026, and which behavioral shifts are you watching most closely?

An interview with a CIO

8:55 - 9:20 Empire Stage

Join Dr. Wojtek Fundamenski, Founder, CEO and CIO of EngSci Capital Management LLC (https://www.engscicapitalmanagement.com/), for an exclusive fireside chat into how a nuclear physicist became a quant investor. Dr. Fundamenski leverages decades of experience modeling the world’s most complex systems from plasma dynamics to global markets, to engineer data-driven investment strategies that fuse scientific rigor with financial innovation. Dr. Fundamenski has pioneered the application of both alternative data and advanced statistical modeling to systematic investment strategies. His tenure with renowned institutions such as Winton Capital, Teza Technologies, Exodus Point and Brevan Howard has shaped how quantitative research drives decision-making in financial markets.

• Ian Webster (Moderator)

• Wojtek Fundamenski, CEO & CIO, EngSci Capital Management

Question: Can you describe the framework you use when models take unexpected actions? How do you determine when humans should intervene to overrule the model or support its recommendation?

The hidden cost of trading on aggregated data

9:20 - 9:45 Empire Stage

Investors increasingly rely on alternative data, but aggregated metrics often mask critical business shifts. This session examines the hidden risks of making investment decisions without understanding who drove a trend change and why it occurred. Through real-world examples using mobile consumer behavior data, attendees will see how standard engagement metrics can tell one story while user-level analysis reveals a different reality, whether hidden risks or overlooked opportunities.

• Jonathan Kay, CEO, Apptopia

Question: Can you connect the granular user-level metrics to how they influence KPI forecasts beyond the current quarter?

Private by design: AI infrastructure for the data-driven market

9:45 - 10:15 Empire Stage

As AI adoption accelerates across financial services, firms face a critical challenge: how to innovate at speed while protecting sensitive data, ensuring compliance, and controlling costs. In this session, discover how leading institutions are building intelligent, secure, and compliant AI infrastructures; turning data into advantage without sacrificing trust or control.

• Tim Yockel, SVP - Global Sales, Options Technology

• Ryan Ballantine, Director - Product Management, Horizon, Options Technology

Question: As firms scale AI adoption, which architectural choices deliver the greatest reduction in compliance and operational risk per dollar invested while also supporting broader integration into daily workflows?

AI adoption and the future of work

10:15 - 10:45 Empire Stage

AI adoption is reaching an inflection point with potential to unlock approximately $920 billion in long-term benefits for S&P 500 companies, translating to an estimated $13-16 trillion in market value creation. New research from Morgan Stanley provides a novel analytical framework assessing both agentic and embodied AI adoption across industries.

• Stephen Byrd, Global Head of Thematic Research and Sustainability Research, Morgan Stanley

Question: Which industries and job functions will see the earliest productivity gains on the path to $13-$16 trillion in market value creation? What traditional or alternative data points are you monitoring to confirm the thesis is on track, and what evidence would cause you to reassess the outlook?

Workshop – Unlocking compliance and value: Insights from DDQs (Data Providers)

10:00 - 10:40 Rockefeller Room

This session will reveal key compliance trends and risks uncovered from anonymised DDQs across the alternative data provider landscape. These real-world insights will allow attendees to benchmark their practices, proactively address emerging risks and adapt to changes in the legal and operational landscape.

• Brittany Thomas, Regulatory Associate, Neudata

Question: Across the DDQs you reviewed, which recurring gaps most reliably signal that a vendor’s operational risk is higher than their marketing suggests?

Fireside chat: How AI is changing web scraping 11:10 – 11:30 Empire Stage Web scraping has never been easier with the advent of AI coding and LLM-powered data extraction. We’ll discuss how firms of all sizes are scaling their web scraping and new use cases emerging across the industry.

• Dorothy Ruderman, Data Strategy & Research, Verition Fund Management (Moderator)

• Tavis Lochhead, Co-Founder, Kadoa

• Michael Hejtmanek, Vice President, Corporate Solutions, Neudata

Question: How do you embed data quality checks in an agentic framework to ensure high precision and recall? How do you help data teams identify errors quickly and trigger recollection before issues propagate?

Small models, big alpha

11:10 - 11:30 Chrysler Stage

This presentation will explore quantitative drivers in 2025 and how fine-tuned LLMs provided an efficient way to boost alpha for quantitative strategies.

• Aditya Sharma, Director, Product Management, S&P Global Market Intelligence

Question: Fine-tuned small language models can run on edge devices and deliver domain-specific insight at a lower cost. Where do you see the greatest potential for these compact models to enhance quantitative strategies, and how should investment teams weigh performance, privacy, and deployment complexity when adopting them?

The risks and rewards of AI-driven investing 11:30 – 12:10 Empire Stage What impact is the growing use of AI LLMs having on the use of alternative data in investment management? This session explores the transformative breakthroughs enabling investors to extract insights from unstructured data, improve forecasting accuracy and unlock new sources of alpha. The conversation also addresses key challenges including data privacy and regulatory compliance.

• Tony Berkman, Former Managing Director, Two Sigma (Moderator)

• Thomas Li, Co-Founder and CEO, Daloopa

• Joseph Aube, US Deputy Head of Data Strategy, Brevan Howard

• Duncan Robinson, Director of AI & Optimization, Franklin Templeton

• Sarkis Agaian, Quantitative Developer, Laurion Capital Management

Question: Systematic strategies risk underperformance when a regime change occurs in the real world, which means the model is not properly calibrated based on the training data. Humans on the loop have provided that oversight and judgement, knowing when to take a model offline for refactoring instead of doubling down on a broken model incorrectly suggesting high alpha opportunities. Over a ten-year horizon, do you believe generative AI can assume that current human oversight role within an agentic framework?

Future proofing your market data strategy

11:30 - 12:10 Chrysler Stage

As investment funds face increasing pressure to modernize their data infrastructure while keeping costs and compliance in check, the market data landscape is evolving rapidly. This panel brings together industry experts to discuss how to make smart, future-proof vendor choices, demand transparency in licensing and pricing, ensure high data quality, and navigate the complex web of contracts and cloud migration challenges.

• Danesh Kissoon, Senior Associate - Market Data Lead, Neudata (Moderator)

• Joe Cappitelli, General Manager and SVP, Dow Jones Newswires

• Eliza Raphael, Head of Data Services, Jump Trading

• Carrie Anton, Director, Data and Research Management, Jain Global

• Brian Wheeler, Senior Director, Head of LMI Data Office, Liberty Mutual Investments

Question: As technology reshapes the market, how do you avoid vendor lock-in while also minimizing switching costs?

New vendor showcase 12:10 - 12:40 Empire Stage In this showcase session hear from some of the industry’s newest providers, as they share the datasets and product launches set to take the world of alternative data by storm. Ensure you’re keeping on top of all the latest updates and case studies to inform your future data acquisition strategy.

• Sergio Toro, CEO, Aterio

• Paul Boynton, COO and Co-Founder, Company Search Incorporated (CSI)

• Nate Singer, Chairman, Blue Heron Research

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

Ranger in review: Neudata’s traditional and market data platform one year on

12:10 - 12:30 Chrysler Stage

Neudata’s Ranger product launched with a bold promise: help investors discover, evaluate and monitor market data more intelligently. Over a year in, what have we learned? In this spotlight session, Neudata shares market trends from the first 12 months including what data buyers are searching for and which data categories get the most traction. Neudata also provides a sneak peak into what the future of Ranger might look like.

• Danesh Kissoon, Senior Associate - Market Data Lead, Neudata

Question: Looking at Ranger’s first year of usage, which buyer behaviors in search, comparison, and monitoring best predict which datasets go on to achieve meaningful commercial traction?

Alternative data in private markets: Gaining the edge

12:30 - 1:10 Chrysler Stage

Private equity and venture capital firms are increasingly turning to alternative data to gain an edge - faster deal sourcing, better benchmarking, and sharper insights. This panel explores how emerging data sources and analytics tools are reshaping private market strategies, from investing in data-driven companies to integrating data into their own workflows. Hear what’s working, what’s next, and how firms are positioning for the future.

• Barney Bruce-Smythe, Vice President, Neudata (Moderator)

• Viacheslav Romanov, Partner and Associate Director, Boston Consulting Group

• Beth Pollack, Operating Partner, Applied AI and Data Strategy, Data Science Advisors

• Lindsi Kimizuka, Vice President - Head of Product, Summit Partners

• Zachary Strasser, Real-World Data Analytics Lead, Patient Square Capital

Question: How should alternative data companies adapt their products and commercial agreements to better support private market data use cases?

Navigating the data management lifecycle

12:40 - 1:10 Empire Stage

This session will explore key challenges investment funds face in managing the data lifecycle, from workflows and tooling decisions to breaking down data silos and managing vendor subscriptions. How do different stakeholders across investment, data science, compliance and operations teams interact with data and how can internal communication be streamlined? How can immediate data needs be balanced with longer-term strategy?

• Alden Sewall, Sales Director, Neudata (Moderator)

• Danielle Castelli, Senior Manager, Research and Data, The Baupost Group

• Natalya Dmitriyeva, Former Global Head of Enterprise Data, Schonfeld

• Steve Hansen, Global Head of Market Data, Davidson Kempner Capital Management

• Rachel Lange, Data Strategy Lead, Point72

Question: Where are you in your centralized versus decentralized journey as data teams, and where do you think the right balance lies? Thinking about this meme for the question :)

Lunch and Learn - Is your data lying? Building the unbreakable, high-confidence identity for investment due diligence (Data buyers and providers)

1:20 - 2:00 Rockefeller Room

For investment professionals, alpha starts with identity. In the rush to integrate new alternative datasets, the biggest risk isn’t the data source - it’s the identity chaos that results. Every disparate piece of information creates noise, making it impossible to confidently identify a person or company and accurately assess risk or signal strength. Join us to explore how modern, AI-powered Entity Resolution (ER) fundamentally changes this dynamic. In this 40-minute Lunch and Learn, we’ll provide a 10-minute blueprint for establishing Trust, Scale, and Defense in your data ecosystem. Then, a 30-minute live demonstration will showcase real-time Senzing ER, proving how simple and fast it is to automatically link, match, and evolve a true, persistent identity across all your datasets. Walk away with the knowledge to move from data fragmentation to a governed, unified entity view.

• Gurpinder Dhillon, Head of Data Partner Strategy and Ecosystem, Senzing

• Jamie Foss, Head of Data Partner GTM, Senzing

Question: Can you share an example where low-quality entity resolution misled investors, and what evidence would indicate that a firm has moved from lagging to good practice in this area?

How to preserve alpha in a macro-driven environment

2:10 - 2:50 Empire Stage

From accelerating regime shifts to persistent rate volatility, the past few years have redefined the macro playbook. This panel will discuss how top investment teams are adjusting their portfolio construction and risk solutions to deal with the increasingly volatile macro environment.

• Mahmood Noorani, CEO and Co-Founder, Quant Insight (Moderator)

• Philip Vasan, Former Managing Director, Co-Head of Fundamental Equities, BlackRock

• Duncan Robinson, Director of AI and Optimization - Principal Research Scientist, Franklin Templeton

• Vishwanath Tirupattur, Chief Fixed Income Strategist and Director of Quantitative Research, Morgan Stanley

• Gjergji Angjeliu, Vice President - Fixed Income Quantitative Research, Analysis and Development, Liberty Mutual Investments

Question: When macro volatility dominates returns, which diagnostics give you the most confidence that a strategy’s alpha is coming from true security selection rather than unintended macro exposure drifting through the portfolio?

Using alternative data to anticipate CPI: State Street PriceStats inflation indices

2:50 - 3:10 Empire Stage

State Street PriceStats tracks billions of prices sold by online retailers to generate daily inflation indices to help investors anticipate and evaluate the impact of inflation and implications for monetary policy. Our methodology is built upon a robust academic foundation through the Billion Prices Project initiative established by Harvard Business School professor Alberto Cavallo. Our research indicates that online prices not only measure inflation effectively, but also anticipate official inflation trends by several months. By providing a daily view of inflation across 27 countries and regional aggregates, State Street PriceStats provides timely insights that can help you get ahead of major shifts in inflation trends.

• Jamie Stewart, Managing Director, State Street Data Intelligence

• Michael Guidi, Managing Director, State Street Associates

Question: Looking back at episodes from disputed or low-quality CPI in some countries to the recent US government shutdown and data blackout, what patterns have you seen that tell you when an online price index is actually more reliable than the official statistics for investors and policymakers?

Trading on trends: Harnessing social media data for macro and market insight

3:10 - 3:30 Empire Stage

Social media data can provide high-frequency, low-latency insights into the opinions and sentiment of consumers, investors and policymakers, so it comes as no surprise that we’ve observed an increase in interest and demand for social media data, given current uncertainty. This presentation will discuss the different use cases for social media data in investment strategy: measuring macro sentiment, monitoring the social media behaviour of retail investors and tracking the consumer engagement for viral products or social media-driven boycotts.

• Drew Davies, Research Analyst, Neudata

Question: Social media sentiment has always been a tricky dataset to accurately generate because of the ongoing evolution of slang and memes combined with sarcasm, satire, and ironic posts. Has the rise of LLMs made this challenge more manageable, allowing for more accurate sentiment scoring?

Alternative data’s relationship with the sellside

3:30 - 3:50 Empire Stage

This session will dive into the types of data providers who work with the sell side and consider whether this is consistent across datatypes. What concerns do data providers have in working with the sell side? Are two-tier offerings common to preserve hedge fund value? Can we beat the sellside? And do we care?

• Finn Cousins, Research Analyst, Neudata

Question: Is it fact or fiction that asset managers avoid purchasing alternative data from a source that is also cited and used in sell-side research reports?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. How to turn raw data into practical, actionable signals

2. How to evaluate and compare signals across dataset and industry domains

3. How practitioners can operationalize new tools and workflows

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of 18 November 2025

Morning Session

9:00 AM - 9:20 AM Unlocking Financial Data with Agentic AI

• Enabling AI in excel for analysts and PMs

• The role good data plays in the AI

• Will MCP replace APIs?

Speakers:

• Tim Harrington (Co-Founder and CEO) at BattleFin

• Thomas Li (Co-Founder/CEO) at Daloopa

Question: Where is agentic AI inside Excel already helping analysts produce a cleaner first model draft faster, and which basic data hygiene practices, such as lineage, refresh cadence, and mapping, have made adoption stick instead of reverting to traditional model building?

9:20 AM - 9:40 AM Healthcare Data Mosaic: Identifying GLP-1 trends and signals with data for 2026

Predictive and enriched data derived from medical device and pharma invoice data from hospitals. Transactions are tickerized and categorized.

Speakers:

• Tim Harrington (Co-Founder and CEO) at BattleFin

• Mark Holmquist (Vice President, Head of MarketPulse) at Sg2

Question: What do recent data trends suggest about shifts in market share among leading GLP-1 providers in the hospital prescription channel, and do these trends translate to other channels?

9:40 AM - 10:00 AM Data-Driven Insights from Securities Finance, ETF Composition and Dividend Forecasting

• Short sentiment metrics from Securities Finance data offer real-time signals into market positioning and alpha opportunities

• Integrating Short sentiment with ETF composition data and forward-looking Dividend forecasts enhance systematic portfolio construction

Speaker:

• Shan Gao (Product Manager) at S&P Global

Question: When short interest, ETF baskets, and dividend forecasts are viewed together, where have practitioners found the most practical applications in portfolios?

10:00 AM - 10:30 AM Consumer Mosaic: Navigating Consumer Trends in Europe

• Real-time consumer and retail trends from credit card, POS, and web-scraped data

• UK tax changes and what effect they may have on the consumer

• Retail Trends Spotlight – highlights Alt Data signals across multiple products

Speakers:

• Tim Harrington (Co-Founder and CEO) at BattleFin

• Chris Stevens (Director, Data Science Research) at Barclays

• Mark Thompson (Primary Research) at Norges Bank

• Gene Gallagher (Director of Research) at Exabel

Question: How would you bring together multiple alternative data sources to monitor and predict the effects of UK tax changes on consumers and the downstream impact on companies? Which data points will matter most for identifying which scenario is unfolding?

Afternoon Session

3:00 PM - 3:15 PM Data Product Showcase – Seeker for Data Strategists

How to Cut Your Alternative Dataset Integration Time by 50%

Speaker:

• Harrison Perl (Director, Alternative Data Strategies) at Exabel

Question: In practice, where does Seeker remove the most friction for data strategists, and which team habits help those time savings appear quickly in live research workflows?

3:15 PM - 3:35 PM Alpha in Advance: Credit-Derived Early Warning Signals

Leveraging advanced data analytics to forecast 2026 risks Predicting Rating Transitions using Machine Learning

Speakers:

• Stas Melnikov (Head of Quantitative Research and Risk Data Solutions) at SAS

• Paul Kamenski (Partner, Co-Head of Credit) at Man

Question: When an early warning signal points to a likely rating change ahead of the market, what is the best way for a credit team to use it given limited liquidity across many publicly traded bonds?

3:35 PM - 3:45 PM Healthcare Data Spotlight

Claims data based on multiple sources—hospital, medical, specialty, retail, etc.

Speaker:

• K’Lene LeCompte, Chief Business Officer, IMX Research

Question: Claims data often have a long lag between when a claim is reported and when the underlying service occurred, creating delays compared with real-time datasets. What are the best use cases that still produce signals before the market recognizes them?

3:45 PM - 3:55 PM New European Transaction Data Provider Spotlight

Daily insights into UK shoppers highlighting consumer trends and company revenue predictions. Speaker:

• Lauren Ambrose

Question: What recent surprises has the dataset uncovered that proved to be on the right side of consensus expectations?

3:55 PM - 4:20 PM Energy/Commodities Data Mosaic

OPEC+, U.S./China tensions, and their ripple effects on oil & gas markets How revisions can effect pricing and inventory projections Supply and demand tools that can give you conviction for your forecasts

Speakers:

• Gene Gallagher (Director of Research) at Exabel

• Naveen Das (Senior Analyst) at Kpler

Question: Which European gas and oil scenarios carry the highest probability heading into winter 2025 to 2026, and which signposts are you watching to confirm or challenge the scenario that is unfolding?

4:20 PM - 04:30 PM Babble Labs—AI-powered media analysis and signal extraction.

• Ticker-mapped alpha signals from YouTube content

• The power of social media monitoring with near real-time analytics

Speaker:

• Ramsey Shaffer (Co-Founder & CEO) at Babbl Labs

Question: How do signals from retail investor activity on YouTube differ from those on Reddit?

4:30 PM - 4:45 PM Surviving the Curveball: Macro Perspectives using NLP and News Articles to predict Trends for 2026

Inflation and labor-force trends heading into 2026

Speakers:

• Tim Harrington (Co-Founder and CEO) at BattleFin

• Dr. Richard Peterson (Founder & CEO) at MarketPsych

• Laurent Bilke (CEO and Head of Research) at Alternative Macro Signals

Question: For inflation and labor trends, how have text-derived signals improved traditional macro analytics, and how do you identify which news signals have causal links to future inflation reports?

4:45 PM - 5:00 PM Leveraging Hierarchical Modeling on Alt Data to go beyond Revenue KPIs

Speaker:

• Zac Yang (VP Product) at Exabel

Question: Which non-revenue KPIs have translated best from early signal to reliable forecast, and which simple stress tests help teams maintain confidence as they scale across sectors?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I offer targeted questions for each speaker and panel. For investment-oriented sessions, the questions connect directly to data, technology, and AI themes rather than individual investment debates or workflows.

There are 3 key themes to my questions:

1. Data and Analytics as an Edge

2. Successful AI Integrations

3. Governance, Risk, and Adaptation

https://berylelites.com/beryl_panel2025

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of October 31, 2025

Monday November 3rd

8:45 – 9:10 The Presentation by the Economist Nouriel Roubini & Opening Remarks by Vidak Radonjic

“Why American Exceptionalism & The Exorbitant Privilege of the US Dollar Are Not Over”

Question: When inflation and geopolitical risks are widespread across regions, the dollar’s value against other currencies may not tell the full story—for instance, gold’s 10-year trend in dollar terms. What data do you track to test the view that the US dollar will maintain its reserve-currency status, and what indicators might shift that view?

9:15 – 9:50 Requiem for a Startup

With most VC-backed startups destined to fail, boards face tough fiduciary, legal, and operational challenges. This panel brings perspectives from lenders, investors, crisis board members, and advisors on alternatives to bankruptcy.

• Moderator: David Johnson, Resolution Financial Advisors

• Claudia Canales, Silicon Valley Bank

• Aron Schwartz, CION Investments *Matt Thompson, CSC Generation

Question: In venture capital, promising progress can quickly unwind and turn into crisis due to many factors. What data and analytical techniques can boards and private investors use to identify early warning signs of distress and act early on non-bankruptcy solutions?

9:55 – 10:45 AI in Action - Driving Value with GenAI and Beyond

AI, GenAI, and agentic systems are reshaping how firms operate, but are they delivering real business value or just hype. What AI/GenAI use cases are clients adopting now?

• Moderator: Kevin Brett, KPMG

• Hemma Retaj, BlackRock

• Tamer Ozmen, Mintus Atul Subbiah, Accenture

• Tim Baker, viaNexus

• Maximilian Gartner, Brightwave

Question: Where do asset managers most often fail when integrating generative AI into workflows? Is it data quality, poor system integration, weak user training, or flawed use-case selection, or something else entirely?

10:50 – 11:30 The Cell Therapy Revolution from Scientific Breakthrough to Scalable Investment

How next-gen therapies are redefining the future of medicine. Cell & gene therapy is undergoing a transformational leap, with next-generation platforms like in vivo CAR-T, allogeneic therapies and automation.

• Moderator: Joy Wang, Dendreon

• Jason Foster, Ori Biotech

• Kristen Hege, Multiple Biotech Boards

• Richard Ransohoff, Third Rock Ventures

• Shyam Patel, CIRM

Question: How do biotech companies measure performance against competitors, and which data and analytics drive their competitive intelligence? Investors seek an edge driven by data and analytics. Corporate competitive intelligence workflows and analytics are often very similar to what investors do. Firms know their own data but lack visibility into peers. So, understanding how they bridge that gap could reveal new opportunities for alternative data and data science.

11:35 – 12:15 Intelligent Data - Rethinking How AI Shapes Investment Decisions:

From finance to healthcare, alternative data and AI are redefining how decisions are made, risks are managed, and opportunities are uncovered. The shift from search to chat - what is the new customer journey?

• Moderator: Rahul Gupta, DRW

• Michael Marrale, M Science

• Kirk McCowen, Carbon Arc

• Richard Lichenstein, Bain & Company

• Vlad Prelevic, Sensor Tower

Question: Despite years of predictions, Excel and BI dashboards remain essential. Will GenAI ultimately replace them or simply reshape how they’re used? Thinking further into the future, if chat interfaces replace dashboards, what might eventually replace chat to bring data-driven decision makers even closer to solving real problems?

12:20 – 1:00 How Technology is Reshaping Private Markets

The opportunities it unlocks with blockchain and AI and the issues it introduces. Private Markets have surged in popularity, but outdated structures lack standardization and create high costs, complexities, limited access compared to public market.

• Moderator: Richa Singh, Lexington Partners

• Stephanie Ramezan, The Crypto Collective

• Dennis Cristallo, J.P. Morgan

• Ivan Mihov, Boltzbit

• Marc Faubeau, Tredit

Question: The opportunities for distributed ledger technology to transform the way capital markets invest in private companies have been discussed and attempted for at least a decade. What has been learned in that process, and how does the industry move past the blockers to unlock the potential of the technology in capital markets?

1:05 – 1:45 The New Moats - Building the Quant Stack for a GenAI Era

A research architecture that thrives on unstructured data, interpretable AI, the creative fusion of human & machine intelligence. From public equities into private markets; migration of quant techniques, factor modeling, headcount analytics, sentiment scoring.

• Moderator: Robert Ciemniak, Robotic Online Intelligence

• Daniel Sandberg, S&P Global

• Spencer Marschall, M Science

• Ehsan Ehsani, Crescendo Partners

• Mike Soss, Millburn

Question: With unstructured data and generative‑AI models potentially creating new sources of edge, what “quant moats” can institutional investors build that combine interpretable AI models, human domain expertise, and scalable infrastructure? How do you ensure model governance and risk management keep pace with experimentation so that innovations do not introduce hidden tail risks?

2:30 – 3:10 The Digital Asset Market – One Year On

How digital asset markets have shifted since last Fall, highlighting major developments, assessing the benefits & challenges. The progression of digital asset trading. The emergence and expansion of derivatives, the evolution of custody and regulatory landscape.

• Moderator: Anthony Tuths, KPMG

• Robert Strebel, DRW

• Adam Vandeboogaard, Copper

• Sam Gaer, Monarq Asset Management

• John D’Agostino, Coinbase

Question: What is the role of data and analytics in modernizing the digital asset market to enable a trusted and well-functioning marketplace? Which metrics will matter most to market participants and regulators to know the market is functioning properly?

3:15 – 4:05 Generative AI from a Business Strategist’s Perspective

Microsoft has published a recent report talking about “The Frontier Firm” – a vision of how companies can give each employee a team of generative AI assistants and agents – employees become agent bosses and thereby gain significant productivity and capacity.

• Moderator: Samantha Subin, CNBC

• William Bundy, McKinsey & Company

• Anthony Martinez, Mercer

• Falgun Desai, Microsoft

• Harvey Stein, Two Sigma

• Yu Yu, BlackRock

Question: Large‑language models and agentic systems promise to make employees “AI managers” rather than task executors. Drawing on your experiences, which processes are you redesigning around GenAI assistants, and how are you measuring productivity while managing risks such as hallucination, security, and reskilling?

4:10 – 5:00 Private Lending, Structured Credit & Insurance Solutions

With 10-year Treasury yields staying higher for longer, managers and investors are reassessing sources of attractive, risk-adjusted returns. How private lending and insurance solutions navigate global risks and opportunities

• Moderator: Kevin Brett, KPMG

• Peter Cecchini, Axonic Capital

• Sulexan Chery, Axonic Insurance

• Jean-Francois Crousillat, Lighthouse Partners

• Rich Nuzum, Franklin Templeton

• Akhil Mago, Indago Capital

Question: In a scenario where interest rates remain in ranges more consistent with historic periods than the early 2020s, what data points and analytics should asset managers prioritize to assess if the current tight credit spreads will persist or if credit spreads will widen to historic levels? How might the analytics be the same or different between public and private credit markets?

5:20 – 6:00 Digital Assets Treasuries (DAT)

Corporate treasuries are no longer just vaults of cash. Public firms are putting Bitcoin, Ethereum, and other digital assets onto balance sheets—not only as inflation hedges, but to unlock capital, boost returns, and build bold equity stories.

• Moderator: Stephanie Ramezan, Crypto Collective

• Melinda Delis, Anchorage Digital

• Amy Oldenburg, Morgan Stanley

• Maja Vujinovic, FG Nexus

• Eva Lawrence, Figment

Question: As more public companies allocate to digital assets, how are treasury functions changing? What technology infrastructure, data analytics, and governance are needed to manage volatility and regulatory risk?

6:05 – 6:45 Allocators & AI - Redefining Alpha in Changing Market

As markets shifts, alternative investments are evolving, with AI and alternative data redefining alpha and risk management. The winners will be those who not only adopt new technologies but also align them with structural market shifts in credit, equity, liquidity. What strategies are allocators deploying the next 12 months.

• Moderator: Jackie Rosner, PAAMCO Prisma

• Rich Nuzum, Franklin Templeton

• Matthew R Gallati, JP Morgan

• Robert Rafter, Morgan Stanley

• Melissa Otto, S&P Global

Question: Ken Griffin recently said that GenAI fails to help hedge funds produce alpha. Do you believe that GenAI can generate Alpha for investors? If so, what examples stand out? If not, what must change to unlock that potential?

6:50 – 7:10 Health & Rescue: Founders Revolutionizing Life-Saving Innovation and Emergency Response

• Moderator: Vidak Radonjic, Beryl Consulting

• Patricia Silverstein, Silverstein Dream Foundation

• Bryan Stern, Grey Bull Rescue Foundation

Question: Outsiders may misunderstand your mission and the impact of your work, limiting support and funding. As a thought exercise, if cost were no constraint, what data and analytics would best tell your foundation’s story? Maybe the answer could inspire a data-driven partnership from a data company in the audience.

Tuesday November 4th

9:00 – 9:40 Macro & Multi-Asset - Navigating Structural Shifts and Market Shocks

Explore how globalization, trade dynamics, demographic trends intersect with credit & interest rate shocks—reshaping equities, fixed income, currencies, alternatives, managed futures, and multi-asset portfolio construction in era of persistent uncertainty.

• Moderator: Stas Melnikov, SAS

• Breanne Dougherty, Bloomberg

• Igor Yelnik, Alphidence Capital

• Judith Gu, Scotia Bank

• Mark Shore, CME Group

Question: Against a backdrop of persistent inflation, shifting supply chains, and divergent monetary policies, what macro scenarios should multi‑asset investors prepare for in 2026? What datasets and analytics are most important to include in the mosaic of insights and factors in quant models?

9:45 – 10:35 Explore the Risks and Returns of Digital Assets

Implications for institutional investors, while exploring strategies to integrate crypto ETFs into traditional portfolios to enhance diversification and returns. Insights into future trends, including central bank digital currencies, Defi, and tokenization. Assess how digital assets are reshaping global economic systems.

• Moderator: Anthony Tuths, KPMG

• Eric M. Pollackov, Invesco

• Matt Kunke, BlackRock

• Kelly Ye, Avenir Group

• Robert Materazzi. Venture Partner

• Frances Newton Stacy, Scarlet Oak Financial

Question: What due diligence frameworks (liquidity, custody, counterparty risk, token economics) do you apply before integrating digital assets into multi‑asset portfolios? How do you address concerns around central bank digital currencies and decentralized finance protocols?

10:40 – 11:30 AI’s Billion-Dollar Bet on Life Sciences - From Drug Discovery to Patient Impact

AI is moving beyond hype to deliver real-world breakthroughs in drug discovery and development. How Machine Learning is Reshaping Biopharma’s Future. AI-driven drugs and biomarkers; challenges in regulation and scaling. Evolving AI-biotech landscape and where capital is flowing.

• Moderator: Wade Schultz, Yale University

• Joy Wang, Dendreon

• Michael Heltzen, eXoZymes

• Sanjiv Kosha, Gilead Sciences

• Sandy Balkin, Royalty Pharma

• Frank Li, Stately Bio

Question: A question to help investors think about data-driven insights in this sector: Without access to non-public information, what data, signals, or observable behaviors could suggest that a biotech company is using AI to build a durable competitive advantage and long-term ROI?

12:00 – 12:50 Trust, Verify, and Survive: Operational Due Diligence Lessons from the Battlefield to the Boardroom

How is ODD evolving across private and public markets — from spotting red flags in fund structures, governance, service providers, and valuation to addressing and assessing concerns such as geopolitics, cybersecurity, regulatory pressures, emerging risks such as ESG verification and AI.

• Moderator: Vidak Radonjic, Beryl Consulting

• Anthony Martinez, Mercer

• Jennifer Keeney, BNY Mellon

• Alena Karr, Russell Investments

• Bryan Stern, Grey Bull Rescue Foundation

• Candace Freeman, Travers Thorp Alberga

Question: Across geopolitics, cyber risk, and AI-in-the-loop processes, what is the most important shift in operational risk today? Which simple, leading indicator would you add to every allocator’s dashboard to detect operational risk early?

12:55 – 1:35 Tokenization 2.0 - The Future of Capital Markets

From real estate and private credit to funds and treasuries, tokenization is reshaping how value is created, traded, and managed. The next wave of blockchain-enabled finance — how tokenized assets are improving market efficiency, expanding investor access, and driving innovation.

• Moderator: Chris Brodersen, EisnerAmper

• Rob Krugman, Broadridge

• Joseph Medioli, Anchorage Digital

• Emily Pollack, Republic

• Ben Sharon, LumiShare

Question: Beyond regulation and custody, what technology and data challenges must be solved for tokenized securities to achieve broad adoption beyond digital currencies?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

1. Model transparency and signal durability

2. AI and data integration

3. Advancing technology and risk management

https://battleofthequants.com/london-2025/

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of 31 October 2025

4 November 2025 Agenda

8:00 AM—Quantitative Allocator Breakfast

Quantitative hedge fund managers will pitch their strategies to tables of allocators over a two-hour period in a ‘speed dating’ format with table rotations taking place every 15 - 20 minutes. The breakfast provides an efficient means of allowing allocators to conduct initial due diligence through a combination of hedge fund presentations and interactive sessions while allocators gain valuable insight into the hedge fund’s strategies.

Question: For allocators meeting fund managers: What was the key breakthrough in factor design, refinement, or model learning that turned average results into sustained outperformance?

8:00 AM—Data Breakfast

Data and signal providers will present their datasets and signals to tables of quantitative portfolio managers, researchers, and data buyers over a two-hour period in a ‘speed dating’ format with table rotations taking place every 15 - 20 minutes. The breakfast provides a focused and efficient means of allowing those attending to conduct initial due diligence through a combination of data and signal provider presentations and interactive sessions while gaining valuable insight into the provider’s solutions as they relate to quantitative finance.

Question: For data buyers meeting vendors: What elements of your dataset’s methodology explain its strongest signals—and what limitations constrain its broader use?

10:10AM—Allocator Panel: Is Now the Time to Invest in Quants?

Global hedge fund AUM is at an all-time high in 2025 and is projected to reach $6 trillion in AUM by the end of 2026. Top-performing strategies include quant equity and quant multi-strategy. According to one investment bank, “Quant equity is expected to be the most in-demand strategy in the second half of the year.” Why are hedge fund strategies back in favor generally and quant strategies specifically? Is your quant allocation based on alpha generation or as a hedging tool?

• Moderator: Bartt Kellermann, CEO & Founder, Global Capital Acquisition

• Richard Hallos, Manager Research Director, Schroders

• Iliya Kutsarov, PhD, Deputy Head of Hedge Funds, FERI

• Jonathan Poon, Investment Director, Stable

• Luca Fuccaro, Quantitative Investment Analyst Investcorp-Tages

• Mathias Piardon, CEO & Founder, 1L Capital

Question: What due diligence frameworks or risk metrics are you emphasizing to differentiate durable alpha from fast‑decaying signals? Does your evaluation differ for multi‑strategy versus single‑strategy managers?

10:50AM Fireside Chat: The Dawning of the Anti-Pod

• Bala Subramanian, PhD, Founding Partner, President and Chief Risk Officer, Engineers Gate

• Nishant Kumar, Chief Correspondent, Bloomberg News

Question: How do quantitative methods enable a more collaborative structure of portfolio managers covering each sleeve of the overall portfolio but also avoid the risk of groupthink causing unintended correlations in the portfolio?

11:55AM—Manager Panel: What Are the Quantitative Strategies That Will Outperform in 2026?

Overall, 2025 is shaping up to be a strong year for quant strategies, with a resurgence of performance and a continued focus on innovative strategies employing AI and new data sources. What are the factors that have contributed to strong performance, and will quants continue their success in 2026?

• Moderator: Simon Legrand-Green, Head of Multi Asset and Systematic Strategies Research, WTW

• Michael Sun, PhD, Director - Quant Capability, Eastspring Investments

• James Murray, Portfolio Manager, Systematic Equities, Jupiter

• Elias Mechabchy, Founder and CEO, ROQS-Partners / MoSAIQ

Question: Which structural factors (macro regime changes, liquidity conditions, or regulatory shifts) do you expect to shape quant returns in 2026? How are you balancing complex machine‑learning models with interpretability and risk management, and are there specific anomalies or data sources you’re most excited or cautious about?

12:25PM—Predictive Analytics: Harnessing Data to Forecast Market Movements

Is the integration of diverse datasets or the refinement of a single source that empowers quantitative managers to forecast market movements? What combination and weightings of data delivers the highest predictive accuracy?

• Moderator: Mark Fleming-Williams, Head of Data Sourcing, CFM

• Jean-François Bacmann, Head of Research, LGT Capital Partners

• Christoph Lehmann, PM, Railpen

• Tim Anderson, Global Head of Tick History and Quantitative Economic Data, LSEG Data & Analytics

• Sayad Baronyan, PhD, Head of Quantitative Research, ISI Markets

Question: Could you share examples where adding “one more dataset” materially improved predictive power and cases where it increased noise or over‑fitting?

1:55PM—Can Quantum Computing in Quantitative Finance Achieve Predictive Superiority?

• Arman Khaledian, PhD, Co-Founder & CEO, Zanista AI

Question: Where do you expect near-term, real-world wins for quantum methods in quant finance, and what evidence supports them? Which use cases (for example, portfolio construction, option pricing, or risk) show the clearest path to faster or higher quality solutions over the next five years?

2:10PM—AI Panel: The Artificial Intelligence Imperative in Quant Funds!

Managing a quantitative strategy almost necessitates the inclusion of a robust AI component that drives alpha. Although AI encompasses a wide diversity of instruments, the one driving force is LLMs and now Agents. How relevant are these cutting-edge technologies, and how are they being used? Or are other AI tools really driving alpha?

• Moderator: Bartt Kellermann, CEO & Founder, Global Capital Acquisition

• Erk Subasi, Head of GAM Cantab, Senior Portfolio Manager - Executive Director, GAM Investments

• Martin Luk, Senior Quant, Man AHL

• Borno Janekovic, CEO, Omphalos Fund

Question: Where can AI agents most effectively support quant researchers: data discovery, feature engineering, signal design, portfolio construction, or execution? Will agents mainly automate repeatable tasks or evolve into creative research partners suggesting novel signals?

3:20PM—As Crypto Becomes Mainstream, How Are Quants Trading This Asset Class?

Crypto trading lends itself to very specific quantitative strategies. What strategies work best, and which are still challenged? Have crypto strategies become non-correlated, and what are the unique characteristics of this asset class? What impact will DATs have on structuring crypto trading funds?

• Moderator: Chris Solarz, CIO, Digital Assets, Amitis Capital Family Office

• Monica Monajem, CEO & Founder, Amitis Capital Family Office

• JM Rynaud, Founding Partner, Toshima Digital

• Sebastian Bea, President & CIO, ReserveOne

• Bob Vosalik, Founder & CIO, 319 Capital

Question: How are you adapting traditional risk and due diligence frameworks to address liquidity, counterparty, custody, and regulatory challenges in crypto?

3:50PM—Fireside Chat: Finding the Truth Behind the Implementation of AI in Quantitative-Based Hedge Funds

• Sid Ghatak, Chief Technical Advisor, National Artificial Intelligence Association

• Bartt Kellermann, CEO & Founder, Global Capital Acquisition

Question: What are the biggest misconceptions about AI’s readiness to deliver alpha in hedge funds? How should managers tackle interpretability, fairness, and compliance when deploying AI strategies?

What questions do you have for the panelists? Leave a comment below

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- Jason DeRise, CFA

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Data Resilience and AI Quality

2. Generative AI and Workflow Transformation

3. Private Market Data Strategy Evolution

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of Oct 27, 2025

Opening remarks

9:15 - 9:30 Main Stage - Nikko III

• Ian Webster, Chief Revenue Officer, Neudata

Question: Which alternative data categories are most established among private market investors, and which emerging types show the strongest momentum?

Keynote Panel—Data sourcing strategy: Resilience in a changing world

9:30 - 10:10 Main Stage - Nikko III Quantitative, Fundamental, Private markets

As the data landscape continues to evolve at pace, identifying and leveraging the right datasets is essential for driving informed financial decisions. Investment funds face mounting challenges, ranging from shifting regulations and data discontinuation to the risk of alpha decay. This session will delve into how resilient sourcing strategies and cutting-edge analytics are helping firms navigate these complexities and maintain a competitive edge.

• Ian Webster, Chief Revenue Officer, Neudata (Moderator)

• Ben Cohen, Managing Director, Data Strategy, Final

• Limin Xiao, Senior Portfolio Manager, Allspring Global Investments

• Artemiza Woodgate, CIO & Founding Partner, Integrated Quantitative Investments

• Ryan Morris, President, Meson Capital Partners, LLC

Question: When a key dataset changes or disappears, what first principles guide your replacement plan so research continuity, compliance, and signal quality stay intact?

Rewriting the sales playbook: Generative AI for account intelligence

10:10 - 10:30 Main Stage - Nikko III Quantitative, Data providers, Fundamental, Private markets

In today’s fragmented data environment, traditional account preparation no longer gives teams the speed or insights they need. Enterprises are turning to generative AI to unify internal and external data, sharpen customer engagement, and drive growth. This session will explore AWS’ journey in transforming account summary tools, examining the impact vs. accessibility framework that shaped development priorities, the challenges of selecting data for generative AI, and the measurable impacts on productivity.

• Fabian Namgalies, Former Head of Generative AI Roll-out, AWS

Question: For teams with messy CRMs and strict privacy rules, what is the simplest starting workflow that showed real win rates or time savings, and why did it work?

Data monetization seminar & networking

10:10 - 11:00 Workshop Room - Carmel I

This exclusive workshop will bring together a dynamic group of data owners and data buyers to explore strategies and opportunities for data monetization. Hear from a panel of Neudata and external experts before taking the opportunity to ask questions, gather invaluable intel, and form connections within a closed networking setting.

• Ian Webster, Chief Revenue Officer, Neudata

• Michael Hetjmanek, Vice President, Corporate Solutions, Neudata

• Robert Britt, Managing Director & Head of Data Science, Blue Owl

• Eric Evan, Head of Business Development, Data Solutions, Trustpilot

Question: What shared success metric can buyers and sellers use after signing that proves real value, is easy to measure, and reduces renewal friction?

New vendor showcase

11:10 - 11:40 Main Stage - Nikko III Quantitative, Fundamental, Private markets

This session will showcase some of the newest vendors, datasets and product launches taking the alternative data industry by storm. Ensure you keep on top of the latest updates and case studies to inform your future data acquisition strategy.

• Joe Vezzani, CEO & Co-Founder, LunarCrush

• Eric Williams, Senior Director of Partnerships, Rippl

• Dylan Johnson, Founding GTM, Wokelo AI

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

From messy records to trusted insights: How entity resolution ensures high-quality data across sources

11:40 - 12:00 — Main Stage - Nikko III Quantitative · Fundamental · Data providers · Private markets

The hidden cost of messy data is staggering. As organisations enrich their data with third-party sources, inconsistencies like ‘Joe’s Pizza’ appearing as ‘Joseph’s Pizza’, or ‘Joey’s Pizzeria’ can multiply across systems, creating confusion. At a global scale, with multiple languages and formats, these challenges become even more acute. This session will explore how Entity Resolution addresses issues of standardization, normalization, duplication and cross-cultural matching, providing the foundation for high-quality, trusted data.

• Gurpinder Dhillon — Head of Data Partner Strategy & Ecosystem, Senzing

Question: How might traditional multisource mastering evolve with generative AI integration? Does it enhance data quality or introduce new noise risks?

Unstructured to unstoppable: AI’s role in unlocking alternative data

12:00 - 12:40 — Main Stage - Nikko III Quantitative · Fundamental · Private markets

AI and LLMS promise to transform the extraction of insights from unstructured data, but what is actually working in practice? What impact is this having on the processing of Alternative Data in the investment vertical? Will the emergence of agentic AI and specialized tools overcome interpretability and explainability challenges? Discover how such breakthroughs could reshape how data supports investment decisions, while balancing compliance and commercial impact.

• Cindy Lin — Adjunct Instructor, WorldQuant University & Quant Researcher (Moderator)

• Cathy Tanimura — Principal, Analytics and Data Science, Summit Partners

• Amanda Widjaja — Vice President, Head of Data Science & Engineering, Norwest Venture Partners

• Monika Aggarwal — Principal Data & AI Leader, Financial Service Market, IBM

Question: As proof of concepts move into products for extracting structured data from unstructured private deal documents, how can users trust the output at scale? What are ways to prevent the user from having to check all records to know which may be incorrect?

What is the risk of AI on a business?

12:40 - 1:00 — Main Stage - Nikko III Private markets

As AI reshapes industries and investment theses, measuring AI adoption and understanding its risk and opportunity profile is becoming an essential component of every PE due diligence. This session will explore how alternative data can be used to quantify AI exposure, identify value creation opportunities, and mitigate emerging risks. Learn how investors and corporates are leveraging non-traditional data sources to navigate one of the most transformative shifts in technology.

• Clark O’Niell — Managing Director and Partner, Boston Consulting Group

Question: When AI serves as a business tailwind, how can investors assess the durability of that advantage amid rapid model innovation?

Insights from the industry: How investors are using data in 2025

2:00 - 2:20 — Breakout Stage - Carmel II Quantitative · Fundamental · Private markets

Join us to be walked through the results of Neudata’s 2025 Industry Survey, this session will explore the latest trends in the investment data landscape as reported by peers. Discover how much investors typically spend on data, what dataset types are most popular, how investors are using AI in their data strategies, and more.

• Ellen Johnson — Content Specialist, Neudata

Question: Which survey results surprised you enough to prompt deeper research, and what follow-up questions should the next survey explore?

Alternative data in diligence: Canva as a case study

2:20 - 2:40 — Breakout Stage - Carmel II Private markets

Using Canva as a case study, this session will explore some of the difficulties with traditional sourcing and the questions alternative data can help to answer. What can web traffic data uncover about the opportunities presented to Canva by AI chatbots? What can B2B panel data reveal about the stickiness of a Canva subscription? Discover how alternative data can play a key role in diligence.

• Nick Huntington-Klein — Senior Data Scientist, Pinegrove Venture Partners

• Alexey Lagerberg — Senior Data Scientist, Pinegrove Venture Partners

Question: When constructing an alternative data mosaic, how do you reconcile conflicting signals across sources?

Private Equity in the AI Age

2:40 - 3:00 — Breakout Stage - Carmel II Private markets

How can private equity investors better screen for and manage AI risk and opportunity? This session will help illustrate how straight-jacketed AI combined with alternative data can help bring structure to a very challenging topic.

• Warren Valdmanis — Managing Partner, FoW Partners

Question: What is a safe first AI workflow for portfolio operators, and how do you measure impact while keeping model risk low?

AI & differentiation in private markets: Enhancing performance

3:00 - 3:40 — Breakout Stage - Carmel II Private markets

As competition for high-quality deal flow intensifies, private markets are turning to AI to gain an investment edge - without the need for extensive internal model building. Discover how novel and high-value data sources are being used to identify opportunities, accelerate due diligence, and unlock measurable value creation across the investment lifecycle. Featuring discussions surrounding the use of paid and open-source models in processing the data and achieving AI innovation.

• Dan Zaelit — Head of Data Platform, Pinegrove Venture Partners (Moderator)

• Annamarie Kelly — Founding Product Engineer, Tidemark

• Anirudh Nair — Founding product Engineer, Base10

• Jerry Ye — Partner, BITKRAFT Ventures

• Christopher Ruiz Chiu — Head of Product and Data, NFX

Question: Where in the private markets deal lifecycle do alternative data and AI add the most value: origination, due diligence, onboarding, monitoring, or exiting the position?

2025 healthcare data trends

4:10 - 4:30 — Breakout Stage - Carmel II Quantitative · Fundamental · Data providers · Private markets

This session will explore popular healthcare data categories as well as considering what non-healthcare data sets are being explored by healthcare investors. Over a year on from significant disruptions in the space, what are the use cases for open vs closed claims data? How can investors stay ahead of the impact of the Trump administration’s bill on healthcare in the US? Dive into these and other key trends in the 2025 healthcare market.

• Matt Yome — Research Analyst, Neudata

Question: Which healthcare datasets are most established, and which are gaining traction? What key risks should investors consider when applying alternative data to healthcare in private markets?

Future proofing your data strategy

4:30 - 5:00 — Breakout Stage - Carmel II Quantitative · Fundamental · Private markets

How are private markets and venture capital funds rethinking their data strategies to ensure long-term success? This panel will explore the value of sourcing novel datasets, adopting tools that streamline the data lifecycle, and fostering a network of in-house talent and advocates. Panelists will discuss how investors are navigating rapid technological change and the rise of AI - seizing new opportunities, navigating risks and ultimately, driving growth.

• Saif Zia — Business Development Manager, Neudata (Moderator)

• Lindsi Kimizuka — Vice President - Product, Summit Partners

• Ryan Peabody — Data Analytics Lead, Patient Square Capital

• Radhika Mardikar — Senior Data Scientist, Lightspeed Venture Partners

• Roshan Raman — Head of Quantitative Research, Woodline Partners LP

Question: How can data and AI buyers avoid vendor lock-in while maximizing value from current providers and pivot quickly if new entrants surpass them?

What questions do you have for the panelists? Leave a comment below

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- Jason DeRise, CFA

Agenda: https://www.battlefin.com/newyork-oct25

To help the data community prepare, I’ve created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Translating Alternative Data into Investment Decisions

2. AI and Automation in Data Processing

3. Data Reliability, Evaluation, and Trust

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you’re an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I’m Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Share

Note: Agenda as of October 6th, 2025

8:45 AM What to expect for Christmas Selling season—Consumer & Supply Chain Data Mosaic

Using Alt Data to Navigate Holiday 2025 Season: early indications from the supply chain, transaction and other categories

Tracking the shifts in consumer behavior and which companies will come out ahead

Going below the surface to show how Mapping SKU-level data can be more accurate and identify trends earlier

• Dorothy Ruderman—Data Strategy & Research, Verition Fund Management

• Michael Gunther—VP, Head of Insights, Consumer Edge

• Richard Davenport—Consultant, Dun & Bradstreet

Question: When SKU-level transactions, card data, and private-company supply-chain signals point in different directions for a specific retailer, what is your analytical process to connect the dots to an actionable investment decision?

9:05 AM Turning Alt-Data into Investor-Ready Forecasts

How the Exabel platform takes raw signals to P&L using the new hierarchical modeling feature.

• Gene Gallagher—Director of Research, Exabel

Question: What evaluation suite and backtests do you require before moving a vendor signal into P&L workflows?

9:20 AM Powering the Next Generation of AI and Finance

How to avoid hallucinations, inaccuracies, and the inability to tie outputs back to trusted sources

• Thomas Li—Co-Founder & CEO, Daloopa

• Tim Harrington—CEO & Co-founder, BattleFin

Questions: A first question on the art of the possible: What is the fastest credible time to model you have achieved with Daloopa, from an IPO S-1 to a production-ready Excel model? What failed in earlier attempts? As a second, more technical question: Where do you draw the line between retrieval-augmented generation and deterministic extraction for SEC text to prevent hallucinated metrics in analyst models?

9:40 AM When Signals Converge: Making Sense of Market Noise

Individual data points tell part of the story; integrated signals reveal the whole. Learn how combining market, corporate, and supply chain views uncovers deeper trade and tariff insights.

• Daniel Sandberg—Global Head of Quantitative Research & Solutions, S&P Global

Question: Could you walk through a 2025 example where adding a supply chain feature flipped an investment thesis that looked “right” on consensus estimates alone?

10:00 AM The 180-year-old alt-data company you’ve never heard of

Using private market data as a precursor to public company movements

• Tim Harrington—BattleFin; • Harris Scher—VP, Business Segment Manager, Private Equity, Dun & Bradstreet

Question: Mapping the private graph to tickers at scale: How do you resolve subsidiaries, DBAs, and roll-ups so a DUNS-level signal flows cleanly to a public company and its peers? What are common challenges in the data during reorganizations or M&A? What audit artifacts do you expose so buy-side users can verify the mapping?

10:40 AM New Vendor Showcase—SRP

• Amélie Labbé—Chief Product Officer, SRP (Derivia Intelligence)

Question: How do you see investors leveraging options-like sensitivity data (“Greeks,” like delta, vega, gamma, etc.) derived from structured product data? Is it primarily for structured products portfolio managers and risk managers, or do you see potential signals and “read across” for other asset classes?

10:45 AM Intro to Parameta—Market Data, Indices & Analytics Solutions

• Kevin O’Keefe—Head of Sales, Americas, Parameta Solutions

Question: Based on your benchmarks/indices that are built on scarce OTC inputs, which index most improved market transparency into 2025 market liquidity, and can you share the insights it unlocked that traditional exchange-based benchmarks couldn’t?

10:50 AM Energy/Commodities Data Mosaic
Refiners vs Producers: using commodity flows to pick winners and losers

• Gene Gallagher—Director of Research, Exabel;

• Matt Smith—Lead Oil Analyst (Americas), Kpler

Question: Over the next one to two quarters, which metrics in your mosaic best separate likely outperformers among refiners versus producers?

11:10 AM How to Cut Your Alternative Dataset Integration Time by 50%

Integrating and evaluating alternative data is a time-consuming process. But it doesn’t have to be. Harrison Perl, Director of Alt Data Strategies at Exabel, demonstrates how data sourcing, data strategy, and data scientist roles can cut the time spent on this process in half.

• Harrison Perl—Director, Alternative Data Strategies, Exabel

Question: A couple questions thinking about AI and ML as solutions to a few of the pain points involved with the ETL process, enriching the data, and running analytics: For messy but promising datasets, do LLMs plus knowledge graphs improve joinability to an asset manager schema? Which standardized and automated evaluations, such as backtesting versus fundamental KPIs and simple alpha signal backtests, help you reach a go or no-go decision?

11:25 AM Rethinking Web Scraping—How AI Redefines Data Collection at Scale

• Sarah McKenna—CEO, Sequentum

Question: With deep mining use cases (e.g., publicly available consumer pricing and inventory data on large retail websites), how can LLMs be leveraged not only as part of the collection methodology but also as checker agents to catch moments where the source data and the web scraping output differ, allowing for a real-time correction before the output data reaches the database?

11:30 AM Data Spotlight—FlexHub: an AI-powered, Voice-Enabled Market Intelligence Platform

• Robert Hable—Founder/Managing Director, 2iQ Research

Question: (Note: I am assuming that Flexhub is a new product from 2iQ Research, but I couldn’t find anything about it in my research.) How do you see adding GenAI capabilities to your core data product that monitors company executives trading in their own company stock as unlocking potential insights, and how do you ensure that voice-to-text and voice-to-voice outputs stay true to the underlying source data?

11:35 AM AI Spotlight—Advances in AI for Finance

• Saul Cohen—Co-Founder & CEO, CliftonAI

Question: Generative AI is seen by users as “creative” when it generates outputs the user likes but is seen as “hallucinating” when the user doesn’t like the output. While facts can be verified with checker agents (like correct financial figures from a 10-K), how do you think about evals to test the viability of “new market knowledge” created by CliftonAI?

11:45 AM Bringing Alt Data into the World of Gen AI

• Zac Yang—VP Product, Exabel

Question: What do you see as the biggest opportunities for incorporating GenAI into the process of generating insights from raw data? Is it in the tagging and mapping of data or the generation of the conclusions based on large volumes of data?

11:55 AM Advertising Data Insights — Media Intelligence

How will AI affect traditional digital advertising, and what trends do we see from the buy side and sell side? Tracking wallet share and competitive sizing. What effect is AI having as we go from SEO to GEO?

• Joseph J. Berger—Managing Director, Financial Vertical Guideline

Question: As advertisers reallocate from Search Engine Optimization (SEO) to Generative Engine Optimization (GEO), which measurable changes in channel mix or creative cadence appear first in your data, and which persist long enough to be investable?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. AI’s role in investing

2. Data economics

3. Infrastructure and governance

Link to agenda: https://www.rebellionresearch.com/cornell-financial-engineering-manhattan-2025-future-of-finance-ai-conference

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Share

Subscribe now

Note: Agenda as of September 16th

08:50 – Opening Address

• Prof. Victoria Averbukh, Director, CFEM

• Alexander Fleiss, CEO, Rebellion Research

Question: Which AI and data engineering skills are most overlooked in today’s financial engineering curricula, and how will CFEM address them?

08:55 – Panel & Keynote Debate

Chat-GPT & The Future of AI in Finance!

Debate: What is the Future of Alternative Data for Markets?

• Tony Berkman (ex-Two Sigma, on garden leave) vs Carson Boneck, Chief Data Officer, Balyasny Asset Mgmt

• Moderator: Dr. Amal Moussa, MD, Head of US Single-Stocks Exotic Derivatives Trading, Goldman Sachs; Adjunct Prof., Columbia

Question: I suspect that both Tony and Carson will take the view that generative AI is a tool for humans to leverage to go deeper into analytics and not a human replacement (my view too). But, as a thought experiment, how could an independent autonomous AI analyst be integrated into the investment decision process with the human-on-the-loop instead of in-the-loop?

09:30 – Panel: Changing Landscape of Data & Investing

• Moderator: Rafa López Espinosa, COO of Equities, Citadel

• Panelists:

  • Natalya Dmitriyeva (Schonfeld),

  • Nancy Davis (Quadratic Capital),

  • Wenqi Zhou (Engineers Gate),

  • Mark Fleming-Williams (CFM),

  • Mike Soss (Millburn)

Question: Looking 10 years ahead, will advances in technology and AI eliminate alpha opportunities in systematic investing? If not, which market, data, or technology dynamics would sustain alpha generation?

10:05 – Conversation: Data Disrupting

• Paul Humphrey, CEO, BMLL

• Christina Qi, CEO, Databento

• Moderator: Tim Baker, CEO viaNexus

Question: Will consumption-based pricing disrupt traditional enterprise models? If so, how might data vendors adapt their product roadmaps and go-to-market strategies?

10:55 – Panel: Where Do Equities & AI Go From Here?

• Moderator: Naim El-Far, Head of Equities Investment Engineering, Bridgewater

• Panelists:

  • Boyu (Daniel) Wu (Vanguard),

  • Samson Qian (Citadel),

  • Renee Yao (Neo Ivy),

  • Mike Purewal (Millennium),

  • Kevin Mahn (Hennion & Walsh)

Question: Which generative AI tools are now used daily in equity signal research, and what measurable uplift have they delivered?

11:35 – Panel: Generative AI for Investment Research

• Moderator: Andrew Chin, Chief AI Officer, AllianceBernstein

• Panelists:

  • Peter Cotton (author Microprediction),

  • Kathryn Zhao (Cantor Fitzgerald),

  • Sean Slotterback (Quant PM),

  • William Wu (Menos AI),

  • Charlie Marin (Quanted)

Question: Acknowledging that today’s AI will be the worst AI we work with going forward, yet large language models have clear limitations in quantitative analytics. Which real applications are already adding production value, and where are researchers still testing boundaries?

1:00 – Panel: Future of AI & Data for Modeling?

• Moderator: Matthew Lyberg, Head of Asset Mgmt AI, Manulife IM

• Panelists:

  • Daniel DeWoskin (Graham Capital),

  • Atlas Wang (XTX Markets),

  • Petros Zerfos (IBM Research)

Question: As firms adopt more advanced AI, what is today’s biggest constraint on modeling: algorithms, data quality and governance, or institutional willingness to productionize under uncertainty? How should these bottlenecks be prioritized over the next few years?

1:40 – Talk: The Current Research Focus of CFEM

Sasha Stoikov, Head of Research, CFEM

Question: If you could launch one open-source benchmark for generative AI in 2026, what would it target?

1:55 – Panel: Building Models From Real-World Data Sets

• Moderator: Gordon Ritter, CEO Ritter Alpha

• Panelists:

  • Ioana Boier (NVIDIA),

  • Evan Reich (Verition),

  • Paul Krueger (Millennium),

  • Alex Unterrainer (DefconQ),

  • Morgan Slade (Exponential Technology)

Question: How do you measure the “new information” in a dataset relative to existing ensembles to avoid overpaying for correlated alpha?

2:35 – Panel: Allocating to Quant Funds

• Moderator: Jonathan Larkin, Columbia Investment Mgmt Co.

• Panelists:

  • Patrick Hop (Draco Ova),

  • Abhi Kane (400 Capital),

  • Yury Rojek (MSIM),

  • Josh Shapiro (UNC Mgmt Co.)

Question: What verifiable artifacts do you require before allocating to a quant manager claiming an ML/AI edge?

3:35 – Panel: Understanding the Future of AI in the 21st Century

• Moderator: Prof. Jim Liew, Johns Hopkins

• Panelists:

  • Zach Golkhou (JPMorgan),

  • Arkin Gupta (Citadel),

  • Keywan Rasekhschaffe (Code Willing),

  • Ramit Sawhney (Tower Research),

  • Igor Halperin (Fidelity)

Question: To unlock AI’s long-term potential, which frontier is most critical in the next two years: (a) building trustworthy enterprise platforms, (b) advancing model architectures for alpha discovery, or (c) creating infrastructure and governance for reliable scaling?

4:15 – Panel: Future of Interpreting Data

• Moderator: Christina Qi, CEO Databento

• Panelists:

  • Nan Xiao (Greenland Capital),

  • Ethan Geismar (Jefferies),

  • Rahul Gupta (DRW),

  • Judith Gu (Scotiabank),

  • Winston Ma (Professor),

  • Andreea Minca (Cornell)

Question: As financial data becomes more complex and central to decision making, what should be the top priority for the industry to build interpretation frameworks that deliver actionable insights while also meeting the rising expectations of regulators and clients for transparency and trust?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the community prepare, I’ve drafted targeted questions for each session. Three themes run through them:

1. Generative AI’s impact on data licensing, rights and governance

2. Vendor choices that shape cost, interoperability, and flexibility.

3. The path from raw data to alpha

Link to Agenda: https://www.neudata.co/events/traditional-and-market-data-summit-2025

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Share

Note: Agenda as of September 15, 2025

Opening remarks

9:00 - 9:15 | Gracechurch Hub | Quantitative | Data providers | Fundamental

• Ian Webster, Chief Revenue Officer, Neudata

Question: How do you see the consolidation of alternative datasets into the offering of traditional market data providers changing the industry?

A fireside chat with Peter Jackson, data innovation pioneer

9:15 - 9:40 | Gracechurch Hub | Quantitative | Data providers | ‍Fundamental

Join Peter Jackson, renowned author and data thought leader, for an exclusive fireside chat. With extensive experience and groundbreaking work, including his books The Chief Data Officer's Playbook and Data Driven Business Transformation, Peter has transformed how organisations, both within and outside of finance, use data to drive strategic decision-making.

• Ian Webster, Chief Revenue Officer, Neudata

• Peter Jackson, Global Head of Data Office, Schroders

Question: As generative AI moves from proofs of concept to production, how should a chief data officer balance experimentation with cutting-edge tools and the need for robust governance and regulatory compliance?

FCA update—Bond and derivatives transparency regime

09:40 - 10:00 | Gracechurch Hub | Quantitative | Fundamental

Receive an update on the new bond and derivatives transparency regime, designed to deliver more timely and effective post-trade transparency while supporting liquidity and strengthening UK markets.

• Fabio Braga, Manager - Trading Policy, Infrastructure & Exchanges, Financial Conduct Authority

Question: Beyond transparency, what other priorities does the FCA have for improving fixed-income market structure, such as a consolidated tape, and how should market participants prepare?

Data in Motion: Building the Framework for the Next Generation of Insights

10:00 - 10:30 | Gracechurch Hub | Quantitative | Fundamental

Join Sophie Beland in conversation with Simon Holden of Morgan Stanley for an exclusive fireside chat, where they will share insights on shaping the future of data and driving next-generation innovation.

• Sophie Beland, Executive Director, EMEA Head of Systematic Advisory Sales, Morgan Stanley

• Simon Holden, Head of Institutional Securities Data Group (IDG), Morgan Stanley

Question: As systematic investing expands beyond equities into multi-asset portfolios, what data gaps persist for fixed-income, commodities, or private-market strategies?

Till data do us part: Picking vendors you won’t regret

10:30 - 11:00 | Gracechurch Hub | Quantitative | Fundamental

Choosing market data vendors is a bit like marriage — easy to get into, hard to get out of. In this lively panel, expert data buyers share war stories, compliance challenges, and the red (and green) flags they look for when building long-term vendor relationships. From scouting scrappy newcomers to knowing which industry giants are still worth the price tag.

• Sophie Beland, Executive Director, EMEA Head of Systematic Advisory Sales, Morgan Stanley

• Leo Murison, Lead Data Scientist, Jupiter Asset Management

• James Maddock, Global Head of Market Data Services, HSBC

• Chelsea Lein, Vice President, Data & Research Compliance, Tudor Investment Corporation

Question: How do you weigh a new vendor that likely adds alpha today against its long-term viability as a business, given the vendor’s need for long-term ROI, potentially driven by higher prices or broader distribution, both of which erode alpha?

The Private Mind: Data You Own. Intelligence You Trust. 11:25 – 11:55 | Gracechurch Hub | Quantitative | Fundamental Join Tim Yockel and Ryan Ballantine as they unveil Private Mind—our secure, finance‑ready AI environment that gives you complete ownership, control and confidence over your data. They’ll show how Private Mind unites compute, AI and financial‑grade security, enabling firms to interrogate their data with precision and privacy. Discover how this breakthrough powers the next era of capital‑markets innovation

• Tim Yockel – SVP – Global Sales, Options Technology

• Ryan Ballantine – Director, Product Management, Options Technology

Question: If Private Mind runs as a self-contained GenAI environment inside an asset manager’s infrastructure with no vendor visibility into prompts or outputs, how do you learn and evolve the product while preserving confidentiality?

Transparency in licensing and pricing

11:55 – 12:25 | Gracechurch Hub | Quantitative | Data providers | Fundamental

This session will address the growing demand for clearer and fairer data pricing and licensing agreements to drive trust and efficiency.

• Keiren Harris – Independent Consultant

• Abigail McInnes – Data Sourcing and Strategy Manager, Man Group

• Reinder Hoiting – Strategic Sourcing & Category Manager, Robeco

• Jag Sidhu – Head of Market Data, Investec

Question: Some data providers are now adding clauses around how data can be used in AI models and in the creation of derived content. Asset managers have a long history of applying AI and analytics to generate insights from data, but the growth of generative AI has heightened provider concerns about usage rights. How can asset managers communicate the importance of derived content to vendors as the reason the data is worth paying for so that protections are respected without needlessly constraining insight generation?

Fixed income and derivatives: Providing data quality and transparency in a changing financial landscape

12:25 – 12:45 | Gracechurch Hub | Quantitative | Fundamental

Investors increasingly demand quality and transparency for data, while banks are striving for new and more complex datasets to meet regulatory demands. Join S&P Global Market Intelligence in this session to hear how data is helping both buy‑ and sell‑side institutions meet their growing requirements.

• Gavan Nolan – Executive Director, Fixed Income Business Development and Research – TBD Derivatives, S&P Global Market Intelligence

• Raphael Bruneau – Director, Derivatives Data Business Development, S&P Global Market Intelligence

Question: Post-trade reporting rules and the push for a consolidated tape in Europe should improve transparency. What role should commercial providers play versus public authorities in building that infrastructure?

The evolving dynamics of index rebalancing

12:45 – 1:00 | Gracechurch Hub | Quantitative | Fundamental

This session will explore how the dynamics of the index effect have changed over time and where market data underpins rebalancing forecasts.

• Ethan Han – Research Analyst, Neudata

Question: Where do index effects remain most material, and what market structure features explain their persistence?

Applying data and AI at scale: Investment research & quant

2:00 – 2:20 | Gracechurch Hub | Quantitative | Fundamental

This session will explore how Large Language Models can be leveraged to generate novel sources of alpha, while integrating both traditional and alternative data to enhance signal discovery. It delves into scalable compute architectures that enable data and model efficiency, as well as approaches to navigating model risk and AI governance in the context of quantitative investing.

• Angana Jacob – Global Head of Investment Data, Bloomberg Enterprise Data

• Abhijeet Gaikwad – Chief Investment Officer, ADG Capital Management

Question: When deploying generative AI at scale in the investment decision process, which is harder to achieve: compute efficiency or data governance? How would you recommend addressing each challenge?

Applying data and AI at scale: Investment research & quant

2:00 – 2:20 | Gracechurch Hub | Quantitative | Fundamental

This session will explore how Large Language Models can be leveraged to generate novel sources of alpha, while integrating both traditional and alternative data to enhance signal discovery. It delves into scalable compute architectures that enable data and model efficiency, as well as approaches to navigating model risk and AI governance in the context of quantitative investing.

• Angana Jacob – Global Head of Investment Data, Bloomberg Enterprise Data

• Abhijeet Gaikwad – Chief Investment Officer, ADG Capital Management

Question: When deploying generative AI at scale in the investment decision process, which is harder to achieve: compute efficiency or data governance? How would you recommend addressing each challenge?

Mastering vendor contract complexity

2:00 – 2:40 | Ludgate Library | Quantitative | Fundamental

Gain practical strategies for optimising contract management, improving transparency, ensuring compliance and controlling costs.

• Reinder Hoiting – Strategic Sourcing & Category Manager, Robeco

• Richard Pedrick – Market Data Compliance Expert

Question: What common pitfalls do you see in data contract negotiations, and how should procurement teams structure RFPs to avoid them?

Market‑data challenger spotlights

2:20 – 2:50 | Gracechurch Hub | Quantitative | Fundamental

In this showcase session hear from some new entrants to the market‑data space, as they share the datasets and product launches set to challenge established vendors on quality, flexibility (and price).

• Tim Tatum – CEO, Securities Quote Xchange

• Liam Rogan – Head of Growth & Business Development, Primer

• Viraj Patel – Deputy Head of Research & Partner, Vanda

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

Neudata Ranger: A year in review

2:50 – 3:10 | Gracechurch Hub | Quantitative | Data providers | Fundamental

Neudata’s Ranger product launched with a bold promise: help investors discover, evaluate and monitor market data more intelligently. One year in, what have we learned? In this spotlight session, Neudata shares market trends from the first twelve months—including what data buyers are searching for and which data categories get the most traction. Neudata also provides a sneak peek into what the future of Ranger might look like.

• Danesh Kissoon – Associate, Neudata

Question: What surprising usage patterns or dataset categories emerged from Ranger over the past year, and what do they reveal about investors’ evolving needs?

From data to alpha: Streamlining the data‑management lifecycle

3:10 – 3:45 | Gracechurch Hub | Quantitative Fundamental

This session will delve into the challenges funds face in managing the data lifecycle—from cataloguing, to trialling, budgeting, governing and migrating data. Panellists will highlight ways to streamline workflows to accommodate evolving regulatory demands whilst maintaining a competitive edge.

• Will Wainewright – Founder, Alternative Fund Insight

• Nico Smuts – Investment & Data Science Leader (Ex‑Citadel)

• Emily Brooke‑Dean – Head of Data iSAM Helix, iSAM

• Mark Thompson – Primary Research, Norges Bank Investment Management

• Ruairi Powers – Senior Vice President, Product Management, Neudata

Question: How do firms maintain reproducibility of analytic results when migrating from one vendor to a direct substitute? How can buyers size switching costs in addition to data fees?

Neutral by design: Building a flexible future for market data

4:10 – 4:30 | Gracechurch Hub | Quantitative | Fundamental

Discover how to drive flexibility, optimise costs and avoid vendor lock‑in by exploring the pros and cons of vendor‑agnostic middleware solutions.

• Bill Bierds – President, BCC Group

• Anna Branch – Head of Strategic Partnerships – TP Quilter, Quilter

• Kevin Morgan – Senior Principal – Market Data, BCG Expand

Question: How do you ensure interoperability between legacy systems and modern APIs without added latency or reduced data quality?

Defining your cloud‑migration strategy

4:30 – 5:00 | Gracechurch Hub | Quantitative | Fundamental

Look at how to manage your data‑migration strategy to avoid common pitfalls and ensure a seamless transition from on‑premise to the cloud.

• Piers Thompson – Chief Technology Officer, Neudata

• Sam Livingstone – Head of Quantitative Strategies & Risk, Ambienta

• Richard Waters – Head of Data Engineering, Systematica

• Maurits Blok – Cloud Architect, Oracle

Question: "Lift and shift" migrations often disappoint due to hidden dependencies. What strategies have you used to re-architect workloads for the cloud rather than replicate on-premises environments?

Have a question for the panelists? Leave a comment below.

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Know someone attending or presenting? Feel free to forward this to them.

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- Jason DeRise, CFA

There are 3 key themes to my questions:

1. AI Agentic frameworks and workflows in Finance

2. Data Vendor differentiation and use cases

3. Alternative Data Market Industry Evolution

Conference Agenda: https://www.eaglealpha.com/2024/04/27/alternative-data-conference-september-2025/

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

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Note: Agenda as of September 12th, 2025

8:50 – 9:05 AM – Welcome & Opening Remarks

• Michael Finnegan, CEO of Eagle Alpha.
  Join us for the official kickoff of the Eagle Alpha Alternative Data Conference with a Welcome & Opening Remarks led by CEO, Michael Finnegan.

Question: After your first year as CEO, how do you assess the alternative-data industry’s performance? How has vendor consolidation shaped it?

9:00 – 9:30 AM – Beyond the Hype: Practical Uses for MCP and What’s Needed to Get Us There

• Sid Ghatak (Increase Alpha),

• Tim Baker (Blue‑Sky Nexus),

• Stuart Reid (Nosible),

• Moderated by Conrad Gann

Discover practical applications for Model Text Protocol (MCP) beyond the hype and explore what the industry needs to unlock its full potential

Question: Anthropic’s Model Context Protocol (MCP) emerged last year as an open-source approach to agentic workflows, with Claude for Financial Services launched in July 2025. How should data vendors leverage MCP to enable easier integration into agentic models while also preparing for Anthropic’s potential push into vertical integration in finance?

9:30 – 10:30 AM – New to Market & Fresh Features – Vendor Presentations

• Various data vendors.

An exciting session where we’ll dive into the latest advancements and features from top data vendors.

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

10:00 – 10:30 AM – Alternative Data in Action: Insights for the Discretionary Investor’s Journey

• Matei Zatreanu (System2),

• Michelle Yang (Daytrix),

• Patricia Tangney (Liberty Mutual Investments),

• Moderated by Adam Braff.

Learn how discretionary investors are starting to harness alternative data, with examples and practical insights to guide your own journey and unlock new decision‑making strategies.

Question: Do you expect generative AI to automate idea generation for discretionary PMs, or will it remain a summarization tool while signals come from other methods?

12:00 – 1:30 PM – Advancing Forecasting Frontiers: SQA Alphathon Mid‑Competition Panel

• Yichuan Zhang (Boltzbit),

• Michael Tiano (Schonfeld),

• Gene Ekster (Maiden Century),

• Rong Zhao (Stony Brook University)

• Christos Koutsoyannis (moderator).

Join the Society of Quantitative Analysts (SQA) for the Alphathon Mid‑Competition event featuring two sessions:

• 12:00–12:45 PM – Learning Machines: Private Data, Compute & Augmenting Fundamental Research with Yichuan Zhang,

• 12:45–1:30 PM – The Cutting Edge: Alternative Data in Investment and Fundamental Research with Michael Tiano, Gene Ekster, and Rong Zhao

Question: Good luck to the competitors. Both ideas sound intriguing. For panelists who teach or mentor early-career talent: what knowledge gaps persist for data-driven investment professionals, and how should academic programs evolve to close them?

12:30 – 12:45 PM – Tracking AI’s Impact: Insights from Mobile App Data

• Thomas Grant (Apptopia)

Discover how mobile app data can reveal the evolving influence of artificial intelligence across industries; Apptopia will showcase how app intelligence uncovers market trends and user‑behavior shifts driven by AI innovations, offering valuable insights for investors and analysts

Question: Is the generative-AI user base still growing on a daily active user basis, and which platforms are gaining share? Can you segment enterprise usage from consumer usage with confidence?

12:45 – 1:15 PM – The Future of Quant Infrastructure: Industry Trends, Buy vs. Build for Institutions and Startup Perspectives

• Jared Broad (QuantConnect),

• Leo Korsunsky (Oleum),

• Moderated by Christina Qi (Databento).

Explore how quantitative investors are expanding beyond traditional factor models by incorporating alternative data sources to enhance alpha generation and improve portfolio performance.

Question: Since 2023, freemium and low monthly pricing have accelerated professional/enterprise adoption for many AI platforms. Can that model work in institutional investor fintech, or do technical, regulatory, and incumbent barriers block this path?

1:15 – 1:30 PM – EventVestor Insights: Driving Smarter Investment Decisions Around M&A

• Anju Marempudi (EventVestor).

Question: Among the many event categories EventVestor tracks, which ones have proven most predictive of stock performance post announcement, and how do you measure the shelf‑life of those signals?

1:30 – 2:00 PM – The Greeks: Data to Trade SWAPs, Derivatives & Structured Products

• Ovie Koloko (Parameta Solutions),

• Amélie Labbé (SRP Greeks),

• Nick Pezolano (NewMark Risk),

• Moderated by Stewart Stimson.

Dive into the critical role of alternative data in trading derivatives, swaps, and structured products, with a focus on understanding the Greeks to improve risk management and enhance trading strategies.

Question: Often alternative data confirms consensus into a print, which offers little for discretionary positions but can inform derivatives-based premium-capture strategies around muted earnings reactions (that the alt data showed would be uneventful print). Are derivative investors already using alt data in this way, or is this a growth opportunity for vendors?

3:30 – 4:00 PM – Macro Data at a Crossroads: A Global View of Data to Fill the Void in Government Statistics

• Apurv Jain (MacroX Studio),

• Toby Dayton (LinkUp),

• Jing Sima (BCA Research),

• Moderated by Brendan Furlong

This session explores the challenges of unreliable U.S. macroeconomic data and discusses innovative approaches to bridge data gaps, providing more accurate and timely insights for investors and analysts.

Question: As a thought experiment, if official statistics diverge from real-time conditions, what happens to market efficiency? Do alternative-data signals degrade, create alpha for those who use them, or improve efficiency by replacing gaps in government data?

4:00 – 4:30 PM – AI, Alt Data & Compliance: From Governance to Exams

• Kelly Koscuiszka (McDermott Will & Schulte),

• Tinika Brown (McDermott Will & Schulte)

• Malik Rollins (McDermott Will & Schulte),

• Moderated by Ciaran Ryan.

As AI shifts from pilots to production, funds face a new compliance frontier. Governance, model vetting, vendor diligence, contractual protections and regulatory expectations are no longer optional; this session offers practical guidance on building policies regulators respect, onboarding models and data with rigor, controlling accuracy and staying exam‑ready.

Question: As AI and alt data move to production, which controls are now table stakes, and where will regulators push next? Will human-in-the-loop be expected whenever gen AI influences decisions, or will agentic systems with checker agents and automated evals satisfy a humans-on-the-loop standard?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:

To help the data community prepare, I've created targeted questions for each speaker and panel.

1. AI-Driven Transformation of Data Workflows

2. Vendor-to-Buy-Side Commercialization Challenges

3. Macroeconomic and Sector Specific Signal Development Using Alternative Data

https://www.battlefin.com/events/new-york-2025-new#event-agenda

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of June 8th, 2025

June 10th, 2025: Quant Focus

2:00 PM Overview, What’s to come from BattleFin & Exabel

• Tim Harrington, CEO, BattleFin

Question: How will you integrate Exabel’s modeling sandbox with BattleFin’s Discovery Day data-vendor pipeline to shorten the cycle from proof-of-concept to production deployment on the buy side?

2:10 PM Setting up for success at BattleFin events

• Sarah Smith, SVP of Sales, Exabel

Question: Based on your interaction with quant teams and the Exable platform, what are the top three misalignments you see between vendor pitch decks and buyside workflows? How can vendors recalibrate?

2:20 PM Making your data AI and Analytics ready

• Daniel Petzold Head of Marketing, Crux

Question: As explainable AI becomes mandatory, which metadata standards should vendors adopt to ensure transparency in automated feature-generation pipelines?

2:40 PM Tactical strategies for data providers to go beyond the first 5 customers

• Alex Nephew, CEO, FirstParty

Question: What frameworks has FirstParty found most effective for helping emerging data vendors get past early trial periods with asset managers on the way to broader success with the account?

3:10 PM Exabel Partner benefits and opportunities. Research, Exposure, Joint Marketing

• Romit Sevani, Partner Growth, Exabel

Question: When evaluating co-marketing programs, which shared KPIs deliver the clearest ROI signals that the collaborative marketing is working for the partner?

3:30 PM Buy side feedback for vendors

• Rich Brown, Global Head of Market Data, Jain Global

Question: From procurement to live-production handoffs, where do you see the biggest friction points in a vendor’s delivery process?

3:50 PM What to expect from BattleFin New York

• Tim Harrington, CEO, BattleFin

Question: What innovations can attendees expect at BattleFin New York 2025 to further facilitate deal flow between data providers and buy-side teams?

June 10th, 2025: Alt Data Content

3:30pm Let's talk AI, Alt Data and the future of finance

How will research and models need to evolve in the world of AI Efficiency or Signal, where will Alt data and AI have the biggest impact

• Tim Harrington, CEO, BattleFin

• Naim El-Far, Head of Equities Investment Engineering, Bridgewater Associates

Question: How should asset managers consider integrating LLM-powered pipelines to enhance the discovery and evaluation of alternative data signals, and what new guardrails should be in place for model governance as AI moves from “assist” to “automate”?

3:50 PM Hierarchical Modeling: Advancing the Frontier of KPI Modeling with Alternative Data

Improving model accuracy and unlocking profitability forecasting by incorporating financial logic into machine learning models

• Zac Yang, VP of Product, Exabel

Question: What were the biggest challenges in scaling that architecture to handle limited-history alt data, and which enhancements are you prioritizing to improve interpretability for buyside stakeholders in Exabel’s multilevel KPI forecasting?

4:05 PM New Vendor/Product Showcases

• Joseph Berger, Managing Director, Guideline

• Ben Darr, Founder, CredolQ

• Paul Mann, Director, Business Development. Zeki Data

• Ethan Atwood, Product Manager, Sherlock Analytics

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

4:25 PM Data Mosaic: A Macro perspective of the global economy using alternative data for a more timely read

Monetary regime change/reflationary world, Deglobalization and supply chain realignment. What effects are tariffs having? How to track these using alt data.

Geopolitical risks, Labor market trends, AI Driven Productivity Boom, Healthspan and Demographic Transitions, Infrastructure and real estate monetization, tokenization

• Scott Asher, Chief Data Officer, Xantium

• Laurent Bilke, CEO & Head of Research, Alternative Macro Signals

• Andrew Harris, Deputy Chief Economist, Fathom Financial Consulting

• Ajit Agrawal, CEO, Akanomics

Question: With deglobalization under way, which alt signals (e.g. AIS, port congestion, payment flows, etc.) have most reliably tracked 2025 tariff impacts?

4:55 PM Are U.S. investors rotating to European and Asian markets? What sectors are avoiding tariff chaos?

A look at the European consumer sector

Asset, Sector and Country Rotations

Is China Bottoming? Tracking Asia Consumer Behavior

• Tim Harrington, CEO, BattleFin

• Sayad Baronyan, Head of Quantitative Research, ISI Markets

• David DeRose, Global Head GTM Sales & Strategy, DataBurning

Question: How do you use investor sentiment indicators and trading flow data to detect sector shifts and geographic investment rotation? Which alternative datasets best complement market-level data?

5:15 PM Data Mosaic: Healthcare - Analyzing Alt Data to Identify Emerging Healthcare Trends

GLP-1: Using data to understand the size of the market (Connector effects like RO, Hims/Hers, etc.)

What other patterns are shifting: grocery health patterns, connect the dots with data

Will less obesity lead to less knee and hip surgeries

• Rayne Gaisford, Chief Risk Officer & Chief Data Strategy Officer, RTW Investments

• Mark Holmquist, Vice President, SG2

• Graham Lincoln, Head of Product, Kyber Data Science

Question: How can hospital-level analytics and consumer behavior data be combined to get ahead of long-term underlying trends as GLP-1 adoption evolves?

5:40 PM Deep Research with Alt Data into the Consumer Sector (Online and In-store Retail Sector Highlights)

What categories of alt data are producing the highest alpha and insights

Understanding consumer behavior patterns, cohort analysis, going beyond calling quarters to identify longer-term themes and changes

• Gene Gallagher, Director of Research, Exabel

• Alyssa Lewis, Vice President and Global Head, Investors Vertical, SimilarWeb

• Jon Ciaio, Senior Director, Client Success, Facteus

• Thomas Paulson, VP and Head of Market Insights, Advan

Question: Can you share examples where combining these datasets uncovers a leading signal missed by each on their own, and what does that imply for designing robust consumer sector investment strategies?

June 11th, 2025: Alt Data Content

8:45 AM New Data Provider Showcase Competition

3 new data providers will present

Datasets will be reviewed over Exabel to showcase specific signal power

• Ish Pandher, Chief Strategy Officer, BattleFin

• Ben Cohen, Ex-WorldQuant

• Alexander Izydorczyk, Data & AI, Something New

• Rayne Gaisford, Chief Risk Officer & Chief Data Officer, RTW Investments

9:15 AM 5-Min Vendor Presentation

Data Showcase: OceanStream powered by SQD

• Howie Altman, Co-Founder, Subsquid

9:20 AM Data Showcase - REsimplifi

• Vicky Reddy, Product, REsimplifi

Question: Like the previous day’s session—good luck to the showcase participants! Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

9:25 AM Focus on AI and Alternative Data Onboarding and Due Diligence

Navigating pain points and hurdles to ensure an efficient and compliant onboarding and due diligence process for AI and alternative data providers, including implementation of appropriate policies and procedures

• Boris Liberman, Partner & Co-Chair, Derivatives and Structured Products, Lowenstein Sandler

• Jordan Hauer, Co-Founder & CEO, Amass Insights

Question: Which policy-template innovations accelerate compliance review without compromising rigor?

10:05 AM Supply Chain Deep Dive

Tariffs and their impact on Supply Chain, Pricing and movement of goods

Which datasets can help monitor supply chain disruptions and identify potential risks?

Does commodity tracking, retail activity and product availability provide insights?

Using Shipping data, weather predictions and social media sentiment to enhance supply chain visibility

• Alexander Izydorczyk, Data & AI, nx1

• John Miller, Chief Economic Analyst, Trade Data Monitor

• Richard Davenport, Contractor, Dun & Bradstreet

Question: What alerting architectures ensure actionable supply-chain warnings hit desks before earnings?

10:35 AM The Future of Expert-Based Research: Quantifying the Qualitative

AI impact on insight generation

Conducting expert-based research at unprecedented scale

Democratization - alt data is no longer just for big funds.

• Dan Entrup, Co-Founder, AggKnowledge

• Aarti Desai, RVP, Financial Services, NewtonX

• Rutwik Ghodadra, CEO, Guidepoint Insights

Question: How might we move beyond the qualitative signal of expert networks and B2B surveys to a back-testable quant signal? Which hybrid human expert + AI workflows have you seen unlock step-change improvements in signal quality of expert networks and B2B surveys?

11:05 AM Data Mosaic: Energy perspective - Oil & Gas demand/supply, Renewable Energy

Data centers, Power grid demand and which datasets can help track these

Global Oil, Gas and LNG flows (Geospatial/Shipping data)

Battery Supply chain and renewable infrastructure buildout

EV Charging Infrastructure (geospatial mapping of charger deployments)

• Tim Harrington, CEO, BattleFin

• Mike McCormick, Partner, Rystad Energy

• Reza Haidari, Head of Power, Gas, Coal, Carbon, LSEG

• Antoine Halff, Co-Founder and Chief Analyst, Kayrros

Question: Which dataset combinations and analytical techniques will help investors best understand the implications of a protectionism scenario for US energy needs?

11:35 AM Follow the Flow: Mapping Business Relationships for Market Insights

BRA network - Business Relationship Analytics (BRA)—a cutting-edge dataset designed to uncover hidden connections and predictive signals across supply chains, customer-supplier networks, and strategic partnerships.

How BRA has been leveraged to track the impact of Trump-era tariffs on key sectors, identifying actionable opportunities and emerging risks through data-driven analysis.

• Dan Sandberg, Global Head of Quantitative Research & Solutions, S&P Global Market Intelligence

Question: Within the BRA network framework, do upstream supplier links or downstream customer links have the most predictive signals of earnings surprises, and how are you embedding causal-edge attribution into your graph-analytics pipelines to distinguish genuine leading signals from spurious correlations?

11:55 AM Data Spotlight - Third Bridge

• Mike Grubert, Managing Director, Third Bridge

Question: What NLP-driven post-call summaries have proven most time-saving for buyside investment teams?

12:10 PM Decoding the Economic Pulse: Unlocking Market Signals from SMB Workforce Data and Alternative Intelligence

SMBs as Early Indicators: Understand why small and mid-sized businesses are key to detecting early shifts in the U.S. economic landscape.

Explore how Paychex’s payroll, hiring, and wage data reveal real-time economic signals.

Democratization - alt data is no longer just for big funds.

Multi-Source Intelligence: See how Carbon Arc combines diverse alternative data sources to build a holistic economic view.

• Tim Harrington, CEO, BattleFin

• Kirk McKeown, Co-Founder, Carbon Arc

• Beaumont Vance, SVP Data and Artificial Intelligence, Paychex

Question: Given Paychex’s granular SMB payroll and wage-growth indexes and Carbon Arc’s high-frequency transaction and geospatial feeds, how would you collaboratively architect a composite real-time economic signal to reliably anticipate turning points in the broader U.S. labor market, and what validation framework would you use to ensure its accuracy across industries and regions?

June 11th, 2025: LLMs, Agents & Edge: The New Data Frontier

9:00 AM The Silent Shift: How AI Has Already Changed Finance Forever

AI isn’t coming—it’s already here. This keynote dives into how LLMs and agents are redefining the research stack and giving early adopters a competitive edge.

Key Themes:

How LLMs are augmenting or replacing traditional workflows

The rise of the AI-first research stack

What does it mean for data providers to be "AI Ready"

• Arkin Gupta, Vice President, Morgan Stanley

• Braden Dennis, Co-Founder & CEO, FinChat

• Tarun Amasa, CEO, Endex

Question: How would you collaboratively define the foundational pillars required to architect an “AI-ready” research stack that maximizes time-to-insight while mitigating operational and compliance risks?

9:35 AM From Search to Synthesis: LLMs Are Changing How We Discover & Value Data

AI-led discovery and tagging

Structured metadata and the future of alt data search

Embedding GPT-style interfaces into analyst workflows

Rethinking the data RFP process

• Ben Cohen, Director of Data Strategy

• Jacob Ayres-Thomson, CEO, 3AI

Question: How do you guard against “hallucinated” metadata when embedding GPT-style interfaces into data catalogs?

10:10 AM AI in Action: How We’re Using LLMs to Add Value to Our Data

Ten-minute pitches from providers deploying real AI capabilities across their data offerings.

• Hendrik Bartel, CEO & Co-Founder, PostSig

• Nicole Königstein, CEO & Co-Chief AI Officer, Quantmate

• Alexander Fleiss, CEO, Rebellion Research

Question: Good luck to each participant! How have you established a performance and governance framework to rigorously quantify and manage an agentic framework output considering latency benchmarks, drift-monitoring protocols, and user-feedback loops?

10:55 AM Buy-Side Chat - Autonomous Agents Are Here: What Happens to the Human Analyst?

Where AI is replacing vs. supporting analysts

How investment workflows are being rebuilt

Use cases: autonomous reporting, modeling, and alerts

Risks: over-reliance, hallucination, and trust-building

• Carson Boneck, Chief Data Officer, Balyasny Asset Management

• Keshavan Seshadri, Senior Machine Learning Engineer, Prudential Financial

• Timothee Consigny, Chief Technology Officer, H20 Asset Management

• Evan Reich, Head of Data Strategy & Sourcing, Verition Fund Management

Question: As your firms evolve from isolated assistants to fully choreographed agentic systems, spanning data ingestion, signal generation, modeling, and even trade execution, what orchestration architecture and human-in-the-loop guardrails are you implementing to manage inter-agent dependencies, detect and correct cascading model drift, and enforce audit-grade accountability? What acceptance tests must be passed prior to deployment given an agentic AI framework is not deterministic?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

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— Jason DeRise, CFA

To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Strategic Adaptability to Market and Macro Shifts

2. Human-Machine Collaboration in the Quant Workflow

3. Infrastructure, Access, and Scalability of Data & Models

https://www.rebellionresearch.com/quantvision-2025-fordhams-quantitative-conference

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of May 17, 2025

08.45 – Opening Address from the Chairs

• Director, MSQF Fordham Director, Professor Qing Sheng,

• Conference Co-Chair Faruque Khan

• Conference Co-Chair Rebellion Research CEO, Alexander Fleiss

Question: How do you see Fordham’s MSQF program evolving its curriculum to integrate real-world AI-for-finance research in partnership with Rebellion Research to better equip graduates for production-level quant roles?

08.50 – Opening Fireside Chat

• Tony Berkman, MD Two Sigma

• Claudia Perlich, MD Two Sigma

Question: Given the pace of technological and data-driven change in markets over the past 15 years, what themes do you expect we’ll be discussing at QuantVision 2040?

09.20 – PANEL DISCUSSION: The Investment Process

• Moderator: Sean Slotterback, Quant Portfolio Manager

• Daniel DeWoskin, Quantitative Research Manager at Graham Capital Management,

• Rahul Gupta, Quant Research and Trading, DRW,

• Zach Squire, Portfolio Manager, Brevan Howard Tekmerion,

• Neal Berger, Hedge Fund Manager,

• Patrick Hop, Chief Investment Officer, Draco Ova Holdings,

• Yosef Zweibach, Chief Operating Officer of Quantic/Walleye Capital

Question: Thinking about uncertainty related to sudden volatility spikes and regime shifts, which systematic signal-management tactics have proven most resilient across your firms?

9:55 – Financial Mathematics or Financial Physics?

• Dr. Peter Cotton

• Dr. Igor Halperin

• Moderated by: Francesco Fabozzi, Research Director of Yale’s International Center of Finance

Question: How do financial mathematics and financial physics differ in managing uncertainty, especially with sparse, noisy, or regime-shifting data? How do your models handle feedback loops where their signals affect market behavior? Can these two approaches reinforce, rather than contradict, each other?

10:25 – Keynote: The Best US Corporate Bond Pricing Model is Deep Learning

• Nathaniel Powell, Deep Market Making, CEO & Fmr Director of Machine Learning Research, JPMorgan

Question: Given the infrequent trading of many corporate bonds, how do you meet market makers' demands for real-time fair-value estimates? Which ML models and strategies have best supported low-latency pricing in live trading workflows?

11.05 – PANEL DISCUSSION : Capturing Alpha & Quantitative Strategies & Global Outlook

• Moderator: Lisa Schirf, MD, Global Head of Data & Analytics, Tradeweb

• Dr. Sudip Gupta, Professor Johns Hopkins University Carey Business School,

• Dr. Argyro (Iro) Tasitsiomi, Head of Investments Data Science at T. Rowe Price,

• Caio Natividade, Global Head Quantitative Investment Solutions Deutsche Bank,

• Jonathan Larkin, Managing Director, Columbia Investment Management Company,

• Paul Krueger, Quant Trader @ Millenium

Question: Assuming ongoing political populism, protectionism, and AI-driven disruption of business models, how should quant strategies adapt to sustain alpha generation?

11.40 – Keynote: Generative AI for Modeling Stock Returns

• Samson Qian, Citadel

Question: In a world where fundamental analysts can “vibe-quant-code” backtested models and implement them leveraging Gen AI co-pilots, how do traditional quant researchers add value? Is it an opportunity or a risk?

1.00 – Keynote Speech: Is There Any Possibility of Cross-Functionality Between Digital Tokens and Tokens Used in Deep Learning?

• Albert An, CEO of Tower Research Capital

Question: How can we reframe market participants’ “jobs to be done” in a way that unlocks new opportunities for cross-functionality between digital and machine-learning tokens?

1.30 – PANEL DISCUSSION: Understanding The Future of Machine Learning in Quantitative Finance

• Moderator: Petter Kolm, NYU Professor & Director of NYU Mathematics in Finance

• Jae Ho Kim, PhD, Head of Portfolio Research, Cubist; Division of Point72,

• *V. Zach Golkhou, Ph.D., Director of GenAI & Data Science, JP Morgan,*

• *Dimitri Bianco, Head of Quant Risk & Research Agora Data,*

• *Arun Verma, Head of Quantitative Research Solutions at Bloomberg, LP,*

• *JD Opdyke, Chief Analytics Officer, DataMineit, LLC,*

• Eric Reiner, Professor, UCLA

Question: Amid the current hype around generative AI, are we overlooking other potential breakthroughs in machine learning? If so, which emerging ML approach is most likely to recapture the market’s imagination?

2.05 – PANEL DISCUSSION: Where is AI Going From Here?

• Moderator: Christina Qi, CEO Databento & Founder Domeyard LP

• Dr. Ioana Boier, Ai Expert, NVIDIA,

• Judith Gu, Head Equities Quantitative Strategist, Scotiabank,

• Petros Zerfos, Principal Research Scientist & Manager at IBM Research, Dr.

• Ihsan Erman Saracgil, CPO & Head of Quant Research, OpenBB,

• Amit Gandhi, Professor, Wharton & Airbnb Technical Fellow, Fred Viole, Hedge Fund Manager,

• Ernest Chan, Hedge Fund Manager,

• Dr Jim Kyung-Soo Liew, Johns Hopkins Professor

Question: Given that Databento and OpenBB democratize access to market data and AI research, which combination of analytical frameworks, technical skills, and investment-process guardrails will most reliably translate these tools into genuine, repeatable outperformance for lean teams and individual allocators?

3.00 – PANEL DISCUSSION: Future & Usability of Data For Quant Funds

• Moderator: Harvey Stein, Two Sigma

• *Samantha Mait, Manager, Equity Data Management, Balyasny Asset Management L.P.,*

• Lauren Crossett, Head of GTM, Spade,

• *Ethan Geismar, Head of US Data Strategy, Jefferies,*

• *Evan Reich, Verition Fund Management Head of Data Strategy & Sourcing,*

• George A. Lentzas, Co-Founder & C.I.O., Springfield Capital | Adjunct Professor, Columbia Business School (Machine Learning & Artificial Intelligence)

Question: How should lean data teams, quant pods, and portfolio managers choose between plug-and-play data subscriptions, open-marketplace offerings, or building in-house to ensure each data dollar spent drives the greatest alpha contribution?

4.00 – PANEL DISCUSSION: US VS. The World in 2025: Where Capital Flows The Next Decade? Can We Model Sudden Shifts in the Macro Environment? Trump & Tariffs: Good or Awful for the Market?

• Moderator: Malcolm Dorson, Senior Portfolio Manager & Head of Emerging Markets Strategy, Mirae Asset & Global X

• Manish Aurora, Hedge Fund Manager Rational Investing LLC,

• Abhi Kane, Managing Director Angel Oak Capital Advisors LLC,

• Samir Shah, Hedge Fund Manager,

• Kevin Gahwyler, Managing Director, Meteora Capital,

• Yury Dubrovsky, CFA Fmr Chief Risk Officer at Lazard Ltd & Lazard Asset Management,

• Nan Xiao, Greenland Capital CTO

Question: How might one design a unified modeling process that proactively anticipates and responds to sudden macro shifts driven by political policy changes, ensuring portfolios across U.S. and global markets are optimally positioned for next-decade capital flows?

4.35 – Innovations in Risk Modeling: Machine Learning Applications in Quantitative Finance:

• Arkin Gupta, Vice President Morgan Stanley

Question: In using black-box neural networks for risk modeling, which interpretability methods have best translated complexity into actionable insights trusted by portfolio managers?

4.55 – Closing Keynote:

• Dr. Dhagash Mehta, Head of Applied Artificial Intelligence Research for Investment Management Blackrock

Question: Which emerging AI paradigms are most likely to transform quant investing? And how can the systematic investing community build stronger academic-industry partnerships to rigorously adopt them?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

To help the data community prepare, I’ve crafted targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Strategic Trust in AI and Data-Driven Narratives

2. Operational Bottlenecks in Data Adoption

3. Macro Uncertainty as a Catalyst for Data Innovation

Link to agenda: https://www.neudata.co/agenda/new-york-summer-data-summit-2025-agenda?_gl=1*1m1pl3q*_gcl_au*OTg3MjM2MTkwLjE3NDIzMzYwNDc.

Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of May 4th, 2025

The Empire Stage

Welcome remarks

8:50 – 9:05 AM · Empire Stage

• Ian Webster, Chief Revenue Officer, Neudata

Question: How do you see the balance between proprietary data development and partnering with market data providers evolving over the next three to five years to sustain a competitive edge?

In conversation: Data, disruption and the future of finance

9:05 – 9:30 AM · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

• Mark Fleming-Williams, Head of Data Sourcing, Capital Fund Management (Moderator)

• Abraham Thomas, Previously Founder and Chief Data Officer, Quandl; Angel Investor and Independent Board Director

Question: Which emerging AI capabilities pose the greatest threat to existing data moats, and how should incumbents respond? I’d encourage the audience to check out Abraham’s Substack to get even more context and insights ahead of the discussion with Mark (who I hope you all already know from The Alternative Data Podcast).

Pivotal

Pivotal is a newsletter about data, investing, and startups. It has three underlying themes: the data explosion, markets in everything, and software eating the world.

By Abraham Thomas

Extracting insights embedded in earnings call transcripts using LLMs1

9:30 – 10:00 AM · Empire Stage · Tracks: Quantitative; Fundamental

• Qingyi Huang, Head of AlphaWise Quant Research, Morgan Stanley

Question: What advice do you have for AI product creators who present AI-generated narrative summaries to senior stakeholders so they trust the nuance without second-guessing every edit?

The analyst of the future

10:00 – 10:30 · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

This session will explore how technology and data are reshaping the process of investment research, from the perspectives of sellside and buyside analysts, and data providers. Is AI a match for human-level analysis or more of a tool than a replacement? What are the benchmarks for funds on the long-term journey to insight automation?

• Jason DeRise, Head of Public Markets & Research Investment Products, Liberty Mutual Investments (Moderator)

• Chris Andrews, Global COO of Research, Morgan Stanley

• Siddhant Jayakumar, Founder & CEO, Finster AI

• Nicole Königstein, Chief AI Officer, quantmate

Question: You’ll need to attend the session to hear the questions I’ll ask while moderating. I’m thrilled to have such an amazing collection of talent on stage with me.

New vendor showcase

10:50 – 11:20 · Empire Stage · Tracks: Quantitative; Private markets; Fundamental

In this showcase session hear from some of the industry’s newest providers, as they share the datasets and product launches set to take the world of alternative data by storm. Ensure you’re keeping on top of all the latest updates and case studies to inform your future data acquisition strategy.

• Jenna Menking, Founder, Crosswalk

• Ben Darr, Founder, CredoIQ

• Joseph Berger, Managing Director, Capital Markets Division – Guideline

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

Industrials neglected no more: Beating the market through nowcasting2 with alternative macro data

11:20 – 11:40 · Empire Stage · Tracks: Quantitative; Fundamental

While consumers leave a footprint of their activities, there is less of that in the business-to-business world. As such, the industrials sector has remained out-of-grasp for PMs trying to get an informational edge. This talk will demonstrate how novel techniques of Nowcasting allow us to turn disparate macro data into structured signals, highlighting which companies are likely to beat or miss consensus revenues.

• Ajit Agrawal, Founder, AKAnomics

Question: Which non-traditional indicators have you found most predictive of industrial revenue surprises in your nowcasts?

Exploring holistic digital behavior across app and web

11:40 – 12:00 · Empire Stage · Tracks: Quantitative; Private markets; Fundamental

This presentation will explore how combining app and web data can offer a more comprehensive view during investment due diligence. Vlad will highlight how Sensor Tower’s True Audience Estimate helps cut through duplication across platforms to reveal a single, unified digital user count.

• Vlad Prelevic, Account Director, Sensor Tower

Question: How do you reconcile discrepancies between in-app engagement and web user journeys when constructing a unified True Audience Estimate?

Consolidating competition: M&A in the data market

12:00 – 12:20 · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

2025 has already brought a number of significant acquisitions in the data market. With consolidation seemingly on the rise, this session will draw on Neudata’s industry intelligence to assess what is driving this trend and what the impact is expected to be on the market.

• Barney Bruce-Smythe, Vice President, Neudata

Question: Given the long selling cycle associated with asset managers, how is the industry managing liquidity while building toward confirmed market fit? How do mergers, auditions and partnerships help shorten the selling cycle or slow cash burn rates?

What does Trump 2.0 mean for the industrials sector?

1:20 – 1:40 · Empire Stage · Track: Fundamental

Tax reform, supply chain disruptions and potential incoming trade policies have all put the US industrials sector in the spotlight for 2025. This session will explore different types of alternative data that can monitor subsectors such as defence, aviation, construction and manufacturing.

• Matt Yome, Research Analyst, Neudata

Question: Because markets are forward-looking, prices are reacting to policy talk before the actual impact is visible. Which datasets can narrow the gap between what’s being said and what’s actually happening such that it’s easier to see what could happen next?

The multimodal economist: A worldwide case study

1:40 – 2:00 · Empire Stage · Tracks: Quantitative; Fundamental

With government data quality declining due to low survey response rates and budget cuts, understanding the effects of growth, inflation, geopolitical shifts, and policy shocks on the worldwide economy is increasingly difficult for asset managers. This session will discuss an innovative multimodal AI approach, designed to increase the depth and breadth of domain expertise, while maintaining explainability. The demonstration will track global labour markets – generating JOLTS+ data and the effect of Trump administration policies – in real time.

• Apurv Jain, Founder and CEO, MacroXStudio

Question: If government data becomes less reliable due to budget cuts or reduced transparency, how can alternative data fill the gap? Are there cases where it already offers a more accurate view of real-world economies than official statistics? Note: I used this question in my preview for Neudata’s London conference in March, where Apurv spoke on this topic, but I’ll be attending this session in person, so I’m asking it again.

Alternative data use cases: Competitive intelligence in private investing

2:00 – 2:40 · Empire Stage · Track: Private markets

While the use cases may differ from public markets, alternative data can be a powerful tool for private market investors. Panelists will explore how venture capital and private equity funds are leveraging emerging data sources, together with new tools for processing data to move faster, benchmark better and gain a competitive edge.

• Keava Low, Senior Analyst, Neudata (Moderator)

• Brian Murphy, Head of Data Science, Salesforce Ventures

• David Teten, Venture Partner, Coolwater Capital; Founder and Managing Partner, Versatile Venture Capital

• Claire Saint-Donat, ex Bain Capital; ex Blackstone Group

• Warren Valdmanis, Managing Partner, FoW Partners

Question: In the due diligence process, private market investors need to ramp up quickly on specific opportunities before the investment window closes. What can data vendors do to better meet the needs of investors in this process?

Navigating market turmoil: Leveraging insights from historical scenario analysis using alternative data

3:00 – 3:20 · Empire Stage · Track: Quantitative

This presentation will explore two compelling case studies that highlight the transformative power of alternative data in decision-making. The first demonstrates how advanced data capabilities can help clients navigate the market environment and make informed allocation decisions. The second showcases how historical scenario analysis and alternative data can impact portfolio-specific strategies.

• Duncan Robinson, Director of Quantitative Insights & Data Science, Allspring Global Investments

Question: Using historical analogies can be powerful for establishing a framework for analyzing the current environment, but it can also be dangerous if there are structural differences between the present problem and the analogous situation. What advice do you have for assessing whether a historical scenario is the right one to apply to a current challenge?

From opportunity to edge: Quant data sourcing and strategy

3:20 – 4:00 · Empire Stage · Track: Quantitative

Quantitative funds have long been a driving force in alternative data adoption. How can quants source and onboard new datasets seamlessly? What dataset types have grown in availability and popularity? Does supply meet demand? What considerations must be made around data quality, cleaning and mapping to ensure funds maximise a dataset’s value?

• Amrita Tiwari, Investment Analytics, New York Life Investment Management (Moderator)

• Ben Cilia, Chief Data Officer, Quantbot

• Alexios Bevratos, Managing Director, Deputy Head of Alpha Research, Capital Fund Management

• Duncan Robinson, Director of Quantitative Insights & Data Science, Allspring Global Investments

Question: Can you share for the data providers what due diligence frameworks quants apply to data to detect hidden overfitting3 risks before onboarding?

The Chrysler Stage

Streamlining data buyer and provider relationships

9:40 – 10:30 · Chrysler Stage · Track: Data providers

This session will explore the evaluation of relationships with data providers, and zero in on the question of when to source a new provider and when to remain in an existing relationship. How can vendors best communicate to existing subscribers about product changes and updates? How willing are buyers to act as an early adopter of a new data product? How can pricing structures best reflect the value a data product can bring?

• Henry Scherman, Data Monetization Consultant, Neudata (Moderator)

• Gregory Kurzman, Senior Associate - Data Sourcing and Strategy, Balyasny Asset Management

• Eliza Raphael, Head of Data Services, Jump Trading

• Philip DiLemme, Data Science, King Street

Question: What should data sellers know about the different buyer personas across the asset management landscape, and how should they adapt the sales cycle for each? What can the data buyers do to help the data vendors meet their needs without sharing proprietary info? Shameless plug for There’s No Such Thing as a Data Buyer

Banking on industry data: Navigating the ebbs and flows of the banking sector

10:50 – 11:10 · Chrysler Stage · Tracks: Quantitative; Fundamental

Over the past two decades, the U.S. banking sector has experienced periods of both exuberance and turbulence. This session will explore the findings of a recent research paper from S&P Global Market Intelligence, aimed at helping investors navigate the sector’s ebbs and flows, offering critical insights into banking characteristics that distinguish winners from losers.

• Daniel Sandberg, Managing Director, Head of Quantitative Research & Solutions, S&P Global Market Intelligence

Question: The research paper leverages SNL financial metrics to break down bank performance into five factors and includes the source code for replication of the methodology (that’s a best practice other companies should follow). Here’s the link: https://www.spglobal.com/market-intelligence/en/news-insights/research/banking-on-industry-data-navigating-the-ebbs-and-flows-of-the-banking-sector. As a follow-up question to the report, what additional data sources or emerging metrics would you recommend investors integrate alongside the traditional, SNL-based factors to ensure their quantitative analyses remain both robust and forward‐looking?

Adopting cloud technology: Where do we start?

11:10 – 11:30 · Chrysler Stage · Tracks: Quantitative; Fundamental

As cloud adoption accelerates across the financial industry, many data providers and buy-side4 participants are left asking: What are the benefits of receiving market data through the cloud, and what are the steps to get started? In this session, we break down the cloud architecture that leading hedge funds use and outline how real-time data can be distributed through the cloud.

• Ethan Han, Research Analyst, Neudata

Question: How do you frame the cloud migration narrative to secure buy-in from IT, compliance and front office stakeholders?

Market vs alternative data: Two sides of the same coin?

11:30 – 11:55 · Chrysler Stage · Tracks: Quantitative; Fundamental

Alternative data is often defined by what it is not, but where do we draw the line between market and alternative data? Panelists will discuss how market, or traditional, data sources can identify trends and inform investment decisions across a range of trading strategies. How can traditional and alternative data sources form part of a cohesive investment approach?

• Farah Ruthnam-Sandys, Business Development Director, Neudata (Moderator)

• Steve Hansen, Global Head of Market Data, Davidson Kempner Capital

• Evan Reich, Global Head of Data Strategy and Sourcing, Verition

Question: Considering the benefits and constraints of traditional market data and alternative data, which tends to lead in signaling regime shifts when market dynamics are uncertain and chaotic? And in stable periods, does the signal hierarchy reverse?

From data to alpha: Streamlining the data management lifecycle

11:55 – 12:20 · Chrysler Stage · Tracks: Quantitative; Private markets; Fundamental

This session will delve into the challenges funds face in managing the data lifecycle, from cataloging, to trialing, budgeting, governing and migrating data. An exclusive update on Neudata’s new SaaS platform, Navigator, will highlight ways to streamline workflows, adapting to evolving regulatory demands whilst maintaining a competitive edge.

• Ruairi Powers, Senior Vice President, Product Manager, Neudata

• Dean Gray, Head of Design, Neudata

Question: How do you prevent siloed ownership from fragmenting a unified data strategy while also enabling innovation and speed across decentralized teams?

Rockefeller Room

Lunch & learn – Small data, big insights: A new playbook for quant signals

12:30 – 1:10 · Rockefeller Room · Track: Quantitative

At Hatched Analytics, we’ve developed a proprietary global dataset grounded in direct, observable measurements, never panel-based assumptions. With growing adoption among pioneering quant funds, momentum is building fast. Join our founders to explore how hidden digital signals deliver precision over assumptions, and how our latest quant product is delivering robust alpha. Plus, hear from Maiden Century on the real-world returns this approach is driving.

• Charmaine Kenny, Co-Founder, Hatched Analytics

• Donal Byrne, Co-Founder & CEO, Hatched Analytics

• Qaisar Hasan, Founder & CEO, Maiden Century

Question: How do you tell the success stories that persuade large quant shops5 to adopt “small data” solutions?

Unlocking AI potential: The critical role of licensed data feeds in business intelligence

1:20 – 2:10 · Rockefeller Room · Tracks: Quantitative; Private markets; Data providers; Fundamental

As Generative AI transforms business intelligence, the role of high-quality, fit-for-purpose, licensed data feeds has never been more critical. This panel brings together leaders from Dow Jones to discuss how real-time news and data power AI applications, the challenges of delivering AI-ready licensed content, and the evolving needs of enterprises integrating AI into their workflows.

• Dan Shar, Executive Vice President and General Manager, Dow Jones Wealth & Investing (Moderator)

• Joe Cappitelli, General Manager, Dow Jones Newswires

• Nicole Spell, Director of Business Management and Commercial Strategy, Dow Jones

• Brian Gelinas, Senior Vice President, B2B Product, Dow Jones

• Jason Malatesta, Head of Partnership and Licensing Americas, Dow Jones

Question: Are data buyers at asset managers and data sellers on the same page about derivative data creation and usage in the age of generative AI? Do data buyers and sellers view generative AI as just another data-processing tool, no different from previous machine learning techniques?

What questions do you have for the panelists? Leave a comment below.

Leave a comment

Know someone attending or presenting at the conference? Feel free to forward this to them.

Share

- Jason DeRise, CFA

1. AI's Transformative Potential vs. Human Judgment

2. Model Resilience and Data Integrity

3. Strategic Decision-Making in Uncertain Macro Contexts

Link to agenda: https://battleofthequants.com/new-york-2025/

Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

Subscribe now

Note: Agenda as of May 3, 2025

Quantitative Allocator Breakfast

Time: 8:00 AM (Two-hour “speed dating” format; rotations every 15–20 minutes)

Quantitative hedge fund1 managers will pitch their strategies to tables of allocators over a two-hour period in a ‘speed dating’ format with table rotations taking place every 15–20 minutes. The breakfast provides an efficient means of allowing allocators to conduct initial due diligence through a combination of hedge fund presentations and interactive sessions while allocators gain valuable insight into the hedge fund’s strategies.

Question: In the process of testing and iterating on the model being shared, what can you share about the biggest breakthrough in factor2 addition, refinement, or learning that changed the model from average performance to material outperformance?

Data Breakfast

Time: 8:00 AM (Two-hour “speed dating” format; rotations every 15–20 minutes)

Data and signal providers will present their datasets and signals to tables of quantitative portfolio managers, researchers and data buyers over a two-hour period in a ‘speed dating’ format with table rotations taking place every 15–20 minutes. The breakfast provides a focused and efficient means of allowing those attending to conduct initial due diligence through a combination of data and signal provider presentations and interactive sessions while gaining valuable insight into the provider’s solutions as they relate to quantitative finance.

Question: Can you share the methodology behind the dataset that explains why the data provides good signals, but also what are the limitations of the methodology that prevent the data from being used for some use cases?

Keynote: How to Grow a Pension Fund from $17 to $52B through Performance-Based Returns

Time: 10:15 AM

Named as one of the top 100 CIOs in the world by Chief Investment Officer magazine, hear how this CIO built a world-class investment team and revolutionized the fund’s investment approach by integrating AI, advanced quantitative analysis and risk management techniques into his portfolio.

• Bruce Cundick, Former CIO, Utah Retirement Systems

Question: Given the trends of increased global protectionism and the advancements of AI, which analytics frameworks will become more important over the next 20 years compared to the prior 20 years? Or are these dynamics just historical patterns repeating which AI and machine learning-driven models can handle?

Buckle Up: The AI Revolution is Coming Fast and Furious, Are You on Board?

Time: 10:45 AM

The adoption of AI in quantitative hedge funds is rapidly advancing, driven by innovations such as customized LLMs3 and emerging implementations of Agentic AI4. These advancements are enhancing portfolio managers’ decision-making processes and equipping quant analysts with efficient, time-saving tools. As these technologies continue to evolve, what impact will this have on quantitative trading?

• Moderator: Bartt Kellermann, CEO & Founder, Global Capital Acquisition

• Adrian de Valois-Franklin, CEO, Castle Ridge

• Hunter Almgren, Distinguished Technologist, Hewlett Packard Enterprise

• Ioana Boier, Global Head of Capital Markets Strategy, Nvidia

• Michael Prichard, CEO & Founder, Shift AI

Question: Could AI empower market-savvy but technically untrained individuals to build quant strategies via “quant-vibe coding5,” and if so, what would be the implications for the markets and quant researchers?

All Bets are Off! Can Your Quant Strategy Survive 2025?

Time: 12:00 PM

Markets are experiencing unprecedented upheaval as major global events unfold almost daily. How are quants navigating these rapidly shifting conditions, where geopolitical forces move markets faster than most investors can react? As the year progresses, will certain strategies prove to be better positioned than others? Can more data, new LLMs, and cutting-edge Agentic AI enhance success in this environment?

• Moderator: Nick Mitsiou, Executive Director, LGT Capital Partners

• Michael Geismar, President & Chief Risk Officer, QIM

• Naim El-Far, Head of Equities Investment Engineering, Bridgewater

• Adam Rej, Head of Macro Alpha, CFM

• Alberto Cozzini, Founder, Polymathique

Question: Can you talk about past “model-breaking” moments where the real world detached from the training data and human intervention was needed to ensure the models didn’t accidentally destroy capital?

Staying Ahead of the Game: Data-Driven Insights in the Tariff Era

Time: 12:30 PM

• Conal Doyle, Senior Presales Engineer, KX

Question: Can you share how time series databases6 help extract causal7 insights from trade data (e.g., AIS, port activity, inventory, and demand) to assess the effects of ongoing trade wars?

What are Institutional Allocators Looking for in a Quant Strategy?

Time: 12:45 PM

Institutional allocators are increasingly turning to quantitative strategies for their ability to deliver consistent, risk-adjusted, and uncorrelated returns, enhancing portfolio performance and diversification. Discover what allocators seek in external managers and how they strategically leverage external strategies to enhance portfolio returns to achieve the greatest success.

• Moderator: Michael Pomada, President and CEO, Crabel

• Jae Yoon, CIO, New York Life Investment Management

• John Trammell, President, New York Episcopalian Diocesan Investment Trust

• Neal Berger, Founder, President and CIO, Eagle’s View

Question: How do you think about the assessment of systematic8 money manager performance? Do you prioritize deeper quantitative metrics or place value on the human skills behind the models?

The Secret Sauce: How to Build a Profitable Systematic Investment Strategy with Alt Data

Time: 2:10 PM

• Qaisar Hasan, Founder & CEO, Maiden Century

Question: What is your advice for quant researchers to maintain their model standards while integrating newer, alternative data9 factors that have a proven ability to predict fundamentals10 but have limited history?

Advances in ML in Quantitative Finance

Time: 2:30 PM

• Gary Kazantsev, Head of Quant Technology Strategy, Bloomberg

Question: Have you seen any geographic regions innovating in alpha generation11 beyond data constraints that US-based quant researchers might learn from?

Predictive Analytics: Harnessing Data to Forecast Market Movements

Time: 2:40 PM

Is the integration of diverse datasets or the refinement of a single source what empowers quantitative managers to forecast market movements? What combination of data delivers the highest predictive accuracy?

• Moderator: Evan Schnidman, PhD, Head of Fidelity Labs, Fidelity Investments

• Mateusz Panasiuk, Chief Scientific Officer, Omphalos Fund

• Kyle Balkissoon, Managing Director & Portfolio Manager, Stance Capital

• Abhi Thakur, Head of Business Development, ArcticDB

• Gary Kazantsev, Head of Quant Technology Strategy, Bloomberg

Question: That first question in the description is a great question! I’ll add another to the description. Does the answer differ about the use of diverse datasets versus factor refinement for different public market asset classes and quant strategies?

Inside the Minds of Pension Funds: What do they Look for in Quant Talent?

Time: 3:50 PM

Pension funds are among the most savvy and well-capitalized allocators in the world, with a mandate to deliver long-term results, and quants are increasingly on their radar. What role do quants play in shaping their portfolios, and how do pension funds weigh internal builds vs. external talent? What are the best ways for quant managers to approach pension plans, and what does it take to earn a Day 1 ticket to their portfolios?

• Moderator: Gene Reilly, CIO, Greenwich Quantitative Research

• Christophe L’Ahélec, Managing Director, Head of Active Public Markets, UPP

• Michael Ijeh, Senior Investment Associate, Teacher Retirement System of Texas

Question: Do pension funds primarily use quant strategies for diversification and risk management or to replace existing strategies and enhance alpha? How should the systematic funds adjust their pitch to reflect this goal?

Fireside Chat: The Future of AI in Finance

Time: 4:15 PM

We stand at the edge of an AI acceleration curve, where systems are not only evolving rapidly but also creating increasingly advanced successors. As financial superintelligence soars, the question becomes: who will emerge as the winners—and how can you best position yourself to ride the wave of this fast-moving AI revolution?

• Samir Varma, Managing Partner, VS Asset Management and Author

• Bartt Kellermann, CEO & Founder, Global Capital Acquisition

Question: Will generative AI make markets more efficient and alpha harder to find, or trigger new dislocations that traditional quant methods can exploit? How might this evolve over the next decade?

What questions do you have for the panelists? Leave a comment below.

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- Jason DeRise, CFA

To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. The path from data to Insight to Alpha

2. Human and Machine Collaboration in the AI Era

3. Operational and strategic dimensions of data acquisition

Feel free to share the article with anyone who would benefit from it

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Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of 22 March, 2025

Opening remarks

8:50 - 9:05 | Leadenhall Hub | Keynote

• Ian Webster, Chief Revenue Officer, Neudata

Question: How do you see the data advisory industry evolving over the next 3 to 5 years, given the rapid expansion of data availability and AI capabilities?

DDQs and data listing optimisation: Best practices for data providers

8:50 - 9:40 | Lothbury Library | Data providers

In this interactive workshop and Q&A, join Neudata’s Vendor Engagement and Regulatory Leads as they share best practices for data providers to maximise success. Hear hints and tips on presenting a dataset as an attractive product and learn strategies for overcoming common compliance and DDQ1 pitfalls, to build confidence with potential buyers.

• Brittany Thomas Senior Regulatory Analyst, Neudata

• Saima Jannath Vendor Engagement Associate, Neudata

Question: What are the most common misconceptions data providers have about key elements of the due diligence process?

Future-proofing: Competing in an AI-driven, constantly changing, quant landscape

9:05 - 9:30 | Leadenhall Hub | Keynote

• Ian Webster Chief Revenue Officer, Neudata

• Paul White, Co-Founder and CEO, Quantbot Technologies

Question: As more data feeds into AI-driven models, is GPU2 demand rising on the buy-side3? Could short-term GPU constraints impact the viability of increasingly compute-intensive strategies? Are users actively optimizing its use of AI or just assuming more compute power will be unlocked in the future?

The ABCs of research management: Automation, big data and content

9:30 - 10:00 | Leadenhall Hub | Keynote

• Nicole Bauthier Executive Director, Head of AlphaWise Data for Europe, Morgan Stanley

• Paul Walsh Associate Director of Research, Morgan Stanley

Question: How might sell-side4 research evolve as GenAI5 becomes more capable of replicating quality research report structure and insights?

From engagement to execution: Selling to data buyers

9:40 - 10:30 | Lothbury Library | Data providers

This exclusive session will offer invaluable guidance and insights from those on the frontlines of data acquisition. Whether at the point of sourcing, onboarding, testing or governance, what are the biggest challenges faced by data buyers and providers? How can vendors streamline their strategies, to ensure best results from initial engagement through to execution?

• Mark Fleming-Williams Head of Data Sourcing, Capital Fund Management

• Leo Murison Lead Data Scientist, Jupiter Asset Management

• Amy Dafnis Data Sourcing Lead, Rokos Capital Management

• Henry Scherman Consultant, Neudata (Moderator)

Question: Since buy-side firms rarely share direct feedback during the data assessment process, how can vendors “read between the lines” to extract useful signals from limited responses?

The European investment market: Volatility, growth and opportunity

10:00 - 10:30 | Leadenhall Hub | Keynote

In an ever-evolving European market, businesses face currency fluctuations, disrupted supply chains and unpredictable consumer behaviour. The risks are clear, but uncertainties also open the door to growth and innovation. In this session, panelists will discuss the alternative data6 sources delivering actionable insights into geopolitical trends, empowering investors to leverage volatility for strategic advantage.

• Alba Seoane Head of Data Research, Europe, Point72 Asset Management

• Ian Morley Chairman, Wentworth Hall

• Savvas Savouri Head of Macro, Chief Economist & Strategist, Quantmetriks Research

• Nick Greenstock CEO, Gatehouse Advisory Partners

Question: With anticipated increases in European aerospace and defense spending, how can investors monitor government allocations and assess localized economic impact (e.g., jobs created near manufacturing sites)? Can this be overlaid with consumer brand and retailer exposure to identify companies best positioned to benefit? Conversely, is it possible to gauge public sentiment and taxpayer willingness to support this spending trend?

Shark Tank: New vendor showcase

11:00 - 11:40 | Leadenhall Hub | Data buyers

In this showcase session hear from some of the industry's newest providers, as they share the datasets and product launches set to take the world of alternative data by storm. Ensure you're keeping on top of all the latest updates and case studies to inform your future data acquisition strategy.

• Paul Mann Director of Business Development, Zeki Data

• Carolyn Ryan Chief Strategy Officer, Trustpilot

• Ariel Duarte López Director of Data Science, Acuity Trading

Question: Good luck to the showcase participants. Can you highlight the strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

Quantext: Invest trends in textual and alternative quant data

11:00 - 11:20 | Threadneedle Hub | Data buyers

• Aditya Sharma Director, Alpha Signals, S&P Global Market Intelligence

Question: Since the release of GPT-3, a plethora of services using LLMs to explain earnings calls and earnings release statements have come to market. Have investor sentiment signals from earnings calls and earnings releases reached the point of being a coincident signal in quant models, explaining bets more so than alpha? Or does the post-earnings share price drift still happen?

A real China: From space to insight

11:20 - 11:40 | Threadneedle Hub | Data buyers

This session will examine how alternative data sources can address the global research gap in Chinese markets, alleviating institutional investors' data pain points. Skysight Technologies will showcase how use of dynamic satellite imagery technology to conduct real-time tracking and monitoring, can provide decision-making support for financial investment institutions.

• Julie Liu Chief Marketing Officer, Skysight Technology

Question: Monitoring large construction projects has been a proven use case for satellite imagery, while efforts like tracking fast-moving events (e.g., car counts) have been less reliable. What are some underappreciated but high-value applications of satellite-derived data?

Unlocking holistic digital trends across app and web

11:40 - 12:00 | Leadenhall Hub | Data buyers

This session will explore the advantages of leveraging both app and web data in tandem, when doing investment due diligence. Andrew will touch upon analysis that utilises Sensor Tower's True Audience Estimate, which deduplicates users across web and app to uncover one true digital user figure.

• Andrew Sprague Head of Investor Vertical, Sensor Tower

Question: Could you walk us through your deduplication methodology for unifying app and web data across devices? What caveats should investors keep in mind when merging these datasets?

Alternative perspectives for private equity

11:40 - 12:20 | Threadneedle Hub | Keynote

2024 saw major activity in the private markets space. With rebounding valuation levels, the continued expansion of AI and an expected M&A surge, 2025 brings no signs of slowing down. This session will explore how private equity firms are using alternative data sources to better inform new strategies, from deal sourcing to diligence and value creation.

• Jon Steinberg Founding Partner, Mountside Ventures

• Aman Aneja Director of Analytics, Fairview Equity Partners

• Steven Millar Senior Manager, PWC Deals Analytics UK

• Michael Hejtmanek Vice President, Corporate Solutions, Neudata (Moderator)

Question: What changes have successful data companies made to serve the distinct needs of private market buyers versus public market buyers?

Transaction data: Tracking grocery and general merchandising retailers

12:00 - 12:20 | Leadenhall Hub | Keynote

• Finn Cousins Research Analyst, Neudata

Question: How should data be combined to better understand revenue growth drivers in grocery and general merchandise retail? For example, some use transaction size as an inflation proxy, which requires a lot of assumptions (e.g., constant basket size, price mix, and geography mix). How would you approach this challenge using multiple datasets?

Patterns and predictions: Unlocking the power of consumer data

12:20 - 12:50 | Leadenhall Hub | Keynote

Consumer transactional and behavioral data remains one of the most popular and widely deployed categories of alternative data, providing indicators for both short-term trading decisions and long-term valuations. This panel will explore the key dataset types helping investors to understand and react to current market trends, whilst predicting future performance and economic shifts.

• Aditi Sawhney Specialist Data Science Lead, Man Group

• Suraj Gohil Co-Founder and Chief Commercial Officer, Fable Data

• Nicholas Neary Senior Data Scouting Analyst, Neudata (Moderator)

Question: Can you share some best practices for sophisticated asset managers to follow when playing the prediction game within the prediction game? By this I mean some data points move the market when released, affecting perceptions of future results, so some investors try to predict the data released by the data vendor with other data sources. And then there are times when the released data point is not the right read versus the real future company results, so market participants also attempt to predict the real result and arbitrage the difference.

From data to alpha: Streamlining the data management lifecycle

12:20 - 12:50 | Lothbury Library | Data buyers

This session will delve into the challenges funds face in managing the data lifecycle, from cataloging, to trialing, budgeting, governing and migrating data. Panelists will consider ways to streamline workflows, adapting to evolving regulatory demands whilst maintaining a competitive edge. Featuring an introduction to Neudata's new SaaS platform, Navigator.

• Rado Lipuš Founder and CEO, Neudata

• Ruairi Powers Senior Vice President, Product Manager, Neudata

• Dean Gray Head of Design, Neudata

Question: Based on your work with the buy-side, how would you benchmark the range of capabilities in managing the data lifecycle from lagging to leading? What are the typical signs of lagging and leading capabilities?

Lunch and learn: How investors use gig mobility data to understand consumer behaviour

1:00 - 1:40 | Exhibition Area | Data buyers

The gig economy is set to surpass $873 billion by 2027, marking it as one of the fastest-growing sectors globally. Yet insights on gig mobility remain largely inaccessible and fragmented. In this session, we’ll explore how investors are harnessing gig mobility data to gain a differentiated view of the market and uncover insights into the performance of hundreds of publicly traded and thousands of privately held companies worldwide.

• Ryan Green CEO, Gridwise

Question: How does Gridwise address the challenge of fragmented gig mobility data, especially given the limited transparency from major ride-hailing and delivery platforms?

The multimodal economist - A worldwide case study

1:50 - 2:10 | Leadenhall Hub | Keynote

With government data quality declining due to low survey response rates and budget cuts, understanding the effects of growth, inflation, geopolitical shifts, and policy shocks on the worldwide economy is increasingly difficult for asset managers. This session will discuss an innovative multimodal AI approach, designed to increase the depth and breadth of domain expertise, while maintaining explainability. The demonstration will track global labour markets - generating JOLTS+ data and the effect of Trump administration policies - in real-time.

• Apurv Jain Founder and CEO, MacroXStudio

Question: If government data becomes less reliable due to budget cuts or reduced transparency, how can alternative data fill the gap? Are there cases where it already offers a more accurate view of real-world economies than official statistics?

How investors are using alternative data to track industrials companies

2:10 - 2:30 | Leadenhall Hub | Keynote

This session will outline global trends impacting the industrials sector, including trade policies, geopolitics and supply chains. What are the common challenges faced by investors looking to track this industry and which dataset types are growing in popularity and impact?

• Matt Yome Research Analyst, Neudata

Question: Beyond tracking supply chain shifts, how can industrial data be used to assess whether productivity is improving—potentially offsetting other pressures on output?

ESG data in 2025: Declining demand or more relevant than ever?

2:30 - 2:50 | Leadenhall Hub | Keynote

The continued rise of ESG investing has fueled a new branch of alternative data, but despite growing popularity, ESG policies and practices continue to attract criticism from many. This session will consider some of the problems with ESG data, look at the current regulatory landscape and highlight the potential advantages and use cases for risk mitigation and alpha generation.

Antti Savilaakso CRO, Impact Cubed

Question: ESG data, in my view, is just investment data. It’s not a box-ticking exercise. Could you share examples where ESG indicators flagged early risk or identified outperformers in a market?

The future of data through a quant lens

2:50 - 3:30 | Leadenhall Hub | Keynote

As technological advancement continues to have a profound impact on the evolution of quantitative investing, how are funds leveraging alternative data to deliver alpha and manage risks? What challenges do quants face in sourcing and integrating new datasets? How can artificial intelligence optimise investment strategies and where does human oversight remain essential?

• Kristina Usaite Senior Quantitative Researcher, Robeco Investment Management

• Florian Koch Quantitative Researcher and Portfolio Manager, Lynx Asset Management

• Vik Bansal Systematic Portfolio Manager, Centiva Capital

• Sophie Beland Head of Systematic Advisory Sales - EMEA, Morgan Stanley (Moderator)

Question: Building on the panel's strong framing—how might synthetic data7 generated by AI be used to stress-test models more effectively? Conversely, could it amplify the flaws in existing training datasets?

Why macro-quantamental data matters in an age of uncertainty: A back-to-basics approach

4:00 - 4:20 | Leadenhall Hub | Keynote

• Lasse de la Porte Simonsen Director of Systems and Advanced Analytics, Macrosynergy

Question: I wish I could be in London to hear the presentation. This is a critical moment to revisit how we apply historical frameworks to evolving macro conditions. In that context, how do you approach the first principles of macro-quantitative8 investing, especially when the underlying causal forces of globalization (post–Berlin Wall) may no longer hold in an era of national protectionism? How might this shift challenge the validity of leading indicators9 that worked reliably over the past 30 years? Or does a first-principles approach suggest these frameworks can still hold, regardless of structural regime change?

AI-Washing in focus: Reviewing the impact of artificial intelligence

4:20 - 5:00 | Leadenhall Hub | Keynote

Amid the AI-boom, some companies have faced accusations of ‘AI-Washing’, or falsely overstating the use of Artificial Intelligence to capitalize on the attention this trending technology attracts. How can alternative data help to expose AI innovation vs hype? Where is AI having a tangible impact on data optimisation in the investment sector? What shifts are we seeing in the regulatory landscape?

• Sam Livingstone Head of Quantitative Strategies & Risk, Ambienta Public Markets

• Sanne de Boer Head of Quantitative Equities, Voya Investment Management

• Timothée Consigny Chief Technology Officer, H20 Asset Management

• Bhavin Kapadia Senior Advisor, Financial Services (Moderator)

Question: Great questions in the summary! As a follow-up, how can due diligence questionnaires help uncover “AI-washing”? What are the telltale signs that AI isn’t meaningfully embedded in a product? Or more importantly, how is AI actually embedded and does it meet responsible AI standards in order for an asset manager to have trust in purchasing the product?

What questions do you have for the panelists? Leave a comment below

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Would this content help someone attending or presenting at the conference? Feel free to forward it on.

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- Jason DeRise, CFA

https://www.eaglealpha.com/2024/05/12/alt-data-conference-february-2025/

There’s 3 key themes to the top questions on my mind

1. The Future of AI, Data Infrastructure, and Investment Technology

2. The Unique Challenges of Alternative Data in Private Markets

3. Sector-Specific and Niche Applications of Data

Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. After moving on from UBS Evidence Lab, I’ve remained active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser, user, and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.

The Data Score introduced premium content on February 1st, which will be behind a paywall. Data Playbook, Data-Driven Investing, and Dataset Deep Dives will be premium content. These sections provide actionable strategies, in-depth case studies, and exclusive expertise tailored for data-driven professionals.

Top Questions conference preview content, like this article, will remain free for all subscribers! The subscription for premium content is priced at $9.99 per month or $99.99 annually.

Subscribe now

Eagle Alpha New York Alternative Data Conference Agenda: Wednesday, February 12th, 2025

Main Track, Morning

Welcome & Opening Statements

• Michael Finnegan, CEO of Eagle Alpha

• 8:50AM - 9:00AM

• MAIN ROOM

• Join us for the official kickoff of the Eagle Alpha Alternative Data Conference with a welcome & opening remarks led by CEO Michael Finnegan.

Question: What key factors will drive the evolution of the alternative data industry in 2025? Will 2024’s trends accelerate, plateau, or shift?

Engineering Tomorrow’s Hedge Fund Technology: Avoiding Data Infrastructure Lock-in and Deploying Useful AI Applications

• Michael Watson, Hedgineer in conversation with Thomas Combes, Eagle Alpha

• 9:00AM - 9:30AM

• MAIN ROOM

• Join us for a discussion with Michael Watson, founder of Hedgineer on the future of hedge fund technology. We’ll explore how open table formats1 and hybrid compute engines2 can prevent data infrastructure lock-in3 and delve into real-world applications of AI that are transforming how hedge funds operate.

Question: It seems like each week there is a new generative AI4 model available with either better reasoning performance or is more cost-effective. What parts of the data and tech stack should remain flexible to accommodate evolving AI models, and what should be stable for long-term reliability?

New to Market & Fresh Features – Vendor Presentations

• An exciting session where we’ll dive into the latest advancements and features from top data vendors.

• 9:30AM - 10:30AM

• MAIN ROOM

Question: For the new vendors, keep these questions in mind while presenting—How does your data solve real investment questions? What sets your methodology apart from competitors? How do you ensure compliance and trust? Good luck with the presentations.

Private Equity Track, 9:15am- 12:30pm

Private Equity Track – How Investors and Lenders in Private Companies are Using Technology & Analytics as a Competitive Weapon

• David Teten, Versatile VC

• 9:15AM - 9:45AM

• BREAKOUT ROOM

• Explore the current state of alternative data adoption in the PE/VC industry with practitioners who are actively using these data sources. This session will provide a firsthand look at how firms are integrating alternative data into their investment strategies.

Question: Where are leading asset managers seeing the biggest impact from data and analytics—deal origination, due diligence, operations, portfolio insights, or elsewhere?

Private Equity Track – The Fund of the Future: Embracing Technology and Data in Private Equity

• Trevor Ross, West Monroe

• 9:45AM - 10:00AM

• BREAKOUT ROOM

• Explore how private equity firms can leverage technology, data analytics, and innovation to gain a competitive edge. West Monroe will break down the “Fund of the Future” framework, offering strategies to drive growth, efficiency, and value creation in today’s evolving market.

Question: How does the ideal data and tech stack differ between large private market and public market asset managers? What are the shared and distinct infrastructure requirements?

Private Equity Track – Alternative Data and Private Equity – Use Cases and Applications

• With G2, Dodge Construction Data and Clootrack, moderated by Brendan Furlong, Eagle Alpha

• 10:00AM - 10:30AM

• BREAKOUT ROOM

• Join leading data vendors for a panel discussion on how alternative data is transforming the private equity landscape, with a focus on real-world applications and actionable strategies.

Question: Given the lack of continuous feedback loops in private markets, short decision time frames, and limited data tagging, how should data providers and asset managers rethink their approach to alternative data? Some context behind the question: unlike private markets, public markets offer a continuous feedback loop where consistently processed data insights scale the decision-making process. There is a short window for making a private equity investment decision instead of the ongoing decision-making process in public markets. The underlying data is not likely well tagged related to the private companies compared to the much better tagging of data to tickers in public markets.

Private Equity Track – Fireside Chat: Value Creation Themes: Talent, Revenue, Operations

• With Tom Liu, Managing Partner / Founder of Ideate Capital, in conversation with Conor Taggart, Eagle Alpha

• 12:00PM - 12:30PM

• BREAKOUT ROOM

• Join us for a fireside chat with a former Head of Data at a top private equity firm as we dive into the cutting edge of value creation. We’ll explore how top firms are securing elite talent, leveraging enriched data to drive higher close rates, and harnessing LLMs5 to optimize every stage of the deal cycle—from sourcing to contract execution.

Question: What advice would you give PE firms to foster strong collaboration between investors and data teams as new data and technology capabilities are applied to PE investments? How can data companies better support the data teams at PE asset managers?

Main Track, Afternoon

Consumer Insights and Product Developments from Leading Vendors

• Brian Callahan, Facteus, Thomas Grant, Apptopia and Charm.io, moderated by Brendan Furlong, Eagle Alpha

• 12:00PM - 12:30PM

• MAIN ROOM

• Gain valuable insights into consumer behavior and witness the latest product innovations as a panel of leading vendors share their expertise. This interactive session will explore the intersection of consumer needs and cutting-edge product development.

Question: How have recent trends in premium and luxury consumer brands differed from 2024? How does this compare to mass-market and value-focused brands?

The Opportunities And Nuances of Deploying Unstructured Data In Quant Trading Models

• Professor Francesco Fabozzi, Yale University; Didier Lopez, OpenBB; and Dan Joldzic, Alexandria Technology; moderated by Christos Koutsoyannis, Atlas Ridge Capital.

• 1:15PM - 2:00PM

• MAIN ROOM

• Join leading practitioners for a discussion on the practical applications of unstructured data in quant6 trading. This session will address the opportunities and nuances of deploying these models, including data sourcing, processing, and integration.

Question: One aspect of leveraging unstructured data in systematic investing7 is extracting sentiment as a signal from news sources. However, some news is recapping a previously known impact on share prices, while others are providing new information that would affect share prices. Have systematic funds successfully built scalable causal AI models8 that identify true new signals versus priced-in insights, or is this still an experimental stage?

2025 Compliance: What to Expect from the New Administration

• Sergio Pagliery, Schulte Roth & Zabel and Emilie Abate, Iron Road Partners, moderated by Ciaran Ryan, Eagle Alpha

• 3:30PM - 4:00PM

• MAIN ROOM

Question: There is a general narrative of deregulation in the current US government. If US regulations and oversight are relaxed for alternative data providers and asset managers who use alt data, will the industry be able to self-police effectively to ensure the long-term viability of the industry?

Alien Intelligence, Myth and Reality

• Frederic Siboulet, consultant (former Deloitte, EY & IBM)

• 4:00PM - 4:30PM

• MAIN ROOM

• Over the past three years, there has been a rapid acceleration in the role of artificial intelligence, particularly with GPTs and LLMs. We examine why, how, and where AI may be augmenting human performance in quantitative and operational tasks. We also consider how this new paradigm relates to model structures and data utilization, both during training and inference. In addition, we discuss the relationship between model size and model risk, and explore the potential evolution of data science given the new agentic frameworks9 available to businesses.

Question: Which asset managers or data firms are seeing the most value from agentic AI frameworks, and which use cases have made it to production? Clever presentation title!

Uncorking Alpha: Data-Driven Strategies in Fine Wine Investing

• Tommy Jensen, Wine Capital Fund in conversation with Ciaran Ryan, Eagle Alpha

• 4:30PM - 5:00PM

• MAIN ROOM

• Data driven wine investing? Yes please. Join Tommy Nordam Jensen (CEO, Wine Capital) and Ciaran Ryan as they discuss Selection, Storage and Selling of tier 1 investment grade wines.

Questions: Fun topic to end the day. I think I might have all the questions—but here’s a few! What measurable weather patterns produce top-rated wines by region, and which recent vintages benefited those conditions? Can the Normalized Difference Vegetation Index (NDVI)10 and other satellite-derived data predict vintage quality? How can data identify oversupply risks that impact investment returns (good for consumers, bad for investors)? Which wine marketing gimmicks fail to add investment value (e.g., aging wine under water as if it were discovered after being lost at sea)? And most importantly—will there be samples?

What questions do you have for the panelists? Leave a comment below

Leave a comment

Would this content help someone attending or presenting at the conference? Feel free to forward it on.

Share

- Jason DeRise, CFA

Another reoccurring theme about data conference agendas is that they love jargon. A lot to define below (ok, to be fair, I added some jargon in my questions too). A bunch of new jargon terms to add to the Jargonator: