Top Questions: Battle of the Quants NYC 2025
Battle of the Quants is in it’s 20th year; here’s my questions for the speakers joining the May 6th, 2025 edition
Battle of the Quants returns to NYC at The Yale Club on May 6th, 2025. To help attendees prepare, I've crafted targeted questions for each speaker and panel, focused on three key themes:
AI's Transformative Potential vs. Human Judgment
Model Resilience and Data Integrity
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.
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.
Would this content help someone attending or presenting at the conference? Feel free to forward it on.
- Jason DeRise, CFA
Quant funds: Short for "quantitative funds," also referred to as systematic funds. Systematic refers to a quantitative (quant) approach to portfolio allocation based on advanced statistical models and machine learning (with varying degrees of human involvement “in the loop” or “on the loop” managing the programmatic decision-making).
Factor investing: an investment approach that involves targeting quantifiable firm characteristics or “factors” that can explain differences in stock returns. Security characteristics that may be included in a factor-based approach include size, low volatility, value, momentum, asset growth, profitability, leverage, term and carry. https://en.wikipedia.org/wiki/Factor_investing
Large Language Models (LLMs): These are machine learning models trained on a large volume of text data. LLMs, such as GPT-4 or ChatGPT, are designed to understand context, generate human-like text, and respond to prompts based on the input they're given. It is designed to simulate human-like conversation and can be used in a range of applications, from drafting emails to writing Python code and more. It analyzes the input it receives and then generates an appropriate response, all based on the vast amount of text data it was trained on.
Agentic AI Frameworks: A type of AI system that autonomously makes decisions and executes tasks with minimal human intervention, often used in data-driven workflows to enhance efficiency and automation. An example could be a large language model chat model used as an interface that calls other AI models depending on the prompt and, in turn, leverages other specific AI agents that handle specific tasks to enable the outcome.
Vibecoding: Computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla, introduced the term vibe coding in February 2025. The concept refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing it. https://en.wikipedia.org/wiki/Vibe_coding
Time Series Database: From the KX website: https://kx.com/time-series-database/#:~:text=A%20time%20series%20database%20is,system%20monitoring%2C%20and%20IoT%20data. A time series database is optimized to store, retrieve, and manage timestamped data points. These databases are designed to handle high ingestion and query throughput for applications that track changes over time, such as stock market analysis, system monitoring, and IoT data.
Unlike traditional relational databases, time series databases offer:
Time-based indexing for fast access to historical and real-time data.
Optimized compression for efficient storage.
Scalable performance for handling vast datasets.
Causal Analysis/Causal AI: A method of identifying relationships that suggest causation rather than mere correlation in statistical data, focusing on determining what affects an outcome.
Systematic Fund: Systematic refers to a quantitative (quant) approach to portfolio allocation based on advanced statistical models, and machine learning (with varying degrees of human involvement “in the loop” or “on the loop” managing the programmatic decision making).
Alternative data: Alternative data refers to data that is not traditional or conventional in the context of the finance and investing industries. Traditional data often includes factors like share prices, a company's earnings, valuation ratios, and other widely available financial data. Alternative data can include anything from transaction data, social media data, web traffic data, web mined data, satellite images, and more. This data is typically unstructured and requires more advanced data engineering and science skills to generate insights.
Fundamental analysis: Assessing investment assets based on underlying economic and financial factors, typically by creating a model that forecasts the financial statements of an entity, sector, or financial market. Valuation methodologies are then typically applied to the forecasted financial statements to derive the value of the entity.
Alpha: A term used in finance to describe an investment strategy's ability to beat the market or generate excess returns. A simple way to think about alpha is that it’s a measure of the outperformance of a portfolio compared to a pre-defined benchmark for performance. Investopedia has a lot more detail https://www.investopedia.com/terms/a/alpha.asp