Top questions ahead of Neudata's Summer Data Summit

Prepare for the upcoming Neudata conference in New York: spotlighting key questions to understand novel data & tech integration, AI in investing, and new data-driven insights.

May 30, 2023

Welcome to the Data Score newsletter, 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 alternative data 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 data. Before that, I successfully built a sellside equity research franchise based on proprietary data and non-consensus insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective that allows me to collaborate with end-users to generate meaningful insights.

This week, Neudata is hosting its Summer Data Summit in NYC on June 1st. https://www.neudata.co/events/new-york-data-summit-2023

Ahead of the event, I thought I’d share the questions on my mind for each panel/presentation. Check out the questions below.

The purpose is to stimulate conversation and encourage the consideration of additional questions for the upcoming conference Let me know what additional questions you have.

There are a few key themes in my questions:

Where are the potential gaps, novel use-cases, and new integration opportunities in the investing process?
What’s the impact of AI on the investing process and decision makers?
What additional technology investments can improve the ability to get insight from data?

I am looking forward to seeing many of you in person at the event. Feel free to contact me if you’ll be there and want to say hi!

DataChorus LLC business email: jason.derise@datachorus.net

LinkedIn: https://www.linkedin.com/in/jasonderise/

The conference agenda and the key questions on my mind

THE FORUM TRACK

09:00 The Impact of ChatGPT
Moderator: Ian Webster, Managing Director, Neudata
Matthew Rothman, Millennium Management
Nicholas Venuti, Executive Director, machine learning research, Morgan Stanley
Parnaz Tabrizian, Director investment science, CPP Investment Board
Jonathan Chin, co-founder, Facteus

Question: What are use cases for generative AI1 applied to alternative datasets that could be implemented today? What use cases for generative AI on the near future for alternative data?

09:40 The latest & greatest in alternative data
Moderator: Daryl Smith, head of research, Neudata
Paul White, CEO, Quantbot
Lilian Quah, managing director, portfolio manager, head of quant research, Epoch Investment Partners
Sheedsa Ali, managing director, Pinebridge Investments

Question: One of the biggest gaps in the alternative data markets are high-frequency B2B datasets, what new datasets are available in this area?

10:20 How big picture trends impact investments
Yesim Tokat-Acikel, managing director, portfolio management, Principal Asset Management
Danesh Kissoon, research analyst, Neudata
Duncan Robinson, director of scientific learning, Allspring Global Investments

Question: Given that impact investing2 is often misunderstood by financial markets and data providers, I’m looking to hear examples where impact investing has successfully generated alpha3 while driving positive changes in ESG4 outcomes?

11:20 Leveraging Systematic Tools and Dynamic Infrastructure for Portfolio Construction
Lila Martz, Vice President, structured derivative sales, Morgan Stanley

Question: Which datasets and technologies are being combined to generate alpha, where the approach is surprising or different than the typical systematic approach.

11:50 Freight transportation: The largest commodity in the world
Daniel Pickett, chief data & technology officer, FreightWaves

Question: How are FrieghtWaves’ clients adjusting capacity in relation to their Sonar product’s freight rate forecasts.

12:20 Changing winds: tracking and assessing global supply chains
Barney Bruce-Smythe, senior analyst, Neudata

Question: In March 2023, the NY Fed claimed supply chains are back to normal. Does the data show the same? Are there any pockets of concern? https://www.reuters.com/business/ny-fed-says-supply-chain-pressures-normalized-february-2023-03-06/

12:40 Real-time macro data for investment strategies
Thanh-Long Huynh, CEO, QuantCube

Question: Nowcasting5 models often need recalibration because most alternative data time series histories are shorter than a full economic cycle, let alone no two economic cycles are exactly the same. How does QuantCube approach this challenge? Which economic situations or models are they actively monitoring for future adjustments?

2:00 Regulation updates
Don D’Amico, managing director, Neudata
Adam Storch, associate director, event & emerging risks examinations team lead, SEC
Maurya C. Keating, associate regional director for examinations in New York regional office, SEC

Question: What are the common shortfalls the SEC has seen in reviews of buyside6 of alt data practices and compliance?

2:40 Generating alpha with mobile device data: Social media
Tom Grant, VP of research, Apptopia

Question: How have TikTok user trends been affected by the political debate in US (if at all)? Is any competitor seeing a change in user overlap with TikTok, either winning or losing share in the process?

3:20 The art of financial clairvoyance: How zodiac signs, mood rings and advanced market data can inform trading decisions
Henry Schwartz, VP, global head of client engagement, CBOE Global Markets

Question: I decided to ask ChatGPT4.0 what this presentation is likely about. I’m looking forward to seeing if this is right!

“This presentation, based on the title, is likely to address unconventional and innovative methods for informing trading decisions, including a tongue-in-cheek reference to zodiac signs and mood rings as a metaphor for unpredictable or seemingly nonsensical market influences. The primary focus, however, would be on advanced market data. Here are some potential topics that might be covered:

Financial Clairvoyance: The speaker, Henry Schwartz, might discuss the need for foresight in trading, how certain indicators can be interpreted to make predictions, and the challenge of separating signal from noise in the abundance of available data.
Zodiac Signs and Mood Rings: This whimsical reference likely suggests a discussion of unconventional or subjective factors in market prediction. It could serve as an analogy for market sentiment or behavioral finance, where trader emotions and psychology can often drive market movements.
Advanced Market Data: As the Global Head of Client Engagement at Cboe Global Markets, Schwartz will likely focus heavily on the use of advanced market data. This could involve sophisticated statistical analyses, AI-driven predictive modeling, and real-time analytics that can help traders make more informed decisions.
Client Engagement: Schwartz might discuss how understanding these diverse and complex factors can help engage clients more effectively, providing them with unique insights and better investment strategies.
Risk and Reward: The presentation might delve into how unconventional predictors, combined with advanced market data, can contribute to risk assessment and potential reward scenarios.

The talk will likely be a blend of humor, anecdotal evidence, and hard data, aimed at showing the importance of innovative thinking and advanced data analysis in modern trading.” — ChatGPT prediction of what the presentation will be about.

3:40 The data view at scale
Gunja Gargeshwari, Chief Revenue Officer, Bright Data

Question: Is their web mined pricing, inventory, and demand data showing signs that inflation is starting to affect demand or that price increases by brands are no longer sticking?

4:00 Using sentiment data to drive alpha and insights
Tom Endean, Product leader, Symphony
Nate Storch, Franchise leader, Data & Analytics, Symphony

Question: What high-impact use cases for sentiment data generated by natural language processing (NLP) are currently being overlooked by the buyside?

4:40 Investing on intangible assets
Mikhail Samonov, CEO lead portfolio manager, Two Centuries Investments

Question: I wonder if this approach to investing based on rising or highly scored intangible assets also works in investing environments when value is in favor? As context, it seems that the companies scoring the best on alternative datasets focused on off balance sheet attributes and intangible assets are often high-quality companies with quality governance. Is this priced into the valuations as higher price/book ratios than peers? If so, there are times when lower quality companies outperform the market (eg when value investing is in favor or cost cutting, turnaround stories are in favor).

5:00 New vendor highlight session
Lobbying Data, Neil Bhatia
Comparables.ai, Niko Nalli
D.A.T.A, Jason Voss
Judges:
Josef Schmalfuss, guest lecturer, Oxford University
Mikhail Samonov, CEO, lead portfolio manager, Two Centuries Investments
Duncan Robinson, director, scientific learning, Allspring Global Investments

Question: Just getting my popcorn to see outcome

Watching Popcorn GIF - Watching Popcorn Eating GIFs

THE WEST HUB TRACK

9:00 Sitting on a data gold mine: Extracting investment use cases
Omer Yackov Laor, head of consultancy, Neudata
Scott Trabucco, director of account management, Similarweb

Question: What are some less obvious, but high-impact use cases for clickstream data that the buyside may be missing?

9:20 Deception And Truth Analysis: How to rapidly surface actionable investment insights.
Jason Voss, co-founder & CEO, Deception and Truth Analysis (D.A.T.A.)

Question: I’m curious if they’ve tried to apply the capability to sellside research. Sellside research firm’s Supervisory Analyst teams could benefit from the technology.

9:40 How alternative data is transforming private market investing
Moderator: Saif Zia, business development manager, Neudata
Aneesha Raghunathan, vice president, Susquehanna Growth Equity (SGE)
Beth Pollack, Managing Partner, Decision Science Advisors
Chandler Klose, VP of Engineering Vendor Management, Insight Partners

Question: How do the panelists view the role of alternative data in sourcing, due diligence, and portfolio operations? It’s still early days for PE/VC and alt data. Each fund seems to be taking a different approach to sourcing, due diligence, and portfolio operations. The last few panel discussions at other conferences with VC and PE data professionals were eye-opening. I took very detailed notes. I look forward to the same level of insight into their process in each of the three phases.

10:20 Traditional vs trending: A fireside chat on market data and alternative data
Moderator: Konstantinos Vafeidis, analyst, Neudata
Aditya Sharma, CFA, Director, Data Valuations and Analytics, S&P Global Market Intelligence
Eliza Raphael, Head of Market Data Services, Schonfeld Strategic Advisors

Not real question: if we as an industry decide we don’t want to call it “alternative” data anymore, what should we call it?

Real question: Is it still possible to generate alpha from traditional data alone or has that potential been competed away?

11:20 Small but mighty: The rise of emerging managers in alternative data investing
Moderator: Farah Ruthnam-Sandys, business development manager, Neudata
Garence Staraci, Analyst, Wellington
Daniel Sheyner, CIO, Chimera
Josef Schmalfuss, Guest Lecturer, Oxford University

Question: What are some non-consensus use cases for alt data that generate alpha?

11:50 Is it your tech stack or your people stack?
Moderator: Tipsy Talwar, managing director, Morgan Stanley
Sham Mustafa, co-founder & co-CEO, Correlation One
Carson Boneck, CFA, Chief Data Officer, Balyasny Asset Management

Question: I suspect the answer will be that the people stack is more important than the tech stack. However, under what conditions could the opposite be true?

12:20 Where’s technology used in hedge funds?
Michael Watson, founder, **Hedgineer.io**

Question: What are the most common use cases where generative AI is already generating alpha for hedge funds?

12:40 Macroeconomic forecasts: earlier & more accurate with machine learning
Professor Anne-Louise Ponsonby, Head | Research, Dysrupt Labs

Question: With an increasing number of market participants now forecasting macro indicators, how has the ability to generate alpha evolved?

Think this is useful for someone attending Neudata? Feel free to forward it on.

What questions would you ask? Leave a comment.

- Jason DeRise, CFA

Generative AI: AI models that can generate data like text, images, etc. For example, a generative AI model can write an article, paint a picture, or even compose music.

Impact Investing: Investments made into companies, organizations, and funds with the intention to generate a measurable, beneficial social or environmental impact alongside a financial return.

Alpha generation: 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 its 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

ESG Outcomes: Environmental, Social, and Governance (ESG) refers to the three central factors in measuring the sustainability and societal impact of an investment in a company or business.

Nowcasting: In order to systematically forecast the next reported economic or company specific financial result, multiple sources of high frequency data are combined. The model continuously updates the forecast with increasing accuracy as the volume of data covering the unknown period increases.

Buyside vs Sellside: Buyside typically refers to institutional investors (Hedge funds, mutual funds, etc) who invest large amounts of capital and Sellside typically refers to investment banking and research who provide execution and advisory services to institutional investors.

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