Top questions ahead of Eagle Alpha's Unbound New York Conference

Data Conference Season Continues with great presentations and panels - and also more questions from me for your use at the conference too.

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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.

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This week, Eagle Alpha is hosting its Unbound conference in New York on June 6th.

https://www.eaglealpha.com/2023/01/24/unbound-conference-new-york-june-6-2023/

Ahead of the event, I am sharing the questions on my mind for each panel or 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:

1. How might AI change the way the alternative data industry operates?

2. What’s the potential for successful data monetization¹ strategies to spark new valuable data to reach the alternative data market?

3. Compliance is critical for the entire data industry: where are the areas that need more attention from buyers and suppliers?

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 FORUM TRACK

Opening Speaker - How Data & Advanced Computing Will Change the industry

• 08:45 - 09:00(EDT)

• RJ Assaly – Chief Product Officer, TOGGLE AI

Question: Toggle created a ChatGPT like LLM² to provide investment guidance. Toggle(https://toggle.ai/) notes that its tech can’t predict the future but can provide resources for screening, analyzing, and testing scenarios. There are typically conflicting estimates and views on stocks across the web and across the professional sellside; how would the LLM decipher which text is most credible to use to answer a prompt?

Future of Asset Management - ArcticDB - A High Performance Dataframe Database For Real-World Data Science Challenges

• 09:00 - 09:20(EDT)

• Tom Taylor – Man Group

Question: Man Group's ArcticDB (https://arcticdb.io/) is announced to be built into Bloomberg BQuant. What are the opportunities for clients of both firms generated by the partnership?

Future of Asset Management - Multiplication By Addition: Saying Goodbye To The Hunger Games

• 09:20 - 09:40(EDT)

• Qaisar Hasan – Founder & CEO, Maiden Century

Question: Maiden Century’s whitepaper (https://maidencentury.com/resources) highlights the benefits of the mosaic approach to forecasting by combining datasets. For investors who do not have the budget to add 10+ data sources, would it be better to think horizontally across a dataset type, expanding geographic and sector coverage with similar datasets (e.g., multiple US transaction sources + Europe transactions + China transactions) or is there more alpha³ by thinking vertically, covering different types of datasets on the same company or geography (e.g., click steam + app data + transaction data + web mines pricing, etc.)?

Natural Language Processing: Where We Are and What's To Come

• 01:15 - 01:45(EDT)

• Panelists & Presenters:

  • Peter Licursi – Chief Strategy Officer, Kensho

  • Chris Tanner – Head of Research & Development, Kensho

Question: What kinds of texts will FinLM be trained on, and how much history is included? What use cases will it be best suited for, and what use cases should investors avoid?

What If We Applied Chat GPT To Consumer Transaction Data

• 01:45 - 02:00(EDT)

• Panelists & Presenters:

  • Jonathan Chin – Co-Founder, Head of Data & Growth Strategy, Facteus

  • Lorn Davis – Head of Corporate and Product Strategy, Facteus

Question: Are the best use cases labeling the data and finding outliners, or has Facteus figured out an approach with ChatGPT to generate insights from their data?

Macro Insights: The Gap Between Alternative Data & Central Bank Forecasts

• 03:00 - 03:30(EDT)

• Panelists & Presenters:

  • Niall Boland – Founder, ClearMacro

  • Ben Zweig – CEO, Revelio Labs

  • Jason Klotch – VP, Diversified Markets, TransUnion

  • Jon Liggett – Financial Services Data Strategy Lead, i360

Question: There have been times when alternative data has trended differently than government statistics, such as cost of housing inflation or employment statistics, due to differences in methodology. How should the industry think about this when government statistics may be less accurate than alternative data but still move the financial market, unlike most alternative data?

Industry Leading Perspectives From Compliance to Workflow Management: How Customer Needs Are Evolving for Solution Providers

• 03:30 - 04:00(EDT)

• Panelists & Presenters:

  • Chris Beels – CTO, GoldenTree Asset Management

  • Eric Marks – Managing Director, Head of Risk for Global Research and Evidence Lab, UBS

  • Happy Lombardy – Director of Market Data, Elliott Partners

Question: What specific new trends in data company policy and procedures are causing increased concern for financial market data buyers?

Controversial Data - The Role Of Alternative Data In Litigation, Short Selling, Asset Recovery & Warfare

• 05:00 - 05:30(EDT)

• Sebastian Neave – Senior Director, JS Held

Question: What parallels can be drawn between the use of data in litigation, short selling, asset recovery, and warfare, and its use in investment scenarios? Could the data used in these fields be adapted to address investment debates?

Data Incubator and Corporate Data (Sponsored by S&P Global Market Intelligence and Similarweb)

Data Monetization - Differentiated Data with EY & Corporate Partners

• 11:15 - 11:45(EDT)

• Joseph Sommer – EY, Managing Director, Data & Analytics

Question: Corporations have been interested in monetizing their data but don’t know if it's worth the effort to sell it to the investment community. What are some success stories that would show it’s possible to generate a meaningful revenue stream from a data monetization strategy?

Data, PE & Portfolio Companies: Differentiated Use Cases & Forward Thinking

• 01:15 - 02:00(EDT)

• Presenters:

  • Scott Trabucco – Director of Account Management, SimilarWeb

  • Adam Nahari – Data Science Lead, Berkshire Partners

Question: What are some less-followed, alpha-generating use cases of clickstream data that should be considered more often?

Data Securitization & Monetization - An Innovative Approach To Leverage Corporate Data Assets

• 03:30 - 04:00(EDT)

• Presenters:

  • Rob Gonda – CEO & Founder, ExoData

  • John Edge – Operating Partner, Broadhaven Capital

Question: What are ways that the alternative data community could remove blockers or concerns from the C-Suite and board when they consider monetizing their data beyond the walls of their firm?

Compliance Hub (Sponsored by Schulte Roth & Zabel and Sequentum)

Use Cases for LLMs in Minimizing The Compliance Risk - DDQs, Monitoring Negative Press About Data Sets, Staying On Top Of The Latest Litigation Related To Data And Its Use

• 10:45 - 11:15(EDT)

• Sarah McKenna – CEO, Sequentum

Question: Large language models are great at summarizing texts and noting differences between bodies of text. But, even managing the temperature settings would leave the potential for inaccurate text to be shown in response to the prompt. What approaches could be applied at scale to verify the accuracy, beside having a human in-the-loop sanity checking the information?

How To Drive Visibility Into A Data Operation To Satisfy SEC Exam Requirements

• 01:30 - 02:00(EDT)

• Presenters:

  • Peter Greene – Partner, Co-Head of the Investment Management Group, Schulte Roth & Zabel

  • Ben Kozinn – Partner, Schulte Roth & Zabel

Question: Documentation of the vetting process and the logic behind decisions is important. What separates the best from the rest in their ability to manage this process effectively?

Generative AI and New Risks Associated With Web Data: Will Web Data Carry Any Form of Copyright?

• 02:30 - 03:00(EDT)

• Presenters:

  • Peter Greene – Partner, Co-Head of the Investment Management Group, Schulte Roth & Zabel

  • Ben Kozinn – Partner, Schulte Roth & Zabel

Question: Most of the web already has a copyright, and facts are typically not considered under copyright law. Is that where the distinction between what can be collected and stored from web sites is between the facts and the content that would fall under copyright law?

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Think this is useful for someone attending Eagle Alpha Unbound? Feel free to forward it on.

What questions would you ask? Leave a comment.

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- Jason DeRise, CFA

1. Data Monetization: As referred to in this context, data monetization involves the process by which corporations, that accumulate large quantities of data through their day-to-day operations, create an additional revenue stream. This is achieved by refining (cleansing and enriching) and structuring their data in a way that it can be packaged and sold, often to external entities such as investment communities, for various purposes.

2. Large Language Models (LLMs): These are machine learning models trained on a large volume of text data. LLMs, such as GPT-3 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. LLMs represent a significant advancement in the field of AI text generation.

3. 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