Top Questions ahead of Eagle Alpha’s Next Level Alternative Data Conference
Eagle Alpha hosts its Next Level Alternative Data Conference on January 18th, 2024 in Midtown New York. Here are some questions for the speakers that are top of mind.
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. 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 and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.
On January 18th, 2024, Eagle Alpha hosts its Next Level Alternative Data Conference in Midtown New York City. https://www.eaglealpha.com/2023/10/17/new-york-next-level-alternative-data-conference-january-18th-2024/#agenda
Ahead of the event, I’m sharing key questions on my mind for each speaker and panel.
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There are three broad themes that my questions address
The future of the alternative data1 industry.
Applications, contraints, and implications of using generative AI2 in the financial markets.
Corporate data strategy: use cases, monetization, and blockers.
Check out the questions below
Let me know what additional questions you have for the panelists.
And I’ll be there as well, so feel free to reach out to set up a time for us to connect in person.
8:45am Opening Remarks
Niall Hurley, CEO, Eagle Alpha
Questions: Over the last 5 years, the demand for and supply of alternative datasets have grown dramatically. And the ability of technology to process data into insights has grown sharply. Looking 5 years ahead, where do you think the alternative data industry will be in its maturity? What activities do you see the smartest asset managers taking to prepare for that future?
9:00 am Keynote: The Future of Data in Asset Management
Rich Brown, Global Head of Market Data, Jain Global
Questions: Over the last 5 years, the availability of alternative data has grown, making it effectively part of the “core data stack.” As the pursuit of alpha3 from alternative data becomes increasingly competitive, what pivotal shifts in strategy and methodology should the buyside4 anticipate and implement? Will it be trying to answer more challenging and different investment questions than have historically been answered by alternative data? Or will it be about making early bets on new or less popular datasets before others are able to uncover their predictive power?
9:20 am Large Language Model Showcase - Applications of Generative AI to Complex Data
Chris Coulthrust, Sr. Cloud Architect, Microsoft
Aidan North, Commercial Lead, Orbit
Thomas Combes, CTO, Eagle Alpha
Questions: Over the last year in the financial markets, there’s been an explosion of rhetoric about use cases for generative AI (Gen AI), and more specifically, large language models (LLM)5. Can the panel highlight a few successful real-world applications of generative AI in production and explain the required data preparation steps that were key to their value creation for end users?
10:00 am Next Wave of Data Discovery: Corporate Data Monetization
Host: Amy Young, Director, Strategic Partnerships, Financial Services, Microsoft
Lauren Cascio, Co-founder, Gulp Data
Trevor Ross, Data Science Strategy Manager, West Monroe
Tom Liu, Co-founder, Ideate AI
Questions: With the growing recognition of data as an asset class, interest in its monetization is rising. What are the primary challenges corporations must overcome to realize tangible value from their data?
10:30 am Newly Profiled Vendors and Fresh Features - Session 1
11:00 am One-on-One Meetings
12:30 pm Will the Magnificent Seven Outperformance Persist in 2024? The Data Specialists Speak
An invite-only session will be running during lunch for fundamental analysts:
Questions: Assuming the “Magnificent Seven” refers to Google, Amazon, Apple, Meta, Microsoft, NVIDIA, and Tesla—and that we as an industry haven’t applied the nickname to 7 magnificent datasets :) — For the panelists discussing their views based on the data, I’m curious what they view as the critical investment questions that the market needs answered in 2024. From there, I’d like to hear how they connect the dots between the questions and potential outcomes as measured in the data. What would we need to see in the datasets to know if one answer to the critical investment questions is more likely than the others?
1:30 pm Newly Profiled Vendors and Fresh Features - Session 2
2:00 pm Macro, Credit Conditions & The Bond Market - What Is The Data Saying? 02:00 - 02:30(EDT)
William Peters, Quant Product Specialist, Macrobond Financial
Brian Callahan, Director of Product, Facteus
Ed Lavery, VP, Investor Intelligence, Placer.ai
Questions: How does the data line up with the narrative that the Federal Reserve has navigated an easier inflation environment without sending the US economy into a recession? Are there indicators in the data suggesting potential upside or downside risks to the prevailing consensus?"
2:30 am One-on-One Meetings
4:00 pm Compliance Considerations for Generative AI and Large Language Models
Jessica Margolies, Special Counsel, Schulte Roth & Zabel
Emilie Abate, Director, Iron Road Partners
Alik Sokolov, CEO, Responsibli
Questions: How is copyright law potential changing in light of generative AI in terms of both the inputs as part of training materials and the outputs of the models? Do industry best practices on data sourcing and data use cases cover the implications of the availability of generative AI, or will new standards need to be established?
4:30 pm Investing In Entertainment -The WGA and SAG-AFTRA Strikes Impact on Streamers, Traditional Broadcasters and Talent - A Data Pespective.
David Viviano, Chief Economist, SAG – AFTRA
Guy Bisson, Executive Director & Co-founder, Ampere Analysis
Brian Fuhrer, SVP Product Strategy, Nielsen
Janet Tamaro, Screenwriter/Executive Producer, Hurdler Productions
Questions: What does the data suggest is the most likely positive scenario for the entertainment industry’s profitability when it is becoming increasingly easy for consumers to generate their own entertainment via AI?
What questions would you ask? Leave a comment below.
- Jason DeRise, CFA
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.
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.
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
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 firms that provide execution and advisory services (research reports, investment recommendations, and financial analyses) to institutional investors.
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.