Top Questions ahead of Neudata’s New York Summer Data Summit 2024

Neudata’s New York Data Summit is on June 4th. Here are the questions on my mind for the speakers.

May 31, 2024

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 June 4th, data conference season continues with Neudata’s Summer Data Summit in New York.

Link to the agenda: https://webflow2.neudata.co/agenda/new-york-summer-data-summit-2024

3 themes across my questions for the speakers

Alternative Data1 Utilization and Trends: Partnerships, gaps in data coverage, predictive uses of alternative data, and its application in various investment strategies.
Quantitative and Financial Strategies: Building a successful quant fund2, avoiding pitfalls, market outlooks, and the role of historical data in trading strategies.
Innovation and Future Outlook: The future of alternative data, ESG3 data's realistic potential, regulatory updates, and the impact of AI and alternative data on finance and investments.

Hall 2 - Empire Stage
8:30 - 8:50 Opening Remarks
Rado Lipus, Founder & CEO, Neudata
Ian Webster, Managing Director, Neudata

Questions: What benefits and innovations do you anticipate from the recently announced partnership with Althub? Do you expect partnerships and mergers to accelerate in the alternative data space?

Hall 2 - Empire Stage
8:50 - 9:10 Fireside Chat: How to Build a Successful Quant Business From the Ground Up
Moderator: Ian Webster, Managing Director, Neudata
Paul White, CEO and Co-Founder, Quantbot Technologies LP

Question: What are the most common pitfalls to avoid in setting up a quant practice, and how can new firms navigate these challenges effectively?

Hall 2 - Empire Stage
9:10 - 9:40 Keynote: US Equity Market Outlook & Key Themes
‍Michelle Weaver, US Thematic Research Strategist, Morgan Stanley

Questions: What primary market scenarios is Morgan Stanley considering for the US equity market, and what specific data points would signal that these scenarios are unfolding?

Hub 1-3 - Chrysler Stage
9:10 - 9:40 Panel: Latest Quant Data Trends
Moderator: Julia Asri Meigh, Head of ESG and Macro Research, Neudata
‍Roshan Raman, Head of Quantitative Research, Woodline Partners
Eugene Miculet, Director, Data Sourcing and Strategy, WorldQuant
Eliza Raphael, Head of Data Services, Jump Trading

Question: In 2023, one of themes of the industry was that there are gaps in industrial and B2B tech sectors from an alternative data coverage point of view. Have those gaps been closed and if so, where are the high priority gaps in coverage?

Hall 2 - Empire Stage
9:40 - 9:50 Using Alternative Data to Predict the US Elections
Barney Bruce-Smythe, Senior Associate, Neudata

Question: I know I used this question before, but I’m still interested in hearing more takes on the usefulness of data from betting and prediction markets to assess what will happen in November. What are your thoughts on the data type?

Hub 1-3 - Chrysler Stage
9:40 - 10:00 Fireside Chat: Data from an Asset Owners Perspective
Moderator: Julia Asri Meigh, Head of ESG and Macro Research, Neudata
Michael Oliver Weinberg, Adjunct Professor of Finance and Economics, Columbia Business School

Question: What advice would you give asset managers who lack the resources of the largest hedge funds to be able to leverage data-driven insights effectively and remain competitive?

Hall 2 - Empire Stage
9:50 - 10:00 Market Movers: Corporate Events as Catalysts for Superior Returns
Anju Marempudi, Founder and CEO, EventVestor

Question: Some corporate events are so high profile that when they get announced, the market efficiently prices the new news right away. What are the conditions that allow for additional alpha4 to be generated post-announcement of a corporate action5?

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Hall 2 - Empire Stage
10:20 - 10:40 Unveiling Data Insights on Sustainability, Research Trends and a Look at AI Sentiment Analytics
Bryan Christian, Head of Business Development, Symphony
Rachna Srivastava, Head of Product, AI, Data & Platform, Symphony
Brian Herlihy, Partner, CFO, 22V Research

Question: There was a period of time where ESG data sets were a top priority for most asset managers, but like most hype cycles, there was also a period where the data type reached the “trough of dissolutionment6,” where the market has become far too pessimistic about the data compared to reality. What do you feel are the realistic possibilities for risk management and alpha generation using ESG data?

Hall 2 - Empire Stage
10:40 - 11:00 1 + 1 = 3: Best Practices to Unlock the Power of Data in Your Investment Operations
Shawn Kenyon, Senior Director, Head of Cloud Product Strategy, SS&C Technologies
Matthew Carroll, Head of Data Lens Sales, SS&C Technologies

Question: For asset managers who haven’t adopted data-driven insights at scale, what do you think are the biggest blockers?

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Hall 2 - Empire Stage
11:00 - 11:20 Does Generative AI7 hiring look different for finance compared to tech?
Ben Zweig, Founder and CEO, Revelio Labs

Question: Do you think the biggest driver of differences between finance and tech hiring stems from the difference in the magnitude of outcomes? By this, I mean an asset manager could make or lose millions of dollars on a single investment decision, but decisions on showing the right ad to a consumer, recommending the next media to consume, or handling customer service may have a material impact on aggregate but each individual outcome has lower stakes.

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Hall 2 - Empire Stage
11:20 - 12:00Panel: Exploring AltData for Non-Equity Use Cases
Moderator: Dan Entrup, Founder, It's Pronounced Data
John Curaba, Director of Sales, Finance, Placer.ai
Rainbow Chik, Director of Data Insight & ESG Analytics, GoldenTree Asset Management
Jason DeRise, Head of Data and Analytics Products, Liberty Mutual Investments

Question: This is awkward. should I ask myself a question? What do you want to ask us?

- 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.

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).

ESG: 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.

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

Corporate Actions: Events initiated by a public company that bring changes to its securities, such as stock splits, dividends, mergers, and acquisitions.

Trough of Disillusionment: A phase in the Gartner Hype Cycle where interest wanes as experiments and implementations fail to deliver, often leading to negative media and analyst attention.

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.

Portfolio Company (PortCo): A company in which a private equity firm or venture capital firm has invested.

Master data management (MDM) is a discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared single source of data truth.^{https://en.wikipedia.org/wiki/Master_data_management}

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.

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