Top Questions ahead of Rebellion Research's 2024 Future of Finance & AI Conference
The Cornell Financial Engineering Manhattan 2024 Future of Finance & AI Conference is on September 13, 2024, kicking off the fall data conference season.
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 September 13th, Rebellion Research is hosting the 2024 Future of Finance and AI conference, located on Roosevelt Island, New York City, at Cornell Financial Engineering, Manhattan. “Rebellion Research is a global machine learning1 think tank, artificial intelligence financial advisor, & hedge fund.”
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Three themes from my questions ahead of the conference
The implications of evolving technologies
Competitive dynamics in systematic2 trading
Maintaining AI models in production
Link to the agenda: https://www.rebellionresearch.com/cornell-financial-engineering-manhattan-rebellion-research-2024-future-of-finance-conference
08.50 – Opening Address from the Chairs: Director, Cornell Financial Engineering Manhattan, Professor Victoria Averbukh & Conference Host Rebellion Research CEO, Alexander Fleiss
Question: Given the ongoing debate on whether AI is a bubble, how should data and tech teams within asset managers allocate budgets to develop AI and ML capabilities in the short and long term?
08.55 – Special Guest Lecture : Mark Fisher, MBF CEO, Legendary Trader & Fund Manager
Question: In the commodity markets, what factors3 are often overlooked, and how can data help accurately navigate these opportunities?
9.15 PANEL DISCUSSION: Chat-GPT & The Future of Ai in Finance!
Moderator: Koren Picariello, Head of Generative AI Strategy and Execution Morgan Stanley
Yu Yu, Director of Data Science at BlackRock, Tony Berkman, Managing Director, Two Sigma, Samson Qian, Citadel, Didier Rodrigues Lopes, Founder and CEO @ OpenBB
Question: Professor Damodaran of NYU recently posted on LinkedIn that a colleague created a “Damodaran Bot” from all his public documents, videos and interviews: https://www.linkedin.com/pulse/beat-your-bot-building-moat-against-ai-aswath-damodaran-wnepc/?trackingId=1wKj8GY4QpK0Un2KiU0IlQ%3D%3D. It caused some dread for him, as he thought if the bot worked, he'd be out of a job and if it didn’t work, it meant his teachings didn’t add value. How does the panel think about creating a moat in the age of Generative AI?
9.55 – PANEL DISCUSSION: The Quantitative Investment Process
Moderator: Gordon Ritter, Hedge Fund Manager, Ritter Alpha LP, 2019 Quant of the Year
Caio Natividade, Global Head Quantitative Investment Solutions, Deutsche Bank, Sameer Gupta, Head of Data, Point72, Dr. Gil Haddad, Head Of Investment Decision Science Fidelity Investments, Dr. Gueorgui S. Konstantinov, Globally Renowned Quant, Petter Kolm, 2021 Quant Of The Year
Question: How have markets targeted by systematic methods evolved as alpha generation4 in public equities faces increasing competition? Which asset classes are the next frontier for quants5?
10.40 – PANEL DISCUSSION**:** Changing Landscape of Investing
Moderator: Kathryn Zhao, Global Head of Electronic Trading, Cantor Fitzgerald
Christina Qi, CEO Databento & Founder Domeyard LP, Jess Stauth, Chief Investment Officer – Systematic Equity, Fidelity, Jae Ho Kim, Head of Research Cubist/Point72
Question: Are barriers to entry in systematic trading lowering, and if so, what are the implications for institutional investors and data companies?
11.25 – PANEL DISCUSSION**:** Where Does Machine Learning Go From Here?
Moderator: Osho Jha, CEO dClimate & Arbol Co-founder
Dr. Ioana Boier, Ai Research, Nvidia, Ruchir Puri, Chief Scientist IBM Research, Dr. Igor Halperin, Ai Research, Fidelity, Evan Reich, Verition Fund Management Head of Data Strategy & Sourcing
Question: The world is potentially overly focused on Generative AI, but other types of machine learning and AI continues to be developed. What other machine learning and AI capabilities are likely to achieve significant breakthroughs in the next five years due to increased computational power?
12.05 – Keynote Debate : Machine Learning vs Optimal Control
Moderator: Gordon Ritter 2019 Quant of the Year
Andrew Chin, Chief Ai Officer Alliance Bernstein Vs. Peter Cotton Rebellion Research’s 2022 Book Of The Year Author ‘Microprediction: Building an Open AI Network’
Question: What are practical strategies for creating models that combine the strengths of both machine learning and optimal control6 approaches?
1:45 – Keynote Debate : Future of Data Science & Machine Learning
Claudia Perlich, Two Sigma Vs. Lisa Huang, Head of AI Investment Management and Planning, Fidelity
Question: Without knowing the specific aspect of the debate in question (assuming neither are arguing there is no future for data science nor machine learning), what are the steelman arguments that traditional, data-light investment techniques can outperform data science-driven strategies?
2:15 – The Current Research Focus of Cornell Financial Engineering Sasha Stoikov Head of Research Cornell Financial Engineering Manhattan
Question: In addition to quantitative research for investing, Sasha does research on algorithmic biases in music popularity. But let’s flip it around: How can the data on music popularity inform our understanding of how investment strategies become popular—and potentially over-crowded?
2.30 – PANEL DISCUSSION**:** Building Models From Real World Data Sets
Moderator: Matthew Lyberg, Director, Quantitative Development Manulife
Eren Kurshan, Columbia Professor, Executive in residence – Princeton & Morgan Stanley Head of AI/ML Research & Methodology, Natalya Dmitriyeva, Global Head of Data Content at Schonfeld, Samantha Mait, Manager, Equity Data Management, Balyasny Asset Management L.P., Qaisar Hasan, CEO Maiden Century, Dr Dhagash Mehta, Head of Blackrock Applied Artificial Intelligence Research for Investment Management
Question: How has the market evolved in using models to predict near-term revenue trends? Are these models becoming more precise, or is the focus shifting to other key performance indicators (KPIs)?
3.15 – PANEL DISCUSSION: Understanding The Future of Natural Language Processing in the 21st Century
Moderator: Jonathan Regenstein, Head of Financial Services Data Science Snowflake
Sahana Athreya, CFA, Millenium, Professor Andreea Minca, Cornell University, Richard Peterson, CEO, MarketPsych(division of London Stock Exchange), Wenqi Zhou, Systematic Trading and Research, (non compete) Fmr Balyasny Asset Management L.P., Judith Gu, Head Quant, Scotia Bank, Kelly Ye, Portfolio Manager, Decentral Park Capital
Question: I notice a number of people are treating natural language processing and large language models7 as synonymous. For which investment use cases are traditional NLP techniques more effective than large language models (LLMs)? FYI, my overly simple explanation of differences is that NLP takes text and turns it into numbers, NLG takes numbers and turns it into text, and LLMs convert input text to text output via converting text into numbers first.
4.00 – PANEL DISCUSSION: Future of Interpreting Data
Moderator: Christina Qi, CEO Databento & Founder Domeyard LP
Sharon Asaf, Director of Global Climate Risk, Citibank, Nan Xiao, CTO, Greenland Capital Management, Professor Vivian Fang, Indiana Kelley, Natascha Hey, Capital Fund Management, Dimitri Bianco FRM, Head of Quant Risk & Research, Agora Data, Inc, Patrick Dote, Head of Research IntelligentCross
Question: As generative AI that converts data into insights moves from proof of concepts to applications in production, how can decision-makers build confidence in the outputs of AI-driven models? What strategies can enhance model explainability8 to ensure confidence in data interpretation?
4.45 – PANEL DISCUSSION: Future of Systematic Credit Trading : Quant Credit and Quant Equity – Are we all going the factor zoo way?
Dr. Riti Samanta Systematic, Senior Director, Fixed Income Solutions Portfolio Manager, Russell Investments
Arik Ben Dor Head of Quantitative Equity Research Barclays, Dr. Joseph Simonian, Senior Investment Strategist, Scientific Beta, Lisa Schirf, Managing Director, Global Head of Data & Analytics, Tradeweb, Manish Aurora, Hedge Fund Manager Rational Investing LLC, Karishma Kaul, CFA, Head of Systematic Fixed Income Strategies at Fidelity
Question: While having fewer factors in a model is a best practice for avoiding overfitting9, simple models are less likely to be truly proprietary, which would suggest there is less (or no?) alpha available from simple quant models? How do you balance the need for proprietary factors to generate alpha with the risk of overfitting, where a model performs well in backtesting10 but fails in real-world deployment?
What questions would you ask? Leave a comment below.
If you think this is useful for someone attending the conference, please feel free to forward it on.
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- Jason DeRise, CFA
Lots of Jargon today. I had to add a few terms to the Jargonator as well:
Machine Learning (ML): An application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
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).
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
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
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).
Optimal control: A mathematical optimization method for determining a control policy that will achieve the best possible outcome in a dynamic system. It is used in various fields, including finance, to optimize decision-making processes.
Natural Language Processing (NLP): An AI technology that allows computers to understand, interpret, and respond to human language in a quantitative way, generating statistical measures of sentiment and importance of topics.
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.
Explainability: In the context of machine learning, this refers to the degree to which a machine learning model's behavior can be understood by humans.
Overfitting: When a model matches the training data very well when back-tested but fails in real-world use cases when the model is applied to new data.
Backtesting: The process of testing a trading strategy or model using historical data to evaluate its performance before applying it in production. The goal is to determine how well the model would have performed in the past and, by extension, how it might perform in the future.