Top Questions ahead of the Battle of the Quants

The Battle of the Quants event in New York City is on May 9th, 2024. Here are the questions on my mind for the speakers.

May 07, 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 May 9th, 2024, "The Battle of the Quants” takes place in New York City. https://battleofthequants.com/new-york-2024/

There are 3 themes to my proposed questions for the panelists:

The Integration of AI and Machine Learning1 in Finance: Exploring how advancements in AI and machine learning are reshaping capital management and quantitative2 trading strategies.
The Impact of Regulatory and Technological Changes: Addressing how the finance industry and quant managers are adapting to regulatory environments and technological disruptions.
The Role of Data in Quantitative Strategies: Evaluating the influence of traditional and alternative data3 sources on investment strategies and the potential for these strategies to evolve as data becomes more integrated.

As a quick note, the agenda is an image on the website. I used ChatGPT4.0 to convert the agenda from an image into text. It did pretty well, but started to halucinate4 some of the information despite multiple prompts encouraging exactness and comparing its generated text back to the image. Was it a time saver when I had to go back through and compare each word side by side with the original image, rewrite parts of the text, add in missing speakers, and correct speaker names and job titles? I want to think so. But maybe not. I apologize in advance if anyone’s name is misspelled or missed and I didn’t catch it in my manual editing.

Also, there’s a lot of technical jargon in this article. I added footnotes throughout. Please let me know if I missed anything that should be defined.

10:15am Keynote: The Future and Deep Future of AI in Quantitative Finance
Matthew Griffin, CEO and Futurist in Chief of the 311 Institute, is the world’s leading authority on emerging technology, trends, foresight, and the future and depp future. His ability ot identify, track, and explain the impacts of hudreds of emerging technologies ans trends on global business, culture, and society has earned him a powerful reputation and a roster of clients that include royal households, world leaders, all G7 and G20 governments, and many of the worlds most respected brands. Matt predicted the rise of LLMs5 at his 2018 ‘Battle’ appearance and he is back to lay the intellectual groundwork for the future of capital markets from New York City to Abu Dhabi to Shanghai. Hear Matt’s global perspective so you can prepare for the paradigm shifts impacting quantitative trading opportunities.
Speaker: Matt Griffin, Futurist, 311 Institute

Questions: How do you envision the regulatory environment adapting to the advancements in AI, both in the finance sector and more broadly?

10:45am Institutional Quantitative Allocator Perspectives on Quantitative Strategies
Are institutional allocators increasingly exploring capital management through artificial intelligence and machine learning approaches? How do these allocators navigate the realm of quantitative strategies? When it comes to quantitative strategies, what factors influence the decision between building them internally or sourcing them externally? Furthermore, how can institutional portfolios strategically leverage external strategies, and what makes this approach particularly effective? Lastly, which approaches have demonstrated the most success in this evolving landscape?
Moderator: Bartt Kellermann, CEO & Founder, Global Capital Acquisition
Yury Rojek, Executive Director, Head of Systematic6 Strategies, MSIM AIP
Samuel Weissen, Executive Director - Portfolio Manager Alternatives, LGT Capital Partners
Edward Aw, Head of Quantitative Strategies, Managing Director, Bessemer Trust
Vincent Weber, CEO & Co-Founder, Resonanz Capital

Questions: They kind of did “my job” of asking questions in their summary. Those are some good questions to answer :)

11:55am Current Quant Strategies for 2024: AI, ML, ETFs7, LGT, and the New Quants!
There are more multibillion-dollar quant funds now than is the history of the industry. Currently, a staggering 8 out of the top 10 hedge funds are quantitative, and the influx of new funds into the market shows no signs of slowing down. What determines success in this space and how can new managers challenge the status quo?
Moderator: Jackie Rosner, Managing Director, PAAMCO Prisma
Shafiq Ebrahim, Managing Director, Systematic Strategies Group, CPP Investments
Dr. Anne-Sophie van Royen, PhD, CIO - Quantitative Strategies, Asset Management One USA Inc.
Wayne Ferbert, Managing Director & Co-Founder, Alpha DNA Investment Management

Questions: In industries that see increasing competition, there are typically lower returns. In other industries, scale is a barrier to entry, but new technologies can level the playing field. Is this an industry where scale will win or can technology be disruptive as a sustainable advantage?

12:25pm Breaking out of the Matrix: Value Drivers in Multi-Manager (MultiStrat) Hedge Funds8
An excellent review of the characteristics of multi-manager hedge funds from a description of “What is a Multi-Manager Hedge Fund” to why you should or should not be interested.
Frederik Middelhoff, Investment Manager, Resonanz Capital

Questions: Could you compare the various approaches to data and technology investments among multi-manager funds? Additionally, do you anticipate a convergence in data strategies over time?

12:40pm The Power of the MultiStrat!
In the financial landscape of 2023, multistrats emerged as the undisputed leaders in the hedge fund space, experiencing a surge in inflows, delivering impressive returns, and fueling a growing demand for skilled managers. As the industry thrives under this prevailing trend, a compelling question arises: Will fund managers continue to ride the wave of collective success, or will they carve unique paths, guided by their own self-directed risk parameters?
Moderator: Frederik Middelhoff, Investment Manager, Resonanz Capital
Brian Hurst, CIO, ClearAlpha Technologies (AQR Capital Management - day one employee and first non-founding partner)
Marc Vesecky, Senior Managing Director, Quantitative Strategies, Verition Fund
Ken D’Souza, Managing Director and quantitative portfolio manager, PGIM
Philip Didio, Portfolio management, SECOR Asset Management

Questions: How could the role of centralized data teams at multi-strategy hedge funds, who work with multiple portfolio managers on the platform, help shape the answer to the question posed in the summary of the session above?

2:10pm Keynote: Quant Strategies 2.0 - The Advent of Machine Learning
Machine learning, data-driven modeling, and AI techniques are reshaping quantitative finance. How are these algorithms different from traditional quant strategies based on statistical models? How do algo-driven markets differ from markets with human traders? What are the implications for risk management and market dynamics?
Professor Rama Cont, Scientific Advisor and Co-Founder, Oxford Algorithms and Professor of Mathematics, University of Oxford

Questions: What are the best practices for ensuring explainability and transparency of the decision logic in AI-driven quant trading models?

2:30pm The Continued Exponential Growth of Alternative Data: Predictive or Noise?
As data, the fuel that powers quantitative strategies, becomes increasingly ubiquitous, the challenge lies in discerning the predictive quality from the superfluous and confusing data. How can these strategies effectively parse through the abundance of information to extract meaningful insights?
Moderator: Saeed Amen, Author of 'The Book of Alternative Data' & Visiting Lecturer on Quantitative Finance, Queen Mary University of London Jianjian Jin, PhD, Associate director, Quantitative Research UPP
Natalya Dmitriyeva, Global Head of Data Strategy and Analytics, Schonfeld
Sheesha Ali, Managing Director - Head of Systematic Strategies, PineBridge Investments
Nicole Königstein, Chief Data Scientist, Head of AI & Quant Research, Wyden Capital
Lee Morakis, Senior Quantitative Implementation Consultant, EFPR

Questions: In the early days of the alternative data, most quant users were reluctant to veer from using the tried and true market data sources that had full coverage and a long history. Over time, alternative data has been incorporated more often in models. Have new modeling techniques enabled the inclusion of alternative data sources with high predictive value but limited history or breadth of coverage? Or has the increased historical data and coverage within the data sources driven their adoption?

3:35pm Should you Build a Generative AI9 Model for your Trading Process?
The holy grail for quants will be to turn LLMs into predictive tools. Currently, the distinctive challenges frequently render the current generation of "off the shelf" LLMs ineffective due to their inherent limitations. Tailoring a bespoke solution specific to finance merges highly advantageous and increasingly essential. The question arises: should managers embark on constructing their own Generative AI tools from scratch or enhance an existing model through customization, such as fine-tuning? Exploring the concept of Agents sheds light on the next evolutionary phase of AI - what are agents and why are they poised to shift the AI landscape?
Moderator: Mario Parlo, Instructor, Columbia University (Former Member Investment Committee and Risk Manager, Multi-Billion Dollar European Family Office)
Gary Kazantsev, Head of Quant Technology Strategy, Bloomberg
Charlie Flanagan, Head of Applied AI, Balyasny Asset Management
Stefano Pasquali, Managing Director, Head of Investment AI Modeling and Research, BlackRock
Giuseppe Russo, CEO & Co-Founder, EXVAi Research

Questions: How should quant managers mitigate the risk of 'catastrophic forgetting10' in in-house customizations when base models, like those from OpenAI, are updated?

4:05pm How China Quants Will Overcome the Challenges in 2024 after a Magnificent 2023
With China ranking as the second-fastest-growing economy globally and holding a substantial 20% share of the global financial community, the question arises: Is it truly an uninvestable market? The era of beta11 play has concluded, making way for astute quants who are now harvesting alpha12 in a dynamic, uncrowded, liquid, and non-correlated marketplace. What specific quantitative strategies are proving successful in China?
Moderator: Winston Ma, NYU Adjunct Professor and Partner, Dragon Global Family Office
Gene Reilly, CIO, Greenwich Quantitative Research
Kevin Chen, Chief Economist, Horizon Financial
Derek Yan, Senior Investment Strategist, KraneShares

Questions: How do quant managers manage the impact of potential regulation changes by the Chinese government, both within financial markets and across the economy as a whole? Is this a case where a human in the loop is needed or can the models appropriately respond when regulation changes cause the underlying investments to behave differently than the past?

4:35pm Closing Fireside Chat: How Quants Leverage Causal Analysis13 for Alpha Generation
Utilizing causal analysis can empower quantitative managers to identify sources of alpha in the market by identifying variables that have a causal influence on asset prices. By focusing on factors that drive market movements, can quants develop trading strategies that exploit mispricing and inefficiencies in the market? How can managers identify emerging trends and adjust their strategies accordingly to maintain a competitive advantage?
Michael Weinberg, Adjunct Professor of Finance and Economics, Columbia Business School
Joseph Miller, PhD, Serial Founder and Scientist (formerly Manager, Technology & Intelligence, Bridgewater)

Questions: Before beginning to create a new causal model, how do you think about assessing the potential factors and independent variables that are most likely to lead to a successfully built model?

What questions would you ask? Leave a comment below.

- Jason DeRise, CFA

Machine Learning (ML): An application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

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).

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.

Hallucination: In AI, hallucination refers to instances where the model generates information that wasn't in the training data, makes unsupported assumptions, or provides outputs that don't align with reality. Or as Marc Andreessen noted on the Lex Fridman podcast, “Hallucinations is what we call it when we don’t like it, and creativity is what we call it when we do like it.”

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.

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).

ETF (Exchange-Traded Fund): An ETF is an investment fund traded on stock exchanges, much like stocks. An ETF holds assets such as stocks, commodities, or bonds and generally operates with an arbitrage mechanism designed to keep it trading close to its net asset value, although deviations can occasionally occur. ETFs offer a cost-effective, liquid, and flexible way for investors to purchase a diversified portfolio that tracks a particular index, sector, commodity, or other asset classes. Unlike mutual funds, which are priced at the end of each trading day, ETFs are bought and sold throughout the day at market price, offering more flexibility for investors.

Multi-Manager (MultiStrat) Hedge Funds: Hedge funds that allocate capital across multiple portfolio managers, each managing a distinct segment of the fund’s total assets, often employing diverse strategies.

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.

Catastrophic Forgetting: A phenomenon in machine learning where a model, after being trained on new tasks, completely forgets the old tasks it was trained on. This is a significant issue in neural networks and an ongoing area of research in the development of AI models that can retain knowledge from previous tasks while learning new ones.

Beta: In finance, beta is a measure of investment portfolio risk. It represents the sensitivity of a portfolio's returns to changes in the market's returns. A beta of 1 means the investment's price will move with the market, while a beta less than 1 means the investment will be less volatile than the market.

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

Causal Analysis/Causal AI: A method of identifying relationships that suggest causation rather than mere correlation in statistical data, focusing on determining what affects an outcome.

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