Hedge Fund Manager Ernie Chan: Use GenAI to Manage Risk, Not Predict Return

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In this episode of Odds on Open, Ethan Kho sits down with Dr. Ernest P. Chan — former quant at Millennium and Morgan Stanley, and now founder of PredictNow.ai and QTS Capital Management. Ernie is one of the best-known voices in quant finance, author of Quantitative Trading, and a pioneer in systematic trading strategies.

We cover:
When machine learning trading models work in markets — and when they fail
Why financial markets suffer from data sparsity, and how regime shifts and black swan events in finance break models
How quants use AI in trading for risk management and portfolio optimization
The promise of LLMs for financial markets and how generative AI can overcome data scarcity
Semi-supervised learning explained, with real examples from analyst reports and Fed speeches
Where quants can still find alpha generation when new technologies become widely available
How PredictNow helps banks and hedge funds apply AI risk management at scale
Lessons from launching QTS Capital and running independent quant trading strategies such as crisis alpha
The role of alternative data in hedge funds and what actually drives performance post-2008
What it was like working alongside quants at Millennium, Morgan Stanley, Credit Suisse — and how Renaissance Technologies influenced Ernie’s career
The traits that make a great quant, and why creativity still matters in quantitative trading strategies
= Advice for students and professionals entering quant finance in the age of financial big data and generative AI
How to spot overfitting in backtests and apply the scientific method in systematic trading strategies
Why risk awareness separates long-term success from blow-ups in post-2008 quant strategies

These are rare insight from one of the leading voices in modern finance, perfect listening for an aspiring quant, a hedge fund professional, or anyone simply curious about quantitative trading strategies, systematic trading strategies, and AI in trading.

00:00 Intro
01:08 When machine learning trading models succeed or fail
05:08 Data sparsity, regime shifts, and black swan events
06:45 AI in trading for portfolio risk management
08:38 Generative AI and LLMs in financial markets
13:01 Semi-supervised learning examples in quantitative finance
15:22 Finding alpha generation with new trading technology
18:35 Do big hedge funds have AI advantages?
22:00 PredictNow.ai solving AI risk management challenges
24:24 Independent quant trading and crisis alpha strategies
27:11 Handling regime changes in systematic trading strategies
30:36 Renaissance Technologies’ influence on quant careers
33:38 What makes a good quant researcher
35:17 Career advice for aspiring hedge fund quants
38:39 Who should pursue quant trading careers
42:24 Getting started in independent quant trading
45:33 Core basics of quantitative trading strategies
47:24 Key questions before running systematic capital
49:13 Backtesting pitfalls, overfitting, and Sharpe ratio traps
51:36 Final lesson: risk management in post-2008 quant strategies