Odds on Open

In this episode of Odds on Open, Ethan Kho sits down with Joe Mezrich, Founder of Metafoura LLC and former Managing Director at Nomura Quant Strategies, to reflect on nearly 40 years in quant finance. Joe’s career spans the early days at Salomon Brothers—where he helped pioneer factor models, risk modeling, and even early machine learning in finance—through senior sell-side research roles at Morgan Stanley, UBS, and Nomura.Joe shares how the sell side effectively built modern factor investing, why models like the Barra risk model failed in crises such as the Tech Bubble (2000) and the Quant Crisis (2007–08), and how market-neutral strategies and algorithmic trading continue to shape today’s buy-side. He also explains why interpretability, from CART decision trees to today’s LLMs for trading, is critical for robust risk management.We cover:- Origins of quant finance on the sell side at Salomon Brothers.- Early factor models, the Barra risk model, and portfolio risk modeling.- Use of robust statistics and CART decision trees in machine learning for finance.- Why risk models failed in the Tech Bubble (2000) and Quant Crisis (2007–08).- Growth of market-neutral strategies and interaction between sell-side research and the buy side.- Crisis lessons: liquidity concentration, model speed, and explainability.- Evolution of factor investing into overlays and ETFs.- How quant researchers balance complexity vs. interpretability with LLMs for trading.- Role of alternative data, point-in-time datasets, and data visualization in alpha.- Wall Street culture: Liar’s Poker-era Salomon, Morgan Stanley, UBS, Nomura.- Impact of interest rates, earnings vs. sales growth, and macro regimes on factors.- Sustainability of multi-manager pod shops (Citadel, Millennium) and implications for quants.- Career lessons: curiosity, humility, and finding beauty in quant models.Whether you’re a quant researcher, an aspiring algorithmic trading professional, or an allocator seeking to understand systematic funds, give this a listen.00:00 Intro and Episode Overview00:46 Origins of Quant Finance at Salomon Brothers02:56 Early Factor Models and Barra Risk Model05:51 Robust Statistics and CART Decision Trees08:58 Machine Learning in Finance 1990s Experiments12:06 Why Risk Models Failed in Tech Bubble15:31 Lessons from the 2007 Quant Crisis18:51 Rise of Market Neutral and Sell-Side Research22:26 Evolution of Factor Investing to ETFs26:01 Balancing Complexity and Explainability for Quants29:16 Alternative Data and Point-in-Time Datasets32:46 Wall Street Culture Salomon Morgan UBS Nomura38:08 Interest Rates Macro Regimes and Factor Drivers41:51 Are Multi-Manager Pod Shops Sustainable?46:04 What Makes Exceptional Quant Researchers Last49:26 Curiosity Humility and Risk Management52:56 Finding Beauty in Quant Models and Data56:16 Final Lessons from 40 Years in Quant Finance

Former Cargill Global Trading & Risk Management Director Kristine Engman Hochbaum explains how commodities trading strategies and quant trading strategies really work. On Odds on Open, she breaks down the sources of trading edge and alpha generation in today’s commodities markets, from agriculture trading to energy and metals.We cover why physical vs. financial commodities trade differently, how systematic trading and traditional players (hedge funds vs. ABCDs) approach markets, and the lessons of the 2022 commodities boom. With Ethan, Kristine unpacks real-world risk management strategies around delta, liquidity, forecast accuracy, and headline shocks — from Russia–Ukraine war trading impact to weather, inflation, and supply chain disruptions.Key topics in this episode:- How relationships and brokers create trading edge in commodities- Why capital allocation matters for building positions- Headline risk and the impact of social media on commodity prices- Managing liquidity risk and delta in physical books- Weather risk and forecast accuracy in agriculture and energy trading- The role of supply chain disruptions in commodities markets- Differences in hedge fund vs. ABCD trading strategies- Lessons from the Russia–Ukraine war trading impact and inflation and commodities- Stories from real trades: negative power prices and long oil during crisis- Career lessons: what makes a great trader and how to keep learning commodities marketsAlong the way, she shares what separates good traders from great ones, why inflation and commodities are inseparable, and how to keep learning in fast-changing markets. If you’ve ever wondered what makes a great trader or how to start learning commodities markets, this episode is for you.00:00 Intro01:15 Edge in commodities trading? Relationships, capital, information04:40 Commodities market efficiency, information flows, and AI08:32 Hedge funds vs. ABCDs, commodity trading strategies13:34 When commodities outperform equities, the 2022 boom17:45 Headline risk, social media impact on markets19:08 Risk management strategies in physical commodities trading26:14 Probability, forecasting, and scenarios for trading decisions31:00 What makes a great commodities trader today37:53 Contrarian trading strategies, alpha generation in commodities42:24 Russia–Ukraine war impact on commodity markets, trading45:35 Life and career lessons from commodities trading51:30 Careers, uncertainty, and learning in commodities markets

Join Ernest P. Chan, a leading figure in quantitative finance and founder of PredictNow.ai, as he shares his insights on the intersection of AI and trading. He reveals when machine learning models thrive and the pitfalls of data sparsity in financial markets. Discover how generative AI enhances risk management and portfolio optimization. Ernie also discusses alternatives to find alpha, the evolution of his quant strategies, and the vital traits for success in this evolving field. A must-listen for aspiring quants!

Join Vinesh Jha, founder of Extract Alpha and former director at PDT Partners, as he dives into the tumultuous lessons from the 2007 Quant Quake. He reveals the inside story of PDT's $500M loss and the cultural differences with other hedge funds. Vinesh discusses the rise of alternative data, how smaller firms can thrive against giants, and the impact of AI on quant finance careers. He also explores the intricacies of predictive modeling and the importance of adaptability in today's trading landscape.

Bill Mann, a former fundamental researcher at Two Sigma and founder of Harmonic Insights, shares his insights into the world of quantitative trading. He discusses the transformative influence of AI, LLMs, and automation on research and trading workflows. Mann explains how hedge funds can leverage proprietary data pipelines for alpha generation while avoiding crowding in popular factors. He emphasizes the importance of hacker creativity in systematic investing and outlines the skills junior quants need to thrive in an AI-driven landscape.

Chris Kantos, Head of Quantitative Research at Alexandria Technology, specializes in using natural language processing and AI to generate investment insights. He discusses how hedge funds analyze social media and earnings calls to identify alpha signals. Kantos highlights the importance of tailored NLP techniques and the challenges of data commoditization. He also explains the future role of AI in systematic investing and how sentiment analysis from platforms like Reddit can inform trading strategies. Tune in for invaluable insights into navigating the noisy world of financial data!

Grant Stenger, the founder of Kinetic and a former intern at Jane Street, dives into the similarities between trading and poker. With a passion for games and mathematical edge, he shares insights from his experiences at QuantRes and Numerai, emphasizing decision-making under uncertainty. He explains how decentralized exchanges operate and the advantages of trading on Solana over Ethereum. Grant also reflects on the collapse of FTX, discussing the importance of being early in emerging asset classes and the parallels between gambling strategies and market trading.

In this engaging discussion, Robert Carver, former head of fixed income at Man AHL—a massive systematic trading hedge fund—dives into his journey as an independent quant trader. He shares vital insights on breaking into quantitative trading without a PhD and the essential skills required. Carver critiques the Sharpe ratio, advocating for better risk metrics, and emphasizes the importance of realistic backtesting. He also discusses managing 200+ trading strategies solo and navigating alpha decay, offering practical advice for aspiring traders.

Agustin Lebron, a former quant trader at Jane Street and author of The Laws of Trading, shares deep insights into the evolving world of trading. He explains the concept of 'edge' and its significance in distinguishing top traders. Agustin discusses how elite firms maintain their competitive advantage, and the role of statistical analysis and judgment in trading. He also gives practical advice for aspiring traders, covering essential skills, internship realities, and the future of quant trading in a tech-driven landscape.

If venture capital underperforms the public markets, is it still worth the investment? For Avik Ashar, the answer is yes—but not for the reasons you think. Avik, a Principal at Riverwalk Holdings, an India-focused VC firm, argues that most people misunderstand how venture capital works and how to fairly evaluate VC fund performance.Venture capital, he explains, isn’t just about chasing unicorns or short-term IRRs. It’s a strategic investment tool, especially in Asia, used by family offices and conglomerates not only for returns but also for M&A, R&D, and market expansion. In markets like India, venture capital is helping industrial groups future-proof their businesses while tapping into innovation. He also highlights how India’s maturing public markets and mutual fund sector are making early-stage investing and startup exits far more viable than in places like Southeast Asia, where liquidity is still limited.He breaks down:– Why VC returns vs public markets often look misleading—unless you know how to analyze them– How family offices in India are using venture funding for strategic acquisitions– Why M&A is finally taking off in Asia—and what that means for founders– The key differences between venture capital in India, Southeast Asia, and the U.S.– Why India’s public markets are becoming a critical exit path– How startup exits work in markets without strong IPO pipelines– Why Southeast Asia’s VC boom from 2014–2018 underperformed– What Gen Z needs to understand about building in a noisy, AI-native world– How venture capital vs private equity differs in terms of outcomes, strategy, and timelinesHe also shares an important reminder for our age of endless short-form content: “The most expensive thing you can give today isn’t your time—it’s your attention.”00:00 Intro00:33 Why invest in venture capital?01:01 How venture returns work03:01 Venture as an R&D and acquisition pipeline04:11 The outlier nature of VC returns05:11 Why family offices invest in venture05:52 Examples of conglomerate acquisitions in India09:43 Differences in VC ecosystems: US, Singapore, and India16:29 Do family-backed VCs perform better in India?16:50 Riverwalk Holdings’ experience in India18:22 Maturity of India’s financial ecosystem for startups23:59 Where will venture gains come from?27:51 Indian conglomerates embracing startups28:52 Challenges of building companies in Asia30:22 Advice for young people in an uncertain world32:44 Tech’s share of the US market cap38:04 Staying focused amidst noise40:57 Advice for recent graduates42:24 Outro