Cliff Asness, founder of AQR Capital Management, shares his remarkable journey in quantitative investing. He reflects on his transformative years at the University of Chicago under Gene Fama and discusses pivotal moments at Goldman Sachs. Topics include the evolution of market inefficiencies, the impact of behavioral economics, and how crowd psychology influences trading. Additionally, he dives into the interplay between AI and investment strategies, all while revealing personal anecdotes that shaped his career.
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From Underachiever to Scholar
Cliff Asness was labeled an underachiever in high school but excelled on standardized tests, which helped him get into college.
A chance job coding for Wharton professors sparked his interest in academic finance and led him to pursue a PhD at University of Chicago.
insights INSIGHT
Fama-French Model's Lasting Impact
Fama-French papers in 1992-93 cemented empirical asset pricing beyond CAPM, showing beta did not explain returns well.
Their three-factor model with market, size, and value factors became the scaffold for much academic finance work.
insights INSIGHT
Risk Vs. Behavioral Factors
Systematic investing strategies can work either due to risk premiums or behavioral inefficiencies.
Asness leans toward behavioral explanations but acknowledges the real world likely mixes both, varying over time.
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This book provides a detailed framework for assessing expected returns and risk in investment management. It covers three different ways to analyze expected returns: by asset classes (stocks, credits, government bonds, and alternative investments), strategy styles (value, trend, carry, and volatility), and underlying risk factors (growth, illiquidity, inflation, and tail risks). The authors emphasize the time-varying nature of expected returns, the importance of diversification, and the use of leverage on low volatility assets. The book is praised for its well-researched and insightful content, making it a valuable resource for both practitioners and academics in the field of finance.
Investing Amid Low Expected Returns
Making the Most When Markets Offer the Least
Antti Ilmanen
Investing Amid Low Expected Returns provides an evidence-based approach to successful investing in a climate of low returns. It emphasizes timeless investment practices like discipline, humility, and patience, and offers insights into portfolio construction, risk management, and diversification. The book is ideal for institutional and active individual investors seeking to adapt to challenging financial conditions.
Men at Work
Men at Work
Nick Eberstadt
Why Machines Learn
The Elegant Math Behind Modern AI
Anil Ananthaswamy
In this book, Anil Ananthaswamy provides a rich, narrative explanation of the mathematics that has driven the development of machine learning and artificial intelligence. The book delves into the historical and mathematical foundations of AI, including linear algebra and calculus, and explores how these concepts have led to significant advancements in fields such as chemistry, biology, physics, and more. Ananthaswamy also discusses the potential of AI to transform everyday activities and highlights the importance of understanding both the capabilities and limitations of AI. The book is praised for making complex mathematical concepts accessible and engaging for a broad audience.
In this episode, Cliff Asness joins Tano Santos and Michael Mauboussin for a conversation that spans the evolution of quantitative investing, lessons from market crises, and the enduring tension between risk and behavioral explanations in finance. From his formative years at the University of Chicago under Gene Fama to building AQR into a quant powerhouse, Cliff reflects candidly on theory, performance, and how markets may have become less efficient in recent years.
Key Topics:
Tano and Michael return from sabbatical and reflect on recent academic and classroom experiences (0:00)
Overview of Cliff’s career and contributions to quant investing and academic finance (1:13)
Cliff recounts his underachiever label, how standardized tests changed his path, and why he chose Penn’s M&T program (2:54)
How Cliff’s coding work for Andy Lo inspired his academic path and led to Chicago (5:03)
A breakdown of the 1992 and 1993 Fama-French papers, and how they reshaped asset pricing (8:45)
Cliff discusses the theoretical divide between Fama and Thaler and his own evolution toward a behavioral perspective (13:08)
Memories of presenting momentum to Fama, intellectual honesty, and voice-shaking dissertation defenses (17:13)
Why Cliff chose Goldman over academia, his role in developing Goldman’s quant group, and the influence of LTCM (22:00)
Launching in August 1998 during the Russia default; early drawdowns and lessons from the tech bubble (27:50)
How quant signals hold up, risks of crowding, and the difference between short-term and long-term capacity (34:32)
Momentum held, but value strategies collapsed. How AQR dealt with long underperformance (43:31)
Valuation starting points can obscure long-term performance; recent decades viewed in proper context (49:22)
Cliff's provocative “Less Efficient Market Hypothesis” and three key drivers: indexing, interest rates, and social media (50:54)
Is passive investing weakening price discovery? Reflections on Sharp’s arithmetic and Grossman-Stiglitz (54:12)
How echo chambers and meme stocks challenge traditional models of rational price formation (58:28)
Why companies aren’t issuing more equity despite sky-high valuations, and the fading role of smart capital allocators (1:00:46)And much more!
Value Investing with Legends 
