Cliff Asness - Surviving the Meme Stock Bubble (Ep. 298)
Timestamps are as accurate as they can be but may be slightly off. We encourage you to listen to the full context.
Podcast Summary
In this engaging conversation, legendary quantitative investor Cliff Asness of AQR Capital Management sits down with Jim O'Shaughnessy to discuss some of the most pressing issues in modern investing. (00:00) The discussion opens with Cliff's candid admission about prospect theory - that losses hurt far more than gains feel good, even for someone who's built his career on rational, systematic investing. (01:03) From there, the conversation weaves through the evolution of quantitative investing, the challenges of valuation bubbles, the rise of meme stocks, and the increasing role of artificial intelligence in investment management.
- Main Theme: The conversation explores the intersection of human psychology and quantitative investing, examining how even the most systematic approaches must grapple with behavioral biases, market irrationality, and the evolving landscape of modern finance.
Speakers
Cliff Asness
Cliff Asness is the co-founder, managing principal, and chief investment officer at AQR Capital Management, one of the world's largest and most influential quantitative investment firms. With over 30 years of experience in quantitative investing, Asness has been a pioneer in factor-based investing and is known for his candid commentary on market dynamics and investment strategies.
Jim O'Shaughnessy
Jim O'Shaughnessy is the host of Infinite Loops and a veteran quantitative investor. He previously founded and ran O'Shaughnessy Asset Management (OSAM) and is known for his research-driven approach to investing and his book "What Works on Wall Street."
Key Takeaways
Get More Aggressive During Bad Periods for the Right Reasons
Asness explains that when AQR loses money due to valuation-based reasons - where markets become increasingly irrational - he becomes more publicly aggressive in defending their strategies. (02:58) However, if losses come from momentum or quality factors not working, he takes a different approach. This distinction is crucial because losing due to market irrationality suggests the fundamentals remain sound, while losing due to factor failure requires reassessment. Practical Example: When value strategies underperform during bubble periods, double down on explaining the thesis rather than abandoning it.
Embrace Limited AI Opacity for Better Results
Despite being traditional quants who demand understanding of every component, Asness describes how AQR has "surrendered a little bit to the machines." (44:07) He argues that if artificial intelligence were completely transparent, it wouldn't be adding value beyond what simple statistics could achieve. The key insight is that some opacity is necessary for AI to provide genuine improvements over traditional methods. Practical Example: Use natural language processing for sentiment analysis even if you can't explain every vector component, as long as results correlate with simpler measures.
Question the Volatility Laundering of Private Equity
Asness coined the term "volatility laundering" to describe how private equity artificially appears less risky than public markets simply because it's not marked to market daily. (65:03) He argues that private equity is essentially "active levered equity" that just isn't being marked accurately. The real risk hasn't disappeared - it's just hidden from view. Practical Example: When evaluating private investments, consider what the daily volatility would actually be if these assets were publicly traded and marked to market.
Understand That Investment Constraints Are Always Costly
When discussing ESG investing, Asness explains that investment constraints can at best have zero expected value, but more likely have negative expected value. (67:37) He argues that the mechanism by which ESG works - raising the cost of capital for "bad" companies - inherently means ESG investors should expect lower returns. This isn't necessarily wrong if the goal is impact, but investors shouldn't expect to "have their cake and eat it too." Practical Example: If you exclude a third of available stocks from your investment universe, you're limiting your opportunity set and should expect lower returns as a cost of your values-based approach.
Focus on High-Risk Adjusted Returns Rather Than Absolute Returns
Asness advocates for running alternative strategies at higher volatility levels and then equitizing them (adding beta back) rather than running them at low volatility. (99:26) This approach maximizes capital efficiency - investors can achieve the same exposure with less capital deployed to the strategy, freeing up capital for other investments. The key is thinking about risk-adjusted returns rather than absolute returns. Practical Example: Instead of investing $100 in a 5% volatility hedge fund, invest $25 in a 20% volatility version of the same strategy and put the remaining $75 in low-risk assets.
Statistics & Facts
- The correlation of AQR's strategies to basic value measures (like Fama French HML) is about 0.2 - much lower than many assume. (03:47) This statistic demonstrates that quantitative strategies can be much more diversified than traditional value investing, reducing concentration risk.
- 68% of quantitative managers overrode their models during the 2008 Global Financial Crisis. (15:04) This highlights how even systematic investors abandon their discipline during periods of extreme stress, often to their detriment.
- 75-85% of US outperformance versus international markets over the past 25 years has come from valuation expansion rather than fundamental improvements. (60:37) This suggests much of US superiority may be due to getting more expensive rather than genuinely better fundamentals.
