Episode Summary
Executive Summary: Patrick O'Shaughnessy interviews Cliff Asness of AQR about the state of quant investing: factor efficacy, whether value is crowded or broken, how AQR balances research openness with proprietary work, machine learning’s role, fees, private markets, and the future of active management. The core message is that quant edges still exist but require realistic expectations, diversification, and discipline through bad cycles.
Main Topics: How institutions should use factors (Priority: 5/5): Asness argues for matching factor implementation to client constraints on leverage, shorting, and stickiness. Why bad factor years do not imply failure (Priority: 5/5): He says value’s drawdowns are explainable by spread compression or factor mix, not necessarily strategy decay. Crowding, capacity, and factor robustness (Priority: 5/5): He says too much money can matter, but the evidence does not yet show value or related factors have been arbitraged away. Research culture and sharing (Priority: 4/5): AQR distinguishes between widely known styles and proprietary alpha, while keeping a strong academic, open research process. Machine learning at AQR (Priority: 4/5): ML is treated as evolutionary and interpretable, used to find nonlinearities without abandoning economic intuition. Market timing, asset allocation, and private markets (Priority: 4/5): He extends factor logic to asset allocation cautiously, and sees private markets as partly a behavioral choice to avoid mark-to-market. The role of fees and active management (Priority: 3/5): He expects more indexing and lower fees, but not a world without active managers or public markets.
Key Arguments: Best use of factors depends on client constraints; the better strategy you can’t stick with isn’t better. Too much money can hurt factors, but crowding would make them look random, not simply bad. Value remains within historical valuation bands except the tech bubble; it is not obviously broken. AQR diversifies across many value measures and mostly within industries, improving robustness. ML should be evolutionary and interpretable, with economic explanations required at AQR. Three-sharp-ratio strategies are usually tiny or non-scalable; real capacity matters more. Public markets still have enough stocks; active managers remain necessary for price discovery. Private markets often smooth returns and help investors tolerate risk, but opacity is part of the appeal.
Data Points: AQR assets under management: $226 billion - Describes AQR’s scale 20 years after founding. AQR founding year: 1998 - Historical reference for the firm Cliff Asness founded. Initial Goldman/AQR-style start: 1994 - Asness says systematic value and momentum started at Goldman in late 1994. Value spread norm: three to six times - Historical band for expensive stocks relative to cheap stocks on price-to-book. Tech bubble value spread: 12 to 14 times - Late 1999/2000 extreme for price-to-book spread versus cheap stocks. Mutual-fund indexing share: mid 30% - Asness says mutual-fund indexed assets were in the mid-30s. Typical target Sharpe range: 0.5 to 1 - Range Asness says many real-world institutional strategies target. Bad periods at AQR: three tough periods - He says AQR has had about three major difficult stretches over 20 years. Time spacing of bad periods: about 10 years apart - He notes the tough periods occurred roughly a decade apart. Two sets of twins: 2 - He mentions raising four children born as two sets of twins. Children: 4 teenagers - Family discussion at the end of the interview. Relationship between children birth timing: a year and a half apart - He says the two sets of twins were born a year and a half apart. Stock return timing horizon in CAPE work: next 10 years - He references tests of CAPE versus subsequent 10-year equity returns. Tech bubble era valuation anomaly: late 99, 2000 - The only major exception to normal price-to-book spread bands.
Pivotal Quotes: "The better strategy you can't stick with is not the better strategy." — Cliff Asness: He explains that implementation must fit an investor’s behavior and constraints. "If it's in the data, write the paper." — Gene Fama (quoted by Cliff Asness): Asness recalls Fama’s response to his momentum research as an act of intellectual honesty. "We think you're the most attractive thing in our portfolio right now." — Unnamed client: A client said this while still redeeming because recovery was expected to take too long.
Implications: The unresolved question is not whether quant works, but how much capacity and complexity investors can truly tolerate; the next test is whether newer tools like ML add durable, interpretable edge.
From the Transcript
Some even single factor on the momentum side, where we think maybe that's a compliment to people who already have a value. There, we can't claim the return against the benchmark is the same risk-adjusted return as when you're allowed to do every asset class using leverage or deleveraging to balance them. And I've heard you talk about this too: the better strategy you can't stick with is not the better strategy. So, we try to spend a lot of time talking to people about where on the spectrum, really with a few different dials. One being how aggressive you can be. One thing about being quant, I think it's way easier than for a traditional manager to dial the risk to different levels, essentially. If someone's more aggressive, you just do everything at a larger size. In principle, that should be possible for a more traditional manager. But when you're running kind of a concentrated book of a few stocks, what that even means gets a little murkier. So, one is how much risk you can take. Two, a very correlated concept because it's about being able to stick with it, how unconventional.
Bad graduation strategy, bad dissertation strategy. But Gene said to me, and it's funny, it took me a little while, but this was probably one of the nicest things I've ever heard. And it was almost a religious statement from him. He kind of paused for a second and he said, if it's in the data, write the paper. Which for him was kind of like the ultimate ethical statement. And he was, and I did, and he wanted me to go in the academic job market for that. I ended up staying at Goldman, but he was very supportive of the paper, even though to this day, I don't think he's the biggest fan of momentum investing out there. The firm he works with, DFA, does incorporate it into their process. So I'm sure that means Gene must have signed off at some point because I don't think anyone's allowed to do something if Gene absolutely slips it in. No, when you Gene Fama, you get a say. So I'm assuming he at least allows a little, but it's not his favorite result to this date. But the intellectual honesty, I will take as an act of kindness. Wonderful. Well, this has been so much fun. I can do this for hours.
Decade. For about a decade afterwards, I would tell this story as if I was talking to someone who was a little crazy. I now think I was talking to someone I actually feel bad for because I think they were put in a pretty bad situation of having to explain to me. And the person was quite honorable. They could have just sold their boss out. They could have just looked at me and said, Hey, I want a stick. But that guy, Phil, in the corner, he's, he, but that was probably the wackiest conversation I've ever had. We think you're the most attractive thing in our portfolio. See, we need help. So I always think one of the most valuable. Contributions that quant research can make is helping people understand what not to do. You wrote in the liquid alt-ragnarog piece about this kind of pejorative three-sharp ratio strategies. I want to talk a little bit about that and how, maybe, from an allocator's perspective, those could be almost like the lottery tickets of our world. Maybe describe what you mean by a three-sharp ratio strategy and why pursuing them or chasing them may not be the best idea. First, your listeners don't know this, but you properly made air quotes when you said three-sharp ratio strategy.
About Invest Like the Best with Patrick O'Shaughnessy
Conversations with the best investors and business builders in the world.
View all episodes from Invest Like the Best with Patrick O'Shaughnessy