May 13, 2024 · 1h 13m · capital-allocators
Cliff Asness - Simple Investing is Hard (EP.385)
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode of Capital Allocators, host Ted Seides interviews Cliff Asness, founder and CIO of AQR Capital Management, exploring the empirical foundations of quantitative factor investing, the discipline required to endure severe performance drawdowns, and candid critiques of institutional portfolio construction.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Ted holds 18.1% of the talking time here. How this is scored →
Ted as informed peer 3.9 Guest teaching 4.0 Guest disagreement 2.4 Ted pushing back 0.8
speaking balance: gold is Ted, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 59:06 Cliff venting on private equity valuation asymmetry
Cliff forcefully denounces the hypocrisy of institutional accounting that permits private equity to smooth marks while penalizing public quant managers for mark-to-market drawdowns.
Hardest push from Ted ▶ 58:44 Ted challenging Cliff's private equity volatility laundering premiseTed directly pushes back on Cliff's thesis by proposing that private equity marks could genuinely reflect fundamental value better than erratic public markets.
Biggest teaching moment ▶ 13:13 Cliff educating Ted on Fama and market efficiencyCliff reframes Ted's question by correcting the common misconception that Gene Fama believed markets are perfectly efficient, citing foundational literature like Grossman-Stiglitz.
Ted holds their own ▶ 33:51 Ted pressing on machine learning vs economic intuitionTed demonstrates sharp industry grasp by contrasting Renaissance's black-box ML approach with AQR's economically grounded, behavioral factor framework.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Ted as informed peer | Guest teaching | Guest disagreement | Ted pushing back | Why |
|---|---|---|---|---|---|---|
| Cliff Asness's Early Life and Academic Foundations | 3 | 2 | 1 | 0 | Ted guides the biographical discussion with broad narrative prompts about Cliff's early life and academic trajectory. Cliff shares self-deprecating anecdotes about his underachievement, college stress, and choosing Chicago over Stanford. | |
| Challenging Perfect Market Efficiency with Momentum | 4 | 6 | 3 | 1 | Ted asks what first signaled that Eugene Fama's efficient markets theory was flawed. Cliff corrects Ted's framing as unfair to Fama, explaining that Fama himself acknowledged markets are not perfectly efficient, before outlining his thesis on momentum. | |
| Factor Decay, Arbitrage Dynamics, and Holding Conviction | 4 | 4 | 2 | 0 | Ted inquires about calibrating regime shifts and factor decay as markets adapt. Cliff explains why factors attenuate but rarely disappear completely due to arbitrage costs and the pain required to hold them. | |
| Navigating Major Drawdowns and Organizational Survival | 4 | 5 | 2 | 1 | Ted prompts Cliff to explore navigating severe multi-year drawdowns at AQR, including the tech bubble and 2018-2020 value drawdown. Cliff details the psychological toll, client behavior, and his early mistake of launching only high-volatility products. | |
| Machine Learning, Complexity, and Factor Innovation | 5 | 5 | 2 | 2 | Ted asks a nuanced question comparing economic hypothesis testing to pure machine learning black boxes like Renaissance. Cliff candidly admits his initial skepticism slowed AQR's ML adoption before explaining how they bound ML within economic priors. | |
| Market Structure: Indexing Dynamics and Active Management | 4 | 4 | 3 | 0 | Ted brings up common market structure narratives around passive indexing. Cliff dissects hyperbolic claims that indexing destroys price discovery, citing Owen Lamont and explaining how the impact depends on whether informed or uninformed investors migrate. | |
| Evaluating the Multi-Manager Pod Shop Model | 4 | 4 | 2 | 1 | Ted asks about the explosive growth of multi-manager pod shops. Cliff admits his prior thesis that the model would fail due to high pass-through fees and rapid firing of managers was wrong, crediting top platforms with genuine managerial selection alpha. | |
| Systematic Strategies Across Global Asset Classes | 3 | 3 | 1 | 0 | Ted asks how systematic factor approaches translate outside equities into macro asset classes. Cliff explains that applying value and momentum across global bond, currency, and commodity markets serves as the ultimate out-of-sample test. | |
| Cognitive Dissonance in Institutional Portfolio Construction | 4 | 5 | 4 | 1 | Ted tees up Cliff's writings on cognitive dissonance among institutional allocators. Cliff vigorously critiques the industry's double standards on international diversification, performance chasing, and irrational leverage aversion relative to concentration risk. | |
| Private Equity Marks vs. Public Market Pricing | 5 | 4 | 5 | 3 | Ted plays devil's advocate, challenging Cliff on whether smoothed private equity valuations might be fundamentally more accurate than volatile public marks. Cliff accepts the theoretical premise but attacks the regulatory and accounting asymmetry that allows PE to hide mark-to-market volatility while public managers are penalized. | |
| Flaws in Investment Committee Governance | 4 | 4 | 3 | 1 | Ted asks about Cliff's experience serving on investment committees. Cliff details structural governance dysfunctions, including line-item fixation, asymmetric career risk, and donor bias driving pro-cyclical allocation choices. | |
| AQR's Future Outlook, Reorganization, and Research Frontiers | 3 | 2 | 1 | 0 | Ted asks about the future outlook for AQR's research. Cliff outlines organizational restructuring into smaller, nimble commando research teams and his excitement regarding tax optimization and machine learning applications. |
Statements from this episode (24)
Asness: Grossman-Stiglitz paradox proves markets cannot be perfectly efficient
“Grossman and Stiglitz wrote a paper introducing a paradox about a perfectly efficient market a long time ago that people need to spend a lot of money making something perfectly efficient on research, on time, and why are they going to do that if it's perfectly…”
Asness: Excess investment returns stem from either undiversifiable risk or behavioral errors
“With almost any strategy that we believe produces an excess return, I always say we believe, you never know if you're right, but if you believe it, there are almost always two twin competing explanations. An efficient markets explanation. One that says you sho…”
Asness: Markets are more efficient and harder to beat than active managers think
“Even today, I think the markets are more efficient, I think, probably than the average active manager. I think they're hard to beat.”
Asness: Inefficiencies persist because arbitraging residual errors yields poor risk-adjusted returns
“If there's an error people make on average, and people catch on to it, maybe they invest enough dollars to arbitrage half that error away. But then it gets to be a pretty low-risk-adjusted return to do the last half.”
Asness: AQR models live factor returns at half the backtested result
“Literally, since our Goldman Sachs days, this is more than 25 years ago, we've generally used half a backtest out of sample as a bogey. And life has worked out fairly close to that.”
Asness: Painful three-year drawdowns protect value factors from being arbitraged away
“The famous value factor, that can kill your world for three years. That's a terrible thing to live through, a wonderful thing if you don't want a factor to be arbitraged away.”
Asness: A prolonged shallow drawdown damages asset managers more than a fast crash
“Duration of pain is as important as intensity of pain to how damaging it is to both your future life expectancy and to your business. A crash that quickly reverses itself. Almost by definition, people are tense. Why'd that crash? You have to talk about it. You…”
Asness: Price-to-book is the value metric most distorted by intangible assets
“Everyone thinks the value is price to book. I blame my heroes, Fama and French, for that. Most modern managers use a very broad set of measures, but price to book, It's probably the most susceptible to being warped by intangible values.”
Asness: Personal cynicism delayed AQR's machine learning adoption by years
“I think I probably slowed us down on some of the machine learning stuff by a couple years with a little cynicism along the lines of exactly what you're talking about, that we could turn over more of the decision to the machines.”
Asness: Unconstrained factor machine learning yields zero out-of-sample power
“If you let it look at every possible factor, it's going to be a giant insane data mining exercise. I believe it'll have zero out of sample power.”
Asness: Machine learning will not help determine the equity risk premium
“I'll give you an example of something ML I don't think will help us on, and maybe I'll be proven wrong about this, but vital numbers to people like us. What is the equity risk premium? What is the premium for high quality against low quality stocks? There, you…”
Asness: Value spreads suggest smart money has also moved to indexing
“Given what we've seen with particularly value strategies and this fact that spreads between cheap and expensive are still considerably wider and recently hit their widest level ever, I don't think there's a lot of evidence that only the dumb money has left and…”
Asness: Multi-manager pod shop model completely defied his past intuition
“If you had told me 20 years ago that here's my strategy, I'm gonna go find people I think are really good at this, Pay them a lot. Charge that to the underlying investor. So the fees are, when you add it all up, are gigantic. And then I'm going to fire them ra…”
Asness: Multi-manager pod shops probably cannot grow by another 5x
“I tend to think those shops probably can't grow by another factor of five. There's just a limit to alpha. There are limits how many great traders that are out there.”
Asness: AQR trades crypto exclusively via small trend-following positions
“The most extreme example I'll give you is there's a one place in our entire business. We trade crypto, pure trend following strategies in places that we have a broader model where we're doing macro that is valuation carry doesn't have any of those, or at least…”
Asness: Private equity volatility is higher than public markets despite reported metrics
“Where private equity is well to the left of public equity, and it's concentrated off in levered equity. So if that's vol on the x-axis, it is not to the left.”
Asness: Expanding multiples drove 85% of US equity outperformance over 30 years
“Shows that U.S. Equities have indeed crushed global equities in the last, call it, 30 years. Don't hold me to the exact numbers, but call it 80, 85% of that victory has been multiples going up. U.S. Started out cheaper and is now considerably more expensive in…”
Asness: Social media and ubiquitous data make market inefficiencies longer-lasting
“I think the ubiquity of data, social media, maybe indexing, these things have added up to a market where inefficiencies are bigger and last longer.”
Asness: Investors Are Too Accepting of Concentration and Overly Frightened of Leverage
“We think people are essentially too accepting of concentration risk and too frightened of leverage risk.”
Asness: Investment committee members face asymmetric downside risk with no upside
“Individual members of the committee get far more punished for disasters than they get for good portfolio returns. That's true in a lot of walks of investing. I think it's particularly true of committees where they don't own anything, they don't get the upside,…”
Asness: University investment committees suffer anti-contrarian bias due to donor selection
“Those on the committee are often some of the biggest donors over the last 10 years. Let's just be honest. The biggest donors are the most successful parts of the financial world over the last 10 years. Therefore, committees tend to be people of goodwill who be…”
Asness: Committees should scrutinize their best-performing managers alongside their worst
“One of the things I do when I'm on a committee, I'll make suggestions like, if we're bringing in our three worst managers, let's bring in our three best, too, because those are anomalous returns also. They're more pleasant to have anomalous big returns, but th…”
Asness: AQR shrank by half and restructured into small research teams
“I think, and this was excruciating and had a human cost, but having to shrink by half made us make very hard decisions about being blunt, who are absolute best commando team researchers, men and women who just excel. We started out this way and we're back to t…”