E427: AQR's Peter Hecht on AI, Market Bubbles & How the Best Investors Build Portfolios

How I Invest with David Weisburd

In this episode of 'How I Invest,' David Weisburd sits down with Peter Hecht, Managing Director at AQR Capital Management, to discuss core inve

Key takeaways

• Diversification should be measured by underlying risk exposure rather than the number of investments.
• Tracking error is necessary for active managers to generate alpha but must align with an investor's risk tolerance.

Main topics

• Diversification in portfolio construction
• Tracking error and active management risk

Notable quotes

We're militant about thinking about are these trades correlated with each other? Through that diversification, we can actually achieve an attractive return for a given level of risk.

Conclusion

Peter Hecht underscores that successful investing hinges on disciplined risk management,

Transcript preview

Speaker 1 (0:00) Pete, at AQR, you have $242 billion assets under management. You're one of the largest hedge funds in the world. And one of the things I wanted to start with is one of my biggest pet peeves in this industry is this concept of a tracking error. Tell me about tracking error and is it a misnomer? We're Speaker 2 (0:19) familiar with concepts like volatility, which sort of gives you a sense of the range of outcomes for an investment's return. Tracking error is the volatility, but it's the volatility of the relative return, your return versus a benchmark. Okay, so let's say the S &P 500. Okay, so tracking error is going to give you a sense of how far a manager's relative return how different it can be from the S &P 500. So like a tracking error of 3 % means if you think it's a normal distribution, right, you're going to have a return that's going to be either 3 % ahead of the S &P 500. 3 % behind the S &P 500 with roughly a 66 % probability. So it gives you a sense of how much active risk am I taking and is it consistent with my risk tolerance in terms of my willingness and ability to tolerate lagging the benchmark. Can tracking error be a good thing? Yes, because if you want to beat the benchmark, if you want to try to add value, you have to take tracking error. So I always tell people, You can take bad tracking error, right? That's with a manager that does not have a good process for determining what's an attractive security and not an unattractive security. Or you can invest with the manager you think has a good process. You have to take tracking error to beat your benchmark. The more tracking error you take gives you more possibility for beating the benchmark, but it also means you might actually lag the benchmark by more when your manager experiences Speaker 1 (1:54) bad luck. Speaking of the markets, They don't operate in a vacuum. The more passive investing becomes a norm. So now we're still in this era of passive investing. Active investing seems to have more room for alpha. Is that generally true? Speaker 2 (2:12) There's a big debate there in terms of whether the rise of passive is distorting prices, making it harder for active managers. I would say from my vantage point, while passive has become more important and it is a large part of the market, I still believe there are enough active managers that are going to take the time to read the information, process the information, and pound that information into prices. And so whether they're being more passive or not is good for active managers sort of depends, right? Because it sort of depends on who's getting fired to go passive. If it's really bad active managers, that are getting fired and should be going passive, right? Now, all of a sudden, the remaining active managers on average are higher quality. But if it's actually some good active managers actually getting fired, maybe there's less competition in terms of being able to harvest, identify and harvest alpha. So it sort of depends on what you believe in. So there is no hard set answer. I Speaker 1 (3:18) spoke to former president of Peter Thiel's hedge fund, and he talked about this reflexivity and the volatility that's caused by the passive amount of capital. So now you see oftentimes in the market, markets go up or down because everybody's making the same trade. Does that make the market inherently more volatile? There have been some academic papers, Speaker 2 (3:39) and it's early innings, and there's people who disagree with it, that believe that the rise of passive have made markets more inelastic. More inelastic means flows will actually move prices, even if those flows are for non-fundamental reasons, which would lead to a more volatile stock market, for example. That's possible, that with more passive investors, people who just take prices as given, aren't as sensitive to prices. I can see the possibility. that you would see more inelasticity and thus for a given flow, maybe today, maybe there is slightly more sort of market impact flow-based movements and prices than what we saw before, which would lead to more volatility. It's early innings and in academia, sometimes Speaker 1 (4:27) it takes 20 years to figure out who is right. PQR has $242 billion. You have all these different strategies, but at the core, how would you describe your philosophy? First off, we believe in diversification. Speaker 2 (4:41) So we are a systematic fundamental manager. Some people call us quants. I like to actually say systematic because within quant, there are people who are mathematicians. Like I have a PhD in finance. Okay. When you're most systematic managers have a small edge for any single security that they trade. And you might ask, well, then how do you actually develop strategies that have good returns? Well, like a casino. A casino would never exist if it could only play blackjack against you, one person. They bring in thousands of uncorrelated blackjack customers. That's like bringing in thousands of uncorrelated trades. So we believe in diversification. We're militant about thinking about are these trades correlated with each other? And through that diversification, through that portfolio construction, we can actually achieve. an attractive return for a given level of risk, whether that's in a market neutral implementation where someone doesn't want any beta or in an implementation where someone wants full market exposure beta one. So that is a core tenant diversification across all of our products. Speaker 1 (5:51) When somebody comes to you and says, does AQR generate alpha? How do you answer that question? There are many ways to measuring alpha. It's an art. So first off, finance is a social science. It's not physics. It's not math. It's probabilistic in nature. Speaker 2 (6:08) Yeah, it's probabilistic in nature. And alpha is always relative to what model? What is your risk model? And this was like what Gene Fama taught us in the PhD program at the University of Chicago on the first day of the PhD program. It's all relative to your market equilibrium. If your model of market equilibrium that determines what are the risk factors and those risk factors should be compensated, if one of those risk factors is a value type factor like book to price, going long book to price, short book to price. Gene Fama would say if your return can be explained by that, that's not alpha because it's part of his risk factor framework. But other people would debate why is a book to price a risk factor? Maybe it's just something that's picking up the fact that people have over and under reacted to past earnings and high book to price stocks that look cheap have just had disappointing earnings and people over extrapolate. There's behavioral finance. stories. So alpha really depends on the risk model you use. So like anything in finance, because it is a social science, treat it like a painting. You're going to do multiple ways of measuring alpha. So the one way might just be let's control for market exposure only. So there were some very famous models in the 1960s, Bill Sharp run. Speaker 2 (7:38) won the Nobel Prize and other people developed a capital asset pricing model that controls for market beta, something people call, use the word beta all the time. You could just look at it versus beta, a standard market beta. You could then say, hey, Gene Fama and Ken French, they are famous. If it's a stock selection strategy, let's expand controlling for alpha. Let's put in a value factor and some of the Fama French factors. And if your return can beat those factors, then we're going to call that alpha. And then people would disagree and say, you know what? I'm going to throw in 10 factors that I think are well known and are associated with the risk factor. So from my standpoint, it's always conditional on the model you assume. But you could also sort of just step back. At the end of the day. You might not care whether something's alpha or not in some academic sense. You just want to know by having it in my portfolio, does it improve my return for a given level of risk? And I can actually measure that by running a regression of the possible new investment that Speaker 1 (8:50) you're considering on the current portfolio you hold. Is there essentially two definitions of alpha? One is... risk-adjusted return and the other one is worse in the context of a benchmark? Are there really two different definitions? They're all related. Speaker 2 (9:02) So alpha is just a very, you know, like I always tell people, if you ask me what I had for dinner and I said food, you would say that's not very specific. And then Speaker 1 (9:12) if I just said fish, that's not very specific. Speaker 2 (9:15) So saying alpha is sort of like saying food. And then saying risk adjuster return versus a benchmark is sort of telling me, are we talking fish or meat? And so there are many ways of doing risk adjustment. And I would argue part of the reason why my group exists is because there is that subjectivity and there is an art to understand what's appropriate, what's not appropriate. But the best way to handle any situation where there isn't a definitive answer is to do it multiple ways. A Sharpe ratio is one way of calculating risk-adjusted return, but it Speaker 1 (9:52) doesn't control for beta. And I know you were an assistant professor at Harvard five years after finishing your PhD, but for you today, the source of truth is the customer. What does the customer want? Yeah. To me, the most potent thing is to actually Speaker 2 (10:07) calculate alpha relative to their current opportunity set, which is the portfolio they hold. The opportunity cost. Yeah. So it's by having access to this, do I improve? the return of your portfolio without taking more risk. And that can actually be calculated. It's called an appraisal ratio. It goes by many names. But to me, that's the most important thing I'm trying to do. If some theoretical exercise says it has alpha but doesn't improve your portfolio, it's dead to you as far as I'm concerned. Speaker 1 (10:36) A mutual friend told me that you're one of two TAs at AQR from Eugene Fama, famous Nobel Prize winner, who popularized this idea of small in value. Today, many people, many very smart people challenge that small in value still persists. What do you say to that? We would have questioned Speaker 2 (10:55) the validity of small decades ago. So I remember some of the early paper that brought the small cap effect. to light in the academic literature, first off, had data errors in the paper. So most people don't know about that. There were like positive signs that should have been negative signs on returns. The second thing is, let's say small caps beat large caps. OK, but small caps have higher beta than large caps. So small caps on average beat large caps, but they're higher beta. Bill Sharp told us in a rational economy. Higher beta stocks should have higher average returns. So are you just picking up higher beta stocks once you control for beta differences? We found there was no. There was no small cap effect. So even before people started saying there was no small in the more recent time period, we would have questioned that. So while something like size can be a risk factor, we didn't think it was a rewarded risk factor with it. positive return associated with it. Speaker 1 (12:01) Said another way, you could have just levered the larger stock to the same beta level and outperformed the small cap. Or had the same performance so that there was Speaker 2 (12:12) no difference whether you were in small or large cap stocks. And to your point on value, obviously Fama French made it very famous with their sort of seminal paper where they looked at book to price. Back in the day, we would have said there were always so many different ways of measuring value, first off. And what made the Fama French Insight amazing wasn't because it was just book to price. It was the larger underlying guiding principle that something that's fundamentals scaled by price might be informative about future returns. Even like back in the day, two decades ago. Most managers who were doing some type of value inclusion in their process weren't just using book to price. There are many ways of measuring cheap versus expensive. And I would say today there are, that process has even evolved. People are using machine learning techniques to come up with better. value measures. One thing in Fama French, and I understand why they did this, they didn't control for industries. So let's say where someone says my value measure is a PE ratio. And I'm going to compare a PE ratio of a tech company to a utility company and say the tech company is expensive because it has a high PE. The utility company has a low PE. PE, it's cheap. And it's like they're different industries. They have different fundamental growth. When we think about implementing value and a lot of thoughtful managers, it's within a peer group where it's apples to apples. It's a utility company versus utility company. It's a tech company versus a tech. While the simple Fama French was just across the entire universe. So all of those types of design choices, managers, thoughtful managers were doing a decade or two ago. And even today, things have evolved. with the explosion of like machine Speaker 1 (14:07) learning and there's always innovation going on at firms. These memes in the market, even when they're untrue, just persist, could persist for decades, even among some of the most intelligent people. One way to protect yourself Speaker 2 (14:19) against that, because it's obviously very hard to try to predict that and to predict how long it will last, is go back to one of the sort of guiding principle that sort of is part of all of our implementations is diversification. Right. If you're holding a thousand stocks long and short and they all have small weights, then if a couple of them go wrong. Even if they go wrong in ways where it's not just like you're short and it was up a couple percent, maybe it was up 10 % or 15%, you can really minimize the damage to the overall portfolio. In contrast to like a concentrated manager who's long 20 names, short 20 names, if they're on the wrong side of one of those stocks, that can ruin their entire year. So our diversification. is consistent with us being a systematic manager and having a small edge, but it has the risk management sort of positive collateral benefit because that means when one of our longs goes down or one of our shorts is going up, we can sort of contain the amount of damage. Speaker 1 (15:25) Everyone I talked to on the show is chasing the same thing, an edge. And more and more, the edge comes down to your information, not just having it, but being able to trust it when the stakes are highest. AI is doing more of the information gathering for you every day, and most tools are very good at sounding right. 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Speaker 1 (16:35) alpha-sense.com, how I invest. I've spoken to quite a few quant investors and they're saying everyone's just loading information to Claude and following what Claude is telling