A Hedge Fund Veteran Is Rethinking Star Manager Selection

Moneyball for allocators is the theme of Joe Peta’s new book.


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Joe Peta, a veteran of Lehman Brothers, Kingsford Capital, and Point 72, says he has come up with a new methodology that would help allocators predict which managers will outperform — at least in the short term.

Peta acknowledges it’s a tall order, but says he does so by measuring “skill not returns.” To make his case, he has written a book called “Moneyball for the Money Set” that describes his methodology, which uses analytics to ascertain the skills of the portfolio manager in a very different way from what is traditionally done.

He takes the title (and theme) from the 2003 Michael Lewis book “Moneyball: The Art of Winning an Unfair Game,” which detailed Oakland Athletics team manager Billy Beane’s data-driven approach to scout and analyze players. (Peta’s book is subtitled “Using Sports Analytics to Predict the Returns of Portfolio Managers with Startling Accuracy.”

A baseball fan, Peta was intrigued by the Moneyball analysis when he was working on a Nasdaq market making trading desk at Lehman in the early 2000s and soon began proposing data-based methods of ranking the firm’s traders. His early metrics, he admits, were crude. And while he was soon helping Lehman in its push into hedge funds, Peta said the analytics he was using raised more questions than answers. He went on to perfect his methodology first at Novus Research, then at Kingsford, a short-only hedge fund, and finally at Point 72, where he was part of the quant division called Fusion as the head of performance analytics until mid-2021.

In an interview with Institutional Investor, Peta said the traditional methods most allocators and funds themselves use to identify manager talent aren’t working. These talk about a manager’s “batting average,” which Peta said “is a near worthless calculation. If I tell you a portfolio manager had a batting average of 53 percent either last month or last year, you don’t know if that’s good or bad. And even worse, one PM with a batting average of 53 percent might not be as good as another PM with a 49 percent or 50 percent batting average.”


Next, traditional analysis considers the slugging percentage. “In other words, what’s the magnitude of the winners? And again, it turns out that’s a worthless calculation.” He said that doesn’t say “whether the number “is good or not” and does not predict the future.”

The two key differentials in his analysis are what he calls consistency skill and explosiveness skill.

Peta said he used the logic of sports analytics to build a framework that distinguished between skills that were repeatable and those that weren’t, resulting in a predictive model of future performance. (Other firms are also working on new methodologies to identify managers who will outperform in the future. Essentia Analytics scores managers on their demonstrated investment decision-making skills, including stock picking, sizing, and timing.) “When the rankings of future PM returns, relative to the market, were compared to their actual returns, the model’s accuracy, in terms of rank correlation readings, far exceeded any known industry methods,” he wrote in his Moneyball book.

To determine consistency, Peta looks at a year or two years of daily stock position data for a manager’s portfolio. “Once you have 500 daily observations, it turns out that the ability to hold or to identify more outperforming stocks than underperforming stocks becomes indicative of the true skill level of the portfolio manager and it becomes predictive of their future ability to identify outperformers” against a benchmark, he told II.

What he calls explosiveness answers questions, such as “How do your outperformers do? Do you hold the best of outperformers?

“A way to describe it may be that the consistency calculation measures the science of stock selection, the art of stock selection is measured by the explosiveness because that really is capturing a PM’s ability to not blow themselves up and to get the most out of their winners when they do identify outperformers,” he said.

Although Peta left Point72 when it shut down the quant division, he said his analysis — had it been used — might have been able to predict the blowup of Melvin Capital, in which Point72 founder Steve Cohen had invested.

Peta has been working as a consultant since leaving Point 72 and in one case he looked at a hedge fund with two managers with low “explosiveness” readings. “Both had problems with GameStop,” he said, in reference to the stock Melvin’s Gabe Plotkin was famously and disastrously short. “The explosiveness reading, he said, might have been able to “suss out” problems with those PMs “in a way that a human eye would not have.”

Peta argued that his analysis is particularly useful for allocators like pension funds, endowments, and family offices that are choosing managers. But he said it’s also useful for multi-manager hedge fund platforms.

“If you’ve got a dozen PMs that have very similar alpha generation over the past year, what this framework does is it gets to look at those PMs and say, ‘Yes, their returns are the same, but the skills they’re exhibiting will show different persistence going forward,’” Peta said in the interview.

But he acknowledged that there’s a limit to the predictive capabilities of his approach. “At the granular level, what I’m really doing is predicting next month or next quarter.”