What Major League Baseball Trick Should Wall Street Follow?

NEW YORK (MainStreet) — Would the Great Recession in 2008 have been as catastrophic if Wall Street traders implemented more of the tactics Major League Baseball managers use? That’s the fundamental question veteran Lehman trader Joe Peta chews on in the book Trading Bases, A Story About Wall Street, Gambling, and Baseball (Random House, 2013).

The fanfare around sabermetrics took off after Moneyball’s excavation of Billy Beane’s statistics-driven management style, and Peta gained renewed interest in the statistical applications to Wall Street by chance.

Chance in the form of a fluky accident.

An ambulance plowed into Peta as he crossed the street in New York City’s TriBeCa at the beginning of 2011. The leg injury, which left him wheelchair-ridden and stranded from his family in San Francisco, effectively curtailed his decade and a half career on Wall Street. But his convalescence allowed for a renewed interest in the interconnectivity of sports management and Wall Street. His interest in gambling theory — particularly Andrew Beyer’s Picking Winners, an examination of horse racing handicapping — and Trading Bases becomes an exegesis on using Big Data to increase one’s chances of success and an indictment on Wall Street’s inability to mimic Big League strategy.

This morning Peta talked with us about his book, which comes out today. He admited his favorite baseball manager is Joe Maddon of the Tampa Bay Devil Rays, and as a native of West Chester, Pa., Peta found that absorbing the pain of the wretched Philadelphia Phillies teams was crucial to his development as a trader.

Tell us about the genesis of this book and how your injury led to a new clarity of ideas?

Peta: Bored and bitter that spring, with nothing but time on my hands, as I recuperated and rehabilitated my leg I threw myself into the baseball season for the first time in many years. I started building my own sabermetric based models to project team performance for the 2011 season, and I quickly began to see abundant similarities between baseball and the financial industry, between trading stocks and betting games, and between probability-based decisions made by both managers on the baseball field and traders on the trading floor.

At that point inspiration hit, and I realized that despite the fact that my bookshelves were filled with sabermetric-based baseball books, Wall Street tomes on investing, and Vegas-based books, including lots of poker titles no one had ever merged all three of those topics into one book -- despite the fact that they all celebrated critical reasoning. From there it was born.

What is the essence of your theory, and why is there such a strong connection between sports dynamics and economics?

Peta: I'd sum it up like this: Baseball, thanks to the sabermetric revolution brought to the attention of a broad public audience by Michael Lewis's Moneyball, uses data in a way to better understand the skill sets of its employees and performers. The financial industry is awash in incredibly rich data, thanks to the electronic nature of trading, which essentially records every decision a trader or portfolio manager makes. I believe the financial industry could adopt some of the best practices of baseball and do a better job of evaluating its employees, specifically focusing on identifying skill sets instead of focusing on results. Incidentally, I find it ironic that baseball is ahead of the curve in this. The baseball industry as a whole is valued at about $30 billion; there are multiple single entities in the financial industry that are worth more than that, so obvious there is more at stake, profit-wise, in the financial industry.

It's been said that if your theory were applied to the economy rather than the baseball diamond, it might have saved the country from the economic disaster of 2008. How so?

Peta: This is my favorite chapter in the book, and the longest because it's really a discussion about Dick Fuld. I worked for Lehman Brothers for 13 years starting as an intern in the summer of 1995 while getting my MBA at Stanford University. In the fall of 1994, just months after Lehman became a wounded, very flawed spun-off public company from American Express, Dick Fuld came to Stanford to personally recruit MBAs for his equity trading floor. Despite the fact that nothing about Dick was smooth—in contrast to every other bank's recruiting team—I was enthralled by Dick and through a number of anecdotes in the chapter I hope I capture his rough charm and the resulting esprit de corps at Lehman he fostered.

However, the stain on Dick's reputation that will accompany him to his New York Times' obituary is deserved. I relate this back to an infamous memo I wrote (reprinted in the book) in 1995 that eerily foreshadowed the demise of the investment bank funding model -- even though I didn't realize it myself. I talk about how any no entity that has an edge, or in business parlance, an activity with a positive expected value in the marketplace, should squander that asset in the future with a risky activity in the present. As any business student knows who has ever valued a company via the Dividend Discount Model knows, the bulk of the value of any company lies in the value of its future earnings -- or the tail as MBAs call it. Therefore no division, no company, and no CEO should ever undertake an activity in the present that imperils its ability to reap the benefits of its money-making advantage in the future. By placing bets on American real estate so large it reduced the profits of its bond and stock traders to rounding errors, Lehman's management led by Dick Fuld did just that. By the way, the US government/bankruptcy court did the same when it gave those future profits to Barclays, a British bank, allowing those future profits to leave our shores and accrue to the British banking system instead of ours.

What does that have to do with sports betting? More than meets the eye. It meant in building my model I would never place a bet on one day that would imperil my ability to exploit my advantage on the next day or the next week, or month. If Dick Fuld and the management of other banks run just as recklessly had understood this, the financial crisis would have never happened.

So you’re something of an evangelist for Big Data on Wall Street?

Peta: Baseball gets criticized sometimes for its acronym heavy statistics but I believe other industries should embrace the practice rather than ridicule it. Brokers possess tremendous amounts of trading data on its customers that is incredibly valuable from a sentiment standpoint but they don't do anywhere close to what they could do to analyze it.

So moving beyond the RBI and WHIP we see in baseball, what would some key acronyms on Wall Street be?

Peta: I talk in the book about the creation of Additional Loss Above Replacement Monkey (ALARM) and Trader Upside Above Cost (TuPac) that my boss and I used on the last trading desk I worked on to evaluate traders. Rather than looking at final P&Ls, we were trying to identify skill sets. This is akin to baseball learning to discount wins and losses on a pitcher's record and focus on the pitchers strikeout, walk, and ground ball rates. Those are the factors he can control; his final wins and losses are dependent on a huge amount of factors the pitcher has no control over. It can be the same with traders and PMs. They don't necessarily have control over what sector they trade, access to capital, etc. Management should try to identify skill sets. The acronyms may be whimsical, but the process of using your data more effectively is a serious endeavor.

 

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