Doing Capitalism in Financial Services
Doing Capitalism in Financial Services
Today: Notes on my reading of Gregory Zuckerman’s book about Jim Simons, "The Man Who Solved the Market".
The Agenda 👇
Everyone has been writing about Jim Simons recently
It intrigued me, and I started reading The Man Who Solved the Market
It tells a compelling yet rather common story: that of doing capitalism
Only in this case, it’s about doing capitalism in financial services
And that’s neither as obvious nor as easy as it sounds! Just ask Jim Simons 😉
Below are my notes on the first half of the book
I’ve been intrigued recently by all the writing about legendary quant investor Jim Simons—a mathematician turned Cold War code breaker turned hedge fund billionaire.
Marc Rubinstein dedicated a fascinating edition of his Net Interest newsletter to Simons and his firm Renaissance Technologies (I highly recommend it if you’re interested in the thorny question of fees in asset management).
More recently, Noah Smith of Bloomberg dedicated a column to Simons’s impressive return performance over the years and why it’s almost impossible to emulate, especially at a large scale:
Undoubtedly, the reason Medallion stays relatively small is that its strategies don’t scale up. The market inefficiencies that allow brilliant mathematicians to make steady money aren’t infinite; bet enough money on these strategies, and they stop making huge profits, as prices move into a more efficient alignment. This is why Renaissance doesn’t let investors keep all their money in Medallion, and no longer solicits money from new outside investors — more money would drive down returns.Renaissance does operate a number of other funds, which use different strategies that scale up better, and are available to outside investors. But these more capacious funds necessarily lack Medallion’s special sauce.
Well, Marc and Noah were enough to convince me to buy Gregory Zuckerman’s book, The Man Who Solved the Market, of which I have read about half by now.
I wouldn’t say it’s an excellent book. But I also recognize how difficult it is to write about Simons and his business because there’s so much technical material in both the mathematical and financial aspects of it. All in all, it’s entertaining and it provides a good perspective on the history of quant investing.
Today I just wanted to share a first impression—mainly that Jim Simons is clearly one, among many, who contributed to converting the financial services industry to “capitalism” as opposed to the “market economy” (in Fernand Braudel’s framework).
You could think financial services, since it’s about moving money around, is naturally all about capitalism—but in fact, a significant part of the industry functions in the market economy: people buy securities, then they sell them at a slightly higher price and pocket the difference. Isn’t that what market trading is all about?
What you learn reading Zuckerman’s book is that for a long time it was more rewarding to simply trade securities than to insert capital in the production process under the form of computing power, data, and sophisticated algorithms. One of Simons’s early business partners, another mathematician named Leonard Baum, became so passionate about trading that he began wondering if all the math they were trying to insert made any difference (p. 62):
“Why do I need to develop those models?” Baum asked his daughter Stefi. “It’s so much easier making millions in the market than finding mathematical proof.”
Simons respected Baum too much to tell him how to trade. Besides, Baum was on a roll, and the firm’s computer firepower was limited, making any kind of automated system likely impossible to implement.
That’s an important point: even if Simons had raised more capital to insert into his firm’s production process, it would have been hard to find machines powerful enough to run the algorithms coming out of his or Baum’s brain. This was still the early 1980s, after all. And no matter the era, it’s not enough to have capital to deploy—you actually need something (more advanced technologies) to insert it into.
Besides, at some points in 1980-1982, Baum was on a roll indeed. But then he lost money. And as Simons once remarked (p. 67): “If you make money, you feel like a genius. If you lose, you’re a dope. It’s just too hard to do it this way. I have to do it mathematically.”
This is a very important insight: capitalism eventually feels easy compared to the market economy. The cult of the small business owner (that individual who owns a restaurant or a barbershop) didn’t develop because they contribute much to adding value in the economy—it’s because it’s so damn hard to make money in the market economy, and so we all have to respect the (oftentimes in vain) effort!
In comparison, if you find a way to insert capital into the production process and thus trigger increasing returns to scale, you end up making money while you sleep, which seems relatively easy when compared to having to constantly hustle on the market.
That’s exactly what Simons has always been after. He comes from academia, after all. He’s always favored the easy way to make money (capitalism) as opposed to the hard way (trading on the market and feeling like a “dope” more often than not).
This, by the way, is the key to unlocking productivity: technological progress and increased productivity happen because inputs (such as labor) cost too much, and some people like Jim Simons are ready to experiment a lot so as to find an easier way to make money. That’s what capitalism is all about!
All that being said, everyone in the startup world knows it’s not that easy. For every founder that has found the recipe for increasing returns, many others completely fail to build a successful business. It all requires “relentless resourcefulness” (to quote Paul Graham) as well as a great deal of luck. And indeed, the first half of Zuckerman’s book is filled with characters who had the resourcefulness but not quite the patience to pursue their experimentation with new trading models. Here’s the example of French mathematician René Carmona (p. 83):
By 1987, Carmona was plagued by guilt. His pay came from a portion of [Simons’s business partner James] Ax’s personal bonus, yet Carmona was contributing next to nothing to the company… “I was taking money from them and nothing was really working,” he says.
What prevented Carmona’s work from unleashing capitalist value creation? The lack of computing power! Later on in the book (p. 95), there’s a mention of things becoming easier thanks to technological progress in the realm of computing:
Computing power had improved and become cheaper, allowing the team to produce more sophisticated trading models, including Carmona’s kernel methods—the early, machine-learning strategy that had made Simons so uncomfortable. With those advantages, Axcom [the subsidiary of Renaissance Technologies employing Carmona] averaged annual gains of about 20 percent, topping most rivals.
Yet even then, it still felt easier to make money in the market economy rather than by doing capitalism. As written on p. 99,
Ax relied on his instincts for a portion of the portfolio, edging away from trading based on the sophisticated models he and [Sandor] Straus [the guy in charge of data] had developed, much as Baum had drifted toward traditional trading years earlier and Simons was initially uncomfortable with Carmona’s “kernels”. It seemed quantitative investing didn’t come naturally, even to math professors.
Reading all of the above, you could have the impression that more capital deployed in better investing was all about computing power. But it was also about data. One prominent member of Jim Simons’s early team, Sandor Straus, was in charge of building a more comprehensive and accurate database for training the mathematician’s models (p. 112):
As the researchers worked to identify historic market behavior, they wielded a big advantage: They had more accurate pricing information than their rivals. For years, Straus had collected the tick data featuring intraday volume and pricing information for various futures, even as most investors ignored such granular information. Until 1989, Axcom generally relied on opening and closing data, like most other investors; to that point, much of the intraday data Straus had collected was pretty much useless. But the more modern and powerful MIPS (million instructions per second) computers in their new offices gave the firm the ability to quickly parse all the pricing data in Straus’s collection, generating thousands of statistically significant observations within the trading data to help reveal previously undetected pricing patterns.
I admit this edition is a bit messy—just a few quotes from Zuckerman’s book with additional comments. But I think it’s worth insisting on the following ideas:
It took a very long time to bring a capitalism approach into the financial services industry (which, by the way, effectively turned this segment of the economy into an actual ‘industry’).
Operating in the market economy is hard, but doing capitalism is not for the faint of heart, either: it takes “relentless resourcefulness” and in many cases leads to failure. You need to be patient and resilient if you want to get lucky.
One question I’ve raised many times in the past is the following: Can venture capital turn into a capitalist enterprise? We’re not there yet, but precedents like Jim Simons’s suggest it will eventually happen—all as part of the Diffraction of Venture Capital!
What do you think?
No need for a reading list since I already compiled all my past writings related to the diffraction of venture capital: All About Diffracted Venture Capital.
If you enjoyed this edition of European Straits, you should subscribe so as not to miss the next ones.
From Munich, Germany 🇩🇪
Nicolas
