The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

4.5 | 4,545 ratings

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Last update: 05-21-2024

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New York Times best seller

Shortlisted for the Financial Times/McKinsey Business Book of the Year Award

The perfect gift for the avid reader on your list: the unbelievable story of a secretive mathematician who pioneered the era of the algorithm - and made $23 billion doing it.

Jim Simons is the greatest money maker in modern financial history. No other investor - Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros - can touch his record. Since 1988, Renaissance's signature Medallion fund has generated average annual returns of 66 percent. The firm has earned profits of more than $100 billion; Simons is worth 23 billion dollars.

Drawing on unprecedented access to Simons and dozens of current and former employees, Zuckerman, a veteran Wall Street Journal investigative reporter, tells the gripping story of how a world-class mathematician and former code breaker mastered the market. Simons pioneered a data-driven, algorithmic approach that's sweeping the world.

As Renaissance became a market force, its executives began influencing the world beyond finance. Simons became a major figure in scientific research, education, and liberal politics. Senior executive Robert Mercer is more responsible than anyone else for the Trump presidency, placing Steve Bannon in the campaign and funding Trump's victorious 2016 effort. Mercer also impacted the campaign behind Brexit.

The Man Who Solved the Market is a portrait of a modern-day Midas who remade markets in his own image, but failed to anticipate how his success would impact his firm and his country. It's also a story of what Simons' revolution means for the rest of us.

Includes a PDF of Appendices 1 and 2 with charts

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

Y. Yuan

Great book that revealed many secrets of Simons’s unprecedented success

Reviewed in the United States on April 27, 2020

I have been live-trading, with my Fidelity IRA account, using the signals generated by the models as introduced in Forecasting and Timing Markets: A Quantitative Approach (ASIN:B0875JBWBQ ). Started from March 09 this year, I have achieved a net profit of 24.54% as of April 24, which is impressive given the market volatility induced by the COVID-19. Coincidentally, I learned from an online post that Simons’s Medallion Fund also achieved an over 24% return during this same period of time. I was motivated to find out more about his Medallion Fund and thus bought this book.

I eagerly read through the entire book so that I could assess how different his quantitative approach is against the AlphaCovaria System I have been relying on as mentioned above. I am so grateful for Mr. Zuckerman who dug out so many details about how Simons’s models have been built. Here is a summary of what I have learned from a quantitative trader’s perspective:

(1) First, a little background. While at IDA during his earlier career, Simons and his colleagues wrote a research paper that determined that markets existed in various hidden states that could be identified with mathematical models. At IDA, they built computer models to spot "signals" hidden in the noise of the communications of the United States' enemies. This was the precursor to Simons’s later persistent pursuit to testing the approach in real life.
(2) Performance-wise, Simons has been the most successful one in trading, given the performance comparisons of this list: Jim Simons (Medallion) 39.1%, George Soros (Quantum Fund) 32%, Steven Cohen (SAC) 30%, Peter Lynch (Magellan Fund)29%, Warren Buffett (Berkshire Hathaway) 20.5%, and Ray Dalio (Pure Alpha) 12%. One of the factors that Simons could succeed so much is that he is a strongly principled person with a strong belief in "Work with the smartest people you can, hopefully, smarter than you... be persistent, don't give up easily." So he is not only a great mathematician but also a great visionary and business manager.
(3) Their model dev process: By 1997, Medallion's staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals: (1) Identify anomalous patterns in historic pricing data, (2) make sure the anomalies were statistically significant, consistent over time, and nonrandom , and (3) see if the identified pricing behavior could be explained in a reasonable way.
(4) Trading frequency: Medallion made between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting the market prices.
(5) Data granularity: They use five-minute bars as the ideal way to carve things up. Their data hunter Laufer's five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects.
(6) Holding period: Medallion still held thousands of long and short positions at any time. Its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques.
(7) Their performance as measured by Sharpe ratio. 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion's trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year. No one had achieved what Simons and his team had-a portfolio as big as $5 billion delivering this kind of astonishing performance. In 2004, Medallion's Sharpe ratio even hit 7.5, a jaw-dropping figure. Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting that the fund's gains came with low volatility and risk.
(8) Their portfolio composition. They started with commodity, bond, and currency, but later expanded into equities, which became the major source of profits after many years of efforts.
(9) Does Simons strictly stick to their models? In general, yes, but he made calls when he saw models were malfunctioning due to extreme market conditions.
(10) How have their models worked under various market conditions? Their models are mostly neutral, which was made possible by making quick trades only to eliminate unforeseeable events. They claimed that they could make models that would work with long-term investments, but it seems that they have not done so.
(11) What is the most secret juice with their models? Medallion found itself making its largest profits during times of extreme turbulence in financial markets. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts. "We make money from reactions people have to price moves." They look for smaller, short-term opportunities-get in and get out. The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. "We are right 50.75 percent of the time... but we're 100 percent right 50.75 percent of the time," Mercer told a friend. "You can make billions that way."
(12) How long was their learning curve? Simons spent 12 full years searching for a successful investing formula, without much success until he and Berlekamp built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process.
(13) Size of their computing infrastructure. On page 248, it says their computer room was the size of a couple of tennis courts. I arrived at a guestimate that they might have about ~13,000 servers, computed like this: 2x78x27 (two tennis courts) x 0.6 (total area occupied by racks) / (2x4 (rack area)) x 40 (servers per rack) = 12,636. This should not be too far away from what they have.

I strongly encourage every serious quant to read through the entire book for a lot of other secret juices.

mattdbergman

Worthwhile Investment History

Reviewed in the United States on November 18, 2019

I enjoyed Mr. Zuckerman’s effort very much - it was a book I didn’t particularly want to put down and it makes for a fun and quick read. Think folks would do well to consider their perspectives/objectives for the book. I’m not primarily a professional money manager, have an M.S. in finance but no advanced training in complex mathematics. I think quantitative rules-based investing systems have significant value. I came to the book under no illusion that it was going to reveal any “secrets” that could be put into action and that held true. But I found it to be a nicely done history of quantitative investing & how technology enabled the scale and complexity. Here are some specific notes:

1. I’m not sure the title does justice to the book or to participants. There’s no doubting the obscenely amazing performance of the Medallion funds. Mr. SImons, as founder and CEO, certainly has earned a large place in financial history. But while the book does a terrific job communicating Mr. Simons’ early work as a cryptographer & his academic achievements in respect of geometers etc., it seems to describe a man who had a vision for how the market might be solved & drove the funding/infrastructure for realization - but not a man who actually developed the specifics of the fund’s model. It really seems to be several of the other key characters who poured endlessly over pricing history, identified & tested anomalies and wrote the algorithmic codes (beginning with commodities & fixed income, equities later on).

2. The author does a very good job, IMHO, of discussing concepts like factor investing, statistical arbitrage, paired trades, hedges, market neutral, etc. And he takes the time to nicely reference some of the underlying math for those who have the interest, touching on concepts ranging from differential equations to mean reversion to Brownian motion to embedded Markov processes. The author doesn’t purport to try and teach readers how they might use those ideas - appropriately so - but it’s meaningful perspective.

3. Not surprisingly, there’s a dichotomy re “how” the market was “solved.” There won’t be much new here for traders. At the broadest level of generality, certain pricing anomalies were identified & incorporated into algorithms that turned the raw data into trading signals. Harnessing computing power, the fund trades a ton, such that it doesn’t need to make much on each trade and only needs to get it right a bit over half the time - returns are then amplified by liberal employment of leverage; the systematic model is trained - application of machine learning - to continue to improve precision on its own and to determine trades/positions. Beyond that, though - & it shouldn’t be folks’ expectation- the book doesn’t go granular on the model’s inputs. It can’t and doesn’t give away the particulars of the black box. The author should be credited for his tackling of the funds’ initial problems with slippage and for reporting on how the funds had no choice but to move into equities in order to attain such massive AUM. Also great history on early and superior efforts to obtain/recreate pricing data & good discussion of the core fund’s preference for extremely short holding periods.

4. There’s some pretty riveting investing history here, ranging from early developments in technical analysis to the long and steady rise of fundamentals-based investing to the profound skepticism with which systematic quant trading was treated for an exceptionally long time.

5. The narrative is at times beautiful , at others choppy and abrupt. Probably too many cases of basically “the fund was in trouble” to “the fund was thriving”. It’s like, “oh, that’s good”

6. In terms of personal biography, my understanding is that Mr. Simons is intensely private - under those constraints, the author does well in tracing his life and career, though for me, a truly strong and well developed portrait remains elusive. The author comes closer to that mark in telling the stories of several of the other key participants in the firm’s rise over time.

7. Later in the book, a ton of space is devoted to Robert Mercer’s public politics and how it impacted the firm. I thought it was interesting stuff, but some may find it loses focus, e.g. there’s quite a bit on Rebekah Mercer that just doesn’t have much relation to the core story.

This was an ambitious endeavor and Mr. Zuckerman should be credited for that. As personal biography, it’s s fine effort. As financial history, I’d characterize it as informative, accessible and entertaining. But I’m not sure I’d say it’s of huge importance. The telling of the story isn’t, in my view, likely to have any real impact on the methods and practice of finance. But for finance junkies, there’s a ton of on point info, perspective, teaching and fun. Thanks much to the author.

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