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![The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by [Gregory Zuckerman]](https://m.media-amazon.com/images/I/51TwZcYI1DL._SY346_.jpg)
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Shortlisted for the Financial Times/McKinsey Business Book of the Year Award
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 twenty-three 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's revolution means for the rest of us.
- LanguageEnglish
- PublisherPortfolio
- Publication dateNovember 5, 2019
- File size12826 KB
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Editorial Reviews
Review
“A compelling read.” —The Economist
“Reads like a delicious page-turning novel.” —Barry Ritholtz, Bloomberg
“One of the most important stories of our time.” —Financial Times
“Zuckerman brings the reader so close to the firm’s inner workings that you can almost catch a whiff of the billionaire’s Merit cigarette.” —Brandon Kochkodin, Bloomberg
“A gripping biography of investment game changer Jim Simons… readers looking to understand how the economy got where it is should eat this up.” —Publishers Weekly
"Worthwhile reading for budding plutocrats and numerate investors alike." —Kirkus
“Immensely enjoyable.” —Edward O. Thorp, author of A Man for All Markets
“An extremely well-written and engaging book . . . a must read, and a fun one at that.” —Mohamed A. El-Erian, author of The Only Game in Town
“Leave it to the Wall Street Journal’s Greg Zuckerman to lay open the golden mysteries of quantitative investing. With this fine, humane, and eye-opening book, he’s well and truly broken the code.” —James Grant, Grant’s Interest Rate Observer
"Page-turning tale…bravura storytelling." —Gary Shteyngart, author of Lake Success
About the Author
Excerpt. © Reprinted by permission. All rights reserved.
Introduction
You do know— no one will speak with you, right?”
I was picking at a salad at a fish restaurant in Cambridge, Massachusetts, in early September 2017, trying my best to get a British mathematician named Nick Patterson to open up about his former company, Renaissance Technologies. I wasn’t having much luck.
I told Patterson that I wanted to write a book about how James Simons, Renaissance’s founder, had created the greatest moneymaking machine in financial history. Renaissance generated so much wealth that Simons and his colleagues had begun to wield enormous influence in the worlds of politics, science, education, and philanthropy. Anticipating dramatic societal shifts, Simons harnessed algorithms, computer models, and big data before Mark Zuckerberg and his peers had a chance to finish nursery school.
Patterson wasn’t very encouraging. By then, Simons and his representatives had told me they weren’t going to provide much help, either. Renaissance executives and others close to Simons— ven those I once considered friends— ouldn’t return my calls or emails. Even archrivals begged out of meetings at Simons’s request, as if he was a Mafia boss they dared not offend.
Over and over, I was reminded of the iron- lad, thirty- age nondisclosure agreements the firm forced employees to sign, preventing even retirees from divulging much. I got it, guys. But come on. I’d been at the Wall Street Journal for a couple of decades; I knew how the game was played. Subjects, even recalcitrant ones, usually come around. After all, who doesn’t want a book written about them? Jim Simons and Renaissance Technologies, apparently.
I wasn’t entirely shocked. Simons and his team are among the most secretive traders Wall Street has encountered, loath to drop even a hint of how they’d conquered financial markets, lest a competitor seize on any clue. Employees avoid media appearances and steer clear of industry conferences and most public gatherings. Simons once quoted Benjamin, the donkey in Animal Farm, to explain his attitude: “ ‘God gave me a tail to keep off the flies. But I’d rather have had no tail and no flies.’ That’s kind of the way I feel about publicity.”
I looked up from my meal and forced a smile.
This is going to be a battle.
I kept at it, probing defenses, looking for openings. Writing about Simons and learning his secrets became my fixation. The obstacles he put up only added allure to the chase.
There were compelling reasons I was determined to tell Simons’s story. A former math professor, Simons is arguably the most successful trader in the history of modern finance. Since 1988, Renaissance’s flagship Medallion hedge fund has generated average annual returns of 66 percent, racking up trading profits of more than $100 billion (see Appendix 1 for how I arrive at these numbers). No one in the investment world comes close. Warren Buffett, George Soros, Peter Lynch, Steve Cohen, and Ray Dalio all fall short (see Appendix 2).
In recent years, Renaissance has been scoring over $7 billion annually in trading gains. That’s more than the annual revenues of brand- name corporations including Under Armour, Levi Strauss, Hasbro, and Hyatt Hotels. Here’s the absurd thing— while those other companies have tens of thousands of employees, there are just three hundred or so at Renaissance.
I’ve determined that Simons is worth about $23 billion, making him wealthier than Elon Musk of Tesla Motors, Rupert Murdoch of News Corp, and Laurene Powell Jobs, Steve Jobs’s widow. Others at the firm are also billionaires. The average Renaissance employee has nearly $50 million just in the firm’s own hedge funds. Simons and his team truly create wealth in the manner of fairy tales full of kings, straw, and lots and lots of gold.
More than the trading successes intrigued me. Early on, Simons made a decision to dig through mountains of data, employ advanced mathematics, and develop cutting- edge computer models, while others were still relying on intuition, instinct, and old- fashioned research for their own predictions. Simons inspired a revolution that has since swept the investing world. By early 2019, hedge funds and other quantitative, or quant, investors had emerged as the market’s largest players, controlling about 30 percent of stock trading, topping the activity of both individual investors and traditional investing firms.2 MBAs once scoffed at the thought of relying on a scientific and systematic approach to investing, confident they could hire coders if they were ever needed. Today, coders say the same about MBAs, if they think about them at all.
Simons’s pioneering methods have been embraced in almost every industry, and reach nearly every corner of everyday life. He and his team were crunching statistics, turning tasks over to machines, and relying on algorithms more than three decades ago— long before these tactics were embraced in Silicon Valley, the halls of government, sports stadiums, doctors’ offices, military command centers, and pretty much everywhere else forecasting is required.
Simons developed strategies to corral and manage talent, turning raw brainpower and mathematical aptitude into astonishing wealth. He made money from math, and a lot of money, at that. A few decades ago, it wasn’t remotely possible.
Lately, Simons has emerged as a modern- day Medici, subsidizing the salaries of thousands of public- school math and science teachers, working to cure autism and expand our understanding of the origins of life. His efforts, while valuable, raise the question of whether one individual should enjoy so much influence. So, too, does the clout of his senior executive, Robert Mercer, who is perhaps the individual most responsible for Donald Trump’s presidential victory in 2016. Mercer, Trump’s biggest financial supporter, plucked Steve Bannon and Kellyanne Conway from obscurity and inserted them into the Trump campaign, stabilizing it during a difficult period. Companies formerly owned by Mercer and now in the hands of his daughter Rebekah played key roles in the successful campaign to encourage the United Kingdom to leave the European Union. Simons, Mercer, and others at Renaissance will continue to have broad impact for years to come.
The successes of Simons and his team prompt a number of challenging questions. What does it say about financial markets that mathematicians and scientists are better at predicting their direction than veteran investors at the largest traditional firms? Do Simons and his colleagues enjoy a fundamental understanding of investing that eludes the rest of us? Do Simons’s achievements prove human judgment and intuition are inherently flawed, and that only models and automated systems can handle the deluge of data that seems to overwhelm us? Do the triumph and popularity of Simons’s quantitative methods create new, overlooked risks?
I was most fascinated by a striking paradox: Simons and his team shouldn’t have been the ones to master the market. Simons never took a single finance class, didn’t care very much for business, and, until he turned forty, only dabbled in trading. A decade later, he still hadn’t made much headway.
Heck, Simons didn’t even do applied mathematics, he did theoretical math, the most impractical kind. His firm, located in a sleepy town on the North Shore of Long Island, hires mathematicians and scientists who don’t know anything about investing or the ways of Wall Street. Some are even outright suspicious of capitalism. Yet, Simons and his colleagues are the ones who changed the way investors approach financial markets, leaving an industry of traders, investors, and other pros in the dust. It’s as if a group of tourists, on their first trip to South America, with a few odd- looking tools and meager provisions, discovered El Dorado and proceeded to plunder the golden city, as hardened explorers looked on in frustration.
Finally, I hit my own pay dirt. I learned about Simons’s early life, his tenure as a groundbreaking mathematician and Cold War code- breaker, and the volatile early period of his firm. Contacts shared details about Renaissance’s most important breakthroughs as well as recent events featuring more drama and intrigue than I had imagined. Eventually, I conducted more than four hundred interviews with more than thirty current and former Renaissance employees. I spoke with an even larger number of Simons’s friends, family members, and others who participated in, or were familiar with, the events I describe. I owe deep gratitude to each individual who spent time sharing memories, observations, and insights. Some accepted substantial personal risk to help me tell this story. I hope I rewarded their faith.
Even Simons spoke with me, eventually. He asked me not to write this book and never truly warmed to the project. But Simons was gracious enough to spend more than ten hours discussing certain periods of his life, while refusing to discuss Renaissance’s trading and most other activities. His thoughts were valuable and appreciated.
This book is a work of nonfiction. It is based on first- person accounts and recollections of those who witnessed or were aware of the events I depict. I understand that memories fade, so I’ve done my best to check and confirm every fact, incident, and quote.
I’ve tried to tell Simons’s story in a way that will appeal to the general reader as well as to professionals in quantitative finance and mathematics. I will refer to hidden Markov models, kernel methods of machine learning, and stochastic differential equations, but there also will be broken marriages, corporate intrigue, and panicked traders.
For all his insights and prescience, Simons was blindsided by much that took place in his life. That may be the most enduring lesson of his remarkable story.
*Mercer no longer is Renaissance’s co‑CEO but he remains a senior employee of the firm.
Product details
- ASIN : B07P1NNTSD
- Publisher : Portfolio (November 5, 2019)
- Publication date : November 5, 2019
- Language : English
- File size : 12826 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Enabled
- Word Wise : Enabled
- Sticky notes : On Kindle Scribe
- Print length : 382 pages
- Best Sellers Rank: #66,189 in Kindle Store (See Top 100 in Kindle Store)
- #22 in Company Histories
- #32 in Biographies of Business Professionals
- #99 in Company Business Profiles (Books)
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About the author

Gregory Zuckerman is a Special Writer at The Wall Street Journal. He is an investigative reporter who writes about various investing and business topics.
Greg is the author of A Shot to Save the World: A Shot to Save the World: The Inside Story of the Life-or-Death Race for a COVID-19 Vaccine, published by PenguinRandomHouse’s Portfolio division October 2021.
Greg is also the author of The Man Who Solved the Market: How Jim Simons Launched a Quant Revolution, a New York Times and Wall Street Journal bestseller. The book, which is being translated into 17 languages, was shortlisted by the Financial Times/McKinsey and the Society for Advancing Business Editing and Writing as one of the best business books of 2019.
Greg also is the author of The Frackers: The Outrageous Inside Story of the New Billionaire Wildcatters, a national bestseller published October 2014 that describes how several unlikely individuals created an American energy renaissance that has brought OPEC to its knees. The Frackers was named among 2014’s best books by The Financial Times and Forbes Magazine. Previously, Greg wrote The Greatest Trade Ever: The Behind-the-Scenes Story of How John Paulson Defied Wall Street and Made Financial History, a New York Times and Wall Street Journal best seller published December 2010.
Greg and his two sons wrote Rising Above: How 11 Athletes Overcame Challenges in their Youth to Become Stars and Rising Above-Inspiring Women in Sports, books that are aimed at inspiring young readers with stories of how stars in various sports overcame imposing setbacks in their youth. The books were chosen by Scholastic Teacher magazine as top picks in 2016 and 2017.
Greg is a three-time winner of the Gerald Loeb award, the highest honor in business journalism. He won the Loeb Award in 2015 for a series of stories revealing discord between Bill Gross, founder of bond powerhouse Pimco, and others at the firm, stories that led to his departure. In 2012, Greg broke news about huge, disastrous trades by the J.P. Morgan trader nicknamed the “London Whale,” trades that resulted in $6.2 billion losses for the bank.
Greg appears regularly on CNBC, Fox Business and other networks and he makes appearances on radio stations around the globe.
Greg joined the Journal in 1996 after writing about media companies for the New York Post. He graduated from Brandeis University in 1988. Greg lives with his wife and two sons in West Orange, N.J., where they enjoy the New York Yankees in the summer, root for the Giants in the fall, and reminisce about Linsanity in the winter.
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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.
Unfortunately, at the end of the book, you still don't know much of the market inefficiencies identified by these quants and that have created enormous wealth for Mr. Simons and his team. But, the fact that these were kept secret for so many years and the innovative machine learning approaches were successfully developed and used in trading first time indicates Mr. Simons not only brilliant scientist but also very successful businessman and also great humanitarian (where he's channelled some of his wealth for meaningful causes like autism research, understanding origin of universe rather than cambridge analytica to replicate lies to manipulate millions on scale with the help of no-funny felonious gru and associated clowns.)
I don’t understand why some reviewers complain about the book not revealing any trading secrets. What were they expecting? The master code driving all RenTec trades? I think the book gives enough details for anybody to understand what these guys were into as they developed their trading systems. I know I personally would have loved having this book in my hands 8+ years ago. It would have saved me a lot of time. That’s because a book like this helps you understand how far behind you are in the process of developing a trading system (and, as a corollary, how impossible it would be for you -an individual investor- to beat these guys). We are talking about an army of PhDs using machine learning and speech recognition models to try to identify market patterns...in the 80s!!! Today, they have (real time) data on every potentially measurable thing on earth (and out of it) and they are putting that on the hands of the most talented people with the most advanced techniques! The most appealing thing of all is that they are not even high frequency traders. They are an investment firm (i.e. they are on the buy side). They don’t play with the advantage of knowing how orders are coming in and trading against that. Now, that’s probably the only advantage they don’t have. There are a lot of very popular quantitative funds out there and their returns are not even close to those of RenTec. It seems as if this was yet another example of a the winner-takes-all situation. So, RenTec would be like the Microsoft of the trading firms.
As I’m writing this, we are in the middle of the coronavirus lockdown in the US. It’s April 6th and some are saying that we are hitting the apex. Hopefully that’s the case. Anyways, the reason why I mention this is because anyone who’s been following the markets in the last few years know that the last couple of years have been extremely volatile compared to the previous 10 years. So, reading a book like this in that context is a extremely humbling experience. It reinforced my conviction that the most an individual investor with limited time and a full time job can do is asset management. By that I mean that it will be very difficult for a retail investor to beat the S&P 500 on an absolute basis. So, the best they can do is to try to beat it on a risk-adjusted basis (based on metrics like the Sharpe or Sortino ratios for example).
So, back to the book. Before Jim Simons started his firm, he had been working for the government as a code breaker. Apparently, the statistical/mathematical techniques used in code breaking are similar to those used in speech recognition (which is what Mercer and Brown were doing for IBM before joining RenTec). In particular, they were using Hidden Markov Models to predict sequences of data. In other words, they were using an extremely advanced form of Technical Analysis. And that was just when they were starting in the 80s.
At the beginning of this review I said that this could also be tagged as a book on politics. And that’s because of the critical role that Bob Mercer played in the last presidential elections. It would not be far-fetched saying that Mercer is who made Donald Trump president of the US. The author goes into a lot of detail describing the developments that took place during the presidential campaign. It made me aware of how flawed the system could be. Mercer just happened to be a shy and nerdy scientist how found himself so rich that he could spare millions of dollars financing his libertarian hobbies. Unfortunately, he was smart enough not to fund the libertarian party but the Republican one. My final take on him is that he was just a conservative and racist person.
Finally, I found the last chapter of the book really interesting. In there, the author explains the impact that these quantitative funds are having on the money management industry and the market as a whole. He does seem to confuse an important concept though. He likens “active” managers to “traditional” managers. And he confronts active managers vs quantitative ones. As everyone knows, quantitative funds can be actively managed (RenTec’s Medallion fund is actually a perfect example). The only difference is that -in these cases- the trading decisions are made by an algorithm, not a person. On the other hand, you can perfectly have traditional/discretionary managers that are quite passive (Warren Buffett). In any case, this is actually not that important since the reader can still get the main point the author is trying to make (which is that discretionary managers are nowadays outperformed by systematic/quantitative algorithmic funds). He actually gives a bunch of good examples as to how this occurs.
In short, a very enjoyable and interesting read.
Top reviews from other countries

The key insight of the book is that Jim Simons and his colleagues realised that markets were not efficient, in contrast to the mainstream view of market efficiency, and that the inefficiency could be exploited for profit. Lots of it. And they were right.
So this book is well worth reading. It’s well written and it skips along at a relatively decent pace. I don’t think it’s a five star book on my scale, and I doubt it will quite make the top step in the FT Business Book of the Year, but it is still a book you probably do want to read sometime soon if you work in and around trading financial markets.

What I liked - Useful biographies of key team players that advanced the success of Jim Simons's Medallion hedge fund and the Renaissance technologies founder. Good index enabling links and cross reference of hedge fund events - Global Alpha Cliff Asness and the Quant quake (refer Greg Smith's why I left Goldman Sachs). Capital and VC involvement described from David Sussman's refusal to GAM's agreement.
What is missing - Old style hold strategy with long event lines was robbable by the quant funds whose techniques was to reduce the event time lines and increase the number of trades. Profitability per trade would diminish but the task was to increase exponentially the number of trades in managed pattern moves - page 223 Medallion was trading up to 300,000 contracts a day. Simplification graphics would help to better grasp essential features of machine managed control like event line time reduction: page 101 halts long term trades, page 113 reduction from 1 week and 1/2 to 1 day and 1/2, page 190 trades average hold 2 days, page 271 hold time 1 or 2 days increases to 1 or 2 weeks. Sorting the Sharpe ratio and evidencing its shape change through a year eventually pushes the ratio out to 7.5 needs illuminating.
The future - investor nervousness. Retrenchment trades and fake chaff news leading to daily 100 point volatility swings in the Dow, Nikkei, Dax, are good for quant funds but negative for investor confidence and micro second entry and exit decisions - IPO management becomes precarious and issues are pulled.

Investors like Soros have given up a lot more of their methods - in part because their strategies relied on directional views of macroeconomic factors are harder to replicate in the future. You would assume that if you had knowledge of the code used at Renissance today you could rack up some pretty mean trading profits - since that is exactly what they are doing!
Still I think this is a must for any mathematician, and offers insight to one (of many) ways that mathematics can be applied to the world we live in.
Personal highlight? The joke at the start of Ch 2:
Q: What's the difference between a PhD in mathematics and a large pizza?
A: A large pizza feeds a family of 4.

