Hello, welcome to Investor’s Bookshelf. Today I will explain to you a book titled The Man Who Solved the Market. Do you know who is the most profitable investor in the world today? If you think it is Warren Buffett, George Soros, or Ray Dalio, then you are mistaken. This person is a mathematician who once collaborated with the famous Chinese-American mathematician Shiing-Shen Chern to develop a renowned geometric theory, and he also won the highest award in mathematics. His name is Jim Simons. From 1988 to 2018, the fund he managed achieved an annualized return of 66%, earning more than 100 billion dollars through trading, setting the highest record of the time.
Why would a mathematician switch careers to become a financial investor? And what enabled him to dominate the financial markets? This is the secret the book wants to reveal to us. You might guess that Simons’ success has something to do with mathematics. That’s right. The method Simons used is called quantitative investing, which is different from traditional investment methods. For example, traditional investing usually encourages people to reduce trading frequency and minimize transaction costs. Quantitative investing, however, takes the opposite approach—it is not afraid of frequent trading and even relies on large-scale, high-frequency transactions to make profits. Why is that?
Because quantitative investing builds mathematical models and uses computer programs to execute trades. As long as its algorithms have more than a 51% chance of winning, the outcome of a single trade does not matter. Instead, only through large-scale, high-frequency trading can small gains accumulate into substantial profits. Precisely for this reason, compared with traditional investing, quantitative investing emphasizes standardization and systematization, avoiding the influence of human emotions and subjective bias, which allows it to surpass traditional investing.
The relationship between the two can be summarized as follows: Traditional investing relies mainly on personal experience and subjective judgment for decision-making, depending on the investor’s research into listed companies. Quantitative investing, by contrast, depends on algorithmic models to scan the market and then makes judgments based on the scanning results. Its decision-making has broader applicability.
Therefore, quantitative investing has rapidly developed over the past 30 years, becoming a major trend in financial markets, with Jim Simons and his Renaissance Technologies as outstanding representatives. However, as a financial investor, Simons believed in “making a fortune in silence,” unwilling to let competitors discover his money-making secrets. He always maintained a low profile with the media, never disclosing his investment methods or entrepreneurial details. It was not until the publication of this book that a corner of the mystery was lifted.
The author of this book is Gregory Zuckerman, a well-known American financial journalist. He has won the prestigious Gerald Loeb Award three times and is the author of The Greatest Trade Ever and The Frackers. Through his relentless effort—conducting more than 400 interviews with over 30 current and former Renaissance employees, as well as conversations with Simons’ family, friends, and Simons himself—he finally wrote this “debut biography” of Simons. The book introduces the technical principles of quantitative investing, discusses Simons’ life experiences, and reveals Renaissance Technologies’ workplace secrets. It is a work that unveils the mysteries of quantitative investing and narrates the legendary life of the “King of Quants.”
Next, I will explain this book from two perspectives. First, why can quantitative investing create miraculous profits in financial markets? In other words, how did quantitative funds, represented by Simons, surpass traditional investment institutions? Second, why was Simons able to build such a powerful quantitative investment team? As a brilliant mathematician, why did Simons decide to switch halfway into financial investing, and what drove him and his team to create such an extraordinary miracle?
I hope that after listening, you will not only understand the important trend of financial investing in today’s world but also gain inspiration about courage, imagination, and spiritual freedom from Simons’ unconventional life trajectory.
Part One
First, let us look at why quantitative investing can create miraculous profits in financial markets. In other words, what advantages does quantitative investing have compared with traditional investing, and what unique features set Jim Simons’ quantitative fund apart?
Let’s begin with the story of one man. His name was Edward Thorp, the first modern mathematician to use quantitative strategies for large-scale investing, predating Simons. In the 1960s, Thorp developed a strategy through mathematical research that increased the probability of winning the blackjack game. He tested this method in Las Vegas casinos and swept the tables, forcing many casinos to change their rules. Later, Thorp applied his method to financial markets, using computer programs to trade stock warrants and founding a professional investment fund. By the late 1980s, Thorp’s fund had grown from $1.4 million to $300 million, with an after-fee annualized return exceeding 15%, while Simons’ fund was only managing $25 million at the time. Unfortunately, Thorp became entangled in a financial scandal, which led to his fund’s liquidation and ended his ambitions. It was at this point that Simons’ fund entered the fast lane, continuing for the next 30 years to write the legend of quantitative investing.
Like Simons, Thorp was a pioneer in using mathematical models and automated programs for quantitative investing. They both believed that financial market prices are unpredictable, influenced by numerous variables, including some difficult-to-define ones that may be unrelated to company fundamentals. Therefore, it is unnecessary to understand every cause of market fluctuations. The key is to find systematic methods that adapt to the market and produce sustained profits. This is one of the core ideas of quantitative investing.
So, what exactly differentiates quantitative investing from conventional investing? American financial expert Rishi K. Narang, in his book Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading, gives a clear answer. He argues that the greatest features of quantitative investing are systematization and automation—that is, conducting rigorous research and executing trades systematically. The focus lies in the method of execution, not in whether mathematical calculations are used or which investment philosophy is followed. In other words, whether you believe in Buffett’s value investing or prefer trend-following momentum investing, both can be transformed into quantitative strategies.
For example, suppose someone prefers undervalued stocks—that is, those with a low price-to-earnings ratio (P/E). In theory, he could calculate each company’s P/E ratio, manually search for the cheapest stocks, and buy them—this would be traditional investing. Or he could write a computer program to scan all P/E ratio databases, identify pre-defined stocks, and automatically buy them—that would be quantitative investing. The processes differ, but the purchased stocks and the reasons for purchasing are the same.
Of course, with computer assistance, quantitative investing can accomplish what traditional investing cannot. For instance, someone may believe that stock price movements exhibit inertia—that upward or downward trends continue for some time. Thus, by quickly buying a stock when it rises and selling it soon after, one could often capture price spreads. But to spread risk, one would need to buy many such stocks—something nearly impossible manually. With computer programs, however, one can instantly retrieve thousands of stocks’ prices, trading volumes, and P/E ratios, and then execute trades based on pre-set rules. This greatly improves efficiency. It’s like making cars: some assembly steps can be done by hand or machine equally well, but for precision or speed, machines are far more efficient.
In this way, quantitative investing gains several advantages over traditional investing. Besides speed and breadth, it can perform backtesting. That is, once you develop a profit-making strategy, you can test it against historical data to see how it would perform, thereby evaluating whether the strategy might work. More importantly, quantitative investing can overcome human psychological biases. By strictly executing strategies via computer programs, it eliminates the arbitrariness of human judgment and avoids errors caused by emotional swings, thereby increasing the likelihood of stable returns.
Because of these factors, quantitative investing has rapidly developed over the past 30 years. A large number of fund companies using such methods have emerged. The book notes that by 2019, the global scale of quantitative investment funds exceeded $2 trillion, with hedge funds and quantitative trading making up 30% of the market—and this trend is still expanding.
So how did Simons’ investment fund stand out among them? Let us look at his entrepreneurial journey. Originally a mathematician, he started his financial investment venture in 1978. At first, he was not successful. Although his partners were brilliant mathematicians, the company failed to find a stable profit model, making almost no money during the first ten years. It wasn’t until 1989, when partner Leonard Baum encouraged the firm to shift focus to high-frequency trading, that the company began to turn profitable. In 1993, two machine-learning experts joined, bringing key technical breakthroughs that rapidly expanded the fund’s scale. Even when the fund grew to $5–10 billion, it still achieved consistently high returns, finally breaking through.
In the new century, Renaissance Technologies rose to prominence on Wall Street. People were astonished that even during the subprime crisis of 2007, its Medallion Fund still delivered an 85% return. At that time, statistics showed that from 1988 to 2018, the fund’s annualized return was an astounding 66%, earning more than $100 billion through trading. Simons thus earned the title “The King of Quants.”
So, what was Simons’ secret to success? Since Simons placed great importance on guarding trade secrets, Renaissance employees had to sign non-disclosure agreements up to 30 pages long, effective even after retirement, leaving outsiders only able to speculate. Based on this book and related materials, I summarize his success into three key points.
First, focus on market anomalies. The book explains that Renaissance monitored financial markets with computer programs, just as astronomers use advanced instruments to observe space. Once an anomaly was detected—appearing frequently enough and statistically significant—it could serve as a trading signal for backtesting. If proven successful, trades would then be executed via computer. In this way, they captured opportunities invisible to most investors. Of course, Simons and his team did not believe in absurd patterns—for example, the idea that stocks with ticker symbols starting with “A” perform better. But they did rely on signals such as using the ratio of trading volume to price changes over the past three days to reflect market trends. Or patterns like Monday’s prices often extending Friday’s trend, only to reverse on Tuesday.
Second, adopt high-frequency trading. As mentioned earlier, in 1989, Baum pushed Renaissance to shift toward short-term, high-frequency trading, enabling the Medallion Fund to achieve steady profits. Baum’s reasoning was simple: casinos host countless games daily, and as long as they win slightly more than half, the house always profits. The Medallion Fund worked the same way: as long as daily trades had a greater than 51% chance of making money, the fund would profit consistently. This is similar to casino profits—repeated games ensure the law of large numbers works in their favor.
When this method was introduced, Renaissance had only a little over $20 million left. They sharply increased their trading frequency, reducing average holding time from a week and a half to just a day and a half. The effect was immediate: in 1990, their after-fee return reached 55%. By 1993, fund assets reached $280 million; in 1994, returns hit an astonishing 71%. From then on, Renaissance’s legendary journey began. In later years, the company executed 150,000–300,000 trades daily, becoming a magical money-making machine.
Third, continual self-evolution. As mentioned earlier, in 1993, two machine-learning experts joined and enabled critical breakthroughs that rapidly expanded the fund’s scale. According to the book’s author, Simons’ team may have employed a mathematical tool called the Hidden Markov Model to predict stock prices. In mathematics, a Markov model refers to a sequence of events in which the probability of the next event depends on the current state. A Hidden Markov Model (HMM), however, assumes that the event sequence itself is unobservable, making it a more complex double-stochastic process. Without going into academic details, suffice it to say that its dynamics closely resemble financial market price fluctuations.
So why is the Hidden Markov Model powerful in investing? According to Lu Chen, a Ph.D. in applied mathematics at New York University and a financial expert, unlike typical quantitative models that function like parasites dependent on a fixed historical environment, the HMM adapts to changing market conditions, constantly adjusting structural parameters within the model. It uses change to offset change, making it the highest form of evolution among quantitative models. Just like in biological evolution: dinosaurs once ruled as the strongest predators, but they went extinct when the environment changed and they could not adapt. Cockroaches, on the other hand, continuously adapted and survived. This is the strength of the HMM.
At this point, you might ask: no matter how powerful Renaissance’s investment methods are, what do they have to do with ordinary people? Indeed, for those not engaged in professional finance, quantitative investing may not seem directly relevant. Yet even so, we can still draw inspiration from their strategies. For example, be highly sensitive to anomalies—quantitative investing profits by detecting irregularities. Or dare to break conventions—Simons’ fund embraced high-frequency trading, something traditional investing avoided, to achieve stable high returns. Most importantly, keep evolving with the times—just as the Hidden Markov Model embodies, the Medallion Fund continuously adjusted its strategies to adapt to shifting markets. This adaptability may well be the most important reason Simons and his team achieved such extraordinary success.
Part Two
But the next question is: why was Jim Simons able to build such a powerful quantitative investing team, and what drove him and his colleagues to create miracles in the financial markets?
In Simons’ life story and entrepreneurial journey, one can see many highlights—genius, success, and wealth. But I particularly agree with one perspective: the central keyword of Simons’ life is not genius, not success, not wealth, but freedom. Simons moved freely across many rigidly defined roles, embodying a rare sense of freedom. This freedom stemmed from extraordinary courage and imagination, which became the core driving force behind his life’s turning points and business success.
Simons can truly be called a genius. Before entering finance, he was an outstanding mathematician. He received his Ph.D. at the age of 24, worked for the U.S. Department of Defense on code-breaking, and at 30 became chair of the mathematics department at Stony Brook University. In 1976, at age 38, Simons won the Oswald Veblen Prize of the American Mathematical Society for his work with Shiing-Shen Chern on the “Chern–Simons Theory,” which represented the highest level of research in geometry. This theory has applications in many areas of physics, such as condensed matter, string theory, supergravity, and even quantum computing research. It cemented Simons’ status as a master in mathematics and physics.
But in 1978, no one expected Simons to leave academia and start his own investment company. His family, friends, and colleagues were shocked. They believed that someone like Simons would not care for money, since it might distract him from loftier pursuits. Although few said it out loud, many thought he was wasting his talent. The book records that at a family gathering, Simons’ father once told one of his colleagues: “I would rather say I have a professor for a son than a businessman.” Clearly, he was deeply disappointed in Simons’ choice.
From this perspective, Simons’ decision can only be described as brave. He had to endure skeptical looks from those around him, and as a world-class scholar, if he failed, it would be hard to face himself. In fact, the fund struggled in its first decade. From 1979 to 1982, it made $43 million in profit and nearly doubled its capital. But soon after, it suffered a series of setbacks, partnerships broke apart, and by 1989 only about $20 million remained. Simons had to halt many investments and was in serious difficulty; colleagues speculated he might shut down the company. Yet unexpectedly, Simons persisted and eventually saw light at the end of the tunnel.
What sustained Simons’ venture until the turnaround? Of course, there was the desire for wealth. According to the book, Simons had a strong appetite for money. He liked buying fine things, though he did not seek a lavish lifestyle. He understood that wealth brings independence and influence. Friends believed that Simons wanted to use money to change the world.
But more importantly, Simons approached financial markets with a mathematician’s curiosity and imagination. Years before starting his company, he had published an article titled A Probability Model for Predicting Stock Market Behavior, arguing that one could find trading methods with 50% annualized returns. He always believed that prices followed patterns, and he wanted to identify them with mathematical models. In a sense, Simons treated making money in financial markets as solving a mathematical puzzle. Thus, although Renaissance Technologies expanded into currencies, gold, futures, and eventually stocks, the key people Simons recruited were often mathematicians. His ability to attract and lead a team of brilliant minds came precisely from this curiosity and imagination for solving such puzzles. Since the puzzle was dynamic, Renaissance’s strategies kept evolving, and its core team kept changing.
At first, Simons relied on his old colleague Leonard Baum from the Defense Department. After 1984, models developed by another partner, James Ax, dominated most of the firm’s trading. After 1986, the company hired people like René Carmona and Elwyn Berlekamp to strengthen its automated trading systems. Many of these figures were top mathematicians, some with algorithms named after them. As a world-class mathematician, Simons had the vision and connections to assemble a formidable quantitative investing team and push it forward in solving financial puzzles.
Finally, in 1989, Renaissance turned the corner. Berlekamp steered the Medallion Fund toward short-term, high-frequency trading, delivering annualized returns of 33% for three consecutive years and reaching $100 million under management. In 1993, the company decided to move into equities. Simons recruited two machine-learning experts from IBM, Peter Brown and Robert Mercer, who helped achieve breakthrough progress. They developed a stock-trading system that could autonomously learn, adapt, and seek optimal opportunities—exactly what Simons had dreamed of years earlier. With this system, the company entered an explosive growth phase. From 1994 to 1999, annual returns averaged over 50%. By 2000, the Medallion Fund posted a post-fee return of 99%, and assets rose to nearly $4 billion—marking the coronation of the “King of Quants.”
So how did Simons lead such brilliant, strong-willed academic geniuses? He believed scientists and mathematicians needed debate and idea-sharing. While most investment firms allowed researchers to work in isolation, Simons insisted on one unified trading system for the Medallion Fund. All employees had access to the source code behind its algorithms and could attempt to improve it. At Renaissance, peer pressure in a healthy form was a crucial motivator. Researchers and programmers spent significant time presenting results, trying to impress colleagues—or at least avoid embarrassment. If you made little progress, you felt pressure; solving tough problems was the best way to prove your worth.
Of course, financial incentives were also vital. For example, when Simons lured Mercer and Brown from IBM, he doubled their salaries at the start and promoted them to partners within two years, giving them equity. Regular employees, besides salaries, received bonuses every six months. Whether you discovered new trading signals or processed data effectively, excellent work was richly rewarded. As Renaissance grew rapidly, some employees earned millions or even tens of millions annually, creating a wealthy class of engineers and scientists. Many bought mansions near the company. Simons himself bought a villa covering 57,000 square meters with views over Long Island’s coastline and bay. He owned a $100 million yacht equipped with a formal dining room for 20, a wood-burning fireplace, a spacious hot tub, and a grand piano. This symbolized his financial freedom and vividly illustrated how “knowledge creates wealth.”
Yet in my view, Simons’ use of wealth reflected his deeper spiritual freedom. By 2008, his net worth was about $8.5 billion, and by 2022 it had risen to $28.6 billion. Named by the Financial Times as “the world’s smartest billionaire,” Simons and his wife co-founded the Simons Foundation, with an annual budget of $450 million to support philanthropy in medicine, education, and scientific research.
For example, he helped establish the Math for America program, which provides $15,000 annual stipends to more than 1,000 outstanding math teachers in New York City, runs workshops and seminars, and built a passionate, high-level teaching community. The Simons Foundation also focuses on autism. It created a genetic sample bank of 2,800 families to advance targeted treatments, and invested $100 million, with all returns dedicated to autism research and improving patients’ lives.
The Simons Foundation is the second-largest private funder of basic scientific research in the United States. His philanthropy reflects a scientist’s drive to explore the unknown. In 2014, the foundation hired Princeton astrophysicists to tackle the timeless question of how the universe began. It invested $75 million to build a giant observatory in Chile’s Atacama Desert at an altitude of 5,000 meters, equipped with an advanced telescope array. This site is ideal for measuring cosmic microwave background radiation, searching for evidence of the Big Bang, and deepening humanity’s understanding of the universe and the origins of life.
As the book notes, Simons spent much of his life solving puzzles. Early on, he studied mathematical problems and deciphered military codes. Later, he sought to solve the mysteries of financial markets. After acquiring enormous wealth, he devoted himself to studying the origins of the universe. This insatiable curiosity and exploration of the unknown was both the goal of his fortune and the driving force and tool to achieve it. What an enviable kind of freedom that is!
Conclusion
At this point, our interpretation of the book is coming to an end. Let us recap.
In Part One, we discussed the differences between quantitative investing and traditional investing. The key is not whether mathematics is used or which investment philosophy one follows, but rather the method of execution. The greatest feature of quantitative investing is that, with the help of computer technology, it achieves systematization and automation in trading. Whether you believe in Buffett’s value investing or prefer momentum investing, both can be transformed into quantitative strategies. Of course, quantitative investing can also do things that traditional investing cannot, such as operating with greater speed, broader coverage, and convenient backtesting. More importantly, it eliminates the arbitrariness of human judgment, avoids mistakes caused by emotional swings, and is more likely to achieve stable returns.
That said, it is worth noting that quantitative trading is only a tool and a method of investing. No method guarantees profits without losses. If one were to summarize Simons’ core principle of quantitative investing, it would be the lesson embodied in the Hidden Markov Model: “Things are always changing, and one must constantly adapt to the present situation.” In other words, there is no secret formula for success—only continual evolution; no universal equation—only constant revision. This may well be the most important reason Simons and his team succeeded. For ordinary people, the lesson is clear: markets and risks are always changing, and investing must be approached with utmost caution.
In Part Two, we reviewed Simons’ life and entrepreneurial journey. The point I want to convey is this: the central keyword of Simons’ life is not genius, not success, not wealth, but freedom. He roamed across many rigidly defined roles, embodying a rare sense of freedom. This freedom stemmed from extraordinary courage and imagination, which became the driving force behind his turning points and career achievements.
As a highly accomplished mathematician, Simons’ decision to switch to finance midway through his career can only be described as brave. He not only had to face skeptical looks from those around him, but if he failed, it would have been hard to forgive himself. What sustained Simons’ entrepreneurial path? Beyond the motivation of money and wealth, the more important factor was his conviction that prices followed patterns. In a sense, Simons treated investing as a mathematical puzzle. His curiosity and imagination about financial markets enabled him to assemble a world-class quantitative team and, through relentless effort, ultimately achieve success.
It is important to remember that although Simons was a mathematical genius, his specialty was theoretical mathematics, not applied mathematics. In investing, he had no more advantage than an ordinary person. He never took formal finance courses, nor did he study business systematically. At 40, he was only beginning to explore the capital markets, and even ten years later, he had made little progress. Yet in the end, he led a group of fellow “investment novices” who were scientists, and together they created a new miracle. They did this by exploring the underlying logic of things, refusing to blindly follow existing methods, and relying on their expertise and perseverance.
In this story, the qualities most worth remembering are persistence in exploration, independent thinking, and a commitment to truth. These, rather than genius or wealth, are the real lessons we should take from Jim Simons’ remarkable journey.
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