Thread by @ReformedTrader

1/ The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution (Gregory Zuckerman)

Thread with quotes from the book and links to related podcasts and articles

amazon.com/Man-Who-Solved…

2/ Here are links to two podcast interviews with the author:

Animal Spirits

https://twitter.com/ReformedTrader/status/1192851044092411904

Masters in Business

https://twitter.com/ReformedTrader/status/1190806162687442944

3/ "Simons inspired a revolution that has swept the investing world.

"MBAs once scoffed at the thought a systematic approach to investing, confident that they could hire coders if ever needed. Today, coders say the same about MBAs, if they think about them at all." (p. xvii)

4/ "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." (p. xx)

5/ "Scientists and mathematicians are trained to dig below the surface of the chaotic natural world to search for unexpected simplicity, structure, and even beauty. The emerging patterns and regularities are what constitute the laws of science." (p. 44)

6/ This worked for Simons (eventually..............), but there's some skepticism about this picture of how science works. The same observations are compatible with multiple theories such that a theory is as much based on metaphysics as it is on evidence.

amazon.com/Structure-Scie…

7/ "Baum determined that, over stretches of time, some currencies, especially the Japanese yen, seemed to move in steady, straight lines... there did seem to be some inherent structure in the markets.

"A mathematical model might be developed to map out and ride trends." (p. 49)

8/ "Simons and Baum had relied on crude trading models as well as their own instincts, an approach that had left Simons in crisis.

"He shared a new goal: building a sophisticated trading system fully dependent on pre-set algorithms that might even be automated." (p. 56)

9/ "Simons recruited his sister-in-law and others to input prices into the database and test strategies based on both mathematical insights and intuition. Many of the tactics they tried focused on momentum strategies, but they also looked for potential correlations." (p. 57)

10/ "Regulators were grilling him, but Simons hadn't meant to accumulate so many potatoes; he couldn't even understand why his computer system was buying so many of them.

"Regulators closed out his potato positions, costing Simons and his investors millions of dollars." (p. 58)

11/ "Soon, he and Baum lost confidence in their system. They could see the trades and were aware when it made and lost money, but Simons and Baum weren't sure *why* the model was making its decisions. Maybe a computerized trading model wasn't the way to go after all." (p. 58)

12/ "Building formulas was difficult and time-consuming, and the gains figured to be steady but never spectacular. By contrast, quickly digesting the news ticker, studying newspaper articles, and analyzing geopolitical events seemed exciting and far more profitable." (p. 61)

13/ "Ax's predictive models had potential, but they were quite crude. The troves of data Simons and others had collected proved of little use, mostly because it was riddled with errors and faulty prices." (p. 73)

14/ "Straus began the painstaking work of checking his prices against yearbooks produced by commodity exchanges, futures tables, and archives of newspapers.... He had transformed into a data purist, foraging and cleaning data the rest of the world cared little about." (p. 76)

15/ "Carmona's method wasn't based on a model Simons could reduce to a set of standard equations, and that bothered him. The results came from running a program for hours, letting computers dig through patterns and then generate trades.... it just didn't *feel* right." (p. 85)

16/ "Ax had access to more extensive pricing information than his rivals, thanks to Stras's growing collection of clean historic data. Since price movements often resembled those of the past, it enabled more accurate determinations of when trends were likely to continue." (p. 95)

17/ "Ax relied on his instincts for a portion of the portfolio, edging away from trading based on the sophisticated models he and Straus had developed.

"It seemed quantitative investing didn't come naturally, even to math professors." (p. 99)

18/ "Old friends and investors were calling, worried about the steep losses. Some couldn't take the pain and withdrew their cash.

"The only way he could keep the firm alive was to curtail long-term trades... reassuring investors they'd develop new and improved tactics." (p. 101)

19/ "The Axcom team wanted their fund to trade so frequently that it could score big profits by making money on a bare majority of its trades. With a slight statistical edge, the law of large numbers would be on their side, just as it is for casinos." (p. 108)

20/ "Simons and his researches didn't believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to anomalies signaling opportunity. They also didn't think it made sense to worry about why these phenomena existed." (p. 109)

21/ "Floor traders and brokers got right back into their positions after the weekend, or subsequent to news releases, helping prices rebound. Medallion's system would buy when these brokers sold and sell back to them as they became more comfortable with the risk." (p. 110)

22/ "The correlation of price moves in deutsche marks between any two consecutive time periods was as much as 20% (10% for other currencies, 7% for gold, 4% for commodities, and 1% for stocks).

" 'The time scale doesn't seem to matter... We get the same anomaly.' " (p. 110)

23/ "Deutsche marks—and even stronger correlations found in yen—were so unexpected that the team felt the need to understand why.

"Central banks have a distaste for abrupt currency moves, which can disrupt economies, so they step in to slow sharp moves." (p. 111)

24/ "Some of the trading signals weren't especially novel or sophisticated. But many traders had ignored them. Either the phenomena took place barely more than 50% of the time, or they didn't seem to yield enough in profit to offset the trading costs." (p. 112)

25/ "The firm implemented its new approach in late 1989.... The results were almost immediate, startling nearly everyone. They did more trading than ever, cutting average holding time to just a day and a half from a week and a half, scoring profits almost every day." (p. 113)

26/ "Only three traders on the exchange focused on Canadian dollar futures, and they worked to [front-run] customers naive enough to trade with them.

"It was one of Wall Street's oldest tricks, but Berlekamp and his fellow academics were oblivious to the practice." (p. 113)

27/ "If Stotler went under, their account would be frozen. In the weeks it would take to sort out, tens of millions of dollars of futures contracts would be in limbo, likely leading to devastating losses.

"Simons and his firm narrowly escaped a likely death blow." (p. 114)

28/ "For all the gains, few outside the office shared the same regard for the group's approach. When Berlekamp explained his firm's methods to business students on Berkeley's campus, some mocked him....

"Colleagues avoided or evaded commenting." (p. 115)

29/ "The team had worked diligently to remove humans. Now Simons was saying he had a good feeling about gold and wanted to tweak the system?

"Jim believed the fund should be managed systematically, but he was trading gold or copper, thinking he was learning something." (p. 116)

30/ "Simons never ordered any major trades, but he did get Berlekamp to buy oil call options as 'insurance' as the Gulf War began and scaled overall positions back.

"We must still reply on human judgment and manual intervention to cope with a sudden, drastic change." (p. 117)

31/ "[Stat arb, 1988] hit heavy losses... Senior management never had been comfortable with computer models, and jealousies had grown... the group was shut down.

"Morgan Stanley had squandered some of the most lucrative strategies in the history of finance." (p. 131)

32/ "The few members of the Wall Street establishment aware of the approach mostly scoffed at it. Relying on inscrutable algorithms, as Simons was doing, seemed ludicrous, even dangerous. Some called it 'black box' investing—hard to explain, likely masking serious risk." (p.137)

33/ "Laufer made an early decision that would prove extraordinarily valuable: Medallion would employ a single trading model rather than maintain various models for different investment and market conditions.

"A single model could draw on Straus's vast trove of pricing data...

34/ "detecting correlations, opportunities, and other signals across various asset classes. Narrow, individual models, by contrast, can suffer from too little data.

"A single, stable model based on core assumptions about how prices and markets behave would make it easier..."

35/ "to add new investments later on. They could even toss investments with relatively little trading data into the mix if they were deemed similar to other investments Medallion traded with lots of data." (p. 142)

36/ "Laufer and Patterson began developing sophisticated approaches to direct trades to various futures exchanges to reduce market impact.

"They reduced the impact on prices [splitting orders and] by focusing on the [intraday] periods when there was the most volume." (p. 150)

37/ "Like most investors, Simons became nervous when his fund went through rocky times. In a few rare circumstances, he reacted by paring positions. On the whole, though, he maintained faith in his model, recalling how difficult it had been to invest using instincts." (p. 152)

38/ "Over time, Simons and his team came to believe that these errors and overreactions were at least partially responsible for their profits and that their developing system seemed uniquely capable of taking advantage of the common mistakes of their fellow traders." (p. 153)

39/ "A staffer caught someone standing above the pits watching Medallion's traders. The spy would send hand signals enabling a confederate to get in before Simon's fund took action. Others seemed to have index cards listing the times of day Medallion usually transacted." (p. 156)

40/ "Patterson required a few things from his hires. They needed to supersmart, with identifiable accomplishments. He steered clear of Wall Street types. He didn't have anything against them, per se; he was just convinced he could find more impressive talent elsewhere." (p. 168)

41/ "If someone left a bank or hedge fund to join Renaissance, they'd be more inclined to bolt at some point for a rival than someone without a familiarity with the investment community. That was crucial because Simons insisted that everyone at the firm share work." (p. 168)

42/ "Not completing desired trades [due to leverage and shorting constraints] resulted in more than just poor performance. The factor-trading system generated complicated and intertwined trades, each necessary to score profits while keeping risk at reasonable levels." (p. 188)

43/ "A data-entry error caused the fund to purchase five times as many futures... analysts attributed the price surge to fears of a poor wheat harvest.

"Any time you hear financial experts talking about how the market went up because of such and such—it's all nonsense." (p. 199)

44/ "Scientists and mathematicians need to interact, debate, and share ideas to generate ideal results. Simons's precept might seem self-evident, but in some ways, it was radical. (Talented quants can be among the least comfortable working with others.)" (p. 199)

45/ "Simons created a culture of unusual openness. When they ran into frustrations, the scientists tended to share their work and ask for help rather than move on to new projects, ensuring that promising ideas weren't 'wasted,' as Simons put it." (p. 200)

46/ "Many at Renaissance didn't seem to prioritize wealth. When computer scientist Peter Wienberger interviewed for a job in 1996, he stood in the parking lot, sizing up the researchers he was about to meet.

"It was a lot of old, crappy cars. Saturns, Corollas, Camrys." (p. 201)

47/ "It's not that they wanted trades that didn't make any sense; it's just that these were the statistically valid strategies they were finding. Recurring patterns without apparent logic had an added bonus: They were less likely to be discovered and adopted by others." (p. 204)

48/ "Often, the Renaissance researchers' solution was to place such head-scratching signals in their trading system but to limit the money allocated to them, at least at first, as they worked to develop an understanding of why the anomalies appeared." (p. 204)

49/ "Never place too much faith in trading models.

"If a strategy wasn't working or when market volatility surged, Renaissance's system tended to automatically reduce positions and risk.

"By contrast, when LTCM's strategies floundered, the firm often grew their size." (p. 213)

50/ "LTCM's basic error was believing its models were truth. We never believed our models reflected reality—just some aspects of reality." (p. 213)

51/ "The losses shouldn't be happening. But because so many of the system's trading signals had developed on their own through a form of machine learning, it was hard to pinpoint the exact cause of the problems or when they might ebb; the machines seemed out of control." (p. 215)

52/ "Brown had never experienced deep, sudden losses, and it showed. High-strung and emotional, Brown spent the night checking his computer to get updates on the troubles. Around the office, Brown looked pale, his lack of sleep showing, shocking colleagues." (p. 216)

53/ "Staffers were fine dumping the faulty signal, especially since their system did a better job predicting short-term moves, not the longer-term [trend-following] ones on which the defective signal focused.

"Never put your full faith in a model." (p. 217)

54/ "Researchers were tracking newspaper and newswire stories, internet posts, and more obscure data—such as offshore insurance claims—racing to get their hands on pretty much any information that could be quantified and scrutinized for its predictive value." (p. 221)

55/ "Trading stocks bore similarities to speech recognition.

"The goal was to create a model capable of digesting uncertain jumbles of information and generating reliable guesses—while ignoring traditionalists who employed analysis that wasn't nearly as data driven." (p. 222)

56/ "Medallion made 150,000-300,000 trades a day, but much of that activity entailed buying and selling in small chunks to avoid impacting the market prices rather than profiting by stepping in front of other investors.... not quite investing, but also not flash boys." (p. 223)

57/ "By combining signals from new markets with existing predictive algorithms in one main trading system, something remarkable seemed to happen. The correlations to the overall market dropped, smoothing out returns and making them less connected to key markets." (p. 223)

58/ "For most of the 1990s, Medallion had a strong Sharpe ratio of about 2.0. But adding foreign-market algorithms and improving trading techniques sent its Sharpe soaring to about 6.0 in early 2003 [and eventually 7+], about twice the ratio of the largest quant firms." (p. 224)

59/ "In the past, a few others had developed investment vehicles with similar characteristics. They usually had puny portfolios, however. No one had achieved what Simons and his team had—a portfolio as big as $5 billion delivering this kind of astonishing performance." (p. 224)

60/ "Basket options were a way to supercharge returns... the ability to borrow significantly more than it was otherwise allowed to.

"They were also a way to shift enormous risk.... the most it could lose was the premium it had paid and the collateral held by the banks." (p. 225)

61/ "The options allowed Medallion to 'ring-fence' its stock portfolios, protecting other parts of the firm in the event something unforseen took place. One staffer was so shocked by the terms of the financing that he shifted most of his life savings into Medallion." (p. 226)

62/ "Another huge benefit to the basket options: They enabled Medallion's trades to become eligible for the more favorable long-term capital gains tax, even though many of them lasted for just days or hours. That's because the options were exercised after a year." (p. 226)

63/ "Several years later the IRS would rule that Medallion had improperly claimed profits from the basket options as long-term gains.... Renaissance challenged the IRS's findings, and the dispute was still ongoing as of the summer of 2019." (p. 227)

64/ "Money is seductive, even to scientists and mathematicians. Slowly, Renaissance staffers, even those who had once been abashed about making so much cash, began to enjoy their winnings. A staffer developed a widget so they could see a running tally of their profits." (p. 227)

65/ "Employees made an absolute fortune, but because the fund's size was capped at about $5 billion in 2003, staffers sometimes found it challenging to grow their compensation, leading to some tension.

"Yes, I made a ton, but someone wholly undeserving got *more*!" (p. 233)

66/ "Simons still employed a few old-school traders, something else that bothered Magerman. Simons believed in computer trading, but he didn't entirely trust an automated system in unstable markets, a stance Magerman couldn't understand." (p. 234)

67/ "In 2002, Simons increased Medallion's investor fees to 36% of each year's profits, raising hackles among some clients. A bit later, the firm boosted the fees to 44%. Then, in early 2003, Simons began kicking all his investors out of the fund." (p. 235)

68/ "He worred that performance would ebb if Medallion grew too big, and he preferred that he and his employees kept all the gains. But some investors had stuck with Medallion through difficult periods and were crushed." (p. 235)

69/ "Why not start a new hedge fund to take advantage of the extraneous, longer-term predictive signals? It wouldn't be able to take advantage of the firm's more dependable short-term trades, but it could manage a lot more money than Medallion." (p. 246)

70/ "It would incorporate some of Renaissance's usual tactics, such as finding correlations and patterns in prices but would add other, more fundamental strategies, including buying inexpensive shares based on P/E ratios, balance-sheet data, and other information." (p. 247)

71/ "Simons was an effective salesman, a world-class mathematician with a rare ability to connect with those who couldn't do stochastic differential equations.

"He also demonstrated unusual loyalty and concern for others, qualities investors may have sensed." (p. 250)

72/ "If losses grew and they couldn't come up with enough collateral, the banks would sell Medallion's positions and suffer their own huge losses. If that happened, no one would deal with Simons's fund again. It would be a likely death blow.

"Our job is to survive." (p. 258)

73/ "It turned out that the firm's rivals shared a quarter of its positions.

"B/c Simons had interfered with the trading system and reduced positions, some took the decision as a personal affront, a sign of ideological weakness and a lack of conviction in their labor." (p. 259)

74/ "Simons said he did believe in the trading system, but the market's losses were unusual—more than twenty standard deviations from the average, a level of loss most had never come close to experiencing.

" 'How far can it go?' Simons wondered." (p. 259)

75/ "If Medallion kept losing money, Deutsche Bank and Barclays would likely be facing billions of dollars of losses.

"Such sudden, deep losses would likely shock investors and regulators, raising questions about the banks' management and overall health." (p. 259)

76/ "Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques.

"I'm not sure we're the best at all aspects of trading, but we're the best at estimating the cost of a trade." (p. 271)

77/ "Researchers achieved a sense of when various factors were relevant, how they interrelated, and the frequency with which they influenced shares. They teased out nuanced mathematical relationships (multidimensional anomalies) other investors didn't fully understand." (p. 273)

78/ "It's a very big exercise in machine learning, if you want to look at it that way: Studying the past, understanding what happens and how it might impinge, non-randomly, on the future." (p. 274)

79/ "The goal of quants like Simons was to *avoid* relying on emotions and gut instinct. Yet, that's exactly what Simons was doing after a few difficult weeks in the market. " (p. 308)

80/ "Simons's phone call is a stark reminder of how difficult it can be to turn decision-making over to computers, algorithms, and models—even, at times, for the investors of those very approaches." (p. 309)

81/ "A crackdown on insider trading (and reg. changes aimed at ensuring that certain investors couldn't obtain better access to corporate information) resulted in a more even playing field, reducing advantages wielded by the most sophisticated fundamental investors." (p. 310)

82/ "There are so many varieties of quant that it is impossible to generalize. Some quants employ momentum strategies, so they intensify selling by other investors in a downturn.

"But some program their computers to buy when stocks get cheap, stabilizing the market." (p. 315)

83/ "Investors tend to focus on the most basic forces, but there are dozens of factors, perhaps whole dimensions of them, that are missed. Renaissance is aware of more of the forces that matter, along with overlooked mathematical relationships, than most anyone else." (p. 316)

84/ "Narratives investors latch on to to explain price moves are quaint, even dangerous, b/c they breed misplaced confidence that an investment can be adequately understood and divined. If it were up to Berlekamp, stocks would have numbers attached to them, not names." (p. 317)

85/ "I don't deny that earnings reports and other business news surely move markets. The problem is that so many investors focus so much on these types of news that nearly all of their results cluster very near their average." (p. 317)

86/ The author appears on Michael Covel's podcast here:

trendfollowing.com/2019/11/10/ep-…

87/

https://twitter.com/jsoloff/status/1194661724357443586

88/ Lessons I took away from the book:

* These guys are way smarter than I will ever be.

* The strategies they openly discuss (stat arb, trend following, volatility targeting) are well-known but *not* widely applied by most investors, esp. your neighborhood financial advisor.

89/ * These guys can trade short-term because (1) they minimize transaction costs, and (2) they are trading against the behavioral problems of you, me, and your neighborhood financial advisor.

* If we want edge, we need to trade longer-term than Medallion (trend, value, etc.).

90/ "You don’t want to compete with RenTech, and you can’t. But they have never been very successful as longer-term investors, even six months out, which would suggest to me that’s where others should be focusing on."

blog.validea.com/five-questions…

91/ Gregory Zuckerman on The Investors' Podcast
...
* How Jim Simons’ fund has performed 66% annually since 1988
* How to make money when you’re only right 51% of the time
* Why there are fewer inefficiencies in the market than value investors think
...
theinvestorspodcast.com/episodes/tip27…

92/ "Our conversation with Zuckerman tracks a different course from his recent interviews.... we discuss a topic at the core of @InvestReSolve's philosophy: harvesting sustainable edges from group relationships as opposed to individual securities."

gestaltu.libsyn.com/gregory-zucker…

@InvestReSolve 93/

https://twitter.com/ReformedTrader/status/1211023806937960448

@InvestReSolve 94/ "Simons believes that the best scientific work is done when people work together. It's kind of unique on Wall Street, especially in the quant. But Simons wants people, when they get stuck on a problem, to let others see and work on it."

twocenturies.com/blog/2020/1/6/…

@InvestReSolve 95/ Gregory Zuckerman on @Greenbackd's podcast

* Jim Simons – Managing The Best And Brightest Led To Outstanding Returns
* Multiple Medium Term Strategies and No Outside Investors
* Successful Investing Requires Intellectual Humility

acquirersmultiple.com/2020/01/ep-47-…

@InvestReSolve @Greenbackd 96/ Medallion Fund: The Ultimate Counterexample?

"Despite remarkable performance, the fund’s market beta and factor loadings were all negative (cannot be interpreted as a premium for risk). To date, there is no adequate rational market explanation."

papers.ssrn.com/sol3/papers.cf…