iPad Notebook export for The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

June 15, 2020

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution Zuckerman, Gregory
Citation (APA): Zuckerman, G. (2019). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution [Kindle iOS version]. Retrieved from Amazon.com

Some quick quotes from the book above are below.

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Part One: Money Isn’t Everything
Highlight(pink) – Chapter Six > Page 109 · Location 1754
Laufer also uncovered how the previous day’s trading often can predict the next day’s activity, something he termed the twenty-four-hour effect. The Medallion model began to buy late in the day on a Friday if a clear up-trend existed, for instance, and then sell early Monday, taking advantage of what they called the weekend effect.
Highlight(pink) – Chapter Seven > Page 131 · Location 2073
The Morgan Stanley traders became some of the first to embrace the strategy of statistical arbitrage, or stat arb. This generally means making lots of concurrent trades, most of which aren’t correlated to the overall market but are aimed at taking advantage of statistical anomalies or other market behavior. The team’s software ranked stocks by their gains or losses over the previous weeks, for example. APT would then sell short, or bet against, the top 10 percent of the winners within an industry while buying the bottom 10 percent of the losers on the expectation that these trading patterns would revert. Highlight(pink) – Chapter Seven > Page 132 · Location 2089
Frey proposed deconstructing the movements of various stocks by identifying the independent variables responsible for those moves. A surge in Exxon, for example, could be attributable to multiple factors, such as moves in oil prices, the value of the dollar, the momentum of the overall market, and more. A rise in Procter & Gamble might be most attributable to its healthy balance sheet and a growing demand for safe stocks, as investors soured on companies with lots of debt. If so, selling groups of stocks with robust balance sheets and buying those with heavy debt might be called for, if data showed the performance gap between the groups had moved beyond historic bounds. A handful of investors and academics were mulling factor investing around that same time, but Frey wondered if he could do a better job using computational statistics and other mathematical techniques to isolate the true factors moving shares.
Part Two: Money Changes Everything
Highlight(pink) – Chapter Twelve > Page 225 · Location 3435
Basket options are financial instruments whose values are pegged to the performance of a specific basket of stocks. While most options are valued based on an individual stock or financial instrument, basket options are linked to a group of shares. If these underlying stocks rise, the value of the option goes up—it’s like owning the shares without actually doing so. Indeed, the banks were legal owners of shares in the basket, but, for all intents and purposes, they were Medallion’s property. The fund’s computers told the banks which stocks to place in the basket and how they should be traded.
Highlight(pink) – Chapter Sixteen > Page 310 · Location 4705 Quant investors had emerged as the dominant players in the finance business. As of early 2019, they represented close to a third of all stock-market trades, a share that had more than doubled since 2013.6 Highlight(pink) – Chapter Sixteen > Page 311 · Location 4719 The rage among investors is for alternative data, which includes just about everything imaginable, including instant information from sensors and satellite images around the world. Creative investors test for money-making correlations and patterns by scrutinizing the tones of executives on conference calls, traffic in the parking lots of retail stores, records of auto-insurance applications, and
recommendations by social media influencers.
Highlight(pink) – Chapter Sixteen > Page 311 · Location 4727 To explore these new possibilities, hedge funds have begun to hire a new type of employee, what they call data analysts or data hunters, who focus on digging up new data sources, much like what Sandor Straus did for Renaissance in the mid-1980s.
Highlight(pink) – Chapter Sixteen > Page 312 · Location 4744 A quip by novelist Gary Shteyngart sums up the future path of the finance industry, and the direction of broader society: “When the shrinks for their kids are replaced by algorithms, that’ll be the end; there’ll be nothing left.”
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