《The Man Who Solved The Market》中文版翻译连载54

文艺复兴招聘官更偏爱不快乐的聪明人 并有意避开华尔街投资

《The Man Who Solved The Market》中文版翻译连载54

The MAn Who Solved The Market (58)

Kepler’s twist was to apply this approACh to statistical arbitrage, buying stocks that didn’t rise as much as expected based on the historic returns of these various underlying factors, while simultaneously selling short, or wagering against, shares that under-performed. If shares of Apple Computer and Starbucks EAch rose 10 percent amid a market rally, but Apple historically did much better that Starbucks during bullish periods, Kepler might buy Apple and short Starbucks. Using time-serious analysis and other statistical techniques, Frey and a colleague searched for trADIng errors, behavior not fully explained by historic data tracking the key factors, on the assumption that these deviations likely would disappear over time.


Betting on relationships and relatIVe differences between groups of stocks, rather than an outright rise or fall of shares, meant Frey didn’t need to predict where shares were headed, a difficult task for anyone. He and his colleagues also didn’t really care where the overall market was going. As a result, Kepler’s portfolio was market neutral, or reasonably immune to the stock market’s moves. Frey’s models usually just focused on whether relationships between clusters of stocks returned to their historic norms — a reveRSIon-to-the-mean strategy. Constructing a portfolio of these investments figured to dampen the fund’s volatility, giving it a high Sharpe ratio. Named after economist William F. Sharpe, the Sharpe ratio is a commonly used measure of returns that incorporate a portfolio’s risk. A high Sharp suggests a strong and stable historic performance.


Kepler’s hedge fund, eventually renamed Nova, generated middling results that frustrated clients, a few of whom bolted. The fund was subsumed into Medallion while Frey continued his efforts, usually without tremendous success.


The problem wasn’t that Frey’s system couldn’t discover profitable strategies. It was unusually good at identifying profitable trades and forecasting the movement of groups of shares. It was that, too, often, the team’s profits paled in comparison to those predicted by their model. Frey was like a chef with a delicious recipe who cooked a serious of memorable meals but dropped most of them on the way to the dinner table.


Watching Frey and his colleagues flail, some Renaissance staffers began to lose patience. Laufer, Patterson, and the others had developed a sophisticated system to buy and sell various commodities and other investments, featuring a betting algorithm that adjusted its holdings given the range of probabilities of future market moves. Frey’s team had nothing of the sort for stocks. Staffers carped that his trading model seemed much too sensitive to tiny market fluctuations. It sometimes bought shares and sold them before they had a chance to rise, spooked by a sudden move in price. There was too much noise in the market for Frey’s system to hear any of its signals.


It would take two oddballs to help solve the problem for Simons. One rarely talked. The other could barely sit still.


As Nick Patterson worked with Henry Laufer in the early 1990s to improve Medallion’s predictive models, he began a side job he seemed to relish as much as discovering overlooked price trends: recruiting talent for Renaissance’s growing staff. To upgrade the firm’s computer systems, for example. Patterson helped hire Jacqueline Rosinsky as the first systems administrator. Rosinsky, whose husband ditched an accounting career to become a captain in the New York City Fire Department, would eventually head information technology and other areas. (Later, women would manage legal and other departments, but it would be a while before they’d play significant roles onthe research, data, or trading sides of the operation.) Patterson required a few things from his hires. They needed to be supersmart, of course, with identifiable accomplishments, such as academic papers or awards, ideally in fields lending themselves to the work Renaissance was doing. Patterson steered clear of Wall Street types. He didn’t have anything against them, per se; he just was convinced he could find more impressive talent elsewhere.


“We can teach you about money,” Patterson explains. “We can’t teach you about smart.”


Besides, Patterson argued to a colleague, if someone left a bank or hedge fund to join Renaissance, they’d be more inclined to bolt at some point for a rival, if the opportunity ever arose, than someone without a familiarity with the investment community. That was crucial, because Simons insisted that everyone at the firm actively share their work with each other. Simons needed to trust that his staffers weren’t going to take that information and run off to a competitor.


One last thing got Patterson especially excited: if a potential recruit was miserable in their current job.


“I like smart people who were probably unhappy,” Patterson says.


One day, after reading in the morning paper that IBM was slashing costs, Patterson became intrigued. He was aware of the accomplishments of the computer giant’s speech-recognition group and thought their work bore similarity to what Renaissance was doing. In early 1993, Patterson sent separate letters to Peter Brown and Robert Mercer, deputies of the group, inviting them to visit Renaissance’s offices to discuss potential positions.


Brown and Mercer both reacted the exact same way — depositing Patterson’s letter in the closest trash receptacle. They’d reconsider after experiencing family upheaval, laying the groundwork for dramatic change at Jim Simons’s company, and the world as a whole.




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