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《The Man Who Solved The Market》中文版翻译连载45


《The Man Who Solved The Market》中文版翻译连载45

The MAn Who Solved The Market (45)

Within Renaissance, word circulated that Commodities Corporation — a firm credited with launching dominant hedge funds run by commodity-focused traders including Paul Tudor Jones, Louis BACon, and Bruce Kovner — also passed on backing Simons’s fund.


“The view from the industry was — ‘It’s a bunch of mathematicians using computers… What do they know about the business?'” says a friend of Simons. “They had no track record … risk was they were going to put themselves out of business.”


Simons still had his trADIng system, now managing a bit more than $70 million after a gain of 39 percent in 1991. If Simons could figure out a way to extend his winning strEAk, or even improve Medallion’s returns, he was sure investors would eventually come around. BerleKAmp, Ax, and Baum were long gone, though. Straus was in charge of the firm’s trading, data collection, and more, but he wasn’t a researcher capable of uncovering hidden trading signals. With competition growing, Medallion would have to discover new ways to profits. Seeking help, Simons turned to Henry Laufer, a mathematician who already had demonstrated a flair for creatIVe solutions.


Laufer never claimed any of the prestigious mathematics awards given to Simons and Ax, nor did he have a POPular algorithm named after him, like Lenny Baum or Elwyn Berlekamp. Nonetheless, Laufer had scaled his own heights of accomplishment and recognition, and he would prove Simons’s best partner yet.


Laufer had finished his undergraduate work at the City Colleague of New York and graduate school at Princeton UniveRSIty in two years each, earning acclaim for progress he’d made on a stubborn problem in a field of mathematics dealing with functions of complex variables and discovering new examples of embedding, or structures within other mathstructures.


Joining Stony Brook’s math department in 1971, Laufer focused on complex variables and algebraic geometry, veering away from classical areas of complex analysis to develop insights into more contemporary problems. Laufer came alive in the classroom and was popular with students, but he was more timid in his personal life. High school friends remember a bookish introvert who carried a slide rule. Early on at Stony Brook, Laufer told colleagues he wanted to get married and was eager to put himself in the best position to find the right woman. Once, on a ski trip with fellow mathematician Leonard Charlap, Laufer suggested they go down to the hotel’s bar “to meet some girls.”


Charlap looker at his friend and just laughed.


“Henry, you’re not that kind of guy,” Charlap said, knowing Laufer would be too shy to hit on women in a hotel bar.


“He was a nice Jewish boy,” Charlap recalls.


Laufer eventually met and married Marsha Zlatin, a speech-language pathology professor at Stony Brook who shared Laufer’s liberal politics. Marsha had a more upbeat personality, often using the word “swell” to describe her mood, no matter the challenge. After suffering a series of miscarriages, Marsha amazed firiends with her buoyancy, eventually giving birth to healthy children. Later, she earned a PhD in speech-language pathology.


Marsha’s outlook on life seemed to influence Laufer. Among colleagues, he was known as a willing collaborator. They noticed Laufer had a special interest in investing, and they were disappointed, but not shocked, when he rejoined Simons as a full-time employee in 1992.


Academics who shift to trading often turn nervous and edgy, worried about each move in the market, concerns that hounded Baum when he joined Simons. Laufer, then forty-six, had a different reaction — his improved pay relieved stress he had felt about the cost of his daughters’ college education, friends say, and Laufer seemed to relish the intellectual challenge of crafting profitable trading formulas.


For Simons, Laufer’s geniality was a welcome relief after years of dealing with the complicated personalities of Baum, Ax, and Berlekamp. Simons became Renaissance’s big-picture guy, wooing investors, attractive talent, planing for emergencies, and mapping a strategy for how his team — with Laufer leading research in a new Stony Brook office, and Straus running trading in Berkeley — might build on the recent strong returns.


Laufer made an early decision that would prove extraordinarily valuable: Medallion would employ a single trading model rather than maintain various models for different investments and market conditions, a style most Quantitative firms would embrace. A collection of trading models was simpler and easier to pull off, Laufer acknowledged. But, he argued, a single model could draw on Straus’s vast trove of pricing data, detecting correlations, opportunities, and other signals across various asset classes. Narrow, individual models, by contrast, can suffer from too little data.


Just as important, Laufer understood that a single, stable model based on some core assumptions about how prices and markets behave would make it easier 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. Yes, Laufer acknowledged, it’s a challenge to combine various investments, saya currency-futures contract and a US commodity contract. But, he argued, oncethey figured out ways to “smooth” out those wrinkles, the single model would lead to better trading results.


Laufer spent long hours at his desk refining the model. At lunchtime, the team usually piled into Laufer’s aging Lincoln Town Car and headed to a local joint, where the deliberations continued. It didn’t take long to come up with a new way to look at the market.


Straus and others had compiled reams of files tracking decades of prices of dozens of commodities, bonds, and currencies. To make it all easier to digest, they had broken the trading week into ten segments — five overnight sessions, when stocks traded in overseas markets, and five day sessions. In effect, they sliced the day in half, enabling the team to search for repeating patterns and sequences in the various segments. Then, they entered trades in the morning, at noon, and at the end of the day.




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