《The Man Who Solved The Market》中文版翻译连载48
The MAn Who Solved The Market (51)
From the EArliest days of the fund, Simons’s team had been wary of these transACtion costs, which they called slippage. They regularly compared their trades against a model that tracked how much the firm would have profited or lost were it not for those bothersome trADIng costs. The group coined a name for the difference between the prices they were getting and the theoretical trades their model made without the pesky costs. They called it The Devil.
For a while, the actual size of The Devil was something of a guess. But, as Straus collected more data and his computers became more powerful, Laufer and Patterson began writing a computer program to track how far their trades strayed from the ideal state, in which trading costs barely weighed one the fund’s performance. By the time Patterson got to Renaissance, the firm could run a simulator that subtracted these trading costs from the prices they had receIVed, instantly isolating how much they were missing out.
To narrow the gap, Laufer and Patterson began developing sophisticated approaches to direct trades to various futures exchanges to reduce the market impact of each trade. Now Medallion could better determine which investments to pursue, a huge advantage as it began trading new markets and investments. They added German, British, and Italian bonds, then interest-rate contracts in London, and, later, futures on Nikkei Stock Average, Japanese government bonds, and more.
The fund began trading more frequently. Having first sent orders to a team of traders five times a day, it eventually increased to sixteen times a day, reducing the impact on prices by focusing on the periods when there was the most volume. Medallion’s traders still had to pick up the phone to transact, but the fund was on its way toward faster trading.
Until then, Simons and his colleagues hadn’t spent too much time wondering why their growing collection of algorithms predicted prices so presciently. They were scientists and mathematicians, not analysts or economists. If certain signals produced results that were statistically significant, that was enough to include them in the trading model.
“I don’t know why planets orbit the sun,” Simons told a colleague, suggesting one needn’t spend too much time figuring out why the market’s patterns existed. “That doesn’t mean I can’t predict them.”
Still, the returns were piling up so fast, it was getting a bit absurd. Medallion soared over 25 percent just in June 1994, on its way to a 71 percent surge that year, results that even Simons described as “simply remarKAble.” Even more impressive: The gains came in a year the Federal Reserve surprised investors by hiking interest rates repeatedly, leading to deep losses for many investors.
The Renaissance team was curious by natural, as were many of its investors. They couldn’t help wonder what the heck was going on. If Medallion was emerging as a big winner in most of its trades, who was on the other side suffering steady losses?
Over time, Simons came to the conclusion that the losers probably weren’t those who trade infrequently, such as buy-and-hold individual investors, or even the “treasurer of a multinational corporation,” who adjusts her portfolio of foreign currencies every once in a while to suither company’s needs, as Simons told his investors.
Instead, it seemed Renaissance was exploiting the foibles and faults of fellow speculators, both big and small.
“The manager of a global hedge fund who is guessing on a frequent basis the direction of the French bond market may be a more exploitable participant,” Simons said.
Laufer had a slightly differfent explanation for their heady returns. When Patterson came to him, curious about the source of the money they were raking in, Laufer pointed to a different set of traders infamous for both their excessive trading and overconfidence when it came to predicting the direction of the market.
Laufer’s explanation sounds glib, but his perspective, as well as Simons’s viewpoint, can be seen as profound, even radical. At the time, most academics were convinced markets were inherently efficient, suggesting that there were no predictable ways to beat the market’s return, and that the financial decision-making of individuals was largely rational. Simons and his colleagues sensed professors were wrong. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts.
Simons didn’t realize it, but a new strain of economic was emerging that would validate his instincts. In the 1970s, Israeli psychologists Amos Tversky and Daniel Kahneman had explored how individuals make decisions, demonstrating how prone most are to act irrationally. Later, economist Richard Thaler used psychological insights to explain anomalies investor behavior, spurring the growth of the field of behavioral economics, which explored the cognitive biases of individuals and investors. Among those identified: loss aveRSIon, or how investors generally feel the pain from losses twice as much as the pleasure from gains; anchoring, the way judgment is skewed by an initial piece of information or experience; and the endowment effect, how investors assign excessive value to what they already own in their portfolios.
Kahneman and Thaler would win Nobel Prizes for their work. A consensus would emerge that investors act more irrationally than assumed, repeatedly making similar mistakes. Investors overreact to stress and make emotional decisions. Indeed, it’s likely no coincidence that Medallion found itself making its largest profits during times of extreme turbulence in financial markets, a phenomenon that would continue for decades to come.