《The Man Who Solved The Market》中文版翻译连载35
The MAn Who Solved The Market (35)
BerleKAmpand his collEAgues hoped Medallion could resemble a gambling casino. Just as casinos handle so many daily bets that they could need to profit from a bit more than half of those wagers, 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.
“If you trade a lot, you only need to be right 51 percent of the time,” Berlekamp argued to a colleague. “We need a smaller edge on eACh trade.”
As they scrutinized their data, looking for short-term trADIng strategies to add to Medallion’s trading model, the team began identifying certain intriguing oddities in the market. Prices for some investments often fell just before key economic reports and rose right after, but prices didn’t always fall before the reports came out and didn’t always rise in the moments after. For whatever reason, the pattern didn’t hold for the US Department of Labor’s employment statistics and some other data releases. But there was enough data to indicate when the phenomena were most likely to take place, so the model recommended purchases just before the economic releases and sales almost immediately after them.
在仔细研究过数据后，为寻找短期交易策略加入到大奖章的交易模型中，团队开始发现 中某些有趣的奇怪现象。某些投资品的价格通常会在关键经济数据公布前下跌，发布后再立刻回升，但价格并不总是在公布前 ，也不是总在发布后立刻上涨。不管出于什么原因，这种模式在美国劳工部的就业统计和其他一些数据发布过程中并不成立。但有足够的数据表明，这种现象最有可能在什么时候发生，因此该模型建议在 数据发布之前买入，在发布之后立即卖出。
Searching for more, Berlekamp got on the phone with Henry Laufer, who had agreed to spend more time helping Simons turn Medallion around after Ax quit. Laufer was in the basement of Simons’s office on Long Island with a couple of research assistants from the Stony Brook area trying to revamp Medallion’s trading model, just as Berlekamp and Straus were doing in Berkeley.
Sifting through Straus’s data, Laufer discovered certain recurring trading sequences based on the day of the week. Monday’s price action often followed Friday’s, for example, while Tuesday saw reveRSIons to earlier trends. 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.
通过对斯特劳斯的数据筛选，劳弗发现某种基于一周中某天的重复交易序列。比如说，周一的价格走势经常是跟随上周五的，而周二则会逆转回到此前的趋势中。劳弗还揭示了，通过前一天的交易是如何预测第二天的走势，这被他成为“24小时效应”。举例来说，如果周五有明显的 ，大奖章的模型会在当天晚些时候 ，然后利用所谓的周末效应在周一早间 。
Simons and his researchers didn’t believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to the anomalies signaling opportunity. They also didn’t think it made sense to worry about why these phenomena existed. All that mattered was that they happened frequently enough to include in their updated trading system, and that they could be tested to ensure they weren’t statistical flukes.
They did have theories. Berlekamp and others developed a thesis that locals, or floor traders who buy or sell commodities and bonds to keep the market functioning, liked to go home at the end of a trading week holding few or no futures contracts, just in case bad news arose over the weekend that might saddle them with losses. Similarly, brokers on the floors of commodity exchanges seemed to trim futures positions ahead of the economic reports to avoid the possibility that unexpected news might cripple their holdings.
These traders got right back into their positions after the weekend, or subsequent to the news releases, helping prices rebound. Medallion’s system would buy when these brokers sold, and sell the investments back to team as they became more comfortable with the risk.
“We’re in the insurance business,” Berlekamp told Straus.
Oddities in currency markets represented additional attractive trades. Opportunity seemed especially rich in the trading of deutsche marks. When the currency rose one day, it had a surprising likelihood of climbing the next day, as well. And when it fell, it often dropped the next day, too. It didn’t seem to matter if the team looked at the month-to-month, week-to-week, day-to-day, or even hour-to-hour correlations; deutsche marks showed an unusual propensity to trend from one period to the next, trends that lasted longer than one might have expected.
When you flip a coin, you have a 25 percent chance of getting heads twice in a row, but there is no correlation from one flip to the next. By contrast, Straus, Laufer, and Berlekamp determined the correlation of price moves in deutsche marks between any two consecutive time periods was as much as 20 percent, meaning that the sequence repeated more than half of the time. By comparison, the team found a correlation between consecutive periods of 10 percent or so for other currencies, 7 percent for gold, 4 percent for hogs and other commodities, and just 1 percent for stocks.
“The time scale doesn’t seem to matter,” Berlekamp said to a colleague one day, with surprise. “We get the same statistical anomaly.”
Correlations from one period to the next shouldn’t happen with any frequency, at least according to most economics at the time who had embraced the efficient market hypothesis. Under this view, it’s impossible to beat the market by taking advantage of price irregularities — they shouldn’t exist.Once irregularities are discovered, investors should step into remove them, the academics augued.
The sequences witnessed in the trading of deutsche marks — and even stronger correlations found in the yen — were so unexpected that the team felt the need to understand why they might be happening. Straus found academic papers arguing that global central banks have a distaste for abrupt currency moves, which can disrupt economies, so they step in to slow sharp moves in either direction, thereby extending those trends over longer periods of time. To Berlakamp, the slow pace at which big companies like Eastman Kodak made business decisions suggested that the economic forces behind currency shifts likely played out over many months.
“People persist in their habits longer than they should,” he says.
The currency moves were part of Medallion’s growing mix of traceable effects, in their developing parlance. Berlekamp, Laufer, and Straus spent months poring over their data, working long hours glued to their computers, examining how prices reacted to tens of thousands of market events. Simons checked in daily, in person or on the phone, sharing his own ideas to improve the trading system while encouraging the team to focus on uncovering what he called “subtle anomalies” others had overlooked.