《The Man Who Solved The Market》中文版翻译连载42
The MAn Who Solved The Market (42)
Gerry Bamberger had few visions of wEAlth or prominence in the early 1980s. A tall, trim computer-science graduate from Colombia UnIVeRSIty, Bamberger provided analytical and technical support for Morgan Stanley’s stock traders, serving as an underappreciated cog in the investment bank’s mAChine. When the traders prepared to buy and sell big chunks of shares for clients, acquiring a few million dollars of Coca-Cola, for example, they protected themselves by selling an equal amount of something similar, like Pepsi, in what is commonly referred to as a pairs trade. Bamberger created software to update the Morgan Stanley trader’s results, though many of them bristled at the idea of getting assistance from the resident computer nerd.
格里·班伯格在二十世纪八十年代早期对财富和名望几乎没有什么幻想。班伯格身材修长苗条，毕业于哥伦比亚大学计算机科学专业，他为摩根士丹利的股票交易员提供分析和技术支持，在这家投行机器中扮演了一个没有得到充分赏识的小人物。当 们准备为客户买入或卖出大量 时，例如要 几百万美元的可口可乐股票，他们就要 相当数量的类似股票，例如百事的股票，这通常被称为配对 。班伯格开发了一款软件更新摩根士丹利交易员们的业绩，不过许多人对向这位电脑高手求助的想法觉得愤怒。
Watching the traders buy big blocks of shares, Bamberger observed that prices often moved higher, as might be expected. Prices headed lower when Morgan Stanley’s traders sold blocks of shares. Each time, the trADIng activity altered the gap, or spread, between the stock in question and the other company in the pair, even when there was no news in the market. An order to sell a chunk of Coke shares, for instance, might send that stock down a percentage point or even two, even as Pepsi barely moved. Once the effect of their Coke stock selling wore off, the spread between the shares reverted to the norm, which made sense, since there had been no reason for Coke’s drop other than Morgan Stanley’s activity.
Bamberger sensed opportunity. If the bank created a database tracking the historic prices of various paired stocks, it could profit simply by betting on the return of these price-spreads to their historic levels after block trades or other unusual activity. Bamberger’s bosses were swayed, setting him up with half a million dollars and a small staff. Bamberger began developing computer programs to take advantage of “temporary blips” of paired shares. An Orthdox Jew and a heavy smoker with a wry sense of humor, Bamberger brought a tuna sandwich in a brown bag for lunch every single day. By 1985, he was implementing his strategy with six or seven stocks at a time, while managing $30 million, scoring profits for Morgan Stanley.
Big bureaucratic companies often act like, well, big bureaucratic companies. That’s why Morgan Stanley soon gave Bamberger a new boss, Nunzio Tartaglia, a perceived insult that sparked Bamberger to quit. (He joined Ed Thorp’s hedge fund, where he did similar trades and eventually retired a millionaire.)
A short, wiry astrophysicist, Tartaglia managed the Morgan Stanley trading group very differently from his predecessor. A native of Brooklyn who had bounced around Wall Street, Tartaglia’s edges were sharper. Once, when a new colleague approached to introduce himself, Tartaglia immediately cut him off.
“Don’t try to get anything by me because I come from out there,” Tartaglia said, pointing a finger at a nearby window and the streets of New York City.
Tartaglia renamed his group Automated Proprietary Trading, or APT, and moved it to a forty-foot-long room on the nineteenth floor of Morgan Stanley’s headquarters in a midtown Manhattan skyscraper. He added more automation to the system and, by 1987, it was generating $50 million of annual profits. Team members didn’t know a thing about the stocks they traded and didn’t need to — their strategy was simply to wager on the re-emergence of historic relationships between shares, an extension of the age-old “buy low, sell high” investment adage, this time using computer programs and lightning-fast trades.
New hires, including a former Colombia University computer-science professor named David Shaw and mathematician Robert Frey, improved profits. 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. It didn’t always happen, of course, but when implemented enough times, the strategy resulted in annual profits of 20 percent, likely because investors often tend to overreact to both good and bad news before calming down and helping to restore historic relationships between stocks.
By 1988, APT was among the largest and most-secretive trading teams in the world, buying and selling $900 million worth of shares each day. The unit hit heavy losses that year, though, and Morgan Stanley executives slashed APT’s capital by two-thirds. Senior management never had been comfortable investing by relying on computer models, and jealousies had grown about how much money Tartaglia’s team was making. Soon, Tartaglia was out of a job, and the group shut down.
It wouldn’t be clear for many years, but Morgan Stanley had squandered some of the most lucrative trading strategies in history of finance.