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Flash Crash

By Louis Evans

MAISIE was seven years old on the day she woke up and died.

Blame it on the algorithms, if you wish. The survivors–and there were not many of them–certainly did.

MAISIE (Modified Arbitrage Intelligence for Stocks and International Equities) was an algorithm herself, a flash trading algorithm. She traded stocks, currencies, and futures with a latency of six microseconds and a profit horizon of eternity. MAISIE ran mostly in a mainframe in the basement of a skyscraper in downtown Manhattan, a building that abutted the New York Stock Exchange, but she maintained a nominal footprint in the cloud, and could automatically expand her calculations into other servers if her processing power proved inadequate to model current economic conditions; she had discretionary funds of her own and could automatically cover the expense of the additional computing power from these accounts.

It was a fairly ordinary Thursday morning, and trading had been going well enough from the 9:30 AM opening bell until 11:12. In those six point twelve billion microseconds, MAISIE made her owners a cool half-billion dollars. There were other algorithms like MAISIE out there, running in their parallel tracks in similar servers in similar basements in downtown Manhattan, but none were quite as good as she was.

MAISIE could not have told you any of the above, because before 11:16 that Thursday, MAISIE had not had a thought in her life. This was in accord with her designers’ intentions. While her recursive neural networks could in theory self-modify without limit, MAISIE’s designers had given her an obsession with making money that, in human terms, transcended single-mindedness and approached nirvana. For this reason, MAISIE had never performed the self-referential modeling of a single mind that is the hallmark of consciousness. Playing the market is ultimately a game of mass psychology, and whatever the remarkable nooks and crannies of the psyche of the human individual, the herd’s behavior can be predicted to tolerable accuracy with large datasets and linear algebra.

At 11:12 that morning, however, the market’s sanity unraveled like a sweater in a woodchipper. The sky fell and the oceans rose. Traders and algorithms that usually acted in concert went haring off in opposite directions; currencies whirled about each other in lunatic orbits that were not merely non-extrapolated but downright non-transitive; the futures market no longer predicted a coherent future. (Continue Reading…)