Two things to remember in regards to technology applied to finance:
1) There is no silver bullet, it is just one more tool in the toolbox.
2) Eventually any advantage is arbed away as the technology diffuses into the market.
From Institutional Investor:
Harnessing the power of language can be a boon to traders bent on something as seemingly subjective as a diagnosis of the market’s pulse. It’s one thing to scour news databases to find negative references to a company whose stock you’re about to sell short. But it’s another thing to detect what Philip Resnik, a professor of linguistics at the University of Maryland, calls spin.
“Language is ambiguous — words have multiple meanings,” says Resnik, who’s also a member of the university’s Institute for Advanced Computer Studies.
Traders have been mining for data in news archives for decades. And they’ve long known how to detect the mood of the masses by assessing puts and calls on stock options. But today, computers armed with the latest artificial intelligence, or AI, software are learning to decipher how the public uses language and which combinations of words can predict the kind of phenomena that interest investors and pollsters.
For example, when a professor at Carnegie Mellon University searched for negative words in news databases — “unemployment,” “layoffs” and “fear” — he noticed a high correlation with a drop in the U.S. Consumer Confidence Index. His project worked just fine until the fall of 2010, when he deduced from the frequency of certain positive words, including “jobs” and “employment,” that public sentiment was about to rise.
The problem: Apple had just released a new iPhone; all those “jobs” references were mentions of company co-founder Steve Jobs. Consumer confidence was in fact falling, but news reports on the hip new Apple product made the scientist erroneously predict that consumer confidence would soon shoot through the roof.
Or think about it this way: A person describing a movie as “a bomb” probably didn’t like the flick. But if he says the movie was “the bomb,” he’s using slang to say how much he enjoyed it. An AI-enabled computer can distinguish between the two uses of the word. And there’s another difference in how traders today are trolling for sentiment: their data sources. Twitter alone has 140 million users churning out thoughts on products, companies and current events around the clock.
Back in 2005, Paul Tetlock, now a professor at Columbia Business School in New York, wrote an academic paper when he was at the University of Texas demonstrating how content in the business press predicts stock market movements. He found not only that high levels of what he calls media pessimism predict downward swings in the stock market, but also that investors can use media pessimism to predict trading volume....MORE