By Michael Maiello – Yale University’s Bryan T. Kelly, Chicago Booth’s Dacheng Xiu, and Booth PhD candidate Shihao Gu investigated 30,000 individual stocks that traded between 1957 and 2016, examining hundreds of possibly predictive signals using several techniques of machine learning, a form of artificial intelligence.
They conclude that ML had significant advantages over conventional analysis in this challenging task.
ML uses statistical techniques to give computers abilities that mimic and sometimes exceed human learning. The idea is that computers will be able to build on solutions to previous problems to eventually tackle issues they weren’t explicitly programmed to take on.
“At the broadest level, we find that machine learning offers an improved description of asset price behavior relative to traditional methods,” the researchers write, suggesting that ML could become the engine of effective portfolio management, able to predict asset-price movements better than human managers.
Of almost 100 characteristics the researchers investigated, the most successful predictors were price trends, liquidity, and volatility. more>
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