Using machine learning algorithms to find patterns in stock prices
Pedro Rodriguez () and
Simon Sosvilla-Rivero ()
No 2006-12, Working Papers from FEDEA
We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the S&P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by first-order serial correlation in stock index returns.
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