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Using machine learning algorithms to find patterns in stock prices

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Author Info
Pedro N. Rodríguez
Simón Sosvilla-Rivero

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Abstract

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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Paper provided by FEDEA in its series Working Papers with number 2006-12.

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Date of creation: Jun 2006
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Handle: RePEc:fda:fdaddt:2006-12

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  1. Jegadeesh, Narasimhan, 1990. " Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-98, July. [Downloadable!] (restricted)
  2. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules1," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February. [Downloadable!] (restricted)
  3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 1(1), pages 41-66. [Downloadable!] (restricted)
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This page was last updated on 2009-11-29.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.