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Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

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  • Andrew W. Lo

    (MIT Sloan School of Management and Yale School of Management)

  • Harry Mamaysky

    (MIT Sloan School of Management and Yale School of Management)

  • Jiang Wang

    (MIT Sloan School of Management and Yale School of Management)

Abstract

Technical analysis, also known as 'charting,' has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis-the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double bottoms-we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. Copyright The American Finance Association 2000.

Suggested Citation

  • Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, August.
  • Handle: RePEc:bla:jfinan:v:55:y:2000:i:4:p:1705-1770
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    References listed on IDEAS

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    1. P.H. Kevin Chang & Carol Osler, 1994. "Evaluating chart-based technical analysis: the head-and-shoulder pattern in foreign exchange," Research Paper 9414, Federal Reserve Bank of New York.
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