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

Authors

  • Andrew W. Lo,

    Search for more papers by this author
    • MIT Sloan School of Management and Yale School of Management. Corresponding author: Andrew W. Lo (alo@mit.edu). This research was partially supported by the MIT Laboratory for Financial Engineering, Merrill Lynch, and the National Science Foundation (Grant SBR–9709976). We thank Ralph Acampora, Franklin Allen, Susan Berger, Mike Epstein, Narasimhan Jegadeesh, Ed Kao, Doug Sanzone, Jeff Simonoff, Tom Stoker, and seminar participants at the Federal Reserve Bank of New York, NYU, and conference participants at the Columbia-JAFEE conference, the 1999 Joint Statistical Meetings, RISK 99, the 1999 Annual Meeting of the Society for Computational Economics, and the 2000 Annual Meeting of the American Finance Association for valuable comments and discussion.
  • Harry Mamaysky,

    Search for more papers by this author
    • MIT Sloan School of Management and Yale School of Management. Corresponding author: Andrew W. Lo (alo@mit.edu). This research was partially supported by the MIT Laboratory for Financial Engineering, Merrill Lynch, and the National Science Foundation (Grant SBR–9709976). We thank Ralph Acampora, Franklin Allen, Susan Berger, Mike Epstein, Narasimhan Jegadeesh, Ed Kao, Doug Sanzone, Jeff Simonoff, Tom Stoker, and seminar participants at the Federal Reserve Bank of New York, NYU, and conference participants at the Columbia-JAFEE conference, the 1999 Joint Statistical Meetings, RISK 99, the 1999 Annual Meeting of the Society for Computational Economics, and the 2000 Annual Meeting of the American Finance Association for valuable comments and discussion.
  • Jiang Wang

    Search for more papers by this author
    • MIT Sloan School of Management and Yale School of Management. Corresponding author: Andrew W. Lo (alo@mit.edu). This research was partially supported by the MIT Laboratory for Financial Engineering, Merrill Lynch, and the National Science Foundation (Grant SBR–9709976). We thank Ralph Acampora, Franklin Allen, Susan Berger, Mike Epstein, Narasimhan Jegadeesh, Ed Kao, Doug Sanzone, Jeff Simonoff, Tom Stoker, and seminar participants at the Federal Reserve Bank of New York, NYU, and conference participants at the Columbia-JAFEE conference, the 1999 Joint Statistical Meetings, RISK 99, the 1999 Annual Meeting of the Society for Computational Economics, and the 2000 Annual Meeting of the American Finance Association for valuable comments and discussion.

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.

Ancillary