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

  • 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)

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.

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Article provided by American Finance Association in its journal The Journal of Finance.

Volume (Year): 55 (2000)
Issue (Month): 4 (08)
Pages: 1705-1770

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Handle: RePEc:bla:jfinan:v:55:y:2000:i:4:p:1705-1770
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  1. Christopher J. Neely & Paul A. Weller & Robert Dittmar, 1997. "Is technical analysis in the foreign exchange market profitable? a genetic programming approach," Working Papers 1996-006, Federal Reserve Bank of St. Louis.
  2. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
  3. Andrew W. Lo & A. Craig MacKinlay, 1987. "Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test," NBER Working Papers 2168, National Bureau of Economic Research, Inc.
  4. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-71, October.
  5. K. Rouwenhorst, 1996. "International Momentum Strategies," Yale School of Management Working Papers ysm36, Yale School of Management, revised 01 Feb 2008.
  6. Andrew W. Lo & A. Craig MacKinlay, 1995. "Maximizing Predictability in the Stock and Bond Markets," NBER Working Papers 5027, National Bureau of Economic Research, Inc.
  7. Treynor, Jack L & Ferguson, Robert, 1985. " In Defense of Technical Analysis," Journal of Finance, American Finance Association, vol. 40(3), pages 757-73, July.
  8. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
  9. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. " Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-81, March.
  10. Christopher Neely & Paul Weller, 1998. "Technical trading rules in the European Monetary System," Working Papers 1997-015, Federal Reserve Bank of St. Louis.
  11. 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.
  12. repec:cup:cbooks:9780521429504 is not listed on IDEAS
  13. C.L. Osler & P.H. Kevin Chang, 1995. "Head and shoulders: not just a flaky pattern," Staff Reports 4, Federal Reserve Bank of New York.
  14. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
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