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

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  • Andrew W. Lo
  • Harry Mamaysky
  • Jiang Wang

Abstract

Technical analysis, also known as charting,' has been 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 apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness to 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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7613.

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Date of creation: Mar 2000
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Publication status: published as Lo, Andrew W., Harry Mamaysky and Jiang Wang. "Foundations Of Technical Analysis: Computational Algorithms, Statistical Inference, And Empirical Implementation," Journal of Finance, 2000, v55(4,Aug), 1705-1765.
Handle: RePEc:nbr:nberwo:7613

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  1. Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers, Wisconsin Madison - Social Systems 90-22, Wisconsin Madison - Social Systems.
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  3. P.H. Kevin Chang & Carol Osler, 1994. "Evaluating chart-based technical analysis: the head-and-shoulder pattern in foreign exchange," Research Paper, Federal Reserve Bank of New York 9414, Federal Reserve Bank of New York.
  4. Treynor, Jack L & Ferguson, Robert, 1985. " In Defense of Technical Analysis," Journal of Finance, American Finance Association, American Finance Association, vol. 40(3), pages 757-73, July.
  5. K. Geert Rouwenhorst, 1998. "International Momentum Strategies," Journal of Finance, American Finance Association, American Finance Association, vol. 53(1), pages 267-284, 02.
  6. 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, University of Chicago Press, vol. 64(4), pages 549-71, October.
  7. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. " Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, American Finance Association, vol. 49(1), pages 153-81, March.
  8. 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.
  9. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521429504.
  10. 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.
  11. Christopher Neely & Paul Weller, 1998. "Technical trading rules in the European Monetary System," Working Papers, Federal Reserve Bank of St. Louis 1997-015, Federal Reserve Bank of St. Louis.
  12. C.L. Osler & P.H. Kevin Chang, 1995. "Head and shoulders: not just a flaky pattern," Staff Reports, Federal Reserve Bank of New York 4, Federal Reserve Bank of New York.
  13. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, American Finance Association, vol. 48(1), pages 65-91, March.
  14. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, Elsevier, vol. 51(2), pages 245-271, February.
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