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

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Author Info
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)

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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.

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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. Carl Chiarella & Xue-Zhong He & Cars Hommes, 2004. "A Dynamic Analysis of Moving Average Rules," Research Paper Series 133, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
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  2. BEN OMRANE, Walid & VAN OPPEN, HervŽ, 2004. "The predictive success and profitability of chart patterns in the Euro/Dollar foreign exchange market," CORE Discussion Papers 2004035, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  3. Yin-wong Cheung, 2006. "An Empirical Model of Daily Highs and Lows," Working Papers 072006, Hong Kong Institute for Monetary Research. [Downloadable!]
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  4. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Quantitative Finance Papers 0901.0447, arXiv.org. [Downloadable!]
  5. Michel Fliess & C\'edric Join, 2008. "Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance," Quantitative Finance Papers 0811.1561, arXiv.org, revised Nov 2008. [Downloadable!]
  6. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter. [Downloadable!] (restricted)
  7. Spyros Skouras, 2001. "Decisionmetrics: A Decision-Based Approach to Econometric Modeling," Working Papers 01-11-064, Santa Fe Institute.
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  8. Carol Osler, 2000. "Support for resistance: technical analysis and intraday exchange rates," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 53-68. [Downloadable!]
  9. Michel Fliess & Cédric Join, 2008. "Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance," Post-Print inria-00338099_v2, HAL. [Downloadable!]
  10. Wing-Keung Wong & Jun Du & Terence Tai-Leung Chong, 2005. "Do the technical indicators reward chartists? A study on the stock markets of China, Hong Kong and Taiwan," SCAPE Policy Research Working Paper Series 0512, National University of Singapore, Department of Economics, SCAPE. [Downloadable!]
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