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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

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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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Handle: RePEc:nbr:nberwo:7613

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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. 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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  3. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Quantitative Finance Papers 0901.0447, arXiv.org. [Downloadable!]
  4. 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!]
  5. Spyros Skouras, 2001. "Decisionmetrics: A Decision-Based Approach to Econometric Modeling," Working Papers 01-11-064, Santa Fe Institute.
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  6. 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!]
  7. 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)
  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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