Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation
AbstractTechnical 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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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 7613.
Date of creation: Mar 2000
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Other versions of this item:
- Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, 08.
- Andrew Lo & Harry Mamaysky & Jiang Wang, 1999. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Computing in Economics and Finance 1999 402, Society for Computational Economics.
- NEP-ALL-2000-05-16 (All new papers)
- NEP-ECM-2000-05-16 (Econometrics)
- NEP-FIN-2000-05-16 (Finance)
- NEP-FMK-2000-05-16 (Financial Markets)
- NEP-HIS-2000-05-16 (Business, Economic & Financial History)
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