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

Andrew Lo (), Harry Mamaysky () and Jiang Wang
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Harry Mamaysky: Massachusetts Institute of Technology
Jiang Wang: Massachusetts Institute of Technology

No 402, Computing in Economics and Finance 1999 from Society for Computational Economics

Abstract: Technical analysis, also known as "charting", has been a part of financial practice for many decades, yet little academic research has been devoted to a systematic evaluation of this discipline. 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 US 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.

Date: 1999-03-19
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin and nep-fmk
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Published, Journal of Finance, 55:4, August 2000, 1705-1765.

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Related works:
Journal Article: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (2000)
Working Paper: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf9:402

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