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

60 Pages Posted: 16 May 2000 Last revised: 10 Apr 2001

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Harry Mamaysky

Columbia University - Columbia Business School

Jiang Wang

Massachusetts Institute of Technology (MIT) - Sloan School of Management; China Academy of Financial Research (CAFR); National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: March 2000

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.

Suggested Citation

Lo, Andrew W. and Mamaysky, Harry and Wang, Jiang, Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (March 2000). NBER Working Paper No. w7613. Available at SSRN: https://ssrn.com/abstract=228099

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

HOME PAGE: http://web.mit.edu/alo/www

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Stata Center
Cambridge, MA 02142
United States

Harry Mamaysky

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Jiang Wang

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E62-614
100 Main Street
Cambridge, MA 02142
United States
617-253-2632 (Phone)
617-258-6855 (Fax)

China Academy of Financial Research (CAFR)

1954 Huashan Road
Shanghai P.R.China, 200030
China

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
930
rank
22,071
Abstract Views
8,628
PlumX