TY  - JOUR
AU  - Lo,Andrew W.
AU  - Mamaysky,Harry
AU  - Wang,Jiang
TI  - Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 7613
PY  - 2000
Y2  - March 2000
UR  - http://www.nber.org/papers/w7613
L1  - http://www.nber.org/papers/w7613.pdf
N1  - Author contact info:
Andrew W. Lo
MIT Sloan School of Management
100 Main Street, E62-618
Cambridge, MA  02142
Tel: 617/253-0920
Fax: 781/891-9783
E-Mail: alo@mit.edu

Harry Mamaysky
Citigroup
Citi Principal Strategies
390 Greenwich St., Floor 7
New York, NY   10013
Tel: 917-514-0845
Fax: 646-224-5619
E-Mail: harry.mamaysky@citi.com

Jiang Wang
MIT Sloan School of Management
100 Main Street, E62-614
Cambridge, MA 02142
Tel: 617/253-2632
Fax: 617/258-6855
E-Mail: wangj@mit.edu
AB  - 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.
ER  -