Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation
ANDREW W. LO, HARRY MAMAYSKY, AND JIANG WANG* ABSTRACT
Technical analysis, also known as “charting,” has been a part of financial practice for many decades, but this discipline has not received the same level of academicscrutiny 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 weapply this method to a large number of U.S. 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 doprovide incremental information and may have some practical value.
ONE OF THE GREATEST GULFS between academic finance and industry practice is the separation that exists between technical analysts and their academic critics. In contrast to fundamental analysis, which was quick to be adopted by the scholars of modern quantitative finance, technical analysis has been an orphan from the very start. Ithas been argued that the difference between fundamental analysis and technical analysis is not unlike the difference between astronomy and astrology. Among some circles, technical analysis is known as “voodoo finance.” And in his inf luential book A Random Walk down Wall Street, Burton Malkiel ~1996! concludes that “@u#nder scientific scrutiny, chart-reading must share a pedestal with alchemy.”However, several academic studies suggest that despite its jargon and methods, technical analysis may well be an effective means for extracting useful information from market prices. For example, in rejecting the Random Walk
* MIT Sloan School of Management and Yale School of Management. Corresponding author: Andrew W. Lo ~firstname.lastname@example.org!. This research was partially supported by the MIT Laboratoryfor Financial Engineering, Merrill Lynch, and the National Science Foundation ~Grant SBR– 9709976!. We thank Ralph Acampora, Franklin Allen, Susan Berger, Mike Epstein, Narasimhan Jegadeesh, Ed Kao, Doug Sanzone, Jeff Simonoff, Tom Stoker, and seminar participants at the Federal Reserve Bank of New York, NYU, and conference participants at the ColumbiaJAFEE conference, the 1999 Joint StatisticalMeetings, RISK 99, the 1999 Annual Meeting of the Society for Computational Economics, and the 2000 Annual Meeting of the American Finance Association for valuable comments and discussion.
The Journal of Finance
Hypothesis for weekly U.S. stock indexes, Lo and MacKinlay ~1988, 1999! have shown that past prices may be used to forecast future returns to some degree, a fact thatall technical analysts take for granted. Studies by Tabell and Tabell ~1964!, Treynor and Ferguson ~1985!, Brown and Jennings ~1989!, Jegadeesh and Titman ~1993!, Blume, Easley, and O’Hara ~1994!, Chan, Jegadeesh, and Lakonishok ~1996!, Lo and MacKinlay ~1997!, Grundy and Martin ~1998!, and Rouwenhorst ~1998! have also provided indirect support for technical analysis, and more direct support hasbeen given by Pruitt and White ~1988!, Neftci ~1991!, Brock, Lakonishok, and LeBaron ~1992!, Neely, Weller, and Dittmar ~1997!, Neely and Weller ~1998!, Chang and Osler ~1994!, Osler and Chang ~1995!, and Allen and Karjalainen ~1999!. One explanation for this state of controversy and confusion is the unique and sometimes impenetrable jargon used by technical analysts, some of which has developed...