2000/08/01 by Andrew W. Lo, Harry Mamaysky, Jiang Wang · 1 citation
Decision Sciences · Mathematics · #Artificial intelligence #Computer science #Econometrics #Economics #Finance #Forecasting Techniques and Applications #Inference #Kernel regression #Machine learning #Mathematics #Nonparametric statistics #Sample (material) #Statistical inference #Statistics #Stock Market Forecasting Methods #Technical analysis
paper · pdf · doi:10.1111/0022-1082.00265
openalex publication_date 2000/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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 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 we apply 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 do provide incremental information and may have some practical value.