This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

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

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Andrew Lo () (Massachusetts Institute of Technology)
Harry Mamaysky () (Massachusetts Institute of Technology)
Jiang Wang (Massachusetts Institute of Technology)

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.nber.org/papers/w7613.pdf
File Format: application/pdf
File Function: main text
Download Restriction: no

Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 402.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 19 Mar 1999
Date of revision:
Publication status: Published, Journal of Finance, 55:4, August 2000, 1705-1765.
Handle: RePEc:sce:scecf9:402

Contact details of provider:
Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

Other versions of this item:

This paper has been announced in the following NEP Reports: Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
  1. Carl Chiarella & Xue-Zhong He & Cars Hommes, 2004. "A Dynamic Analysis of Moving Average Rules," Research Paper Series 133, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
    Other versions:
  2. BEN OMRANE, Walid & VAN OPPEN, HervŽ, 2004. "The predictive success and profitability of chart patterns in the Euro/Dollar foreign exchange market," CORE Discussion Papers 2004035, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  3. Yin-wong Cheung, 2006. "An Empirical Model of Daily Highs and Lows," Working Papers 072006, Hong Kong Institute for Monetary Research. [Downloadable!]
    Other versions:
  4. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Quantitative Finance Papers 0901.0447, arXiv.org. [Downloadable!]
  5. Michel Fliess & C\'edric Join, 2008. "Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance," Quantitative Finance Papers 0811.1561, arXiv.org, revised Nov 2008. [Downloadable!]
  6. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter. [Downloadable!] (restricted)
  7. Spyros Skouras, 2001. "Decisionmetrics: A Decision-Based Approach to Econometric Modeling," Working Papers 01-11-064, Santa Fe Institute.
    Other versions:
  8. Carol Osler, 2000. "Support for resistance: technical analysis and intraday exchange rates," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 53-68. [Downloadable!]
  9. Michel Fliess & Cédric Join, 2008. "Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance," Post-Print inria-00338099_v2, HAL. [Downloadable!]
  10. Wing-Keung Wong & Jun Du & Terence Tai-Leung Chong, 2005. "Do the technical indicators reward chartists? A study on the stock markets of China, Hong Kong and Taiwan," SCAPE Policy Research Working Paper Series 0512, National University of Singapore, Department of Economics, SCAPE. [Downloadable!]
    Other versions:
Statistics
Access and download statistics

Did you know? You can import bibliographic info in various formats into you bibliographic tool, or just into your word processor. See under "publisher info" on each abstract page.

This page was last updated on 2009-11-13.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.