Old School Academics on Moving Average Rules: Remarkable.
Simple Technical Trading Rules and the Stochastic Properties of Stock Returns
- Brock, Lakonishok, and LeBaron
- A version of the paper can be found here.
- Want a summary of academic papers with alpha? Check out our Academic Research Recap Category!
This paper tests two of the simplest and most popular trading rules–moving average and trading range break-by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, our results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.
I’m always interested in anything Josef Lakonishok has written. Why? Well, the “L” in LSV stands for Lakonishok and they managed to create a wonderful business that manages around $100 billion. Not bad.
Lakonishok and his coauthors were academics well ahead of their time. Their paper on simple moving average trading rules was published in the Journal of Finance in 1992. What makes this feat even more amazing is that they were publishing papers in top academic journals on technical trading rules in an environment that was extremely hostile towards all things “chartist.”
A quote from Burt Malkiel’s 1981 Random Walk Down Wall Street says it all:
Obviously, I am biased against the “chartist.” This is not only a personal predilection, but a professional one as well. Technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics’ are prompted by two considerations: (1) the method is patently false; and (2) it’s easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember: His your money we are trying to save.
The results of the study are below.
The authors find that moving average trading rules work pretty well. The best performing rule is actually the 50-day moving average. They also identify that a 1% trading band improves the trading rule across the board.
As a value-investor by nature, reading papers on technical analysis can be a bit gut-wrenching, however, as an evidence-based investor by faith, the results are interesting!
Old School Evidence on a New School Trading Theme
Note: This site provides no information on our value investing ETFs or our momentum investing ETFs. Please refer to this site.
Join thousands of other readers and subscribe to our blog.
Please remember that past performance is not an indicator of future results. Please read our full disclaimer. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.
Definitions of common statistics used in our analysis are available here (towards the bottom)