Some weekend reading for trend-followers who want to question their beliefs.
Valeriy Zakamulin is an animal when it comes to generating research on moving averages. We’ve done a lot of the same work, but we’re too lazy to tabulate the results in an academic paper format.
The king of moving average research.
Check these papers out:
Revisiting the Profitability of Market Timing with Moving Averages
In a recent empirical study by Glabadanidis (“Market Timing With Moving Averages” (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425; the paper is also available on the SSRN and has been downloaded more than 7,500 times) the author reports striking evidence of extraordinary good performance of the moving average trading strategy. In this paper we demonstrate that “too good to be true” reported performance of the moving average strategy is due to simulating the trading with look-ahead bias. We perform the simulations without look-ahead bias and report the true performance of the moving average strategy. We find that at best the performance of the moving average strategy is only marginally better than that of the corresponding buy-and-hold strategy. In statistical terms, the performance of the moving average strategy is indistinguishable from the performance of the buy-and-hold strategy. This paper is supplied with R code that allows every interested reader to reproduce the reported results.
A Comprehensive Look at the Empirical Performance of Moving Average Trading Strategies
by Valeriy Zakamulin, working paper
Despite the enormous current interest in market timing and a series of publications in academic journals, there is still lack of comprehensive research on the evaluation of the profitability of trading rules using methods that are free from the data-snooping bias. In this paper we utilize the longest historical dataset that spans 155 years and extend previous studies on the performance of moving average trading rules in a number of important ways. Among other things, we investigate whether overweighting the recent prices improves the performance of timing rules; whether there is a single optimal lookback period in each trading rule; and how accurately the trading rules identify the bullish and bearish stock market trends. In our study we, for the first time, use both the rolling- and expanding-window estimation scheme in the out-of-sample tests; study the performance of trading rules across bull and bear markets; and perform numerous robustness checks and tests for regime shifts in the stock market dynamics. Our main results can be summarized as follows: There is strong evidence that the stock market dynamics are changing over time. We find no statistically significant evidence that market timing strategies outperformed the market in the second half of our sample. Neither the shape of the weighting function nor the type of the out-of-sample estimation scheme allows a trader to improve the performance of timing rules. All market timing rules generate many false signals during both bullish and bearish stock market trends, yet these rules tend to outperform the market in bear states.
Market Timing With a Robust Moving Average
by Valeriy Zakamulin, working paper
In this paper we entertain a method of finding the most robust moving average weighting scheme to use for the purpose of timing the market. Robustness of a weighting scheme is defined its ability to generate sustainable performance under all possible market scenarios regardless of the size of the averaging window. The method is illustrated using the long-run historical data on the Standard and Poor’s Composite stock price index. We find the most robust moving average weighting scheme, demonstrates its advantages, and discuss its practical implementation.
Anatomy of Market Timing with Moving Averages
by Valeriy Zakamulin, working paper
The underlying concept behind the technical trading indicators based on moving averages of prices has remained unaltered for more than half of a century. The development in this field has consisted in proposing new ad-hoc rules and using more elaborate types of moving averages in the existing rules, without any deeper analysis of commonalities and differences between miscellaneous choices for trading rules and moving averages. In this paper we uncover the anatomy of market timing rules with moving averages. Our analysis offers a new and very insightful reinterpretation of the existing rules and demonstrates that the computation of every trading indicator can equivalently be interpreted as the computation of the weighted moving average of price changes. This knowledge enables a trader to clearly understand the response characteristics of trading indicators and simplify dramatically the search for the best trading rule. As a straightforward application of the useful knowledge revealed by our analysis, in this paper we also entertain a method of finding the most robust moving average weighting scheme. The method is illustrated using the long-run historical data on the Standard and Poor’s Composite stock price index. We find the most robust moving average weighting scheme and demonstrates its advantages.
About the Author: Wesley Gray, PhD
After serving as a Captain in the United States Marine Corps, Dr. Gray earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel Prize Winner Eugene Fama. Next, Wes took an academic job in his wife’s hometown of Philadelphia and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management firm dedicated to an impact mission of empowering investors through education. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes has published multiple academic papers and four books, including Embedded (Naval Institute Press, 2009), Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016).
Dr. Gray currently resides in Palmas Del Mar Puerto Rico with his wife and three children. He recently finished the Leadville 100 ultramarathon race and promises to make better life decisions in the future.
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For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice. Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.
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