The Moving Average Research King: Valeriy Zakamulin

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.
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The current version of the paper on the SSRN

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.

The current version of the paper on the SSRN

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.