The Moving Average Research King: Valeriy Zakamulin
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.
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
The current version of the paper on the SSRN
Market Timing With a Robust Moving Average
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
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