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Open AccessArticle

Long Run Returns Predictability and Volatility with Moving Averages

Department of Applied Economics, Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
Faculty of Management, University of Tampere, FI-33014 Tampere, Finland
Department of Finance, Asia University, Taichung 41354, Taiwan
Discipline of Business Analytics, University of Sydney Business School, Sydney 2006, Australia
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain
Institute of Advanced Sciences, Yokohama National University, Yokohama 240-8501, Japan
Author to whom correspondence should be addressed.
Risks 2018, 6(4), 105;
Received: 13 September 2018 / Revised: 20 September 2018 / Accepted: 21 September 2018 / Published: 22 September 2018
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
PDF [3254 KB, uploaded 22 September 2018]


This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets. View Full-Text
Keywords: trading strategies; risk; moving average; market timing; returns predictability; volatility; rolling window; data frequency trading strategies; risk; moving average; market timing; returns predictability; volatility; rolling window; data frequency

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Chang, C.-L.; Ilomäki, J.; Laurila, H.; McAleer, M. Long Run Returns Predictability and Volatility with Moving Averages. Risks 2018, 6, 105.

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