Next Article in Journal
Effects of the Age Process on Aggregate Discounted Claims
Next Article in Special Issue
The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns
Previous Article in Journal
Three Different Ways Synchronization Can Cause Contagion in Financial Markets
Previous Article in Special Issue
Country Risk Ratings and Stock Market Returns in Brazil, Russia, India, and China (BRICS) Countries: A Nonlinear Dynamic Approach
Open AccessArticle

Long Run Returns Predictability and Volatility with Moving Averages

1
Department of Applied Economics, Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
2
Faculty of Management, University of Tampere, FI-33014 Tampere, Finland
3
Department of Finance, Asia University, Taichung 41354, Taiwan
4
Discipline of Business Analytics, University of Sydney Business School, Sydney 2006, Australia
5
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
6
Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain
7
Institute of Advanced Sciences, Yokohama National University, Yokohama 240-8501, Japan
*
Author to whom correspondence should be addressed.
Risks 2018, 6(4), 105; https://doi.org/10.3390/risks6040105
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]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top