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Long Run Returns Predictability and Volatility with Moving Averages

Author

Listed:
  • Chia-Lin Chang

    (Department of Applied Economics, Department of Finance, National Chung Hsing University, Taichung 402, Taiwan)

  • Jukka Ilomäki

    (Faculty of Management, University of Tampere, FI-33014 Tampere, Finland)

  • Hannu Laurila

    (Faculty of Management, University of Tampere, FI-33014 Tampere, Finland)

  • Michael McAleer

    (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)

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.

Suggested Citation

  • Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:4:p:105-:d:171554
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    Cited by:

    1. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Lord, Montague & Chang, Susan, 2019. "Pre-Feasibility Study of Sarawak-West Kalimantan Cross-Border Value Chains," MPRA Paper 94732, University Library of Munich, Germany.
    3. Ken Chung & Anthony Bellotti, 2021. "Evidence and Behaviour of Support and Resistance Levels in Financial Time Series," Papers 2101.07410, arXiv.org.
    4. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Moving Average Market Timing in European Energy Markets: Production Versus Emissions," Energies, MDPI, vol. 11(12), pages 1-24, November.
    5. Parastoo Mousavi, 2021. "Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns," Mathematics, MDPI, vol. 9(13), pages 1-18, July.

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    More about this item

    Keywords

    trading strategies; risk; moving average; market timing; returns predictability; volatility; rolling window; data frequency;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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