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Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks

Author

Listed:
  • Chia-Lin Chang

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

  • Jukka Ilomäki

    (Faculty of Management University of Tampere, Finland.)

  • Hannu Laurila

    (Faculty of Management University of Tampere, Finland.)

  • Michael McAleer

    (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)

Abstract

The paper examines whether the moving average (MA) technique can beat random market timing in traditional and newer branches of an industrial sector. The sector considered is the energy sector, divided into balanced stock portfolios of fossil and renewable energy companies. Eight representative firms are selected for both portfolios. The paper finds that MA timing outperforms random timing with the portfolio of renewable energy companies, whereas the result is less clear with the portfolio of fossil energy companies. Thus, there seems to be more forecastable stochastic trends in sunrise branches than in sunset branches.

Suggested Citation

  • Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks," Documentos de Trabajo del ICAE 2018-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:1824
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    References listed on IDEAS

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    More about this item

    Keywords

    Moving averages; Market timing; Industrial sector; Energy sector; Fossil fuels; Renewable energy; Random timing; Sunrise branches; Sunset branches.;
    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
    • L71 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Hydrocarbon Fuels
    • L72 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Other Nonrenewable Resources
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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