The paper examines whether the Moving Average (MA) technique can outperform random market timing in the energy sector, compiled of fossil and renewable energy producers. According to the Capital Asset Pricing Model, random timing is a superior trading strategy in the long run. However, the MA technique may be more successful, if there are predictable stochastic trends in the price series. In the paper, eight representative firms are selected for both fossil and renewable portfolios with actually tradable stocks in order to create two Exchange-Traded Funds (ETF). The paper finds that MA timing outperforms random timing for the ETF of renewable energy companies, but not for the ETF of fossil energy companies.

Energy sector, Fossil fuels, Market timing, Moving averages, Random timing, Renewable energy
Time-Series Models; Dynamic Quantile Regressions (jel C22), Time-Series Models; Dynamic Quantile Regressions (jel C32), Mining, Extraction, and Refining: Other Nonrenewable Resources (jel L72), R&D; Agricultural Technology; Agricultural Extension Services (jel Q16), Alternative Energy Sources (jel Q42), Energy Forecasting (jel Q47),
Energy Reports
Department of Econometrics

Chang, C-L, Ilomäki, J, Laurila, H, & McAleer, M.J. (2020). Market timing with moving averages for fossil fuel and renewable energy stocks. Energy Reports, 6, 1798–1810. doi:10.1016/j.egyr.2020.06.029