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.

Moving averages, market timing, industrial sector, energy sector, fossil fuels, renewable, energy, random timing, sunrise branches, sunset branches
Time-Series Models; Dynamic Quantile Regressions (jel C22), Time-Series Models; Dynamic Quantile Regressions (jel C32), Mining, Extraction, and Refining: Hydrocarbon Fuels (jel L71), 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)
Econometric Institute Research Papers
Erasmus School of Economics

Chang, C-L, Ilomäki, J, Laurila, H, & McAleer, M.J. (2018). Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks (No. EI2018-44). Econometric Institute Research Papers. Retrieved from