J. Risk Financial Manag., Volume 14, Issue 2 (February 2021) – 48 articles
Cover Story (view full-size image): Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest toward investing in companies that specialize in clean energy. Well-informed investors need reliable methods for predicting the stock prices of clean energy companies. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90%, while logit models produce accuracy rates of between 55% and 60%. View this paper.
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