Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239464 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. 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%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.
Subjects: 
clean energy stock prices
forecasting
machine learning
random forests
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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