Prediction of WilderHill Clean Energy Index Directional Movement

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dc.contributor.advisorMcGuffey, Elizabeth
dc.contributor.advisorLi, Meng
dc.contributor.authorDu, Yolanda
dc.contributor.authorLu, Lu
dc.contributor.authorDing, Hongkai
dc.date.accessioned2023-05-11T19:21:03Z
dc.date.available2023-05-11T19:21:03Z
dc.date.issued2023-05-08
dc.description.abstractThe popularity of clean energy has risen recently due to concerns about climate change and the exhaustion of traditional energy sources. The stock price of clean energy companies reflects the public’s attention to the industry’s growth potential, and clean energy stocks are among the riskiest stocks to invest in. Thus, it is important to apply quantitative methods to analyze the financial risks and returns of renewable energy stocks. Prior works on the topic are mainly focused on inference rather than predictions of renewable energy stock prices. In this investigation, the directional movement of the WilderHill Clean Energy Index is predicted using machine learning methods including logistic regression, random forest, and neural networks. Using data including technical indicators and macroeconomic variables, the aim is to predict the movement of the WilderHill Clean Energy Index with high accuracy. The results suggest that for the classification models with two directions, random forest and neural networks outperform full logistic regression and stepwise logistic regression. For the classification models with a three-category target variable, random forest and neural networks models outperform full logistic regression and stepwise logistic regression in overall accuracy; however, the methods give varying results for different outcome classes, in regards to sensitivity and specificity. In addition, the relationship between renewable energy stock directional movement and independent variables is investigated. The results suggest that two important macroeconomic variables are West Texas Intermediate crude oil prices and NYSE Arca Tech 100 Index.
dc.format.extent30 pp
dc.identifier.citationDu, Yolanda, Lu, Lu and Ding, Hongkai. "Prediction of WilderHill Clean Energy Index Directional Movement." (2023) Rice University: <a href="https://hdl.handle.net/1911/114877">https://hdl.handle.net/1911/114877</a>.
dc.identifier.digitalWhitePaper_Prediction-WilderHill-Clean-Energy-Index-Directional-Movement
dc.identifier.urihttps://hdl.handle.net/1911/114877
dc.language.isoeng
dc.publisherRice University
dc.rightsCopyright is held by author.
dc.titlePrediction of WilderHill Clean Energy Index Directional Movement
dc.typeWhite paper
dc.type.dcmiText
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