Prediction of WilderHill Clean Energy Index Directional Movement

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dc.contributor.advisorMcGuffey, Elizabethen_US
dc.contributor.advisorLi, Mengen_US
dc.contributor.authorDu, Yolandaen_US
dc.contributor.authorLu, Luen_US
dc.contributor.authorDing, Hongkaien_US
dc.date.accessioned2023-05-11T19:21:03Zen_US
dc.date.available2023-05-11T19:21:03Zen_US
dc.date.issued2023-05-08en_US
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.en_US
dc.format.extent30 ppen_US
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>.en_US
dc.identifier.digitalWhitePaper_Prediction-WilderHill-Clean-Energy-Index-Directional-Movementen_US
dc.identifier.urihttps://hdl.handle.net/1911/114877en_US
dc.language.isoengen_US
dc.publisherRice Universityen_US
dc.rightsCopyright is held by author.en_US
dc.titlePrediction of WilderHill Clean Energy Index Directional Movementen_US
dc.typeWhite paperen_US
dc.type.dcmiTexten_US
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