CoFES White Paper Series

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https://hdl.handle.net/1911/111011

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Browsing CoFES White Paper Series by Author "McGuffey, Elizabeth"

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    Prediction of WilderHill Clean Energy Index Directional Movement
    (Rice University, 2023-05-08) Du, Yolanda; Lu, Lu; Ding, Hongkai; McGuffey, Elizabeth; Li, Meng
    The 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.
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    Understanding Houston’s Growth: Real Estate New Development Data Analysis
    (Rice University, 2024-04-20) Ruan, Jian; Chi, Eric; Ensor, Katherine; McGuffey, Elizabeth
    Market research serves as the foundation for successful real estate investment and development, guiding strategic site selection and opportunity identification. This study focuses on analyzing the evolving landscape of real estate development in Houston through a multifaceted approach encompassing geographic information systems (GIS) visualization to explore the spatial dynamics and diversity of new developments, time series analysis to examine temporal trends of new development applications, and machine learning techniques to predict future urban growth trajectories. K-means clustering is employed to segment the market and reveal unique characteristics based on development distribution, while the impact of new developments on nearby home values is quantified through linear panel regression and tree-based models, offering insights into the complex relationships between development and property valuation. By integrating these complementary analyses, this research aims to provide a comprehensive understanding of Houston’s real estate development landscape, informing strategic decision-making processes and contributing to the interdisciplinary discourse on urban growth dynamics, market trends, and the interplay between development and valuation.