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 "Li, Meng"

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    Fake News Detection with Headlines
    (Rice University, 2023-12-12) Ramirez, Gared; Li, Meng
    Fake news has become an increasing problem due to the rising use of the Internet and social media. It is important to be able to distinguish sources of fake and misleading news articles to ensure that misinformation does not sow discord, erode trust in credible sources, and negatively impact our personal and societal well-being. Moreover, in an age where many people only skim headlines without delving into the full articles, the ability to discern fake news from headlines alone becomes even more crucial. To detect and classify fake news, we implement and compare five machine learning models–naive Bayes, logistic regression, decision tree, random forest, and support vector machine–on two different datasets: a benchmark dataset and a dataset with full articles and headlines. We utilize measures such as term frequency-inverse document frequency and sentiment scores, as predictors in our models. We find that naive Bayes consistently performs best on both datasets with accuracies of 64.40% and 92.56%, respectively.
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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.