We study how negative sentiment around an industry impacts beliefs and behaviors, focusing on demands for racial justice after the murder of George Floyd and the salience of the “defund the police” movement. We assess stakeholder beliefs on the impact of protests on the stock prices of...

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox-Ingersoll-Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for...

We study how negative sentiment around an industry impacts beliefs and behaviors, focusing on demands for racial justice after the murder of George Floyd and the salience of the “defund the police” movement. We assess stakeholder beliefs on the impact of protests on the stock prices of...

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox-Ingersoll-Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for...

Purpose Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid...