Date Thesis Awarded
Honors Thesis -- Access Restricted On-Campus Only
Bachelors of Science (BS)
Many studies have used machine learning techniques, including neural networks, to predict equity returns. This paper uses macroeconomic and fundamental factors, and employs regression analysis and deep learning techniques to build forecasting models for equity returns in the Electric Vehicle market. Our results show that selected macroeconomic and fundamental factors are statistically significant in explaining trends of excess returns. Our deep neural networks achieved great in-sample testing results. Also, the results show that deep neural networks generally outperform shallow neural networks.
Zhang, Xinzhi, "Modeling Effects of Macroeconomic and Fundamental Factors in Equity Returns with Machine Learning" (2021). Undergraduate Honors Theses. William & Mary. Paper 1730.
On-Campus Access Only