Date Thesis Awarded

4-2019

Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)

Department

Computer Science

Advisor

Zhenming Liu

Committee Members

Robert Michael Lewis

Bin Ren

Tyler Frazier

Abstract

This paper examines developing machine learning and statistic models to build forecast models for equity returns in an emergent market, with an emphasis on computing. Distributed systems were pared with random search and Bayesian optimization to find good hyperparameters for neural networks. No significant results were found.

Recommended Citation

Ren, Xida, "Using Gleaned Computing Power to Forecast Emerging-market Equity Returns with Machine Learning" (2019). Undergraduate Honors Theses. William & Mary. Paper 1296.
https://scholarworks.wm.edu/honorstheses/1296