Title
Using Gleaned Computing Power to Forecast Emerging-market Equity Returns with Machine Learning
Date Thesis Awarded
4-2019
Access Type
Honors Thesis -- Open Access
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
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Included in
Computational Engineering Commons, Computer and Systems Architecture Commons, Data Storage Systems Commons, Digital Communications and Networking Commons, Finance and Financial Management Commons, Other Computer Engineering Commons
