Date Thesis Awarded
Honors Thesis -- Access Restricted On-Campus Only
Bachelors of Science (BS)
Robert Michael Lewis
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.
Ren, Xida, "Using Gleaned Computing Power to Forecast Emerging-market Equity Returns with Machine Learning" (2019). Undergraduate Honors Theses. William & Mary. Paper 1296.
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