Date of Award
8-2020
Degree Type
Creative Project
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Tyler Brough
Committee
Tyler Brough
Committee
Pedram Jahangiry
Committee
Paul Fjeldsted
Abstract
This study develops and tests the hypothesis that the machine learning algorithm, Random Forests, can be used to systematically pick financial ratios that would be best for indicating market trends and be used subsequently to perform comparable analysis to speculate whether a firm is over- or under-valued. Results show that financial ratio selection differs depending on the market sector to which a firm pertains. We examine the 11 financial sectors representing the key areas of the economy. We also look at four possible trading strategies that an investor could have: month-long, quarter-long, semi-annual, and annual to capture differing trading horizons.
Recommended Citation
Butterfield, Collin, "We Can Use Machine Learning to Determine Which Financial Ratios are Best for Investors" (2020). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1462.
https://digitalcommons.usu.edu/gradreports/1462
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