Graduation Year
2020
Date of Submission
12-2019
Document Type
Campus Only Senior Thesis
Degree Name
Bachelor of Arts
Department
Economics-Accounting
Reader 1
Professor Lisa Meulbroek
Terms of Use & License Information
Abstract
To assess whether or not fundamental analysis can be improved with more advanced, non-linear statistical techniques like neural networks over linear techniques like lasso and least squares, I construct estimates of market capitalization utilizing the different methodologies proposed and utilize their deviation from actual market values to construct portfolios which I test for significance. Where several different neural-network derived portfolios were able to generate statistically significant risk-adjusted returns, least squares and lasso regression were not under any scenario. This leads me to the conclusion that neural networks are in fact a superior means of conducting fundamental analysis and that the relationship between fundamental values and a firm’s subsequent returns is complex and best explained by a model which is capable of modeling these non-linear relationships.
Recommended Citation
Johnson, Nick, "Does Complexity Pay? A Study on the Effectiveness of Various Forms of Regression at Fundamental Analysis" (2020). CMC Senior Theses. 2294.
https://scholarship.claremont.edu/cmc_theses/2294
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.
