Date of Award
Summer 8-24-2024
Document Type
Dissertation
Degree Name
Ph.D.
Organizational Unit
Daniels College of Business
First Advisor
Jack Strauss
Second Advisor
Chris GauthierDickey
Third Advisor
Alex Petkevich
Fourth Advisor
Shahram Amini
Copyright Statement / License for Reuse
All Rights Reserved.
Keywords
Financial ratios, Industry portfolio allocation, Industry returns, Machine learning, Sector portfolio allocation, Sector returns, Gradient-boosted decision trees, Gradient-boosted regression trees, Extreme gradient boosting (XGBoost)
Abstract
Academics and equity analysts often utilize fundamental financial ratios to evaluate an industry's financial and operational performance. However, sophisticated econometric methods could be improved for estimating these ratios' importance in predicting sector-level stock return performance. To address this gap, gradient-boosted decision tree methods were applied to the WRDS sector-level financial ratio database to identify if financial ratios effectively forecast sector-level stock returns in future periods. The findings show that these ratios are predictive and economically material, both stand alone and in combination with other macroeconomic factors. Using these predictions, long and long-short sector-level portfolios were created, and most of these portfolios consistently outperformed the market in absolute and risk-adjusted returns. Furthermore, these portfolios generate positive alpha in the presence of the well-established Fama-French factor models. This research demonstrates that sector-level information is effective in forecasting equity market performance.
Copyright Date
8-2024
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Greg Kuppenheimer
Provenance
Received from Author
File Format
application/pdf
Language
English (eng)
Extent
109 pgs
File Size
1.3 MB
Recommended Citation
Kuppenheimer, Greg, "Utilizing Machine Learning to Forecast Sector-Level Equity Returns from Sector-Level Financial Ratios" (2024). Electronic Theses and Dissertations. 2451.
https://digitalcommons.du.edu/etd/2451
