Three applications of machine learning methods in corporate finance ...
This thesis focuses on three applications of machine learning methods in corporate finance. The first two applications (Chapter 2 and 3) are dedicated to two applications of double (or debiased) machine learning (DML) on corporate cash holdings, and merger returns, respectively. The third applicatio...
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University of Glasgow
2024
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| Online Access: | https://dx.doi.org/10.5525/gla.thesis.84298 https://theses.gla.ac.uk/id/eprint/84298 |
| Summary: | This thesis focuses on three applications of machine learning methods in corporate finance. The first two applications (Chapter 2 and 3) are dedicated to two applications of double (or debiased) machine learning (DML) on corporate cash holdings, and merger returns, respectively. The third application (Chapter 4) is related to empirical evaluation of the heterogeneous impacts of cost of carry on cash holdings using the causal forest (CF) method. I also provide a comprehensive introduction to machine learning techniques and the potential benefits that these methods can bring to enhance the effectiveness of data analysis in the field of finance (Chapter 1). The motivation for using DML is the existence of a large number of explanatory variables in the relevant literature. The increase of features in a system probably causes a high degree of non-linearities and hidden complex inter-relationships between covariates. Traditional machine learning methods which rely on the linearity assumption, like LASSO, cannot ... |
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