In this post, I explain how we can build and evaluate supervised machine learning classifiers in Python.
The outline of this post is summarized as follows:
- Build classification models using Logistic Regression, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest, and AdaBoost
- Tune hyperparameters using the Grid Search method
- Evaluate the models and select the best model using precision, recall, and AUC
As a working example, I will explain how we can predict whether an IPO firm chooses to reduce its pre-IPO disclosures using its firm characteristics. I use these predictions in the first chapter of my UC Berkeley Haas dissertation (link). Nonetheless, it can be generalized and used to solve many other classification and prediction problems. By the way, a part of the first chapter is co-authored with my UC Berkeley PhD advisors and is published in the Critical Finance Review (a top 4 finance journal). Be sure to check the publication here.
If you have any comments or suggestions, email me at [email protected]. Enjoy reading the rest of the post!
In 2012, the JOBS Act allowed IPO firms to reduce their financial disclosures before going public. In the first chapter of my dissertation, I examine the economic consequences of this Act and find that firms that choose to disclose less (hereafter "reduced-disclosure firms") become more overpriced. To explain the channel, I examine whether the reduction in disclosure requirement is the driver of overpricing or whether certain types of IPOs that are known to be overpriced went public after the JOBS Act.
The purpose of this post is to test the channel. To do so, we will examine whether firms' choices to disclose less can be predicted using their firm characteristics. If we can indeed predict their choices, then we can rule out one of the two possibilities that the reduction in disclosure requirement caused IPOs to become more overpriced.