Chapter 6. Supervised Learning: Classification
Here are some of the key questions that financial analysts attempt to solve:
Is a borrower going to repay their loan or default on it?
Will the instrument price go up or down?
Is this credit card transaction a fraud or not?
All of these problem statements, in which the goal is to predict the categorical class labels, are inherently suitable for classification-based machine learning.
Classification-based algorithms have been used across many areas within finance that require predicting a qualitative response. These include fraud detection, default prediction, credit scoring, directional forecasting of asset price movement, and buy/sell recommendations. There are many other use cases of classification-based supervised learning in portfolio management and algorithmic trading.
In this chapter we cover three such classification-based case studies that span a diverse set of areas, including fraud detection, loan default probability, and formulating a trading strategy.
In “Case Study 1: Fraud Detection”, we use a classification-based algorithm to predict whether a transaction is fraudulent. The focus of this case study is also to deal with an unbalanced dataset, given that the fraud dataset is highly unbalanced with a small number of fraudulent observations.
In “Case Study 2: Loan Default Probability”, we use a classification-based algorithm to predict whether a loan will default. The case study focuses on various techniques and ...