Introduction

This course offers a comprehensive overview of both classical and modern statistical learning methods with a strong emphasis on their application in finance. The classical approaches covered include linear regression, logistic regression, and k-Nearest Neighbors (k-NN), providing foundational tools for prediction and classification. The course will also explore modern methods such as decision trees, ensemble techniques (boosting and bagging), support vector machines,and neural networks, as well as advanced topics like model assessment, feature selection, and unsupervised learning techniques like clustering. Throughout the course, students will apply these methods to real-world financial datasets, gaining hands-on experience in statistical learning as it pertains to asset pricing, portfolio optimization, and other key areas in finance.

Prerequisites - A quantitative background is required, including knowledge of probability, statistics, and calculus. Prior programming experience in languages such as Python or R is highly recommended. Students without programming experience may still enroll in the course but should anticipate challenges in completing assignments and the project. Such students are expected to be highly motivated and willing to acquire the necessary programming skills as the course progresses.

Course Outcomes

The objective of this course is to equip students with a comprehensive understanding of various statistical learning models and their applications in finance. Through a combination of lectures,coding assignments, and applied projects using real-world financial data, students will gain both theoretical knowledge and practical experience in implementing these models.
After successful completion of this course, students will be able to:

1. Distinguish between supervised vs. unsupervised learning and parametric vs. nonparametric models.
2. Identify key statistical learning techniques, including classical and modern approaches
3. Apply these techniques to analyze financial datasets, focusing on asset pricing, portfolio optimization, and market prediction.
4. Evaluate and select the most suitable model for financial data analysis based on performance

Textbook

• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. An Introduction to Statistical Learning with Applications in Python. Springer, 2023

  This book is available free from the author's web site at " https://www.statlearning.com/

Other Readings

• Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer, 2013
• John C. Hull. Machine Learning in Business. Third Edition. 2021
• Marcos Lopez de Prado. Advances in Financial Machine Learning. First Edition. Wiley. 2018
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. 2016