COMM 374 - Applied Financial Markets
This course applies the main concepts of finance established in COMM 370 and COMM 371. It aims at preparing student for the financial industry and is designed to cover broad topics in both corporate finance and investment management.
Students will learn to perform quantitative data-driven analysis. As a first step, they will be introduced to the main sources of financial data. Using analytical tools, they will then convert historical and real-time financial market data into information and actionable insights. They will also interpret the information using finance theory, and learn how to use it to make correct financial decisions. This course relies on Excel – the standard baseline tool in the financial industry.
Learning objectives
Upon successful completion of this course, students should have the ability to:
- Employ widely used financial databases, including Compustat, CRSP, I/B/E/S, Edgar, and Bloomberg. Be familiar with best practices of data workflow from data collection and basic excel manipulation to regression analysis.
- Value companies using different methods: discounted free cash flow, multiples, and characteristic regression. Learn about alternative datasets that are exploited by hedge funds, such as foot traffic and app downloads, to value start-ups where earnings are less informative.
- Apply difference-in-differences techniques to study the causal impact of important policies, such as privacy regulations, COVID vaccine lotteries, environmental acts, or gender initiatives.
- Conduct event studies to examine changes in stock prices around major corporate events, such as earnings announcements, data breaches, pandemics, or patent grant announcements.
- Predict bankruptcy and understand the role of CDS and rating agencies, with applications to sovereign default.
- Evaluate the performance of mutual funds using risk-adjusted measures (e.g., carbon risk, political risk), multi-factor models, and the CAPM.
- Predict returns based on past firm performance and use the prediction to build trading strategies. Understand how machine learning techniques can help us better predict future returns.