FINC672 - Financial Data Analytics

Course Description

This course provides an overview of empirical research in finance. The course is organized into two parts. In the first part, students build foundational programming and data science skills in Python, with a practical focus on collecting, cleaning, transforming, and analyzing financial datasets. These skills are increasingly essential in the era of big data and FinTech. In the second part, students study widely used, state-of-the-art statistical methods in empirical asset pricing and machine learning. Throughout the course, students apply programming tools and statistical techniques to real-world research questions in equity, derivatives, and fixed-income markets.

Learning goals

After completing this course, students will be able to:

• Write Python programs using core concepts, including data types, data structures, functions, control flow, and loops.
• Work effectively with tabular data structures (e.g., dataframes) for financial data analysis.
• Load, store, and manage large datasets; perform advanced data cleaning, conversion, and transformation for market and accounting data.
• Create clear, informative visualizations to explore data and communicate empirical findings.
• Retrieve financial data from online sources using web services/APIs and integrate those data into a research workflow.
• Construct and analyze stock portfolios and implement mean–variance portfolio optimization.
• Estimate and interpret the Capital Asset Pricing Model (CAPM) and multi-factor asset pricing models.
• Design, implement, and backtest systematic investment strategies using historical equity and fixed-income data.
• Compute and interpret performance and risk metrics to evaluate investment strategies and investment funds.
• Apply and evaluate a machine learning model for credit default prediction and interpret model outputs in a finance context.

Class Times

Office Hours

Syllabus