Quantitative Finance

Catalog description

ECO 43000 builds upon the student’s experience using Python programming to perform statistical analysis of historical financial data to understand and evaluate investment products and trading/investing strategies in the context of empirical evidence about return patterns across assets (e.g., the factors such as value/growth, momentum, and carry that drive returns) in multiple markets/asset classes (e.g., domestic and international equities and bonds, currencies, and commodities). This course will be financial economics intensive, but the ability to develop alpha signals through applying fundamentals of statistics to analyze large datasets by programming in Python is crucial to have in a career in marketing, human resources, business operations, and economics. Course topics include developing quantitative trading strategies, foundations of machine learning and artificial intelligence, and application of data science. This course is project based, with students completing a capstone project requiring substantial Python programming to create a trading algorithm and provide statistical support for their algorithm through backtesting their algorithm against a benchmark.