Analytic Finance and Machine Learning

1-6 Hours

Utilize traditional financial datasets (e.g., financial statements, ratio analysis, credit and fixed income information, stock returns) from a variety of data sources and platforms, including WRDS/Compustat/CRSP, Bloomberg, and Compustat. Explore more advanced techniques designed to yield insights from alternative and unstructured datasets (e.g., text and sentiment analysis of earnings conference calls; SEC filings like 10K's, 10Q's, and 8-Ks; message and discussion boards; marketing/customer feedback; and other big data sources). For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.