The University of Texas at Austin 1-6 credit Finance
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. Pre/Corequisites: Graduate standing, Finance 394 and 397, and a strong background in Python, probability and statistics, and regression and machine learning techniques, and consent of instructor. Restrictions: Restricted to students in the Master of Science in Business Analytics program. Also offered as 285F, 385F, 485F, 585F, 685F for different credit.
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