Data Analysis for Finance

Course Description

Recent innovation of information technology along with the fast growth of applications on the Internet have resulted in an explosion of financial data, new ways of data collection and storage, as well as additional opportunities for business and research based on the data. This course enables students to analyze financial data based on traditional financial models. The major topics include asset pricing, capital budgeting, risk management, pension fund management, portfolio analysis, and stock hedging. Students will learn (review) the models with a focus on their implementation using Microsoft Excel, Matlab, or other programming languages. In addition, the basic statistical models, such as regression, time series models and probability models will be used. “Big Data” (data mining) technology will be introduced with a focus on financial data analysis. The main topics include classification, clustering, association analysis and anomaly detection. The key objectives of this course are: (1) to review the classical financial models and statistical models; (2) to teach the concepts of data mining with a focus on financial applications; (3) to provide students extensive hands-on experience in applying the concepts in financial data applications. 3 credits Prerequisite: FIN 502 and admission to the MS in Finance or MBA 502 or 36 credit MBA in Finance Student; FIN 540 Grading Letter graded (A, A-, B+, etc.)

Code

FIN 562

Credits

3

Subject

FIN

Programs That Include This Course

• Master's Master of Science in Decision Analytics
• Master's Master of Science in Finance