FINC 651
Financial Analytics: Applications for Data Analysis and Portfolio Construction
1. Catalog Entry
FINC 651
Financial Analytics: Applications for Data Analysis and Portfolio Construction
Credit hours (3)
Applications for Data Analysis and Portfolio Construction, will apply major financial, statistical, and quantitative techniques to analyze big data and construct optimal financial portfolios. This new course will cover the importance of statistical techniques, advanced econometrics, portfolio theory, and risk/reward interplay to a) understand big data, b) use econometrics to streamline time series, c) develop financial models, and d) analyze and create investment portfolios for individual investors and institutional clients. The course coverage includes use of financial and economic databases, advanced econometrics tools, portfolio theory, SAS and/or Excel and/or SPSS, and research methods in finance.
2. Detailed Description of Course
1) Databases training: Students will learn how to use existing databases such
as Morningstar,
Bloomberg, The Federal Reserve, and other data sources to download a variety
of data that
will be used for analysis purposes.
2) Applied econometrics: Students will learn how to use applied statistics such
as cleaning of
data, identification of outliers, streamline a dirty time series of data,
data manipulation
(such as winsorising data, dropping the extreme outliers, converting nonlinear
data into
linear etc.), regression analysis, and forecasting
3) Portfolio theory
4) SAS (and /or Excel and/or SPSS) Training
5) A Comprehensive Group Research Project (maximum three or four students per
group) based
on databases, portfolio theory, applied econometrics, and software knowledge.
The instructor
will provide research ideas to different groups and also assist groups within
the realm of
instructor support in completing their final projects.
3. Detailed Description of Conduct of Course
It is intended that this course will be first developed as an on campus course, then also offered as a hybrid course and eventually made available entirely online.
4. Goals and Objectives of the Course
Goal: Students will apply financial analytics techniques for personal and professional investment decisions.
Objectives: Students will improve their data analysis skills and use statistical and financial knowledge to develop sound investment decision making approaches.
5. Assessment Measures
Students鈥� progress will be measured by their performance on assignments, tests and in class presentations of their applications of financial analytics in compliance with Quality Matters Standards.
6. Other Course Information
None
Review and Approval
May 11, 2015