The Master of Science in Financial Analytics is a STEM-designated program that educates students with a broad spectrum of knowledge in corporate finance and investments, data analysis, computer programming, Excel-based financial modeling and portfolio and risk management. Graduates emerge well-prepared to think critically, apply analytic tools, answer specific business questions and forecast possible future financial scenarios for organizations.
Fundamental concepts such as time value of money and risk-return relationships in finance are presented in detail. Financial decisions faced by modern business enterprises are analyzed in the framework of financial theories. These decisions include capital budgeting, long-term financing, dividend policy, short-term financial management, and performance evaluations.
Introduces Excel VBA, Monte Carlo simulation, efficient frontier, portfolio optimization, asset pricing models, capital budgeting decisions, yield curves and bond strips.
Course incorporates programming and data analytics. Calculate portfolio performance parameters, conduct security fundamental analysis with ratios and build optimized portfolios using the Index Model. Risk measures such as Value at Risk (VaR), Conditional Tail Expectations (CTE), and Lower Partial Standard Deviation (LPSD) discussed. Risk management strategies through hedging also covered.
Introduces fixed income products and markets. Topics covered: duration/convexity and price approximation, term structure of interest rates, asset-backed securities such as bond-backed securities, stripped products, municipal bonds, sovereign bonds, alternative bonds, federal funds and LIBOR products, repo and reverse repo.
Explores the ways that new technologies in AI, deep learning, blockchain and open APIs are disrupting the financial services industry. Includes payment, credit, trading and risk management.
Focuses on the identification and access of information sources and analyzing the information to make informed decisions and solve managerial problems. Among the topics included are numerical and graphic description of data, confidence intervals, hypothesis testing, regression analysis and predictive modeling, linear allocation models and allocating resources, forecasting, and decision analysis. The course utilizes spreadsheet, statistical and simulation software.
A course designed to teach the fundamentals of programming as it relates to the development of data science and analytics solutions. May include software platforms Python or R.
A course on communication of analytical and data science results using visualization software. May include Tableau, PowerBI and other visualization packages.
An integrated study of systems for collecting, storing and retrieving data with a particular emphasis on relational databases. May include Snowflake, SQL SERVER, MySQL.
Addresses tools and techniques required for analyzing business data for forecasting. Includes time series analysis and time series forecasting, and application of these techniques to support business decision makers.
Prerequisites are not required.
Applicants must demonstrate a basic understanding of analytical techniques and have strong communication skills.
Applications are evaluated on an individual basis considering the following factors:
1. Academic Background (transcript)
2. Professional Experience (resume or CV)
For questions about the application process:
Cindy Treadway
Meinders Graduate Advisor
[email protected]
405-208-5154
For questions about the international application process:
Aaron Wheelbarger
Director of International Admissions
[email protected]
405-208-5006
Located minutes from downtown Oklahoma City, the Meinders School of Business contains cutting-edge technology in more than a dozen teaching rooms, four executive classrooms, large and small conference rooms, four computer labs, including the Bloomberg Finance Lab for access to the latest investment data, breakout rooms for small-group meetings, and a 250-seat, theater-style auditorium.
