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12 Months, 31 Credits

The Saunders Master of Science in Finance program at RIT is designed to prepare students for managerial careers in corporate finance, investment analysis and portfolio management, financial consulting and financial institutions.

The analytical, deductive nature of the program creates financial professionals who can adapt to dynamic changes in the deregulated financial industry and its dramatic growth in investment options. Its courses prepare students to sit for the Chartered Financial Analyst (CFA) exam.

Students may complete the program on a full-time or part-time basis, with the full-time program beginning exclusively in the fall semester.

The program of study consists of 10 courses and a comprehensive exam based on the finance courses completed by the student. The exam will be administered at the end of the student’s last term. Students must pass the exam to earn their degree.

Curriculum Resources

The latest curricula for incoming students is provided below. Current students and alumni – please visit the Office of the Registrar for a History of Course Catalogs to view and download official degree requirements pertaining to the academic year you began your degree.

Curriculum

Finance, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
ACCT-603
Accounting for Decision Makers
A graduate-level introduction to the use of accounting information by decision makers. The focus of the course is on two subject areas: (1) financial reporting concepts/issues and the use of general-purpose financial statements by internal and external decision makers and (2) the development and use of special-purpose financial information intended to assist managers in planning and controlling an organization's activities. Generally accepted accounting principles and issues related to International Financial Reporting Standards are considered while studying the first subject area and ethical issues impacting accounting are considered throughout.
3
FINC-721
Financial Analysis for Managers
An examination of basic financial theories, techniques, and practices. Topics include: time value of money, valuation, capital asset pricing, risk and diversification, cost of capital, capital budgeting techniques and spreadsheet analysis.
3
FINC-725
Securities and Investment Analysis
A survey of topics in investment analysis, including the study of financial markets, features of various financial assets and security pricing. Focus is on individual security analysis (as distinct from portfolio analysis). Asset pricing theory is used in valuing securities. Practical issues in equity valuation are discussed including risk evaluation, macroeconomic/industry/competitive analysis, and the use of corporate SEC filings.
3
FINC-740
Options and Futures
This course focuses on financial derivative securities. Their role in financial management is becoming increasingly important, especially in portfolio management. This course covers valuation of various options and futures as well as their use in risk management. Specific topics include options and futures pricing models, options strategies, and contemporary topics such as index arbitraging.
3
FINC-790
Field Exam Preparatory
All MS-Finance students take a field exam at the end of their program. This course provides basic help to students taking this exam. (all required finance courses in the MS-finance program)
1
Finance Electives
9
STEM Electives
9
Total Semester Credit Hours
31

Finance electives

FINC-722
Financial Management II
This advanced course in corporate finance focuses on financing policies, financial planning/control, and other advanced corporate topics. Specific topics include the financing process, alternative financing instruments, restructuring, cost of capital, corporate applications involving options, working capital management and the use of financial budgets/forecasts.
FINC-732
Portfolio Management
This course extends the knowledge of risk and return in a portfolio context to portfolio management. Topics include portfolio optimization, diversification strategies, hedging strategies and performance evaluation. A variety of investment tools (e.g., fixed income securities) and investment contexts (e.g., pensions) will be studied.
FINC-742
Financial Modeling and Analysis
Students apply computer technology to solve finance-related problems using a variety of analytical methods. Analytical methods include spreadsheet modeling, mathematical optimization, regression, decision tree analysis, and Monte Carlo Simulation. Typical topics covered are financial forecasting, pro-forma financial statements, equity valuation, cash budget forecasts, and portfolio analysis. This is a hands-on course that focuses on collecting, managing and analyzing financial data.
FINC-758
Seminar in Finance
Special topics seminars offer an in-depth examination of current events, issues and problems unique to finance. Specific topics will vary depending upon student and faculty interests and on recent events in the business world. Seminar topics for a specific semester will be announced prior to the course offering. These seminars may be repeated for credit since topics will normally vary from semester to semester. (instructor-determined)
FINC-760
Finance in a Global Environment
This course has a specific focus on international business problems that are financial in nature. Topics include an examination of the international environment the firm operates in, international investment, exchange rates and the management of risks arising from shifting exchange rates, and the problems of short and long-term asset and liability management.
FINC-761
Stock Market Algorithmic Trading
The course is a “hands-on” lab-based class designed to help students develop algorithmic trading strategies to invest in the stock market that can be implemented by retail and professional traders. What sets this course apart from many others is a strong emphasis on practical application with the purpose of building marketable skills for careers in finance. Concepts are not only taught, they are brought to life by learning how to design algorithmic trading models through the use of a computerized trading platform, that allows back-testing of data on thousands of different stocks. The software platform includes an automated wizard for building advanced technical trading models without programming knowledge; but also has an embedded programming language, similar to C-sharp, for those students that have those skills and elect to use them. (Knowledge of programming is not required; and there are no pre or co-requisites; but a lap-top is strongly recommended.).
FINC-772
Equity Analysis
Students learn about various equity markets, trading, and valuation. The focus of this course is on valuing equities using widely used methods and in forming and analyzing equity portfolios. Students also learn portfolio optimization methods.
FINC-773
Debt Analysis
Students learn about various debt markets, trading, and valuation. The focus of this course is on valuing debt instruments using widely used methods and in forming and analyzing debt portfolios.
FINC-780
Financial Analytics
This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

STEM electives

ACCT-745
Accounting Information and Analytics
The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction.
BANA-680
Data Management for Business Analytics
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.
BANA-780
Advanced Business Analytics
This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.
MGIS-650
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
MGIS-725
Data Management and Analytics
This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.
FINC-761
Stock Market Algorithmic Trading
The course is a “hands-on” lab-based class designed to help students develop algorithmic trading strategies to invest in the stock market that can be implemented by retail and professional traders. What sets this course apart from many others is a strong emphasis on practical application with the purpose of building marketable skills for careers in finance. Concepts are not only taught, they are brought to life by learning how to design algorithmic trading models through the use of a computerized trading platform, that allows back-testing of data on thousands of different stocks. The software platform includes an automated wizard for building advanced technical trading models without programming knowledge; but also has an embedded programming language, similar to C-sharp, for those students that have those skills and elect to use them. (Knowledge of programming is not required; and there are no pre or co-requisites; but a lap-top is strongly recommended.).
FINC-773
Debt Analysis
Students learn about various debt markets, trading, and valuation. The focus of this course is on valuing debt instruments using widely used methods and in forming and analyzing debt portfolios.
FINC-780
Financial Analytics
This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

Finance Faculty