Finance Master of Science Degree

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A master of science in finance unlocks emerging FinTech fields (algorithmic trading, blockchain and cryptocurrency) as finance industries become more data-driven and analytics-based.

$74.2K

Average First-Year Salary of RIT Graduates from this degree

#26

Best Master’s of Finance TFE Times, 2023

#29

Best Master’s in Corporate Finance Eduniversal, 2023

Overview for Finance MS

Why Study RIT’s Master of Science in Finance

  • STEM-OPT Visa Eligible: The STEM Optional Practical Training (OPT) program allows full-time, on-campus international students on an F-1 student visa to stay and work in the U.S. for up to three years after graduation.
  • Manage an investment portfolio with the Finance Management Association.
  • Highly flexible program includes exposure to software including Python, R, Tableau, Matlab, SAS, and SQL
  • The curriculum is designed to prepare you to take the Chartered Financial Analyst® (CFA) exam.

The master of science in finance unlocks the world of finance and prepares you for careers in corporate finance, investment analysis, wealth management, portfolio management, financial consulting, commercial banking, investment banking, insurance, cryptocurrencies and FinTech. This is a highly flexible program with a strong emphasis on experiential learning opportunities, enabling you to apply your knowledge in real-world scenarios. Accessing Bloomberg Terminals in the Sklarsky Business Analytics Center, you tap into the latest financial market developments while having the tools to analyze and develop unique insights.

RIT’s Finance Master’s Degree

Today’s finance industries are data-driven and analytics-based. As a result, RIT’s finance master’s degree includes projects and teaching materials that expose you to financial analytics skills and software. In this highly flexible program, you’ll complete courses in accounting, corporate finance, investments, risk management, and more. In addition, you'll choose elective courses in other areas of finance such as banking, algorithmic trading, and financial modeling, as well as data-related courses such as financial analytics. Students also have access to data courses offered elsewhere in our college and build skills on specific languages such as Python or SQL. Top finance master's programs, like RIT's master of science in finance, prepare you to take the Chartered Financial Analyst® (CFA) exam–the most respected and recognized investment management designation in the world.

Leading Finance Faculty

Saunders’ finance faculty are prestigious researchers who actively contribute to elite research journals, covering traditional finance topics and cutting-edge areas such as high-frequency trading, stock/option trading strategies, NFT, cryptocurrencies, and blockchain. Our faculty members bring valuable industry expertise to their classrooms while maintaining solid connections with industry professionals.

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Program Director Hao Zhang discusses the Finance program

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Careers and Experiential Learning

Typical Job Titles

Chief Executive officer (CEO) Chief Financial Officer (CFO) Credit Risk Analyst
Controller Data Analyst Director, Corporate Strategy
Director, Financial Planning and Analysis Financial Advisor Financial Analyst
Finance Director Financial Engineer Financial Planner
Investment Banker Loan Officer Portfolio Manager
Quantitative Analyst Quantitative Investment Analyst Research Associate
Risk Analyst Trader Vice President, Finance

Post-Graduation Salary and Career Info for Finance MS

Cooperative Education and Internships

What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.

Co-ops and internships take your knowledge and turn it into know-how. Business co-ops provide hands-on experience that enables you to apply your knowledge of business, management, finance, accounting, and related fields in professional settings. You'll make valuable connections between course work and real-world applications as you build a network of professional contacts.

Cooperative education is optional but strongly encouraged for graduate students in the finance program.

Finance Careers

Graduates of RIT's master of science in finance are prepared for outstanding career opportunities in a range of finance positions. Our alumni are employed at diverse firms such as AMG Technology, T3 Trading Group, LLC, TD Securities, MAI Capital Management, and more. Our Finance Industry Advisory Board helps students prepare for their careers by ensuring the curriculum is continuously updated to meet employers needs while providing networking and mentorship opportunities.

Our alumni are employed at diverse firms such as AMG Technology, Citi Group, Fannie Mae, Fidelity Investments, Goldman Sachs, KPMG, MAI Capital Management, Paychex, T3 Trading Group, LLC, TD Securities, and more.

Featured Work and Profiles

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Curriculum for 2024-2025 for Finance MS

Current Students: See Curriculum Requirements

View Printable 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. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring, Summer).
 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. (Prerequisites: ACCT-603 or equivalent course.) Lecture 3 (Fall, Spring).
 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. (Prerequisites: FINC-721 or equivalent course.) Lecture 3 (Fall, Spring).
 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. (Prerequisites: FINC-721 or equivalent course.) Lecture 3 (Fall, Spring).
 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) (This course is restricted to FINC-MS Major students.) Comp Exam 1 (Fall, Spring, Summer).
 1
 
Finance Electives
 9
 
STEM Electives
 9
Total Semester Credit Hours
31

Finance electives

FINC-610
Financial Risk Management and Analysis
Students learn about various financial risk measurement and management issues. The focus of this course is on analyzing financial and other risks using widely used methods and discussing various ways of managing the risks. (This course is restricted to FINC-MS Major students.) Lecture 3 (Spring).
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. (Prerequisites: FINC-721 or equivalent course.) Lecture 3 (Fall, Spring).
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. (Prerequisites: FINC-725 or equivalent course.) Lecture 3 (Fall, Spring).
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. (Prerequisites: FINC- 722 and FINC-725 or equivalent courses.) Lecture 3 (Fall, Spring).
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) Lecture 3 .
FINC-760
International Finance
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. (Pre or Corequisites: FINC-721 or equivalent course.) Lecture 3 (Fall, Spring).
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.). Lecture 3 (Spring).
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. (Prerequisites: FINC-671 or equivalent course.) Lecture 3 (Fall).
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. Lecture 3 (Fall).
FINC-795
Computational Finance Experience
Students apply their mathematical, data analytic, and integrative finance skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/co-op followed by an in-depth report may obtain equivalent credit. (This course is restricted to CMPFINC-MS Major students.) Lecture 3 (Summer).
 
FINC, ACCT, and MGIS courses 600-700 level and others as approved

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. (Prerequisites: ACCT-110 or ACCT-603 or equivalent course.) Lecture 3 (Fall, Summer).
ACCT-796
Accounting Capstone Experience
The principal focus of this course is students completing several projects provided by members of CPA firms and industry employers. Employers provide assignments, which may include data or require students to gather relevant data, and students use defined technology, which may include a variety of applications common in technological accounting practice, to complete projects in teams. Students also write comprehensive individual reports and analyses related to the projects. Peripheral work in the course includes examination of theoretical concepts, definitions, and models espoused in the accounting literature and relevant to analyzing various contemporary issues in financial accounting and reporting. The historical development of accounting standards and contemporary issues in financial reporting are integrated. The course requires writing and student presentations. Subject to approval by the Program Director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit. Lecture 3 (Spring).
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. Lecture 3 (Fall).
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. (Prerequisite: BANA-680 or equivalent course.) Lecture 3 (Spring).
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. Lecture 3 (Fall).
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. Lecture 3 (Spring).
FINC-610
Financial Risk Management and Analysis
Students learn about various financial risk measurement and management issues. The focus of this course is on analyzing financial and other risks using widely used methods and discussing various ways of managing the risks. (This course is restricted to FINC-MS Major students.) Lecture 3 (Spring).
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. (Prerequisites: FINC- 722 and FINC-725 or equivalent courses.) Lecture 3 (Fall, Spring).
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.). Lecture 3 (Spring).
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. Lecture 3 (Fall).
FINC-795
Computational Finance Experience
Students apply their mathematical, data analytic, and integrative finance skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/co-op followed by an in-depth report may obtain equivalent credit. (This course is restricted to CMPFINC-MS Major students.) Lecture 3 (Summer).

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Admissions and Financial Aid

This program is available on-campus only.

Offered Admit Term(s) Application Deadline STEM Designated
Full‑time Fall Rolling Yes
Part‑time Fall or Spring Rolling No

Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Finance MS program, candidates must fulfill the following requirements:

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

TOEFL IELTS PTE Academic
88 6.5 60

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

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