The Master of Science in Finance (MSF) online program focuses on a global outlook, practical application, real-world trends, and the importance of ethics to deliver a comprehensive and career-focused education.
The world-class MSF curriculum is available either part-time or full-time over 21 months. The curriculum requirements include six core courses, two advanced courses, and a choice of two elective courses, as well as a weeklong opening residency and the Global Consulting Project Residency, which gives students an opportunity to solve a complex, real-world challenge for an international firm.
Students pursuing the full-time option also take the MSF clinic course and two additional electives.
Residencies (5 credits)
This weeklong residency occurs after students complete their first core course: Financial Markets. While on the Georgetown University campus, students will work together on a case study competition, experience the Georgetown University campus, and get better acquainted with their classmates and professors. Credits: 2
Global Consulting Project
This capstone project challenges students with an invaluable real-world experience. Drawing upon the skills you have learned throughout the program, students conduct a consulting project for a firm in another country. Students will research the history, business, political and regulatory environment of the client’s country as well as consult with them via e-mail, phone and video conferencing. The project includes an intensive, prep weekend on campus in the middle of the course and concludes with a weeklong visit to the client’s country during which the students will finalize their project, present it to the client, and enjoy business and cultural site visits. Credits: 3
Core Curriculum (18 credits)
This course begins with a review of the building block concept of the time value of money and quickly moves onto bond and stock valuation; the relation between risk and return; and the ongoing debate about the workings of financial markets according to the Efficient Market Hypothesis and the alternative hypothesis of Behavioral Finance. Credits: 3
This course focuses on the accumulation, analysis and presentation of relevant accounting data of an enterprise and how it is used to serve the needs of managers, shareholders, creditors and external analysts. Credits: 3
Building on the previous core courses, this course shows you how to maximize shareholder wealth within a legal and ethical framework. Topics covered include capital budgeting, cost of capital, capital structure, payout policy and the fundamentals of derivative pricing. Credits: 3
Focusing on the art and science of making sense of financial data, this course teaches students how to build and analyze large databases using advanced econometric techniques. Credits: 3
Corporate Valuation and Modeling
This course covers advanced valuation topics such as the free cash flow approach to equity valuation, the use of accounting and market data to measure and manage the value of the firm, and parameter estimation errors in valuation. Credits: 3
Principled Financial Leadership
Designed to train women and men to lead with integrity, this course focuses on ethical challenges faced by leaders of financial firms or by leaders in finance positions at non-financial firms. Credits: 3
Advanced Courses (6 credits)
Options Pricing and Risk Management
In recent years, options and other derivatives markets have become increasingly important in the world of finance and investments. Thus, it is essential for all finance professionals to understand how these markets work, how these derivatives can be used, and what determines the prices of derivatives. This course addresses these issues and also examines trading strategies involving options. The course also explores specific applications of options in the corporate setting, including executive stock options and real options. Finally, the course examines how corporations can manage currency, commodity price, interest rate, and other risks they face in doing business in a multinational setting. Credits: 3
Investments and Fixed Income
This course is designed to introduce the student to the modern world of professional asset management. Topics include the identification and analysis of investment opportunities, portfolio analysis and optimization, the identification and execution of investment strategies, and the professional responsibilities of asset managers. Although the equity markets often get more attention in the popular media, the fixed income world is much larger in terms of both outstanding issues as well as annual issuance. Credits: 3
Program Electives (at least 3 of 8.5 credits)
Private Equity Real Estate
This course will focus on the art and science involved in making an investment in commercial and multifamily real estate through a bottom-up (asset level) and top-down (capital markets) approach that is regularly employed in the real world. Lectures will be delivered from the perspective of an equity investor with the intention of instructing each student on the proper approach to measuring risk associated with a real estate transaction. This approach will broadly cover the physical and the financial aspects of the investment, but will delve deeply into a variety of subtopics including leverage, and income tax considerations. Students will gain a broader understanding of the institutional real estate world and how circumstances in the current market impact the ability to acquire (and dispose) of product. The course will also employ a case study approach for applying the course concepts to practical real estate problems. Credits: 1.5
Big data is a relative term—data today are big by reference to the past, and to the methods and devices available to deal with them. The challenge big data presents is often characterized by the four V's -volume, velocity, variety, and veracity. Volume refers to the amount of data. Velocity refers to the flow rate—the speed at which it is being generated and changed. Variety refers to the different types of data being generated (currency, dates, numbers, text, etc.). Veracity refers to the fact that data is being generated by organic distributed processes (e.g., millions of people signing up for services or free downloads) and not subject to the controls or quality checks that apply to data collected for a study. Credits: 1.5
Advanced Financial Modeling
This course focuses on learning fundamental data analysis techniques, using the various libraries and code base of Python. Credits: 1.5
Financial Statement Analysis
Financial Statement Analysis (FSA) is the process of reviewing and evaluating an organization's financial statements to gain an understanding of its economic situation. Good FSA enables effective decision-making. For example, it plays an important role in understanding the potential risk and rewards of an investment. Credits: 1.5
Small Data is an integral part of Business Analytics and it refers to two important concepts in practice:
I. When solving a consulting problem, it is very common to have a small sized data set (say less than 30) in order to solve practical business situations. This data set may not be normal or could be quantitative or qualitative. The data could be categories such a political parties, rankings such as customer preferences or continuous such as the time a customer waits in line on hold with a credit card 800 customer service number.
II. Small data is also a new concept in Business Intelligence which appeared in late 2012, it is the opposite of Big Data and it is an innovative way to understand your customers’ actions.
It refers to all tidbits of information created by customers by means of customers’ preferences, wearables devices and self-tracking. Think of all the digital tidbits consumers leave in their paths as they go through the day. Credit card payments, location fixes, newsletter signups, Facebook likes, tweets and Web searches. Small data are derived from our individual digital traces. Small data is generally small sample sized and not normally distributed. Small Data drives personalization.Credits: 1.5
This 16-week, one-credit, pass-fail, fall semester course provides students with the tools, resources, techniques, and access necessary to be ready to leverage the MSF degree both in the short and the long-term.Credits: 1
Semester structure: 3-credit courses offered in 7-week modules; *1.5 credit courses offered in 4-week modules.
Additional Non-Credit Learning Opportunities for MSF Students
Applied Data Science for Finance Summer Course
During the non-credit data science course, students deepen business and technical finance skills while gaining exposure to Python programming.
Over the span of four weeks, students join MSFLive sessions consisting of 90-minute interactive case discussions and complete a comprehensive data programming assignment.
Learn more about the data science course.