MS IN FINANCE COURSE DESCRIPTIONS
As a student in the online Master of Science in Finance program, you will complete 30 credits over four terms.
Here are the course descriptions for the 30 total credits you will take while enrolled.
To ensure you are prepared for the academic demands of the program, you must have earned a grade of B or higher in introductory courses in four areas: accounting, finance, statistics, and economics. If you haven’t taken a course in each of the four areas, you can meet the prerequisite requirements by taking the following online courses through Fordham University:
Basics of Accounting
Provides a basic understanding of the preparation and analysis of corporate financial statements; introduces generally accepted accounting principles (GAAP) and the standard-setting process; and discusses current issues in the reporting process, such as the benefits and problems of the Sarbanes-Oxley Act.
Basics of Finance
This course is an introduction to the financial system and the basic techniques in valuation of financial and physical assets. The course is primarily meant for someone who has not had a formal introduction to financial markets, institutions, and instruments. The course will cover the topics of financial statement analysis, time value of money, valuation of stocks and bonds, capital budgeting, cost of capital, and the efficient market hypothesis.
Basics of Statistics
Introduces the basic statistical concepts essential for business research and decision-making. These include descriptive statistics, probability distributions, statistical inference, and simple and multiple regressions.
Basics of Economics
Examines microeconomic theory and concepts that strive to explain economic decisions of businesses in the marketplace. The dominant issues addressed are the factors of supply and demand and the relationship of production costs, output, and market structures to pricing. Designed to provide the economic foundation for management decisions.
MS IN FINANCE CORE COURSES
This course introduces students to the different financial data sources used in practice and in research. Students will learn how to access and download data from Bloomberg, financial data websites, and research databases. Students will also be introduced to data manipulation tools and basic statistical tools in Python and will engage in short projects that use the data and implement the tools developed in class. The focus is to provide a knowledge of financial data, Python data-frame techniques, and data visualization and inferences using Python.
Corporate Finance Applications
This course will explain the concepts of corporate finance and their applications in an international setting. Students will examine opportunities and problems that are faced specifically by multinational and foreign corporations and will compare corporate finance practices around the world. Topics covered in the course include foreign exchange rate mechanics, international parity theories, forecasting and hedging, international cost of capital, capital budgeting, capital structure, and valuation of foreign investments.
Financial Markets and Responsibility
Students in this course will discuss the instruments that support and frame the markets, the trading mechanisms, and the regulatory structure. The course is intended to be descriptive and conceptual. The aim is to familiarize students with the breadth and scope of equity, debt, and derivative markets, and will include recent developments in the U.S. and the development of financial markets globally.
The objective of this course is to introduce students to investment principles in the U.S. and in the global capital market. Students in this course will gain an understanding of existing assets and investment vehicles, the functioning of capital market, the theoretical principles that underline asset pricing, and its applications in the valuations of fixed income and equity securities.
Here is a sample of electives you may be able to choose from.
Advanced Corporate Finance
This course teaches the art of applying corporate finance theory and essential tools and techniques to strategic decision-making in critical real-life situations faced by organizations. The course enhances the students’ understanding of corporate finance by providing a comprehensive examination of selected advanced topics, such as alternative valuation methods, real options in corporate finance, decision trees, international operations, mergers and acquisitions, risk arbitrage, debt capacity and leveraged buyouts, private equity, warrants and convertibles, and ethical issues.
Over the past several decades, the field of finance has developed a successful paradigm based on the notions that investors and managers are generally rational and that the prices of securities are generally efficient. In recent years, however, anecdotal evidence as well as theoretical and empirical research has shown this paradigm to be insufficient to describe various features of actual financial markets. In this course we will use psychology and more realistic settings to guide and develop alternative theories of financial markets. We will examine how the insights of behavioral finance complement the traditional paradigm and shed light on investors’ trading patterns, the behavior of asset prices, corporate finance, and various financial market practices through lectures, case studies, and our own discussions.
This course will focus on government policies and their motivation, transmission, and limitations. Students will learn how a country’s investment possibilities and potential GDP is driven by its labor force and productivity. In turn, the level of productivity can be affected by a confluence of monetary, fiscal, currency, and regulatory policies developing at the “emerging growth” phase, when political goals and legal structures are still in transition, financial and government institutions are not yet fully formed, and consumer spending behavior and market availability are evolving.
Covers estimation of parametric and non-parametric techniques commonly used in finance, applying high-frequency financial databases. Discusses properties of financial data, linear time-series data analysis, basic theory of statistical inference with linear models, general linear models, conditional heteroskedasticity models, nonlinear models, and Bayesian inference and estimation.
Develops (using Excel) the type of financial models that businesses use every day to analyze a wide range of financial problems and make decisions. Covers modeling of financial statements and models in many other important practical areas, such as time value of money, project evaluation, bonds, investment management, and derivatives. Emphasizes using the most powerful and useful tools in Excel—such as logical functions, PivotTables, Data Table, Scenario Manager, and Goal Seek—to solve problems that closely resemble real-life situations.
Global Equity Portfolio Management
The objective of this course is to introduce students to modern portfolio analysis and aspects of practical portfolio management. Topics include basic risk measurement, asset allocation and the Markowitz mean-variance analysis model, hedging and Value at Risk (VaR), the Capital Asset Pricing Model (CAPM), and portfolio performance evaluation. Emphasis will be placed on applying theories to aspects of portfolio management using real data. This course is project oriented.
Machine Learning for Finance
Machine learning (ML) methods of data analysis and prediction are transforming the financial landscape. This course provides a broad overview, knowledge, and practical skills of Machine Learning (ML), focusing on applications in Finance. The course will introduce various ML methods including supervised and unsupervised learning, as well as deep and reinforcement learning. Students will understand the general landscape of available ML algorithms and learn to implement the most appropriate solutions of a given problem. The course will use Python programming and open source Python packages, and requires knowledge of statistics. Class sessions will provide the basics of Python, which is not a prerequisite, and resources will be provided for both Python and statistics.
This course covers research methodology, including essential skills for writing a research paper, or conducting an empirical analysis, relating to financial markets and data. It will emphasize where to find the raw data, and, methods of proper treatment to make it suitable for analysis. Students will also learn Tableau, a powerful data visualization software. Students will apply course techniques to a research project which will be provided by an industry practitioner, or a faculty member, or the student.
Sustainability Reporting and Finance
Financial decisions worldwide are increasingly influenced by the unique risks of the 21st Century. All activities demand focus on sustainability issues; from the looming impacts of climate change, to health-and safety-associated risks, to credit and investment gaps that limit business opportunities and hamper economic progress in many parts of the world. As the challenges of scarcity of resources, the search for profits through efficiency, and impact of climate change continue to escalate, environmental, social and governance (ESG) data become essential for prudent decision-making. Along with several multinational investment banks, the Dow Jones has a sustainability index indicating that the search for profitability through efficiency has transcended trends and has now become the new corporate norm. This course studies finance, corporate disclosures and sustainability reporting practices as integrated subjects beginning with an introduction of financial and reporting principles and moving through financial analysis and industry focused disclosures. Additionally, the course covers diverse aspects of sustainable reporting and offers tools for effective risk assessment.
Delve deeper into the fast-paced, competitive world of finance with the Gabelli School of Business’ specialized tracks for Master of Science in Finance students—Portfolio Management, Investment Banking, and Financial Analytics—and optimize your online learning experience. As a student, you have the option to focus your electives and pursue one or more tracks, depending on your course of study. Although tracks will not be noted on your transcript, you are encouraged to include your track(s) on your résumé; your industry insights could help you stand apart in your chosen field.
Portfolio Management: Fine-tune your analytical mind with a track designed for emerging financial experts. The U.S. Bureau of Labor Statistics projects the career outlook for portfolio managers will grow by 17 percent, much faster than the national average.1 With the Portfolio Management track, you will learn how to make strategic stock market decisions, balance and manage assets, leverage multimillion-dollar funds, and anticipate potential investment risks to maximize your clients’ bottom line.
Investment Banking: Develop the leadership and interpersonal skills you need to set yourself apart from competitors in the fast-paced field of corporate finance. This robust track will teach you how to secure capital for growing businesses, understand the nuances of financial modeling, create detail-oriented pitch books, and interpret complicated data into digestible graphs and charts.
Financial Analytics: Connect underlying economic trends to real-world stock market strategy. With a track in Financial Analytics, you will learn how to organize and interpret complex datasets into digestible graphs and charts, forecast financial projections, measure potential profit margins, and make informed recommendations about stock market trading.
Speak with an admissions officer to learn more about these specialized tracks.
TAKE THE NEXT STEP
Learn how our online MS in Finance courses can give you the knowledge and skills to build a global finance career with worldwide impact.
Thinking about an on-campus program? The Gabelli School of Business also offers a portfolio of graduate programs across a variety of business disciplines. Visit the Gabelli School website to learn more.
1Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Financial Managers. Retrieved December 13, 2021.arrow_upwardReturn to footnote reference