Master in Finance Course Tracks
Elective courses can be chosen according to either individual needs and preferences or to conform to one of the suggested tracks listed below. It is not necessary for a student to designate or complete a particular track to satisfy the Master’s requirements; the tracks listed below are merely illustrations of coherent courses of study that students might choose.
Beyond the tracks listed below, we offer a number of electives in corporate finance, dealing with the choice and financing of investment projects, firms’ determination of dividend policy, optimal capital structure, financial reorganization, mergers and acquisitions, start-up financing, deal structure, incentive design, valuation of high risk projects, initial public offerings, etc. However, we believe that our students’ comparative advantage lies in other areas encompassed within the modern investment bank such as asset management, risk management, derivatives pricing and trading, fixed income analytics and other areas where a quantitative background in theoretical and practical aspects of modern finance is essential.
Financial Engineering and Risk Management
Financial engineers design and evaluate products that help organizations manage risk-return trade offs. Financial engineering is no longer limited to quantitative traders and derivatives specialists, but is now used widely throughout the private sector for purposes including hedging foreign currency exposures, financing real investment, and managing real and financial risks. The aim of this track is to provide students with the background they need to be leaders and innovators in this growing field. The track includes courses in probability, optimization under uncertainty, stochastic calculus, dynamic programming, and financial economics. Special attention is given to the development of the efficient computational techniques that are needed in “real-time” computing environments. In addition, students can elect to focus on the computer-based technologies that are becoming increasingly important in finance, such as the design of efficient trading systems, algorithms, interfaces, large databases, and the security of computer networks. Several courses provide students with the opportunity to acquire practical experience. In particular, full-time students will have the opportunity to work in a small group on actual financial engineering problems under the joint guidance of a faculty member and a high-level industry practitioner: see Research.
Quantitative Asset Management and Macroeconomic Forecasting
Highly trained financial specialists are increasingly utilized in the fields of portfolio management and macroeconomic forecasting. Among the quantitative tools used in this area are “attribute” screening, analysis of earnings revisions, and quantitative forecasting methods. Quantitative techniques are widely employed to control portfolio risk and to establish portfolios balanced with different assets (stocks, bonds, real estate, etc.) so as to minimize the variance of returns. Finally, the major commercial banks, life insurance companies, securities firms, asset managers, etc. all employ financial economists to formulate strategies consistent with the expected performance of the macroeconomy; required skills include expertise in applied time series analysis and an understanding of the major statistical macro models.
Data Science & Financial Technologies
Computer-based technologies are becoming increasingly important in finance, such as efficient trading systems, algorithms, large databases, and the security of computer networks. The growth of computer-based trading, the ability to access and process big data sets, and the renewed emphasis on risk management in all firms are creating a new competitive environment where increasing the speed and lowering the costs of trading and other financial operations become essential components of success. This track gives students access to the latest tools and techniques of computer science and computational methods applied to finance (FinTech), including machine learning, artificial intelligence, information retrieval, deep learning and modern statistics.