Tracks and Electives
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 program’s requirements. The tracks listed below are merely illustrations of coherent courses of study that students might choose.
Beyond the tracks listed below, the program offers 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.
Students looking for information on key program requirements and core courses can find that information here.
Three optional program tracks
Financial Engineering and Risk Management Track
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, 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.
Pre-approved Master in Finance Elective Courses
The following courses are pre-approved as eligible electives toward the Master in Finance degree. Some of these courses have prerequisites, or require permission of the respective instructors.
Importantly, any course not on the pre-approved elective list must be pre-approved by the Director of Graduate Studies and will not be considered unless it meets the following criteria: a) having regular homework assignments b) a final exam, and c) is a full semester (not a half semester) course.
Please check this page for the most current updates regarding courses eligible as elective courses, as courses are often added or removed from this list. Not all courses may be offered every year. Please check with the relevant departments to confirm their offerings in any given year.
In addition to browsing the lists below, you can view descriptions of currently-available electives on the Registrar’s Office website.
List 1: Finance Applications Courses
FIN 515 | Portfolio Theory and Asset Management |
FIN 516 | Topics in Corporate Finance, Corporate Governance and Banking |
FIN 517 | Venture Capital and Private Equity Investment |
FIN 518 | International Financial Markets |
FIN 519 | Corporate Restructuring, Mergers and Acquisitions |
FIN 521 |
Fixed Income: Models and Applications |
FIN 522 | Options, Futures and Financial Derivatives |
FIN 523 | Forecasting and Time Series Analysis |
FIN 560 | Master’s Project I |
FIN 561 | Master’s Project II |
FIN 567 | Institutional Finance: Trading and Markets |
FIN 568 | Behavioral Finance |
FIN 570 | Valuation and Security Analysis |
FIN 580 | Quantitative Data Analysis in Finance |
FIN 590 | Financial Accounting |
FIN 591 | Cases in Financial Risk Management |
FIN 592 | Asian Capital Markets |
FIN 593 | Financial Crises |
FIN 594 | Chinese Financial and Monetary Systems |
ECO 414 | Introduction to Economic Dynamics |
ECO 525/FIN 525 | Asset Pricing |
ECO 526/FIN 526 | Corporate Finance |
ECO 527/FIN 527 | Financial Modeling |
ORF 455 | Energy and Commodities Markets |
ORF 527 | Stochastic Calculus and Finance |
ORF 530 | Statistical Analysis of Large Financial Datasets |
ORF 531/FIN 531 | Computational Finance in C++ |
ORF 534/FIN 534 | Financial Engineering |
ORF 535/FIN 535 | Financial Risk Management |
ORF 538 | PDE Methods for Financial Mathematics |
ORF 555 | Fixed Income Models |
ORF 574/FIN 574 | Special Topics in Investment Science: Trading and Risk Management |
List 2: General Methodology for Finance
APC 350 | Introduction to Differential Equations |
APC 503 | Analytical Techniques in Differential Equations |
APC 518/ORF 51 | Applied Stochastic Analysis and Methods |
CEE 513 | Introduction to Finite-element Methods |
CEE 532 | Advanced Finite-element Methods |
CEE 548 | Risk Assessment and Management |
CBE 508 | Numerical Methods for Engineers |
CBE 530 | Systems Engineering |
COS 402 | Machine Learning and AI |
COS 423 | Theory of Algorithms |
COS 425 | Database Information Management Systems |
COS 432/ELE 432 | Information Security |
COS 435 | Information Retrieval, Discovery, and Delivery |
COS 436 | Human-Computer Interface Technology |
COS 448 | Innovating Across Technology, Business, and Marketplaces |
COS 461 | Computer Networks |
COS 511 | Theoretical Machine Learning |
COS 524 | Fundamentals of Machine Learning |
ECO 418 | Strategy and Information |
ECO 501 | Microeconomic Theory I |
ECO 502 | Microeconomic Theory II |
ECO 503 | Macroeconomic Theory I |
ECO 504 | Macroeconomic Theory II |
ECO 511 | Advanced Economic Theory I |
ECO 512 | Advanced Economic Theory II |
ECO 513 | Advanced Econometrics: Time Series Models |
ECO 514 | Game Theory |
ECO 517 | Econometric Theory I |
ECO 518 | Econometric Theory II |
ECO 519 | Advanced Econometrics: Nonlinear Models |
ECO 521 | Advanced Macroeconomic Theory I |
ECO 522 | Advanced Macroeconomic Theory II |
ECO 523 | Public Finance I |
ECO 524 | Public Finance II |
ECO 531 | Economics of Labor |
ECO 541 | Industrial Organization and Public Policy |
ECO 551 | International Trade I |
ECO 552 | International Trade II |
ECO 553 | International Monetary Theory and Policy I |
ECO 554 | International Monetary Theory and Policy II |
ELE 491/EGR 491 | High-Tech Entrepreneurship |
ELE 535 | Machine Learning and Pattern Recognition |
FIN 581 | Entrepreneurial Finance, Private Equity and Venture Capital |
MAE 305/MAT391 | Mathematics in Engineering I (ODE, PDE) |
MAE 306/MAT 392 | Mathematics in Engineering II (PDE, complex variables) |
ORF 311 | Stochastic Optimization & Machine Learning in Finance |
ORF 363/COS323 | Computing and Optimization for the Physical and Social Sciences |
ORF 401 | Electronic Commerce |
ORF 409 | Introduction to Monte Carlo Simulation |
ORF 522 | Linear & Nonlinear Optimization |
ORF 523 | Convex & Conic Optimization |
ORF 524 | Statistical Theory and Methods |
ORF 525 | Statistical Foundations of Data Science |
ORF 526 | Probability Theory |
ORF 533 | Convex Analysis for Mathematical Finance |
ORF 542 | Stochastic Optimal Control |
ORF 545/FIN 545 | High Frequency Trading |
ORF 547 | Dynamic Programming |
ORF 548 | Large Scale Optimization |
ORF 549 | Stochastic Programming |
ORF 551/APC 551 | Random Measures and Levy Processes |
ORF 553 | Stochastic Differential Equations |
ORF 554 | Markov Processes |
SPI 519B/PSY 528B | Negotiation, Persuasion and Social Influence: Theory and Practice |
SPI 523 | Legal & Regulatory Policy Toward Markets |
SPI 524 | The Political Economy of Central Banking |
SPI 544 | International Macroeconomics |
SPI 582C | Topics in Economics: Growth, International Finance & Crisis |
SPI 582F | House of Debt: Understanding Macro & Financial Policy |