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
Quantitative Asset Management
- Focuses on designing and evaluating financial products that help organizations manage risk-return trade-offs.
- Work on portfolio management, risk management, asset pricing and hedging, providing the necessary quantitative background to leaders and innovators in this growing field.
- Highly requested at major investment banks, hedge funds, and asset managers.
- Includes courses in probability, optimization, stochastic calculus, dynamic programming, machine learning, and several disciplines in financial economics.
Data Science & Financial Technologies
- For those interested in computer-based technologies and their increasingly important use of big data in finance.
- Focuses on computational techniques needed in “real-time” computing environments including efficient trading systems, algorithms, high-frequency data analysis, interfaces, processing large databases, and the security of computer networks.
- Essential for competitive financial firms demanding more efficient trading processes, data signal extractions with increased speed and lower costs.
- Courses cover the latest tools and techniques of financial technologies (FinTech), computer science, and computational methods in finance, including machine learning, information retrieval, artificial intelligence, and deep learning.
Valuation and Macroeconomic Analysis
- Focuses on the strategic understanding of a firm’s valuation and structural macroeconomic conditions.
- Studies how to evaluate and finance investment projects, including start-up financing, deal restructuring, and how to determine the optimal capital structure of a firm.
- Formulates strategies consistent with the expected performance of the macroeconomy.
- Fundamental for venture capital funds, global macro funds, strategic consultancies in finance, and pension funds.
- Includes courses in corporate finance, behavioral finance, investment projects, financial reorganization, mergers and acquisitions, incentive design, initial public offerings, time series analysis, macroeconomics, and asset and liability management.
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.
Finance Applications Courses
| FIN 515 |
Portfolio Theory and Asset Management |
| FIN 516 |
Topics in Corporate Finance, FINTECH |
| FIN 517 |
Venture Capital and Private Equity Investment |
| FIN 518 |
International Financial Markets |
| FIN 519 |
Corporate Restructuring, Mergers and Acquisitions |
| FIN 521 |
Fixed Income, Options and Derivatives: Models and Applications |
| FIN 522 |
Financial Derivatives and Currencies |
| FIN 523 |
Forecasting and Time Series Analysis |
| FIN 560 |
Master’s Project I *Does not count towards degree* |
| FIN 561 |
Master’s Project II *Does not count towards degree* |
| FIN 567 |
Institutional Finance: Trading and Markets |
| FIN 568 |
Behavioral Finance |
| FIN 570 |
Alternative Investments |
| FIN 572 |
Hedge Fund Strategies |
| 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 |
| ECO 529 |
Financial and Monetary Economics |
| EGR 395 |
Venture Capital and Finance of Innovation |
| ORF 527 |
Stochastic Calculus |
| ORF 530 |
Financial Data Mining |
| ORF 531/FIN 531 |
Computational Finance in C++ |
| ORF 535/FIN 535 |
Financial Risk and Wealth Management |
| ORF 538 |
PDE Methods for Financial Mathematics |
| ORF 545/FIN 545 |
High Frequency Markets: Models and Data Analysis |
| ORF 555 |
Energy & Commodities Markets |
| ORF 574/FIN 574 |
Special Topics in Investment Science |
General Methodology for Finance Courses
| APC 350 |
Introduction to Differential Equations |
| APC 503 |
Analytical Techniques in Differential Equations |
| APC 524 |
Software Engineering for Scientific Computing |
| COS 423 |
Theory of Algorithms |
| COS 425 |
Database Information Management Systems |
| COS 435/ECE 433 |
Introduction to Reinforcement Learning |
| COS 436 |
Human-Computer Interaction |
| COS 445 |
Economics and Computing |
| COS 448 |
Innovating Across Technology, Business, and Marketplaces |
| COS 461 |
Computer Networks |
| COS 484 |
Natural Language Processing |
| COS 485 |
Neural Networks: Theory and Applications
|
| COS 511 |
Theoretical Machine Learning |
| ECE 432/COS 432 |
Information Security |
| ECE 473/COS 473 |
Elements of Decentralized Finance |
| ECE 524 |
Foundations of Reinforcement Learning |
| ECE 530 |
Theory of Detection, Estimation and Learning |
| ECE 535 |
Machine Learning and Pattern Recognition |
| ECE 539 |
Special Topics in Data and Information Science: Optimization for Machine Learning |
| ECO 342 |
Money and Banking |
| 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 507 |
Introduction to Macro-Finance |
| 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 |
| EGR 491 |
Hightech Entrepreneurship |
| 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 363/COS323 |
Computing and Optimization for the Physical and Social Sciences |
| ORF 409 |
Introduction to Monte Carlo Simulation |
| ORF 418 |
Optimal Learning |
| ORF 474 |
Network Game Theory |
| 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 542 |
Stochastic Optimal Control |
| ORF 543 |
Deep Learning Theory |
| POL 572 |
Quantitative Analysis I |
| SML 505 |
Modern Statistics |
| SPI 524 |
The Political Economy of Central Banking |
| SPI 582C |
Topics in Economics: Growth, International Finance & Crisis |
| SPI 582F |
Topics in Economics: Understanding Macro & Financial Policy * only if full semester course |