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Master in Finance Tracks and Electives

Master in Finance

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 543 Deep Learning Theory
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
SML 505 Modern Statistics
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