The program requirements consist of students taking 5 core courses and 11 elective courses. In addition, an internship is required for two-year students who are not returning to a previous employer. Extensive placement assistance is provided to help students obtain suitable internships.
Fall Semester Deadline:
Dean’s Date: deadline for student submission of written work
Faculty grade: same deadline for submission of graduate course grades
Spring Semester Deadline:
Dean’s Date: deadline for student submission of written work
Faculty grade: May/June, one day prior to advanced degree deadline found HERE
The Masters Project can be undertaken only once for each student during the length of their program, it can either be in either the fall semester FIN560 or the spring semester FIN561
The course requirements are with the following provisions:.
- At least 5 of the elective courses must be at the level 500 or higher.
- At least 5 of the elective courses must be taken from List 1 below.
- Any courses not on the Elective Lists 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; c) is a full semester (not a half) course.
- At most 5 course credits per semester can be earned towards fulfilling the requirements of the program. Each semester, the final list of courses taken for credit needs to be approved by the Director of Graduate Studies before the end of the second week of classes.
- Second year students cannot take 300 or less level courses for credit; First year students can take at most one 300 level course for credit but MUST have permission from the Director of Graduate Studies
- First year students cannot take any Woodrow Wilson School courses for credit; Second year students can, to a maximum of 2, and at most one per semester, for credit after approval from the BCF Director of Graduate Studies.
- Students must maintain an overall grade average of B (GPA = 3.0) or better as well as earn a passing grade in all core and elective courses.
- In case a student completes additional courses beyond the required total number, the GPA is calculated using the grades earned in the most favorable combination of courses that still fulfills the program’s core and elective requirements.
- Audited or P/D/F courses cannot be used to fulfill the program’s requirements.
- While no Master’s thesis is required, second year students interested in independent research may work with a BCF-affiliated faculty member on a topic relevant to finance, and by enrolling in the appropriate courses (FIN 560 in the fall and FIN561 in the spring). See further requirements in the “Masters Project” section below.
One Year Students:
- Students who have been admitted to the one-year program (as stated in their admission letter) must complete a total of 10 courses over two semesters. Individual meetings between the student and the Director of Graduate Studies will determine, on the basis of courses previously completed by the student at Princeton or another institution, which of the 16 core and elective courses need to be taken. Additionally, one year students cannot take WWS classes for credit unless they get permission from the DGS.
Students unable to meet the graduation requirements of the program at the end of their original program length of one or two years, either because their GPA is below the required 3.0 minimum or because they have not completed the required core and elective courses, may apply for an extension by submitting a proposal to the Director of Graduate Studies who will review it and seek the approval of the Dean of Academic Affairs of the Graduate School. The conditions governing such proposals are the following:
- At most two additional semesters will be granted to complete the requirements
- The student will need to re-enroll formally for each additional semester, and full tuition will be charged for each additional semester.
- A maximum of two courses can be re-taken for the purpose of improving the student’s GPA. Students are free to select the two courses they wish to re-take.
- A course re-taken will appear twice on the student transcript, but only the higher grade earned will be used for the purpose of computing the student’s final GPA.
- Courses may not be re-taken until the completion of the student’s original program length.
- A course re-taken counts only once for the purpose of satisfying the required number of core and elective courses.
- The new plan of study needs to be approved before the official beginning of the semester to be added.
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.
Core Courses – Master in Finance
The core courses of the Master in Finance provide students with analytical fundamentals of modern finance, both theoretical and empirical.
FIN 501/ORF 514 – Asset Pricing I: Pricing Models and Derivatives
This course provides an introduction to the modern theory of asset pricing. Topics include: no arbitrage, Arrow-Debreu prices and equivalent martingale measures, security structure and market completeness, mean-variance analysis, Beta-pricing, CAPM, and introduction to derivative pricing.
FIN 505/ORF 505 – Statistical Analysis of Financial Data
The course is divided into three parts of approximately the same lengths:
• Density estimation (heavy tail distributions) and dependence (correlation and copulas)
• Regression analysis (linear, nonlinear, nonparametric) and robust alternatives
• Time series analysis (AR, MA, ARMA) and Filtering
The statistical analyses, computations and numerical simulations will be done in the R software environment for statistical computing and graphics.
FIN 502 – Corporate Finance and Financial Accounting
This course covers the basics of financial statements, the analysis and recording of transactions, and the underlying concepts and procedures. In addition, a more detailed study of some aspects of financial accounting that have widespread significance is undertaken, such as inventories, long-term productive assets, bonds and other liabilities, stockholders equity, and the statement of changes in financial position. The course provides students with the skills necessary to become informed users of financial statements. Problem sets emphasize an ability to interpret and analyze financial statement disclosures.
FIN503/ORF515 – Asset Pricing II, Stochastic Calculus and Advanced Derivatives
This course begins with an overview of basic probability theory and covers the elements of stochastic calculus and stochastic differential equations that are widely used in derivatives modeling, pricing and hedging. Topics include Brownian motion, martingales, and diffusions and their uses in stochastic volatility; volatility smiles; risk management; interest-rate models; and derivatives, swaps, credit risk, and real options.
FIN504/ORF 504 – Financial Econometrics
This course covers econometric and statistical methods as applied to finance. Topics include measurement issues in finance, predictability of asset returns and volatilities, value at risk and extremal events, linear factor pricing and portfolio problems, intertemporal models of the stochastic discount factor and generalized method of moments, vector autoregressive and maximum likelihood methods in finance, risk neutral valuation in discrete time, estimation methods of continuous time models, volatility smiles and alternatives to Black-Scholes, and nonparametric statistical methods for option pricing.
Master in Finance Elective Courses
In addition to core courses, which provide a broad survey of topics and techniques of modern finance, the program will offer students the opportunity to choose among a variety of elective courses. Some of these courses have prerequisites, or require permission of the respective instructors.
Please check this page for updates regarding new courses eligible as elective courses for the program or removals of previously-listed courses from the eligibility list. Not all courses may be offered every year. Please check with the relevant departments to confirm their offerings in any given year.
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 Modelling
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
WWS 594n: Financial Regulation, Crises and Macro Policy
List 2: General Methodology for Finance
APC 350: Introduction to Partial Differential Equations
APC 503: Analytical Techniques in Differential Equations
APC 518/ORF 518: 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 (numerical methods)
COS 402: Machine Learning and AI
COS 423: Theory of Algorithms
COS 424: Fundamentals of Machine Learning
COS 425: Database 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
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: Topics in Empirical Macroeconomics
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: Entrepreneurship, Innovation and Venture Capital
MAE 305/MAT391: Mathematics in Engineering I (ODE, PDE)
MAE 306/MAT 392: Mathematics in Engineering II (PDE, complex variables)
MAE 503: Basic Numerical Methods for Ordinary and Partial Differential Equations
ORF 311: Optimization under Uncertainty
ORF363/COS323: Computing and Optimization for the Physical and Social Sciences
ORF 401: Electronic Commerce
ORF 409: Introduction to Monte Carlo Simulation
ORF 522: Linear Optimization
ORF 523: Nonlinear Optimization
ORF 524: Statistical Theory and Methods
ORF 525: Statistical Learning and Nonparametric Estimation
ORF 526: Probability Theory
ORF 533: Convex Analysis for Mathematical Finance
ORF 542: Stochastic Control and Stochastic Differential Games
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
WWS 519B/PSY 528B: Negotiation, Persuasion and Social Influence: Theory and Practice
WWS 523: Legal & Regulatory Policy Toward Markets
WWS 524: Advanced Macroeconomics: Domestic Policy Issues
WWS 544: International Macroeconomics
WWS 582C: Topics in Applied Economics: Growth, International Finance & Crisis
WWS 582F: Topics in Applied Economics: Financial Markets and Public Policy
MFIN Math & Boot Camps
Each year, the Center holds a Math Camp and a Boot Camp for all incoming Master In Finance students. The purpose of Math Camp is to enrich the finance mathematics background in preparation of the mathematical rigors of the MFIN program. Following the two-week long Math Camp we continue with a three day Boot Camp. This camp focuses on a refresher of various finance topics, the types of careers for which the Master in Finance degree prepares students, and some useful information on networking and interviewing skills.
Master in Finance Students starting in Fall 2019 should plan to arrive in Princeton in time for Monday, August 26, 2019. Graduate Student housing will allow for early arrivals for our students up to three days before.
- Monday, August 26, 2019 – Welcome Breakfast and Introductions
- Monday, August 26, 2019 to Friday, August 30, 2019 – Math Camp, week 1
- Tuesday, September 3, 2019 to Friday, September 6, 2019 – Math Camp, week 2
- Friday, September 6, 2019- Boot Camp Opening Session, afternoon
- Saturday, September 7, 2019- Boot Camp All Day, Alumni/Student Reception
- Monday September 9, 2019- Graduate School Orientation, all Day
- Tuesday, September 10, 2019- Boot Camp, all day
- Friday, September 13, 2019 – Undergrad Boot Camp Session
Other Incoming Student Events
- Monday, August 26, 2019 Begin Individual meetings with Director of Graduate Studies (sign up notice will come from Academic Administrator)
- Monday, August 26, 2019 to Friday, September 6, 2019 – Individual resume review and career development meetings with Director of Corporate Relations
- Sunday, September 1, 2019 – Graduate Student Academic Year Sign-in and Course Enrollments begin
Course credit can be granted to students who elect to carry out a master’s research project under the supervision of a BCF-affiliated faculty member. Currently enrolled students interested in pursuing research opportunities should contact the BCF Director of Graduate Studies, and seek a faculty advisor among the BCF-affiliated faculty. Master’s research projects can only be conducted while the student is enrolled full-time in the program.
The Financial Engineering Laboratory (equipped with personal computers, workstations, and financial data feeds) facilitates such projects. The BCF provides a standardized computing environment, based on Matlab, Mathematica and C++. The BCF also maintains a large financial database.
A list of recent research topics can be found below:
- The S&P 500 P/E Ratio
- Regime Switching in Option Pricing: Estimation and Persistence of Market Volatility
- Estimation of One-factor Affine Models with Flexible Price of Risk
- Estimation of Two-factor Affine Models to the Mexican Bonds Market
- An Examination of Chinese Companies with A-H Share Price Differentials
- A Model for Natural Gas Storage Optionality
- Econometrics of Exchange-Traded Funds
- Hedge Funds Involvement in Economic Crisis
- Three Algorithms for American Options
- Identification and Significance of Momentum in Sovereign and Sub-sovereign Debt
- The Economics of U.S. Horse Racing Markets and an Empirical Analysis of the Gray Horse Bias
- 2007 Chinese Stock Market Crash and Non-tradable Share Release
- Optimal Replication of S&P 500 Index
- Central Banking during the Credit Crisis: A Comparative Analysis between the ECB and the Fed
- Do Industries Lead Stock Markets in Smaller Countries?
- Pricing of Emission Certificates under Cap and Trade Schemes with CDM
- Market versus Statutory Approach to Sovereign Debt Restructuring
- Maximum Likelihood Estimation of Jump-Diffusion Model
- Pricing the Risk of Private Equity Fund Commitments
- Measuring the Risk of Private Equity Fund Commitments
- An Empirical Study of Liquidity Effects in Equity Options Markets
- Analyses of the Efficiency of the Hong Kong Equity Markets
- How Can We Formulate and Convey the Value of Healthcare Link (Account Receivables Product) to Clients?
- Performance Matters: A Study about the Brazilian Fund’s Industry
- A Simple Model of Emerging Markets Sovereign Bond Trading
- Rational Versus Adaptive Expectations in Present Value Models Applied to U.S. Interest Rates
- Reviewing Monetary Policy
- Modeling Asian Spread Options
- Variance and Volatility Swaps: A Review of Popular Methodologies
- Building AMCs with Chinese Characteristics: Application of the RTC Model on NPLs in China
- Opening up Microfinance to Global Capital Markets: Securitization of Microfinance Loans
- The Empirical Research on the Abnormal Returns of Initial Dividend Announcement in China
- A Comparison of Winning Premiums Paid for Companies in Management and Other Buyouts
- An Econmic Explanation to Implied Volatility Smile
- The Pricing of Barrier Options
- Seasonal Component in Temperature Time Series
- Time Series and Weather Derivatives
- The Price Impact of Credit Rating Announcements on Credit Default Swap Markets
- An Empirical Study on the Performance of ETFs in China
- The Spinoff Puzzle and the Carveout Puzzle: a Survey and a Contribution
- The Effect of Monetary Policy on Bank Lending in China: Evidence from Banks’ Balance Sheets
- Risk Ratings of Middle Market Loan Exposure
- Pharmaceutical Firm Research and the Property Rights Framework
- Glamour versus Growth: the Asian Evidence
- The Myth of Usury