MBA Major in Quantitative Finance

The Quantitative Finance major aims to prepare students for a wide range of careers in the financial industry, including quantitative asset management and trading, financial engineering, risk management and applied research. The major places a strong emphasis on financial economics and data analysis, in addition to advanced quantitative and computational methods. It is designed to appeal to students with strong quantitative backgrounds who wish to develop their skills for quantitative applications in finance.

Although based in the Finance Department, the major will also include relevant cross-disciplinary content from accounting, statistics and operations, information and decisions. Some doctoral courses in Finance may also be counted towards this major. MBA students majoring in Quantitative Finance will have both the technical expertise that allows them to compete for quantitative positions in finance, and the generalist MBA experience that provides them with the necessary leadership skills to quickly rise to the top of their organizations.


Why Choose Quantitative Finance?

Industry Advisory Panel


The Finance major requires six credit units. All Finance majors must take FNCE6110 and FNCE6130, unless they successfully apply to substitute an upper-level finance course. Although the Finance Department does not grant waivers, it does permit qualifying students to substitute an upper-level finance course for a core course. Students with a strong background in Finance may apply to replace FNCE 6110 with any upper-level finance course to fulfill their core corporate finance requirement. (Please note, however, that neither Independent Study Projects (FNCE 8990), nor Global Modular Courses can be used for this purpose.) Students who have taken an intermediate macroeconomics course and earned at least a B+ (or equivalent) may apply to replace FNCE 6130 with one of three following upper-level Finance courses: 1. FNCE 7190, International Financial Markets and Cryptocurrencies; 2. FNCE 7320, International Banking; or FNCE 7400 Central Banks, Macroeconomic Policy, and Financial Markets.


To complete the major, students must take an additional four credit units of upper-level Finance electives.

At least 3 c.u. must come from the following courses:
• FNCE 7050-Investment Management
• FNCE 7170- Financial Derivatives
• FNCE 7190- International Financial Market and Cryptocurrencies
• FNCE 7250- Fixed Income Securities
• FNCE 7370- Data Science for Finance
• FNCE 7390- Behavioral Finance
• FNCE 7570- Foundations of Asset Pricing
• FNCE 8920- Financial Engineering

The remaining 1 c.u. may come from the following courses:
• FNCE 9210-  Introduction to Empirical Methods in Finance
• ACCT 7470-  Financial Disclosure Analytics
• OIDD 6530-  Mathematical Modeling and its Application in Finance
• STAT 5330 –  Stochastic Processes
• STAT 7110  –  Forecasting Methods

Courses taken pass/fail cannot be counted towards the major. This major cannot be taken in conjunction with the general FNCE major. Moreover, ACCT 7470, OIDD 6530, STAT 5330 and STAT 7110 count only for the Quantitative Finance major and will not be count as part of the 4 c.u. requirement for the general Finance major.


FNCE 7050 – Investment Management

(Formerly FNCE 7200)This course studies the concepts and evidence relevant to the management of investment portfolios. Topics include diversification, asset allocation, portfolio optimization, factor models, the relation between risk and return, trading, passive (e.g., index-fund) and active (e.g., hedge-fund, long-short) strategies, mutual funds, performance evaluation, long- horizon investing and simulation. The course deals very little with individual security valuation and discretionary investing (i.e., “equity research” or “stock picking”).

FNCE 7170 – Financial Derivatives

This course covers one of the most exciting and fundamental areas in finance. Financial derivatives serve as building blocks to understand broad classes of financial problems, such as complex asset portfolios, strategic corporate decisions, and stages in venture capital investing. The main objective of this course is build intuition and skills on (1) pricing and hedging of derivative securities, and (2) using them for investment and risk management. In terms of methodologies, we apply the non-arbitrage principle and the law of one price to dynamic models through three different approaches: the binomial tree model, the Black-Scholes-Merton option pricing model, and the simulation-based risk neutral pricing approach. The course covers a wide range of applications, including the use of derivatives in asset management, the valuation of corporate securities such as stocks and corporate bonds with embedded options, interest rate and credit derivatives, as well as crude oil derivatives. We emphasize practical considerations of implementing strategies using derivatives as tools, especially when no-arbitrage conditions do not hold.

FNCE 7190 – International Financial Markets and Cryptocurrencies

Major topics in this class include foreign exchange rates, international money markets, currency and interest rate derivatives (forwards, options, and swaps), international stock and bond portfolios, and cryptocurrencies. Students learn about the features of financial instruments and the motivations of market participants. The class focuses on risk management, investing, and arbitrage relations in these markets.

FNCE 7250 – Fixed Income Securities

This course covers fixed income securities (including fixed income derivatives) and provides an introduction to the markets in which they are traded, as well as to the tools that are used to value these securities and to assess and manage their risk. Quantitative models play a key role in the valuation and risk management of these securities. As a result, although every effort will be made to introduce the various pricing models and techniques as intuitively as possible and the technical requirements are limited to basic calculus and statistics, the class is by its nature quantitative and will require a steady amount of work. In addition, some computer proficiency will be required for the assignments, although familiarity with a spreadsheet program (such as Microsoft Excel) will suffice.

FNCE 7370 – Data Science for Finance

This course will introduce students to data science for financial applications using the Python programming language and its ecosystem of packages (e.g., Dask, Matplotlib, Numpy, Numba, Pandas, SciPy, Scikit-Learn, StatsModels). To do so, students will investigate a variety of empirical questions from different areas within finance including: FinTech, investment management, corporate finance, corporate governance, venture capital, private equity, and entrepreneurial finance. The course will highlight how big data and data analytics shape the way finance is practiced. The course objective is twofold: (1) illustrate how data analytics can improve financial decision-making, and (2) provide students with a foundation for performing data analytics in finance-related roles both inside the financial sector (e.g., commercial and investment banking, private equity, asset management) and outside the financial sector (e.g., consulting, corporate development, treasury).

FNCE 7390  –  Behavioral Finance

There is an abundance of evidence suggesting that the standard economic paradigm – rational agents in an efficient market – does not adequately describe behavior in financial markets. In this course, we will survey the evidence and use psychology to guide alternative theories of financial markets. Along the way, we will address the standard argument that smart, profit-seeing agents can correct any distortions caused by irrational investors. Further, we will examine more closely the preferences and trading decisions of individual investors. We will argue that their systematic biases can aggregate into observed market inefficiencies, thus giving rise to apparently profitable trading strategies. The latter part of the course extends the analysis to corporate decision making. We then explore the evidence for both views in the context of capital structure, investment, dividend, and merger decisions. In addition to prerequisites, FNCE 705 is highly recommended but not required.

FNCE 7570  –  Foundations of Asset Pricing

This course will cover methods and topics that form the foundations of modern asset pricing. These include: investment decisions under uncertainty, mean-variance theory, capital market equilibrium, arbitrage pricing theory, state prices, dynamic programming, and risk-neutral valuation as applied to option prices and fixed-income securities. Upon completion of this course, students should acquire a clear understanding of the major principles concerning individuals’ portfolio decisions under uncertainty and the valuations of financial securities.

FNCE 8920 – Financial Engineering

This class covers advanced pricing models for equity, fixed income and credit derivatives. It aims at: 1) Introducing the main models used in practical applications to price and hedge derivatives; 2) Understanding their comparative advantages and limitations, as well as how they are calibrated and applied. As part of team assignments, students will be asked to calibrate and implement the models introduced in the class using software of their choice.

FNCE 9210 – Introduction to Empirical Methods in Finance

This is a Doctoral level course. It provides students with an introduction to the frontier empirical methods commonly employed in finance research. The course is organized around empirical papers with an emphasis on econometric methods. A heavy reliance will be placed on analysis of financial data.

ACCT 7470 – Financial Disclosure Analytics

This course focuses on the analysis of financial communications between corporate managers and outsiders, including the required financial statements, voluntary disclosures, and interactions with investors, analysts, and the media. The course draws on the findings of recent academic research to discuss a number of techniques that outsiders can use to detect potential bias or aggressiveness in financial reporting. FORMAT: Case discussions and lectures. Comprehensive final exam, group project, case write-ups, and class participation.

OIDD 6530 – Mathematical Modeling and its Application in Finance

Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc.

STAT 5330 – Stochastic Processes

An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

STAT 7110 – Forecasting Methods

This course provides an introduction to the wide range of techniques available for statistical forecasting. Qualitative techniques, smoothing and decomposition of time series, regression, adaptive methods, autoregressive-moving average modeling, and ARCH and GARCH formulations will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.



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