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 complete 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.

CORE COURSE REQUIREMENTS

The major requires students to take FNCE611 Corporate Finance and FNCE613 Macroeconomics and the Global Economic Environment and complete (or waive) MGEC611 and MGEC612.

The major requires students to take or replace FNCE611 and FNCE613. Students who waive FNCE 611 may replace it with either FNCE 720, Investment Management, or FNCE 726, Advanced Corporate Finance.  Beginning with academic year 2021-22, however, students who waive FNCE 611 may choose any upper-level finance course to fulfill their core corporate finance requirement.  Students who waive FNCE 613 must take FNCE 893 Central Banks, Monetary Policy and Financial Markets.  Beginning with academic year 2021-22, however, students who waive FNCE 613 may take FNCE 893 or FNCE 719, International Financial Markets, or FNCE 732, International Banking, to fulfill their core macroeconomics requirement.

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

FINANCE ELECTIVES

This major requires a minimum of 4 credit units (cu) of elective coursework.

At least 3 cu must come from the following courses:
• FNCE717  –  Financial Derivatives
• FNCE719  –  International Financial Markets
• FNCE720 –  Investment Management
• FNCE725 –  Fixed Income Securities
• FNCE737 –  Data Science for Finance
• FNCE757 –  Foundations of Asset Pricing
• FNCE892 –  Financial Engineering

The remaining 1 cu may come from the following courses:
• FNCE921 –  Introduction to Empirical Methods in Finance
• ACCT747 –  Financial Disclosure Analytics
• OIDD653 –  Mathematical Modeling and its Application in Finance
• STAT533  –  Stochastic Processes
• STAT711   –  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, ACCT747, OIDD653, STAT533 and STAT711 count only for the Quantitative Finance major and will not be count as part of the 4 cu requirement for the general Finance major.

COURSE DESCRIPTIONS

FNCE717 – Financial Derivatives

In the modern financial architecture, financial derivatives can be the most challenging and exotic securities traded by institutional specialists, while at the same time, they can also be the basic securities commonly traded by retail investors such as S&P Index Options. The basic ideas of financial derivatives also serve as building blocks to understand a much broader class of financial problems, such as complex asset portfolios, strategic corporate decisions, and stages in venture capital investing.  The global derivatives market is one of fastest growing, with over $600 trillion value in total. The main objective of this course is to help students gain the 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. We discuss 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 derivatives, credit derivatives, as well as crude oil derivatives. In addition to theoretical discussions, we also emphasize practical considerations of implementing strategies using derivatives as tools, especially when no-arbitrage conditions do not hold.

FNCE719 – International Financial Markets

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.

FNCE720 – Investment Management

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”).

FNCE725 – 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.

FNCE737 – 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).

FNCE757 – Foundations of Asset Pricing

This course will cover methods and topics that form the foundations of modern as- set 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 under- standing of the major principles concerning portfolio decisions under uncertainty and the valuations of financial securities.

FNCE921 – 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.

ACCT747 – 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.

OIDD653 – 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.

STAT533 – 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.

STAT711 – 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.

 

CONTACTS:

Academic Affairs: (Scheduling/ISPs/Grades)

Stacy Franksstacyf@wharton.upenn.edu

FACULTY ADVISOR: (Curriculum)

Professor Nikolai Roussanov – nroussan@wharton.upenn.edu