The Applied Finance Project (AFP) gives MFE candidates the opportunity to apply knowledge acquired through MFE coursework to solve a practical, real-world financial engineering problem. Students begin working on the AFP in their spring quarter and then complete their project the following fall quarter, allowing for ample time to work with their team and a corporate client. By partnering with a corporate client, students develop and showcase their knowledge of quantitative finance, hone their communication skills and delve more deeply into an area of interest beyond the classroom.
AFP projects concentrate on such areas as quantitative trading strategies, portfolio management, risk management, hedging and derivatives valuation, and are sponsored by top organizations that include PIMCO, Citi, PwC, Aspirant, Accenture, AXA Rosenberg, Hyundai Capital and Research Affiliates. Students interact directly with clients, gaining valuable exposure to potential employers and broadening their professional networks.
Professor: Mikhail Chernov
This course covers the essentials of asset pricing and portfolio choice, standard discounted cash flow approaches and no-arbitrage framework for valuing financial securities. It introduces basic paradigms of asset pricing, such as the capital asset pricing model (CAPM), arbitrage pricing theory (APT) and the Fama-French three-factor model. Students learn the development and illustration of dynamic portfolio selection and optimization approaches.
Financial Accounting (Fall)
Professor: Mark Kim
This is an introduction to the concepts of financial accounting and their underlying assumptions, including an examination of the uses and limitations of financial statements. Procedural aspects of accounting are discussed in order to enhance understanding of the content of financial statements. The course emphasizes using accounting information in the evaluation of business performance and risk. The use of accounting information in research studies is also examined.
Professor: Peter Rossi
This course covers the theory and in-depth application of linear regression. Topics include simple linear regression, multiple regression, prediction in a multiple-regression model, residual diagnostics, detection of outliers and violations of stochastic assumptions.
Stochastic Calculus (Fall)
Professor: Stavros Panageas
This course covers the economic, statistical and mathematical foundations of derivatives markets. It presents the basic discrete-time and continuous-time paradigms used in derivatives finance, including an introduction to stochastic processes, stochastic differential equations, Ito's Lemma and key elements of stochastic calculus. The economic foundations of the Black–Scholes no-arbitrage paradigm are covered, as are the Girsanov theorem and changes of measure, the representation of linear functionals, equivalent martingale measures, risk-neutral valuation, fundamental partial differential equation representations of derivatives prices, market prices of risk and Feynman–Kac representations of solutions to derivatives prices. The role of market completeness and its implications for the hedging and replication of derivatives is covered in depth.
Trading, Market Frictions and FinTech
Professor: Jinyuan Zhang
Empirical Methods in Finance (Winter)
Professor: Lars Lochstoer
This course covers the probability and statistical techniques commonly used in quantitative finance. Students use estimation application software in exercises to estimate volatility, correlations and stability.
Derivative Markets (Winter)
Professor: Andrea Eisfeldt
Derivatives are both exchange-traded and over-the-counter securities. The derivatives markets are the world's largest and most liquid. This course focuses on the organization and role of put and call option markets, futures and forward markets, as well as their interrelations. The emphasis is on arbitrage relations, valuation and hedging with derivatives. The course also covers the implementation of derivatives trading strategies, the perspective of corporate securities as derivatives, the functions of derivatives in securities markets and recent innovations in derivatives markets.
Computational Methods in Finance (Spring)
Professor: Levon Goukasian
This course covers the quantitative and computational tools used in finance. It introduces numerical techniques such as the implementation of binomial and trinomial option pricing, lattice algorithms for computing derivative prices and hedge ratios, simulation-based algorithms for pricing American options and the numerical solution of the partial differential equations that appear in financial engineering.
Fixed Income Markets (Winter)
Professor: Francis Longstaff
This course provides a quantitative approach to fixed-income securities and bond portfolio management, with a focus on fixed-income security markets. The course covers the pricing of bonds and fixed-income derivatives, the measurement and hedging of interest rate risk, dynamic models of interest rates and the management of fixed-income portfolio risk.
Financial Risk Management (Spring)
Professor: Valentin Haddad
This course examines financial risk measurement and management, including market risk, liquidity risk, settlement risk, model risk, volatility risk and kurtosis risk.
Special Topics in Financial Engineering (Electives)
Choose four of seven courses offered, two during Spring term and two during the last Fall term. Special topics courses consist of an in-depth examination of problems or issues in an area of current concern in financial engineering.
Career Management Workshop (Fall, Winter, and Spring)
Career development programming supplies students with necessary career-management skills and tools to effectively identify, compete, and secure professional opportunities.
Quantitative Asset Management (Spring)
Professor: Bernard Herskovic
This course emphasizes the application of state-of-the-art quantitative techniques to asset management problems. The course covers asset-pricing models in depth, portfolio optimization and construction and dynamic strategies such as pairs trading, long-term and short-term momentum trades and strategies that address behavioral finance anomalies. The course also discusses major forms of asset-management structures, such as mutual funds, hedge funds, ETFs and special investment vehicles, and examines some of the primary types of trading strategies used by these organizations.
Financial Innovation (Spring)
Professor: John O'Brien
Study stresses financial innovation in traditional financial markets, and innovation opportunities in newer disciplines of long- and short-term economic markets and public policy. Some examples of latter include livelihood insurance, home-equity markets and insurance, inequality insurance, inter-generational social security, international agreements, and individual defined contribution retirement savings strategies.
Data Analytics and Machine Learning (Spring)
Professor: Lars Lochstoer
Study of data science, oriented toward decision making and predictive analytics. Topics include predictive and prescriptive models, panel regressions, text analysis, model validation and selection, models for discrete outcomes, and machine learning. Uses industry-leading R/Rstudio statistical environment. Examples and homework focus on finance applications including return and earnings prediction, default prediction and lending markets, portfolio choice, and trading models.
Introduction to the Finance of Blockchain Cryptocurrency (Fall)
Professor: Ben Tsai
Credit Markets (Fall)
Professor: Patrick Augustin (visiting professor)
This course provides an introduction to the building and implementation of credit models for use by financial institutions and quantitative investors. The course covers the basics of corporate debt securities and provides an in-depth introduction to the credit derivatives markets. Structured credit products such as cash and synthetic collateralized debt obligations (CDOs) are discussed.
Statistical Arbitrage (Fall)
Professor: Valentin Haddad
Study of quantitative equity market-neutral strategies. At one end of spectrum are high-capacity strategies with multi-year time horizons. At other end are low-capacity strategies with milli-second time horizons. Students place themselves squarely in middle of this trade-off, enabling them to study both slow and fast signals. This is sweet spot where one can have sufficiently high Sharpe ratio to be rewarded on one's own merit rather than on one's verbal ability to explain away bad performance; and one can escape from technologically intensive rat race to have fastest computer co-located closest to stock exchange. Rather than give students list of alphas that are supposed to work, study gives them toolkit necessary to develop their own sources of alpha. Statistical arbitrage is less of formula than ongoing process: one fixes airplane as one is flying it.
Behavioral Finance (Fall)
Professor: Avanidhar Subrahmanyam
Introduction and explanation of evidence of anomalous return behavior found in stock markets. Presentation of details on how "quant" firms apply evidence to manage equity portfolios, and seek to explain trading activity in equity markets. Exploration of some evidence that contradicts standard risk-return paradigm. Introduction of some psychological biases that researchers suspect are inherent to investors. Some results from psychology literature employed to explain irrationalities encountered in financial markets. Discussion of what stock trading strategies to avoid and what strategies to adopt. Latest evidence on why individual investors trade, and how individual and institutional investors form their portfolios.
Applied Finance Project (Fall)
Professor: Ehud Peleg
Every MFE student is required to complete an Applied Finance Project (AFP) that explores a quantitative finance problem. The AFP enables candidates to apply the knowledge and tools they developed through MFE coursework by working directly with clients.
Financial Market Regulation (Fall)
Professor: Alexander Dill
Students gain a solid understanding of the key financial risks facing financial institutions; the role of firms’ corporate governance systems in controlling these risks, including risk management and compliance; and the applicable regulation under the Dodd-Frank Act. Numerous case studies on risk management blowups such as the 1987 market crash, LTCM, the August 2007 “quant crash,” and the 2008 financial crisis shine a light on poor risk management practices and market, liquidity, counterparty credit, and systemic risk. As a result of the 2008 crisis, expertise in risk management and model risk management (MRM) has become highly valued throughout the finance industry. In the team project, students apply MRM in projecting P&L under the Fed’s severely adverse macro stress test scenarios in a sample equity and bond portfolio.