Electives

MFE 230M (2 units)

This empirical course will apply the latest tools of economics and finance to provide a detailed understanding of the structure and operation of the securitized bond markets in the U.S., including what can go wrong. As one of the major innovations in U.S. capital markets, there is robust demand from a wide variety of employers for 230M graduates who intimately understand the complexities of securitization and can work with the massive datasets required thereby. Discussing 230M class projects in interviews provides an opportunity to showcase that skillset to potential employers. We will extend the study of fixed-income securities and credit risk with advanced topics on securitized lending, mortgages, and mortgage-backed securities, applying these lessons to many other asset-backed securities including asset-backed commercial paper, auto loans, credit card receivables, crowdsourced lending, equipment, third-world debt, repo, commercial leases, and energy derivatives. Of necessity, problem sets will involve developing Big Data skills to handle the massive individual-loan datasets underlying many asset-backed securities, as well as Monte Carlo simulation and option-based pricing techniques. We will consider the basic mechanics of structuring ABS deals, including how to value, trade, rate, and stress-test such securities, as well as the risk management techniques employed in both the pooling and slicing (tranching) phases of the securitization process. Finally, we consider a proposal to use medical securitization to cure cancer.

MFE 230GB (1 unit)

The course is dedicated to currency markets: market organization, determination of spot and forward rates, and links to international finance more broadly. Topics include: FX arrangements and capital controls, Different approaches to exchange rate determination/forecasting, Conventional and unconventional monetary policy and exchange rate, among other areas.

MFE 230T (2 units)

The purpose of this class is to bring together students and interdisciplinary experts in Computer

Science and Finance to discuss the emerging area of Decentralized Finance (or DeFi). DeFi has experienced an unprecedented growth, with hundreds of projects and a countless stream of

financial, distributed systems, and blockchain innovations. The total value locked (TVL) in

decentralized finance — a measure of the total value of assets committed to the DeFi ecosystem 

has reached over $80 billion. When compared to the traditional centralized finance (CeFi), DeFi offers products and services serving similar financial goals, but critically innovates with novel capabilities such as instantaneous multi-billion USD flash loans. By utilizing blockchain and smart contract technologies, DeFi as a whole aims to provide a new platform for programmable, automated finance services that remove the reliance on central trust and intermediaries. Our goal is to provide a framework to understand this new area of financial services. For each finance function, we will consider 1) the current intermediated structure, and then b) the DeFi version (actual or proposed) that fulfills the function. Is either one of these optimal? We will evaluate both through the lens of CS and finance. Is the application computable (efficiency, decidable), programmable (automatic)? Is the application welfare-enhancing and stable (not a source of systemic risk). How do the new and old systems interact? We aim for the students to be able to critically evaluate whether a new DeFi protocol is novel and practical. We further will capture the security danger in DeFi, as well as their impact on the underlying consensus security. Lastly, we hope to give an insight into how to program and structure secure and incentive- compatible DeFi applications. Through the exposure to cutting-edge research as well as remaining open challenges, we hope for our students to quickly integrate into academic as well as industrial projects related to DeFi.

MFE 230ZA (1 unit)

In this class, we continue with control, action and reinforcement learning. By the end of this course, students will become familiar with theories and Python implementations of Reinforcement learning methods with applications to finance and economics. Students will also be exposed to an array of data sources, both standard and alternative (e.g., text, image, video, audio) in nature. Reinforcement learning (RL) is becoming increasingly significant in finance and economics due to its potential to solve complex decision-making problems that involve uncertain and dynamic environments.

MFE 230ZB (1 unit)

In this class we focus on unsupervised learning and generative AI. Deep learning has been extremely popular recently, which motivates students to learn the material. This course also aims to provide students with a thorough understanding how deep/machine learning models can or cannot be applied in financial contexts. One of the main goals of this class is to teach students experimental methods to evaluate published models in a critical way. The class therefore aims to prepare students for a real-life situation faced by many quantitative finance researchers in industry. In addition, students will be exposed to an array of data sources, both standard and alternative in nature. Unsupervised learning and generative AI are transforming finance and economics by uncovering hidden patterns in data and generating new, synthetic data for analysis.

MFE 230GA (1 unit)

The course cover active equity portfolio management including the more general quantitative theory of active management. We will view active management as an optimization problem trading off expected returns against risk and the cost of trading. Modules will cover forecasting returns, risk, and cost. We will also cover how to research active strategies and the analytics that support the enterprise. We will discuss various categories of active equity strategies and provide an introduction to current approaches.

MFE students are encouraged to attend weekly discussions held by finance practitioners. In the first term speakers discuss jobs available to graduates of the MFE and the skills needed to contribute to a firm's mission. In the second term, speakers provide insights into the way the financial world is changing: new products and needs; evolving data and information systems; and similar topics.

MFE 230X (2 units)

This course covers topics in high frequency finance and discusses recent developments in market microstructure, electronic trading and data modeling. The course is aimed at students who are considering careers in financial engineering or quantitative trading at institutions involved in automated securities trading on electronic platforms.

MFE 293 (1 - 3 units)

The Independent Study course is your opportunity to do research in an area of interest to you, in which there are no existing courses.

Our students work directly with financial institutions, hedge funds, Fintech firms, etc., on (unpaid) projects for which they receive academic credit.

MFE 230S (2 units)

This course covers elements of behavioral decision theory and its implication in financial markets. Focus is on the psychological processes by which people make judgments and decisions, and the heuristics and biases associated with these decisions.

MFE 230K (2 units)

Covers the strategies for achieving various investment objectives for portfolios/ instruments (equity, fixed income, currency, mortgages, non-traded assets) and applications (investment funds, pension funds, insurance companies, bank investment portfolios).

MFE 230J (2 units)

The objective of the course is to explore modern financial innovation through the lens of data science, and through a combination of lectures, cases, guest speakers, and applied data science projects. By the end of the course, the students will better understand the most significant financial innovations today and the critical role quantitative research can play in determining success.