We offer a range of elective courses which may change each year. Below is a sampling of electives that we have recently offered.
Asset Backed Security Markets
MFE 230M (2 units) - Nancy Wallace
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.
Deep Learning for Financial Time-Series
MFE230T (1 unit) - Laurent El Ghaoui
Topics include time-series models and challenges; Markov chains, stochastic processes, spectral representation, long memory Processes “shallow models”: ARMA, filter banks, SVR/SVM, random forests and Probabilistic graphical networks; deep models: RNNs and CNNs for sequential modeling, attention networks deep learning frameworks, basic models and causal loss functions for financial time-series prediction. Distributed representations of discrete entities and applications in Natural Language Processing data and model fusion strategies, irregular time series low cost modeling strategies (model compression, cascades and low rank modeling).
Financial Practice Seminars
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.
High Frequency Finance
MFE 230X (2 units) - Terry Hendershott and Dmitry Livdan
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.
MFE293 (1 - 3 units) - Choose an advisor
The Independent Study course is your opportunity to do research in an area of interest to you, in which there are no existing courses.
International Equity & Currency Markets
MFE 230G (2 units) - Ron Kahn and Amir Kermani
This course reviews various aspects of equity and currency markets and provides models of and historical evidence on the average returns and volatility of returns on equities, on the trade-to-trade equity price behavior, on trading volume and patterns, and on primary financial risks. The determination of spot and forward exchange rate and the volatility, volume, high frequency dynamics, and dealer behavior in currency markets are considered. Practical considerations involved in the implementation of various strategies are considered.
Introduction to Deep Learning
MFE230T (1 unit) – Gert Lanckreit
Topics include Supervised, Unsupervised, and Reinforcement learning Industry Tools to Develop Machine Learning Systems. Data Collection and Processing (APIs, web scraping, and Hadoop, MapReduce, Spark) Multilayer Perceptron (Deep Neural Nets, Training Deep Neural Nets, Convolutional, Neural Networks, Recurring Neural Networks, Word2Vec). The course will end with a session on solving practical problems with deep learning.
Topics in Financial Engineering
MFE230T (1–5 units)
Advanced study in the field of financial engineering that will address current and emerging issues. Topics will vary with each offering and will be announced at the beginning of each term.
MFE 230S (2 units) -David Sraer
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.
Dynamic Asset Management
MFE 230K (2 units) - Kevin Coldiron
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).
Financial Innovation with Data Science Applications
MFE 230J (2 units) – Ananth Madhavan and Christine Parlour
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.