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.

This course covers an overview of DeFi, Blockchain Technology, Smart Contracts, Decentralized Lending, as well as other topics at the intersection of the DeFi and traditional finance ecosystems. 

MFE230T (1 unit) 

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).

MFE 230G (2 units) 

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.

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.

MFE293 (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.

MFE230T Part 1 (1 unit) 

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.

MFE230T (1 unit) 

Topics include 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

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.