First Semester (Fall)
FIN 500 Introduction to Finance (4 hours): Introduction to financial markets, some of the most common securities traded in financial markets, and theories of valuation, with a brief overview of some of the important ideas in corporate finance. Net present and future value; internal rate of return; Gordon dividend model; fixed-income analysis; random cash flow; Markovitz’ portfolio theory and diversification; Sharpe’s capital asset pricing model; capital market line; free cash flow for equity evaluation; forward markets, futures and options; binomial and Black-Scholes option pricing; capital structure and corporate restructuring.
FIN 580 Machine Learning (second half of Fall semester - 2 hours): Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. In this continuation course students will explore advanced topics in machine learning including neural net architecture, Reinforcement Learning, recurrent neural nets (RNNs) and long short-term memory (LSTMs). Applications include option pricing, portfolio selection and time series modeling. Students will gain practical experience implementing these models in Python with frequently used packages such as TensorFlow.
IE 598 Machine Learning in Finance lab (first half of Fall semester - 2 hours): Machine learning is an increasingly important tool in every financial engineer’s toolbox. Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. In this introductory course students will explore the main concepts behind several different machine learning algorithms and gain practical experience implementing them using Python and several of the most frequently used packages; pandas, NumPy, scikit-learn, etc.. Students will also learn how to construct and interpret their own machine learning models in Python.
IE 522 Statistical Methods in Finance (4 hours): Methods of statistical modeling of signals and systems with an emphasis on finance applications. Review of linear algebra, probability theory, and spectral analysis. Linear Time Invariant (LTI) models, ARX models, Least-squares methods, Maximum likelihood methods, non-parametric and frequency-domain methods, convergence, consistency and identifiability of linear models and asymptotic distribution of parameter estimates, and techniques of model validation. Principal Component Analysis (PCA) for dimension reduction, ARCH and GARCH processes and their related models, implementation/application and case-studies of Recursive Identification, and Monte Carlo Simulation.
IE 523 Financial Computing (4 hours): Review of C++ programming: structures, classes, I/O, C++ standard libraries, Recursion. Linear Algebra tools and MILP solvers in C++. Computational aspects of probability, statistics and simulation in C++. Methods: Root-finding, Taylor’s Expansion, FFTs, Dynamic Programming, Monte Carlo Methods. Financial computing case studies in C++.
FIN 580 Professional Development (1 hour): This course is designed to enrich student coursework experience through a series of invited speaker lectures (MSFE Practitioner Series) field trips and conversation courses aimed at bridging the gap between academic work and industry. Students are expected to meet standards of professional conduct during MSFE related activated -see handbook for more details.
Second Semester (Spring)
FIN 512 Financial Derivatives (4 hours): Introduction to options, futures, swaps and other derivative securities; examination of institutional aspects of the markets; theories of pricing; discussion of simple as well as complicated trading strategies (arbitrage, hedging, and spread); applications for asset and risk management.
FIN 567 Risk Management (4 hours): The course will cover the main ideas and tools for financial risk measurement, as currently practiced at some of the leading financial institutions. The techniques covered will include, among others, value-at-risk, credit value-at-risk, and stress testing. It will consider their strengths and limitations, and the criticisms that have been made of internal risk management. The course will study a number of cases of risk management failures in order to identity the lessons that can be learned from the risk management failures. Students will be expected to prepare for and participate actively in the case discussions.
IE 525 Numerical Methods in Finance (4 hours): Numerical methods for the modeling, pricing, and risk management of various financial instruments including derivatives: (STOCHASTIC) randomization and anti-gaming, Monte Carlo simulation, variance reduction techniques, quasi Monte Carlo methods; (DETERMINISTIC) finite difference methods for ordinary and partial differential equations, explicit and implicit schemes, boundary conditions, and free boundary problems for American options; (DATA) data-driven financial model calibration and optimization, financial data pattern analysis and synthesis, filtering, online learning.
IE 526 Stochastic Calculus in Finance (4 hours): A stochastic calculus approach to the pricing and risk management of financial derivatives: No-arbitrage pricing; the binomial model; Brownian motion; the Black-Scholes-Merton model; stochastic-volatility and jump models. Computational methods including numerical solutions of partial differential equations. Monte Carlo simulation and Fourier transform methods.
IE 527 Professional Development (1 hour): This course is designed to enrich student coursework experience through a series of invited speaker lectures (MSFE Practitioner Series) field trips and conversation courses aimed at bridging the gap between academic work and industry. Students are expected to meet standards of professional conduct during MSFE related activated -see handbook for more details.
Summer Internship Third Semester (Fall)
FIN 516 Term Structure & Valuation (4 hours): The LIBOR market model (LMM), its calibration, implementation, and use in valuing interest rate derivatives, including interest rate exotics and Americanstyle options with the LMM. Review of the simpler Hull-White, Black-Derman-Toy, and Black-Karasinski models that are still in widespread use. Applications of Monte Carlo methods (in the LMM) and finite-difference or “tree” methods (in the other models).
FIN 566 Algorithmic Market MicroStructure (4 hours): This course introduces the modern theoretical, empirical and institutional foundations of market microstructure and trading activity, with an emphasis on applications to algorithmic and high-frequency trading. The first part of the course addresses market microstructure and the algorithmic implementation of traditional microstructure-inspired tasks such as minimizing execution costs. The second part of the course proceeds to examine actual algorithmic strategies, and ultimately high-frequency trading. Recurrent themes throughout the course will be the use of economic theory to simplify computationally challenging problems, and the use of theory-driven structural models to construct more robust trading algorithm
IE 524 Optimization in Finance (4 hours): Basic optimization methods for financial engineering, optimization modeling languages such as AMPL and GAMS, and optimization software including the NEOS server. Linear, quadratic, nonlinear, dynamic, integer, and stochastic programming and their applications to portfolio and asset management. Optimization using values-at-risk, conditional values-atrisk, and other risk measures.
IE 534 Deep Learning (4 hours): This course provides an introduction to neural networks and recent advances in deep learning. Topics include training and implementation of neural networks, convolution neural networks, recurrent neural networks (LSTM and gated recurrent), residual networks, reinforcement learning, and Q-learning with neural networks. A part of the course will especially focus on recent work in deep reinforcement learning. The course will also cover deep learning libraries (e.g., Chainer, Tensorflow) and how to train neural networks using GPUs and GPU clusters.
IE 597/FIN590 Financial Engineering Project (4 hours): Project-based course. Students work individually or in teams to develop solutions to problems in finance supplied by industry or by a faculty member associated with the MSFE program. A midterm and final report summarize the work of the term.
FIN 580 Practical Asset Allocation (4 hours): This is an elective class for students of MSFE. Ph.D. students interested in this class should speak with Professor Morton Lane. Prerequisites are familiarity with modern portfolio theory (e.g., mean-variance optimization), factor models (e.g., Fama and French), and Matlab. Each lecture will be in two parts; the first part is more theoretical and the second will have problems and practical applications.
FIN 580 Option Trading Market Making (4 hours): This course will focus on how option theory is actually applied by option traders and market-makers to achieve their desired goals.Â The class will include sections on the mechanics of trading and market organization, as well as a review of basic option theory and commonly used pricing models.Â From this, we will examine a variety of trading and market-making strategies, with special emphasis on both the theoretical and real-world risks which option trading entails. Lastly, we will look at some of the realized and implied volatility contracts which have become such popular trading instruments.
IE 598 Electronic Trading (4 hours): It behooves participants in today’s electronic markets understand the way the markets work. Few do. The purpose of this course is expose the exact nature of order matching and routing at the compute-packet level in most exchanges. Not knowing the nature the interfaces has led many to analyze market data in comfortingly ‘good fit” predictive models. However, they are in many cases, predicting the past! They need to adjust analyses for speed and time-stamps. The course will address these issues. However, it should be stressed that the course does not purport nor intend to examine nor propose “trading strategies”.