Course Descriptions

First Semester - Fall - 16 hours

Course title and description

Core/Elective

Credit Hours

FIN 500 Introduction to Finance: 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.

Core

4

FIN 553 Machine Learning:  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.

Core

4

IE 522 Statistical Methods in Finance: 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.

Core

4

IE 523  Financial Computing: 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++

Core

4

Second Semester - Spring - 16 hours

FIN 512 Financial Derivatives: 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.

Core

4

IE 525 Stochastic Calculus & Numerical Models in Finance: This course explores PDE and simulation methods for pricing derivatives. Specifically, we will derive the Black-Scholes PDE and develop finite-difference techniques for pricing vanilla, path-dependent and exotic options. Moreover, we will discuss Monte Carlo methods and variance reduction techniques, for pricing options as well as calculating their sensitivities

IE 583 MSFE Practicum Project: 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.^b

Core - optional timing

4