MBA and M.S. in Financial Engineering Dual Degree Master’s Program

Applied Analytics is a capstone course for the analytic-focused MBA program. It is intended to integrate all previously taken coursed in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skill taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

An interdisciplinary course which examines the elements of, and the framework for, developing and implementing organizational strategy and policy in competitive environments. The course analyzes management problems both from a technical-economic perspective and from a behavioral perspective. Topics treated include: assessment of organizational strengths and weaknesses, threats, and opportunities; sources of competitive advantage; organizational structure and strategic planning; and leadership, organizational development, and total quality management. The case method of instruction is used extensively in this course.

Covers the general area of management of operations, both manufacturing and non-manufacturing. The focus of the course is on productivity and total quality management. Topics include quality control and quality management, systems of inventory control, work and materials scheduling, and process management.

This course deals with the basic problems of managing a project, defined as a temporary organization built for the purpose of achieving a specific objective. Both operational and conceptual issues will be considered. Operational issues include definition, planning, implementation, control, and evaluation of the project. Conceptual issues include project management vs. hierarchical management, matrix organization, project authority, motivation, and morale. Cases will be used to illustrate problems in project management and how to resolve them.

Executives make decisions every day in the face of uncertainty. The objective of this course is to help students understand how decisions are made, why they are often less than optimal, and how decision-making can be improved. This course will contrast how managers do make decisions with how they should make decisions, by thinking about how “rational” decision makers should act, by conducting in-class exercises and examining empirical evidence of how individuals do act (often erroneously) in managerial situations. The course will include statistical tools for decision-making, as well as treatment of the psychological factors involved in making decisions.

The study of marketing principles from the conceptual, analytical, and managerial points of view. Topics include: strategic planning, market segmentation, product life-cycle, new product development, advertising and selling, pricing, distribution, governmental, and other environmental influences as these factors relate to markets and the business structure.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

This course provides the mathematical foundation for understanding modern financial theory. It includes topics such as basic probability, random variables, discrete continuous distributions, random processes, Brownian motion, and an introduction to Ito’s calculus. Applications to financial instruments are discussed throughout the course.

This course deals with basic financial derivatives theory, arbitrage, hedging, and risk. The theory discusses Itô's lemma, the diffusion equation and parabolic partial differential equations, the Black-Scholes model and formulae. The course includes applications of asset price random walks, the log-normal distribution, and estimating volatility from historic data. Numerical techniques such as finite difference and binomial methods are used to value options for practical examples. Financial information and software packages available on the Internet are used for modeling and analysis. Prerequisite: Multivariable Calculus, Ma/FE610, and programming in C, C++, or Java.

This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software.

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques such as linear programming. Topics include contingent investment decisions, deferral options, combination options and mergers and acquisitions. The course then focuses on financial risk management with emphasis on Value-at-Risk (VAR) methods using general and parametric distributions and VAR as a risk measure. Real world scenarios are studied.

This course deals with fixed-income securities and interest-rate sensitive instruments. Topics include term structure of interest rates, treasury securities, strips, swaps, swaptions, one-factor, two-factor interest rate models, Heath-Jarrow-Merton (HJM) models and credit derivatives: credit default swaps (CDS), collateralized debt obligations (CDOs), and Mortgage- backed securities (MGS).

This course is designed for FE students undertaking a research or a project in financial engineering either individually or as a group. The project may be suggested either by faculty members or industry senior managers associated with your internship, as well as any internship that a student may receive through this course. The goal of this course is to involve students in developing research skills, communication skills while keeping their interest in result-oriented techniques. The ability to work on a research-oriented project in a group environment and train their professional presentation and scientific writing skills lead to a competitive graduate who is ready to lead in the work place.

Many managerial decisions - regardless of their functional orientation - are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models, and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation, and finance. Business Analytics explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation, and optimization models.

This course introduces managers to the essence of business economics – the theories, concepts and ideas that form the economist’s tool kit encompassing both the microeconomic and macroeconomic environments. Microeconomic topics include demand and supply, elasticity, consumer choice, production, cost, profit maximization, market structure, and game theory while the Macroeconomic topics will be GDP, inflation, unemployment, aggregate demand, aggregate supply, fiscal and monetary policies. In addition the basic concepts in international trade and finance will be discussed.

This concentration emphasizes the design and implementation of financial trading systems in dynamic markets, with special focus on how software and automated decision support systems play roles in trading strategies. To complete this concentration, students must take the following courses:

• FE 545 Design, Patterns and Derivatives Pricing

• FE 570 Market Microstructure and Trading Strategies

• FE 620 Pricing and Hedging

• FE 670 Algorithmic Trading Strategies

The Financial Analytics concentration emphasizes statistical learning methods and database skills, preparing students to develop tools to manage enterprise-level challenges. Students apply data-driven solutions to complex financial problems in preparation for an industry in increasing need of such skills. To complete this concentration, students must take the following courses:

• FE 582 Foundations of Financial Data Science (2 credits)

• FE 513 Financial Lab: Database Design (1-credit lab)

• FE 590 Statistical Learning in Finance

• FA 595 Financial Technology (FinTech)

• FE 550 Data Visualization Applications

Technology’s impact on market fundamentals means managers must understand the financial system, its environment and the risk measures that help quantify risk in its multiple hierarchies. Courses in this concentration emphasize a blend of technology and business to help graduates see the financial landscape from a systemic perspective, and to analyze and manage risk efficiently. To complete this concentration, students must take the following courses:

• FE 535 Introduction to Risk Management

• FE 610 Stochastic Calculus for Financial Engineers

• FE 635 Financial Enterprise Risk Engineering

• FE 655 Systemic Risk and Financial Regulation

Students also complete an additional elective with advisor permission.

Proper statistical analysis, supported by new technology tools, helps managers assess markets and build products to create competitive advantages for the enterprise. This concentration gives students insight into technology-driven opportunities in finance through advanced data analytics. To complete this concentration, students must take the following courses:

• FE 541 Applied Statistics with Applications in Finance

• FE 542 Time Series and Applications to Finance

• FE 590 Statistical Learning in Finance

• FE 610 Stochastic Calculus for Financial Engineers

This concentration goes beyond basic programming and computing skills to teach students to use quantitative models to manage large financial data sets. Students learn financial computing models, financial databases, financial engineering software and specialized programming languages. To complete this concentration, students must take the following courses:

• FE 505 Financial Lab: Technical Writing in Finance (1 credit)

• FE 522 C++ Programming in Finance

• FE 511 Introduction to Bloomberg and Thomson Reuters (1-credit lab)

• FE 699 Project in Financial Computing (2 credits)

Students also complete an additional elective with advisor permission.