Master of Quantitative Finance Curriculum

This course has a broad structure and covers many aspects of modeling and estimating financial/economic time series. In particular, we will be focusing on (i) linear regression models involving variables observed overtime and (ii) “pure” univariate and multivariate time-series models. The objective is that participants gain a thorough understanding of the theory underlying time-series econometrics, which is the basis for any empirical time-series analysis of financial/economic market phenomena. The course places a particular emphasis on clearly identifying which econometric methods are appropriate under which scenarios. Estimation techniques covered will be Ordinary Least Squares (OLS) and Generalized Method of Moments (GMM).

This course examines the problems faced by the corporate financial manager on the theoretical, analytical, and applied levels. The impact of the financing decision upon the value of the firm is analyzed. The course reviews the theory and empirical evidence related to the investment and financing policies of the firm and attempts to develop decision-making ability in these areas.

This course introduces various types of fixed income product including Money Market, Treasury, Corporates, ABS, MBS, CMO, Structure Finance and Credit products. It focuses on various aspects of bond pricing, bond analytics and structuring. In addition, it examines funding methods and tools for fixed income portfolio management.

The purpose of this course is to teach students how to create an actual portfolio that meets the needs of a client in a manner consistent with the investment philosophy of Graham, Dodd, and Buffett. The client (previously an individual, now the Rutgers University Foundation) wishes the portfolio to have a Value orientation with hedge fund characteristics (i.e., the portfolio has both Long and Short positions.) From an organizational standpoint, each student will serve as an analyst responsible for a particular sector or industry. Students will be required to write two comprehensive stock reports (one Long recommendation and one Short recommendation) and present their findings in front of the class. The course will be primarily conducted on an independent study basis with only a moderate number of in-class meetings. We will meet in a classroom setting approximately once every two weeks. Additional communication will be done via phone (e.g. conference calls) and email. All students must have a strong understanding of financial statement analysis in order to effectively participate in the class.

The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to conduct their own research in data mining or to apply data mining techniques to their own research fields.

Interpretation and in-depth analysis of reported financial data. The role of taxes and tax disclosures will be included in the class discussions. Some aspects of valuation will be discussed. Issues include reported numbers making sense; reporting choices made by the preparer/firm when other choices under GAAP are available; strategy of firms in their choice of financial information disclosures; comparison of financial information within and across industries; projection of key information variables like earnings or cash flows into the future; financial reporting information used to gauge the riskiness of firms; prediction of the probability of bankruptcy using financial data.

The purpose of this course is to develop basic econometric estimation and hypothesis testing tools necessary to analyze and interpret the empirical relevance of financial and other economic data. This requires developing statistical methods for estimation of population parameters and testing hypotheses about them using a sample of data drawn from the population distribution, under various assumptions regarding the true population relationship between the observable economic variables. Topics covered include estimation and hypothesis testing using the classical general linear regression model, combining sample and non-sample information, dummy and interaction variables, multicollinearity, and introductions to large sample theory, nonspherical disturbances, panel data, instrumental variables, systems of equations, and their application. The focus is on the theoretical foundations of econometric analysis and strategies for applying these basic econometric methods.

This elective course is designed to provide a deep understanding of the U.S. financial system from a modern financial intermediation perspective. It will examine financial institutions, monetary theory and the money supply process and focus on the recent financial crisis from a scientific data-driven analytical approach.

This is a quantitatively-oriented financial economics course for the Master of Quantitative Finance (MQF) students. The course covers the basic concepts and analytical techniques of modern portfolio theory and asset pricing. Topics include Fisher separation, risk analysis using expected utility theory, mean-variance analysis, capital asset pricing model, arbitrage pricing theory, state preference theory, consumption-based asset pricing, market efficiency, empirical tests of asset pricing models, and market anomalies.

The  course  is  designed  to  teach  students  the  fundamentals  of  pricing  and  risk  management of derivative assets with the continuous time finance techniques. While the focus of the course is on equity derivatives, the short rate and LIBOR market models are also included in the tentative schedule. The course material is designed to address the demands of the modern quant profession and includes lectures on various models addressing the implied volatility surface phenomenon.

This course is designed to provide a practical, useful, and intelligent methodology for understanding and analyzing the financial performance of a business entity or division for professional or personal investments. To recognize the key components of financial and investment activities that marks the difference between successful and failed businesses. To understand and analyze cash flow, credit situation, and equity structure of a business.

This course covers applied statistical methodologies pertaining to financial time series, with an emphasis on model building and accurate prediction. Completion of this course will equip students with insights and modeling tools to analyze real world financial and business time series. Students are expected to have basic working knowledge of probability and statistics including linear regression, estimation and testing from the applied perspective. We will use R throughout the course so prior knowledge of it is welcome, but not required.

Students are guided through a series of lectures, workshops, individual and group activities, and assignments designed to educate, develop, and assist them to successfully navigate the challenging MQF profession. The course begins with “understanding your value” as a potential employee and how best to communicate it to targeted employers. From here it moves on to the job search process and then to the many phases of interviewing and ultimately to negotiating compensation.

Students will have various opportunities during both semesters to connect with industry participants and network. This course provides tools necessary for students to take ownership of their career and give them the competitive advantage critical to achieving their career goals.

This course will provide students with a solid and working understanding of hedge funds. The course will not only cover an overview of the hedge fund industry, but also provide students with a strong understanding of more than a dozen hedge fund strategies, including equity long / short, global macro, statistical arbitrage, merger arbitrage, convertible arbitrage, and fixed income arbitrage. The course will make extensive use of Excel spreadsheets to model specific hedge funds strategies and will also include live instruction on using cutting-edge Internet resources. In my view, often the best way to learn is by doing, so students will also manage a simulated $1 million hedge fund portfolio and design and present a hedge fund investment strategy group project.

This course covers set theory, sample spaces, events, probability functions on sample spaces, combinatorial methods, conditional probability, Bayes' theorem, Markov chains (if time permits), random variables and distributions (discrete, continuous, mixed, multivariate), conditional distributions, functions of random variables, expectations (mean, variance, covariance, correlation, moments, conditional expectations), moment-generating functions, inequalities (Chebyshev, Jensen), limit theorems (laws of large numbers, central limit theorem), large sample approximations (Poisson and normal to binomial, normal to Poisson, normal to the t- distribution, etc.), special distributions (Bernoulli, binomial, multinomial, geometric, negative binomial, hypergeometric, Poisson, exponential, gamma, beta, t, normal and multivariate normal, and chi-square.

This course introduces various asset classes including equities, fixed income, real estate and derivatives. It introduces students to basic Portfolio theory, CAPM, Market Efficiency and their implications in terms of investment. It looks at Risk Factor models versus Expected Return models. The course also looks at various aspects of trading, stock selection, hedging, portfolio strategies, asset allocation and investment philosophy.

This course derives, analyzes, and applies methods used to solve numerical problems with computers; solution of linear and nonlinear algebraic equations by iterations, linear equations and matrices, least squares, interpolation and approximation of functions, numerical differentiation and integration, and numerical solutions of ordinary differential equations.

This course assumes some computer programming language experience like C. It is designed for learning object oriented programming using C++ programming language. Basic concepts such as data types, control structures, classes design, class hierarchy, class libraries, inheritance, polymorphism, I/O handing, exceptions, templates and standard template libraries will be covered. Other C++ features will also be covered. This course is focus on hand-on experience of developing financial related computer applications.

The goal of this year-long sequence of courses is to give a rigorous introduction to computer programming and software engineering with special emphasis on applications to financial engineering. Our primary programming language will be C++. This programming language is fast enough to accommodate the performance demanded in financial environments. At the same time C++ is an object oriented language and, as such, is suitable for modern software design. In this course the assumption is that students have had no background in computer programming, although even people who are familiar with some programming language will hopefully benefit and learn new material. In part I in the Fall semester the course will start with basic concepts of programming, but we quickly get into topics in object oriented programming, UML diagrams, and basic patterns. We will also include introduction to basic algorithms and data structures. In part II in the Spring semester, more advanced topics will be covered, including advanced algorithms and data structures especially through introduction to STL and boost libraries, numerical algorithms and introduction to BLAS and LAPACK libraries, design of graphical user interfaces, and concurrent programming (also known as multiprogramming).

The objective of the course is to provide the students with knowledge and skill sufficient for correct formulation, analysis and solution of optimization models. Particular attention will be devoted to models applicable to various financial planning problems, including models of risk-averse optimization. Specific topics include optimality conditions for linear and nonlinear programming, duality, mean-risk optimization, optimization of coherent measures of risk, and optimization with stochastic dominance constraints. The course will also prepare the students for independent research on problems involving risk modeling and optimization.

The purpose of this course is to provide students with the necessary knowledge on how to use and not to use the models for derivatives. While the course will primarily focus on payoffs, usage, pricing, hedging, and institutional details of the most fundamental or vanilla versions of Options and Futures, it will also deal in some detail with more recent studies in the theory of derivative pricing. Students will acquire a robust conceptual knowledge of the fundamental issues that determine the valuation and behavior of these instruments. Though this course focuses on the intuitive economic insights of those models, some advanced math is required, including stochastic calculus. Be prepared for some necessarily non-trivial math if you take the course.

The purpose of this course is to teach students how to create an actual portfolio that meets the needs of a client in a manner consistent with the investment philosophy of Graham, Dodd, and Buffett. The client (previously an individual, now the Rutgers University Foundation) wishes the portfolio to have a Value orientation with hedge fund characteristics (i.e., the portfolio has both Long and Short positions.) From an organizational standpoint, each student will serve as an analyst responsible for a particular sector or industry. Students will be required to write one comprehensive stock report (one Long recommendation and/or one Short recommendation) and present the findings of their best investment idea in front of the class. Students have the option of writing two stock reports (one Long, one Short sale) to maximize the skill set obtained from the course.

This course provides an overview of financial risk management. Emphasis will be on modeling and quantitative techniques. Students will learn how risk management is carried out in today’s financial firms and about current challenges in financial risk management.

The objective of the course is to provide the students with knowledge and skill sufficient for correct formulation and analysis of continuous-time stochastic models involving stochastic integrals and stochastic differential equations. Particular attention will be devoted to application of stochastic calculus methods in finance, such as models of evolution of stock prices and interest rates, pricing of options, and pricing of other contingent claims.The course will also prepare the students for independent research on problems involving stochastic calculus techniques.

The course covers the theory and modeling of stochastic processes. Topics include, martingales, stopping theorems, elements of large deviations theory, Renewal Theory, Markov Chains, Semi-Markov Chains, Markovian Decision Processes. In addition, the class will cover some applications to finance theory, insurance, queueing and inventory models.

This course covers the use of simulation as a tool for analyzing business and engineering problems. The two primary goals of the course are to learn how to plan, build and use simulation models and to develop an understanding of when simulation is an appropriate tool for analysis. Much of the work in the course will involve learning the mathematical and software tools for building simulation models, performing experiments with them, and interpreting the results.

The MQF internship program is an integral and important enhancement to class lectures, readings, and student assignments. It is designed to provide students practical experience in the quantitative finance field with the opportunity to experience classroom theory in the business environment.

The types of training may include implementation of trading strategies for equities and currencies, analysis of stocks and bonds, identification of mispriced assets, validation of pricing models for options and other derivative securities, analysis and management of risk, and forecast of financial variables.

The student will work under the supervision of an approved employer within a specific department and will be evaluated by both the employer and the program advisor.

This is an advanced, modern finance course with the objective of studying indices and Exchange Traded Funds (ETFs) and their application in investment management. The course is designed to be comprehensive in covering the economic space and investment objectives/strategies represented by indexes and the data and products that underlie ETFs. The contemporary and practical class content is enhanced by exposure to useful industry resources and participants. The course is divided into two major segments. The first provides a deep dive into the theories, methodologies and wide variety of products (covering all asset classes) that are the foundations of index-based investing. The second segment provides a thorough examination of ETFs from an investment strategy standpoint, including their anatomy, mechanics, application, availability, and the ecosystem of industry participants. A semester project is required that involves working on either a current industry challenge or investment opportunity that includes, portfolio design, calculation, backtesting and marketing. The class project includes working on teams, at times directly with industry experts, to practice the application of tools and material learned in class to actual real-world situations. Spring semester only.

This course provides a comprehensive insight into quantitative trading strategies and covers most aspects of the development life cycle of a trading strategy. This includes idea conception, using data for research and alpha generation, appropriate modeling, back-testing and simulation, technology and infrastructure, regulatory compliance, risk management, and others. The course will provide an introduction to financial markets, nature of market and its mechanics, various constituents of the markets and their role, importance of order types and execution details, micro structure and more. It will also introduce few quantitative trading methods, educate on pitfalls and limitations, give a preview of regulatory compliance, and provide experience based view of what it takes to build and deploy a success trading strategy. The course will educate students on responsible capital allocation, risks involved in algorithmic trading, and appropriate performance matrices. An introduction to algorithmic investment management will also be provided.