Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• QST MF 796: Computational Methods of Mathematical Finance
  This course introduces common algorithmic and numerical schemes that are used in practice for pricing and hedging financial derivative products. Among others, the course covers Monte-Carlo simulation methods (generation of random variables, exact simulation, discretization schemes), finite difference schemes to solve partial differential equations, numerical integration, and Fourier transforms. Special attention is given to the computational requirements of these different methods, and the trade-off between computational effort and accuracy. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 810: FinTech Programming
  The course introduces students to a number of efficient algorithms and data structures for computational problems across a variety of areas within FinTech. In the first half of the course, a special programming language for blockchains, such as Solidity, is taught, and TensorFlow, a special Python library for deep learning models, is used to solve stochastic control problems in finance. In the second half of the course, advanced techniques for improving computational performance, including the use of parallel computation and GPU acceleration are surveyed; frameworks for big data analysis such as Apache Hadoop and Apache Spark are studied. Students will have the opportunity to employ these techniques and gain hands-on experience developing advanced applications. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)
• QST MF 815: Advanced Machine Learning Applications for Finance
  This course surveys applications of machine learning techniques to various types of financial datasets. This course starts with financial data structure and features, then introduces deep learning and advanced supervised learning techniques. We will examine several machine learning applications in pricing, hedging, and portfolio management. Advanced methods for clustering and classification such as support vector machine and unsupervised learning will be introduced. Reinforcement learning and its connection with optimal control will be discussed. Text data will be introduced and analyzed using text mining techniques. Machine learning techniques will be applied to asset allocation. Strategy back-testing and strategy risk will also be discussed. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)
• QST MF 821: Algorithmic and High-Frequency Trading
  This course will introduce concepts of electronic markets, and statistical and optimal control techniques to model and trade in these markets. We will begin with a description of the basic elements of electronic markets, some of the features of the data, its empirical implications and simple microeconomic models. Next, we will study statistical tools to estimate and predict price and volatility of the high-frequency price. Then we will investigate algorithmic trading problems from the stochastic optimal control perspective, including the optimal execution problem and show how to modify the classical approaches to include order-flow information and the effect that dark pools have on trading. Trading pairs of assets that mean-revert is another important algorithmic strategy, and we will see how stochastic control methods can be utilized to inform agents how to optimally trade. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 825: Advanced Topics in Investments
  This course is designed for students seeking to work as quants in a quantitative finance investments group. It covers utility theory, portfolio optimization, asset pricing, and some aspects of factor models, incorporating the impact of parameter uncertainty. The course does not cover risk management or fixed income instruments, nor does it describe how the financial services industry works. Rather, it teaches how a quant should optimize a portfolio. The course makes extensive use of R (Excel or VBA are not substitutes), optimization theory, statistics, regression theory (OLS, GLS, testing theory), and matrix algebra. Students should be very comfortable with these concepts before taking the course; further, students should already have taken a finance course covering expected returns models (CAPM), options and futures. The course emphasizes the ability to prove theoretical results and their validity, an essential trait for investments quants. Students who completed QST FE825 may not take this course for credit. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 840: Data Analysis and Financial Econometrics
  This is the second course of the econometrics sequence in the Mathematical Finance program. The course quickly reviews OLS, GLS, the Maximum Likelihood principle (MLE). Then, the core of the course concentrates on Bayesian Inference, now an unavoidable mainstay of Financial Econometrics. After learning the principles of Bayesian Inference, we study their implementation for key models in finance, especially related to portfolio design and volatility forecasting. We also briefly discuss the Lasso and Ridge methods, and contrast them with the Bayesian approach Over the last twenty years, radical developments in simulation methods, such as Markov Chain Monte Carlo (MCMC) have extended the capabilities of Bayesian methods. Therefore, after studying direct Monte Carlo simulation methods, the course covers non-trivial methods of simulation such as Markov Chain Monte Carlo (MCMC), applying them to implement models such as stochastic volatility. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 850: Advanced Computational Methods
  This course explores algorithmic and numerical schemes used in practice for the pricing and hedging of financial derivative products. The focus of this course lies on data analysis. It covers such topics as: stochastic models with jumps, advanced simulation methods, optimization routines, and tree-based approaches. It also introduces machine learning concepts and methodologies, including cross validation, dimensionality reduction, random forests, neural networks, clustering, and support vector machines. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 921: Topics in Dynamic Asset Pricing
  This course provides a comprehensive and in-depth treatment of modern asset pricing theories. Extensive use is made of continuous time stochastic processes, stochastic calculus and optimal control. Particular emphasis will be placed on (i) stochastic calculus with jumps; (ii) asset pricing models with jumps; (iii) the Hamilton-Jacobi-Bellman equation and stochastic control; (iv) numerical methods for stochastic control problems in finance. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 930: Advanced Corporate Finance
  This doctoral level class on corporate finance covers both theoretical and empirical work. Rather than explaining the underpinnings of basic corporate research (e.g., model/applications dealing with asymmetric information, agency problems, and capital market frictions), we go deeper in understanding "how to operationalize" research on concrete topics that are central to contemporary corporate finance, such as bankruptcy, capital structure, mergers and acquisitions, the firm boundaries, investment, and much more. The class also looks at the interface between corporate finance and other research areas, such as asset pricing and banking. The course is a blend of new approaches to modeling in corporate research (e.g., dynamic, structural models of financial policy that generate typically quantitative predictions) and new approaches to testing design (e.g., regression discontinuities and natural experiments). The goal is to expose the students to the "state-of-the-art" of research in corporate finance and prepare them to do research in corporate finance using new methods and tools. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 990: Current Topics Seminar
  For PhD students in the Mathematical Finance program. Registered by permission only.
• QST MF 998: Directed Study: Mathematical Finance
  PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST MF 999: Directed Study: Mathematical Finance
  PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST MG 730: Ethical Leadership in the Global Economy
  The purpose of this course is to explore ethical issues throughout our global economy in a pragmatic, responsible, and decisive manner in order to prepare you to resolve these issues when faced with them in your personal and professional lives. This course will bridge the gap between an individual's personal moral values and the challenges presented by corporate activity in a marketplace -- be it local or global. Our work in this course will raise your awareness of the interrelated legal, moral, and ethical challenges inherent in business. We will critically examine the ethical implications of business decisions and equip you with frameworks and strategies for managing your own and others' behavior. We will formulate a process to evaluate complex leadership decisions and enhance your own ability to effectively navigate multi-faceted decision-making scenarios.
• QST MG 737: Capstone Project
  Questrom's action learning capstone course develops students' ability to apply integrated management perspectives and practices to live business challenges and opportunities. A foundational part of Questrom's Full Time MBA Curriculum, this capstone course provides first year students with the opportunity to work with Host Partners on marketing, operations and/or strategy projects. Student teams have the opportunity to conduct short meetings with host organizations every-other week. Under the guidance and direction of Action Learning Instructors, each team defines a project goal and the research questions to be investigated, conducts research and depth interviews, and develops a set of deliverables or recommendations. This course will include projects serving a wide range of organizations, from long-established companies to emergent startups and founders, from government agencies to university initiatives and non-profits. While the different projects will vary, the goal across all projects is the same: for students to deliver concrete and actionable recommendations for organization partners, while effectively setting and managing their client's expectations, working collaboratively as a team, and acting as principled and ethical leaders.
• QST MK 200: Principles of Marketing
  Open only to non-Questrom students. Marketing elective for Business minors. How is it that some products succeed and some fail? In many instances, the difference is in their marketing. The course examines key areas of marketing including product development, advertising, promotions, pricing, and channels. It uses a combination of in-class exercises, real world examples, cases, lecture, and discussion
• QST MK 323: Marketing Management
  Component of QST SM323, The Cross Functional Core. Introduces students to the field of marketing management: analysis, planning and implementation of marketing strategies as the means for achieving an organization's objectives. Students analyze cases and participate in workshops that focus on key marketing management tasks: marketing research, consumer behavior, segmentation and targeting, sales forecasting, brand management, distribution channels, pricing, promotion and advertising strategies, and marketing ethics. A semester-long business plan project where students collect primary and secondary research explores the interactions and the cross functional integrations between marketing, operations, and finance, while leveraging business analytics. cr. 4
• QST MK 345: Consumer Insights
  Formerly MK445. Provides insight into the motivations, influences, and processes underlying consumption behavior. Considers relevant behavioral science theories/frameworks and their usefulness in formulating and evaluating marketing strategies (i.e., segmentation, positioning, product development, pricing, communications).
• QST MK 435: Introduction to the Music Business and Music Marketing
  Survey of the music industry with a focus on understanding of its structure and the intersection of business and music. Discusses key areas of music marketing, including opportunities for musicians, including publicity, advertising, promotion (online and traditional), digital distribution, touring, licensing/synch, and radio.
• QST MK 442: Digital Marketing Analytics
  This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers. The course develops essential data analytics skills--critical thinking, data mining, experimental analysis and design--applied to ad campaign, ad attribution, and social media data.
• QST MK 447: Marketing Research
  Required for Marketing concentrators. Introduces tools and techniques of marketing research as an aid to marketing decision making. Definition of research problems, selection of research methodologies, design of research projects, interpretation of research results, and translation of research results into action.

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