Financial Analytics
Program Details
Degree
Master of ScienceDepartment
School of Business Graduate ProgramAvailable
On Campus & OnlineThe School of Business is redesigning and enhancing the Financial Analytics Program with the Financial Technology and Analytics program. The last semester we will accept applications to the FA program will be for Fall 2023.
The Master’s in Financial Analytics teaches professionals to apply their physics, statistics or engineering skills to the lucrative finance industry. You’ll learn from faculty whose insights are changing how the industry is thinking about concepts like machine learning, data modeling and optimization in finance. You’ll take courses in one-of-a-kind financial labs, home to the data analysis and visualization tools used on Wall Street.
The Financial Analytics program is structured around a core of courses covering data science, risk management and time series, and their applications in finance. The core concludes with a capstone consulting or research experience. You will customize your degree through a set of electives, including the chance to pursue a structured specialization tailored to your career interests. A close relationship between you and your faculty advisor will help you select the right classes for your future.
Core courses
Courses in this block go beyond important foundational concepts for financial analysts, immersing you in new programming languages, data sets and analytical technologies.
FA 541 Applied Statistics with Applications in Finance
This course prepares students to employ essential ideas and reasoning of applied statistics. The course provides students with a solid foundation for solving empirical problems with the ability to summarize observed uni- and multivariate data, and to calibrate statistical models. While financial applications are emphasized, the course may also serve areas of science and engineering where statistical concepts are needed. The course will familiarize students with the use of R for statistical data analysis.
FA 582 Foundations of Financial Data Science (2 credits)
This course provides an overview of issues and trends in data quality, data storage, data scrubbing and data flows. Topics include data abstractions and integration, enterprise-level data issues, data management issues, similarity and distances, clustering methods, classification methods, text mining, and time series. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues.
Corequisite: FE 513
FE 513 Practical Aspects of Database Design (1 credit)
The course introduces required techniques and fundamental knowledge in data science techniques. It familiarizes students with database and data analysis tools, teaching them to manage databases and solve financial problems using R.
FE 535 Introduction to Financial Risk Management
The course reviews different topics related to risk modeling and management, specifically in line with Financial Risk Manager (FRM) Parts I and II. The class begins with basic topics related to quantitative analysis and financial market products before moving on to advanced topics related to market and credit risk management. Project work and weekly tutorials help reinforce lessons from class.
FA 590 Statistical Learning in Finance
This course provides an applied overview of both classical linear approaches to statistical learning and more modern statistical methods. The classical linear approaches include logistic regression, linear discriminant analysis, k-means clustering and nearest neighbors, while modern approaches include generalized additive models, decision trees, boosting, bagging and support vector machines.
FA 542 Time Series with Applications to Finance
In this course, students learn how to estimate financial data models and predict using time series models. The course covers linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), nonlinear models (TAR, STAR, MSA), nonparametric models (kernel regression, local regression, neural networks) and nonparametric methods of evaluating fit. The course also introduces multivariate time series models, such as VAR.
Prerequisite: Any of FE 541, MA 331, MA 541 or MA 612
Concentrations
There are endless career opportunities for professionals with the skills taught in this program. Four elective courses allow you to further explore a particular career path. Three popular tracks are outlined below.
FA 550 Data Visualization Applications
Effective visualization of complex data allows for useful insights, more effective communication and better decision-making. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a task. Students use a number of tools to refine their data and create visualizations, including: Tableau, R and associated visualization libraries, HTML5 and CSS 3, D3.js and related javascript libraries, Open Refine, basic Python scripting, and image-editing programs.
FA 595 Financial Technology
This course deals with the networking and machine-learning technologies underlying activities of markets, institutions and participants. The overall purpose is to give students a working understanding of a wide variety of the technological tools that permeate modern life. The successful student will be able to extend this knowledge, understand systems currently in place and use new developments in the field as they are created.
FA 690 Machine Learning in Finance
This course focuses on traditional machine learning algorithms (random forests, support vector machines and conditional random fields) as well as recent developments in deep neural networks focusing primarily on the TensorFlow library. The distinctions between various types of algorithms (supervised, unsupervised and reinforcement learning) are developed, as well as the relationship between the quality of the data and complexity of the model.
FA 550 Data Visualization Applications
Effective visualization of complex data allows for useful insights, more effective communication and better decision-making. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a task. Students use a number of tools to refine their data and create visualizations, including: Tableau, R and associated visualization libraries, HTML5 and CSS 3, D3.js and related javascript libraries, Open Refine, basic Python scripting, and image-editing programs.
FA 631 Investment, Portfolio Construction and Trading Analytics
This course explores how to apply fundamental machine learning models to predict financial time series and solve financial problems. Some of the financial applications explored are algorithmic trading, model calibration, portfolio optimization and risk management.
In addition to the two courses above, choose one of the below.
FA 646 Optimization Models and Methods in Finance
This course introduces the approach of modeling financial decisions as optimization problems, then developing appropriate optimization methodologies to solve these problems. The course discusses the main classes of optimization problems encountered in financial engineering: linear and nonlinear programming, integer programming, dynamic programming, stochastic programming and robust optimization. Recent topics about portfolio optimization arising in behavioral finance also are discussed.
MA 575 Optimization Models in Quantitative Finance
This course introduces mathematical models and computational methods for static and dynamic optimization problems occurring in finance. The models involve knowledge of probability, optimality conditions, duality and basic numerical methods. The course discusses approaches to portfolio optimization with fixed income securities, immunization, risky assets, asset-liability management in dynamic models, dynamic optimization techniques and others.
And select one of the following three options.
MA 630 Advanced Optimization Methods
This course introduces several advanced topics in the theory and methods of optimization. The first portion of the class focuses on subgradient calculus for non-smooth convex functions, optimality conditions for non-smooth optimization problems, conjugate and Lagrangian convex duality. The second part of the class discusses numerical methods for non-smooth optimization as well as approaches to large-scale optimization problems.
MA 661 Dynamic Programming and Reinforcement Learning
This course presents an introduction to dynamic programming as the most popular methodology for learning and control of dynamic stochastic systems. The course covers basic models, some theoretical results and numerical methods for these problems. They will be developed starting from basic models of dynamical systems, through finite-horizon stochastic problems, to infinite-horizon stochastic models of fully or partially observable systems. Concepts and methods will be illustrated by various applications, including business and finance.
MA 662 Stochastic Optimization
This course introduces modeling and numerical techniques for optimization under uncertainty and risk. Topics include: generalized concavity of measures, optimization problems with probabilistic constraints, numerical methods for solving problems with probabilistic constraints, two-stage and multi-stage models (structure, optimality, duality), decomposition methods for two-stage and multi-stage models, and risk-averse optimization models.
In addition to the three courses below, you may select an elective of your choice. Consult your advisor for suggested courses that align with your professional goals.
FE 635 Financial Enterprise Risk Engineering
This course begins with risk management case studies, then continues into strategies for quantitative investing. Credit derivatives will be introduced, along with the pricing models using Hazard rates and Copulus. Modern regulatory theory using Basel II, Basel III and CVA as a starting point will be analyzed. Finally, the study of fat-tailed distributions — such as Pareto and those coming from extreme value theory — will be discussed.
FE 655 Systemic Risk and Financial Regulations
This course deals with aspects of systemic risk in financial systems, covering a review of classical risk measures and introducing non-classical measures such as extreme value theory. It also covers the study of financial systems as a system of complex adaptive systems, agent-based modeling, history and analysis of bubble formations as a systemic risk, the role of rating agencies, the financial systems ecosystem, risk and regulatory environment, and risk and the socio-political environment.
FA 636 Advanced Financial Risk Analytics
This course aims to leverage state-of-the-art analytics for financial risk management. The course begins with an overall introduction to risk models such as market, credit and operational risk. The course then evolves to discuss volatility predictive models using time series analysis and machine learning. It will also discuss multivariate risk systems, copulas and shrinkage-based techniques for risk assessment. The second half of the course is mostly dedicated to credit risk management, focusing on use predictive analytics to develop early warning systems for corporate credit risk. The course will cover recent research articles and statistical computing libraries as part of the learning objectives.
A capstone experience is a requirement to ensure mastery of the diverse skills covered in the curriculum. Both courses offer the research-intensive perspective needed to help you think of innovative ways to approach emerging problems in this sector. Most students will take FA 800; FA 900 is a two-semester option for students looking to write a graduate thesis. Choose one of the below.
FA 800 Project in Financial Analytics
This course is designed for FA students undertaking a research or analytical project either individually or as a group. The goal of this course is to train students' ability to work on a research-oriented project in a group environment, and also train their professional presentation and scientific writing skills.
FA 900 Master's Thesis in Financial Analytics
A minimum of six credit hours is required for the thesis. Hours and credits to be arranged. You will need both an advisor and a reader to complete this course; interested students should contact their academic advisor for complete details.
Ideal candidates for the program will have a strong background in statistics, mathematics, physics, engineering or a related discipline, or will have several years' experience working in finance with an interest in exploring these disciplines in great depth.
Application deadlines
Full-time applications to the Financial Analytics program are accepted in three distinct cycles. To be considered for admission, all materials must be submitted by the deadline.
Application cycle | Application deadline | Application decision | Acceptance deadline |
Priority | Oct. 15 | Dec. 1 | Jan. 15 |
Standard | Jan. 15 | March 1 | Included in admission offer |
Final | April 15 | May 1 | June 1 |
Part-time applications to this program are accepted on a rolling basis.