Financial Analytics Curriculum Overview

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

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. 

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.

This course is designed for 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.

The Fintech concentration is focused on decentralized finance, blockchain, and digital payment systems.

FA 591 - Blockchain Technologies and Decentralized Finance (3)

The course will introduce concepts of Blockchain technologies as they apply to decentralized finance. The course starts with cryptocurrency and advances the concept of smart contracts as they apply to financial instruments. The course is technical and requires knowledge of programming in Python as well as financial instruments and concepts. Programming in solidity is learned throughout the class. The course discusses risk management concepts, stable coins as well as how regulations may impact the area.

FA 595 - Financial Technology (3)

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 (3)

This course focuses on advanced machine learning models and their applications to finance. Building on fundamental statistical learning theory, the course covers advanced topics in classification, supervised learning, unsupervised learning, latent space models, graphical models, mixture models, online learning, deep learning, and big-data analytics. Learning and building from financial data sets, the lectures will introduce machine learning models in quantitative investing, portfolio management, algorithmic trading, risk management, client-relationship management, and beyond. A final project on related topics is required.

The Financial Data Science concentration is focusing on automating financial advising with Data Analytics, and managing exposure to financial risk. 

FA 541 - Applied Statistics with Applications in Finance (3)

The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.

FE 535 - Introduction to Financial Risk Management (3)

This course deals with risk management concepts in financial systems. Topics include identifying sources of risk in financial systems, classification of events, probability of undesirable events, risk and uncertainty, risk in games and gambling, risk and insurance, hedging and the use of derivatives, the use of Bayesian analysis to process incomplete information, portfolio beta and diversification, active management of risk/return profile of financial enterprises, propagation of risk, and risk metrics.

FA 542 - Time Series with Applications in Finance (3)

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

The Financial Data Science concentration is focusing on automating financial advising with Data Analytics, and managing exposure to financial risk. 

FA 541 - Applied Statistics with Applications in Finance (3)

The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.

FE 535 - Introduction to Financial Risk Management (3)

This course deals with risk management concepts in financial systems. Topics include identifying sources of risk in financial systems, classification of events, probability of undesirable events, risk and uncertainty, risk in games and gambling, risk and insurance, hedging and the use of derivatives, the use of Bayesian analysis to process incomplete information, portfolio beta and diversification, active management of risk/return profile of financial enterprises, propagation of risk, and risk metrics.

FA 542 - Time Series with Applications in Finance (3)

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

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.

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.

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.

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.

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.

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.

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.

This course is designed for 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.

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.