M.S. in Fintech and M.S. in Financial Technology & Analytics Dual Degree Master's Program

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 - 3 Credits

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 to Finance - 3 Credits

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.

Prerequisite: BIA 652 or MGT 700 or FA 541

FA 550 - Data Visualization Applications - 3 Credits

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing datasets from a variety of perspectives in order to best identify the best solution for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Tableau 2020, Gephi, and web-based applications.

FA 631 Investment, Portfolio Construction and Trading Analytics - 3 Credits

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.

Prerequisites: Any of BIA 656, FE 590, FE 690, MIS 637 or CS 559

FA 691 Deep Learning for Finance - 1.5 Credits

This course focuses on neural network models and their applications to finance. Building on fundamental statistical learning theory, the course covers advanced topics in deep learning and big-data analytics for classification and prediction. 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. Prerequisite: Students must have taken FA590 or comparable introduction to machine learning methods

FA 692 Natural Language Processing for Financial Applications - 1.5 Credits

This course focuses on natural language processing (NLP) models and their applications to finance. Building on fundamental machine learning theory and practice, the course covers advanced topics in natural language processing for analyzing financial reports and news. 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.

Prerequisite: Students must have taken FA 590 or comparable introduction to machine learning methods

FA 800 Project in Financial Analytics - 3 Credits

Three credits for the degree of Master of Science (Financial Analytics). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master's degree program. A department technical report is required as the final product for this course.

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.

Prerequisite: 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.

FA 591 Blockchain Technologies & Decentralized Finance - 3 Credits

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.

Prerequisite: Technical background from either FE, FA, BI&A, or CS. Basic finance principles acquired through FE620, FA 535 or equivalent. Basic programming skills in Python, FE520 or equivalent.

FA 595 - Financial Technology - 3 Credits

This course covers emerging topics in the area of financial technology and also allow students to develop practical programming, statistics, and mathematical skills that are valued in the Fintech industry. We will explore topics from both theoretical and practical perspectives in the areas of digital currency and blockchain technologies, automated wealth management, digital lending, peer-to-peer applications including payments and insurance, machine learning financial applications.

FA 596 Digital Payment Technologies & Trends - 3 Credits

This course introduces students to the up-to-date payment systems and innovative financial technologies (FinTech) in the payment systems. Common payment systems such as checking, ACH, cards, cash, wire transfer are discussed. Students learn the mechanisms of FinTech innovations, including the Non-Fungible Tokens (NFTs). Students also learn how to set up digital wallets and interact with the blockchain.

Prerequisite: FA 591 and [FE 520 Python, or FE 515 R, or FE 516 Matlab], or instructor permission

FA 691 Deep Learning for Finance - 1.5 Credits

This course focuses on neural network models and their applications to finance. Building on fundamental statistical learning theory, the course covers advanced topics in deep learning and big-data analytics for classification and prediction. 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.

Prerequisite: Students must have taken FA590 or comparable introduction to machine learning methods

FA 692 Natural Language Processing for Financial Applications - 1.5 Credits

This course focuses on natural language processing (NLP) models and their applications to finance. Building on fundamental machine learning theory and practice, the course covers advanced topics in natural language processing for analyzing financial reports and news. 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.

Prerequisite: Students must have taken FA 590 or comparable introduction to machine learning methods

FA 800 Project in Financial Analytics - 3 Credits

Three credits for the degree of Master of Science (Financial Analytics). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master's degree program. A department technical report is required as the final product for this course.

Financial Statistics (FIT5003)

This course covers an introductory level of probability and statistical analysis for graduate students who major in finance. We emphasize topics of probability and statistical theories that students will encounter in graduate finance and econometric courses. Topics include probability theory, sampling, statistical estimation, and hypothesis testing.

Investment Analysis (DBA4014)

This course provides students with the theories and practices of modern investment analysis. The topics to be taught include (a) different types of securities, (b) security markets and how securities are traded, (c) security analysis, (d) portfolio theory, (e) asset pricing models, (f) efficiency in financial markets, (g) behavioral finance, (h) portfolio management and evaluation, (i) bond yield and bond portfolio management, (j) introduction of derivative securities.

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Python Programming (FIT5001)

This is a basic course in python programming for FinTech Master or Ph.D. program. Today business professionals require basic knowledge of algorithms concepts and computer programming. These two concepts differ in their scope. An algorithm refers to a set of instructions to be followed to solve a problem. A computer program is as a set of instructions that a computer must follow. The aim of this course is to learn how to define algorithms and to use computer programs to implement them. This course will allow business students not only to communicate with computer science professionals and engineers but also to play an active role in the development of new digital products. The basic knowledge of Python programming is required. We discuss using algorithms and computer programming to solve problems in a business environment. The course consists of three modules. First, we discuss how to define a business problem and the requirements for its solution. Second, we review the principles and fundamental concepts of algorithms. Third, we use Python to translate a generic algorithm to a computer program. This will cover the software development basic cycle.

Data Analytics in Action with Python (WIS5074)

This course uses Python programming language for practicing examples of descriptive statistics, inferential statistics, regression, clustering analysis, as well as machine learning and deep learning. Its focus is more on applications than theory building. Students are encouraged to present the examples they found, and instructor and other students are doing questions and answers. This study is a social science-based trans-disciplinary course, rather than just a methodology or programming course.

Financial Markets and Corporate Finance (DBA4012)

The goal of this graduate level course is to provide an introduction into current theoretical research in corporate finance and financial markets, covering a broad range of topics. Special attention will be paid to developing techniques for empirical data analysis. Students will also have a chance to practice written and oral presentation of their research ideas.

Financial Derivatives (FIT4003)

The objective of this course is to introduce key building blocks of financial derivatives to FinTech-oriented students. By combining analytical models with numerical examples, exercises, and case studies, the course emphasizes practical applications of financial engineering tools. Main topics cover various aspects of forward contracts, futures, options and swaps. The course will focus on hedging and pricing of financial derivatives.

Deep Learning (ADS5019)

This course covers deep learning based on artificial neural network which is advanced on various industries. This course, especially, gives students basic understanding of modern neural networks and their applications in computer vision and natural language understanding. Students learn about Convolutional networks, RNNs, LSTM, Dropout and more. This course introduces the major technology trends driving Deep Learning.

Fintech Internship (CHS4001)

This course is to provide the students with the development of the ability that they can apply their financial knowledge to practice through the internship at fintech startups or financial institutions. The students will learn how the development of fintech contributes to the digital transformation of financial industry.

Behavioral Finance and Fintech (DBA5095)

Finance is the field that is affected the most by technological revolution such as FinTech, Blockchain, and Big Data. Interestingly, the development of FinTech goes hand in hand with the development of "behavioral finance". Until recently, traditional neoclassical economists largely ignored the effect of psychological factors of the CEOs and investors in financial markets because there was not enough data. However, with the explosive growth in computing power and Big Data, researchers keep finding that psychological factors are very important elements in financial market, which established the field of behavioral finance. In this course, we will start by studying the Efficient Market Hypothesis, which is the backbone of neoclassical economics. Then we move on to study behavioral concepts such as momentum and bubble. We also study how narcissism, overconfidence, and biological traits of the CEO affect corporate financial decision making such as financial leverage and M&A. We study how the financial technologies help us capture the psychological biases. Lastly, we study how blockchain technology is changing the game of finance.

Machine Learning in Finance (FIT5019)

The amount of data used in financial practice is increasing, and the trend toward using unstructured data goes beyond traditional structured data. Therefore, it is very important to develop the ability to apply Machine Learning methods to deal with financial tasks in order for students to be competitive and to become leaders in the future of finance. To this end, this course aims to create the ability to use financial data in practice by using R and Python to lay the groundwork for overall data science and machine learning, from data collection, organization, analysis, visualization, machine learning, and report writing.

Financial Data Analysis (FIT5020)

n finance area, basic understanding and analyses of financial data is becoming more crucial. In order to implement meaningful analyses of financial data, students need to know how to get access to the key data sources as well as understand the key structure of the financial data. In this course students will learn how to get access to the key financial data (Compustat, CRSP, Fn DataGuide, etc.), how to handle the large data set, and how to analyze the data. The purpose of this course is to provide exposure to the key financial data, and to provide meaningful experience in handling and analyzing the large data set so that students can have a foundation in advanced data analyses.

This course will require students to use the data handling and analyses skills to replicate the real research papers from the quality international journals. Furthermore, students are going to be required to extend the research paper that they have replicated. This will provide students to get exposure to the whole process of accessing, handling, and analyzing the financial data.

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Blockchain and Financial Application (FIT5006)

Blockchain, which was first introduced in the paper titled “Bitcoin: A Peer-to-Peer Electronic Cash System” in October 2008 by Satoshi Nakamoto, provides a new way of ledger management technology. In recent, blockchain technology, as a cutting-edge technology, is seeking its applications in almost all major industries, including insurance, transportation, healthcare, energy, logistics and delivery, music, manufacturing, IoT, social media and public sectors. Especially, in the field of finance, because of the emergence of cryptocurrency, blockchain technology is widely used than any other field. This lecture introduces overview on blockchain technology and studies the characteristics of cryptocurrency market.

Theories of Artificial Intelligence (AIM5001)

In this course, students will learn the fundamental algorithms of Artificial Intelligence including the problem-solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages.

Advanced Financial Theory (DBA5015)

This course brings the student to the point where she or he can accomplish theoretical research in finance. To accomplish this goal, we discuss the skills commonly used by the theoreticians in finance, major findings in finance last 20 years, and developing an appreciation of literature progress in finance.

Data-Driven Credit Modeling (FIT5021)

The purpose of this course is to help FinTech master program students understand basic credit risk analysis and AI application to credit risk management. It begins with an introduction to commonly used models of credit risk. It then focuses on techniques to measure and manage credit risks in financial industries. The course also discusses with modeling and computing AI skills to solve business problems in credit risk management. At the end of this course, students are expected to obtain a deeper understanding of credit risk management with AI techniques.

AI and Wealth Management (FIT5005)

The course will primarily focus on two quantitative approaches in investment: optimal asset allocation and machine-learning and AI based asset pricing. Although asset allocation is known to be the most important performance determinant, quantitative approaches (portfolio optimization) are rarely used in practice. There are several reasons behind this, such as being a black box and difficult to interpret the outcomes, but the most crucial reason is the high sensitivity of optimal portfolios to the input parameters (expected returns and covariances of returns). With the inevitable estimation errors in these parameters, it is difficult to obtain a robust yet reliable portfolio. Much of the research in portfolio management over the last decades has been devoted to addressing this limitation of the classic portfolio optimization models, and some notable progress has been made, especially using Bayesian methods and shrinkage estimators. The course will assume students are equipped with fundamental portfolio theories and spend most time on introducing recent developments in portfolio optimization.

Fintech Startups and Venture Financing (FIT4001)

This course covers how students can develop a business model and start-ups in fintech area. It examines the process of financing a newly formed corporation in private and public securities markets. The sequence of topics roughly parallels the life cycle of a typical fintech start-ups. We begin by discussing the advantages and disadvantages of different types of venture financing. Second, we study a method of valuing private and public firms, and specifically valuation methods in the venture capital setting. Third, we discuss the process of exiting investments in young firms and the venture capital financing contracts, known as the term sheets. Finally, we analyze the investment banking and initial public equity offering process for firms.

InsurTech Theory and Practice (FIT5024)

When compared to other sectors of “big business”, the insurance industry has for long been left to operate uninterrupted, out of reach from the aggressive startup movement that has radically transformed and reshaped so many other industries. Now is the time of change. Over the last couple of years, startup funding has increased dramatically in the insurance sector fueling what is known as insurance technology companies or InsurTech. In this course, we'll navigate through the new hot area of InsurTech. Firstly, we'll have a quick introduction to InsurTech. Then, we'll move on to have an overview on the insurance industry and its digitization efforts, following that we'll learn the categories of InsurTech companies as well as InsurTech Technology Enablers. From that point, we'll get to learn InsurTech business model, key commercial drivers, and finally we'll explore the future of InsurTech.

FinTech and Financial Law Practice (FIT4002)

This course helps students to understand RegTech and to become more confident and persuasive in their ability to analyze and make recommendations to executives within the finance industry regarding how to react to recent changes in fintech area, e.g. Regulations to cryptocurrencies like BitCoin & Initial Coin Offering (ICO). Students will learn about how FinTech and RegTech disrupt and transform finance industry, such as challenges in protecting data and security with digital forensics, risk management and corporate governance in banking industry in terms of Know Your Customer (KYC) and Anti Money Laundering (AML), and how governments in different countries take initiatives in FinTech and RegTech.

Seminar in Investment (DBA5013)

This course is designed to provide students with a framework for the analysis of the capital markets. Each meeting will cover leading theoretical and empirical papers on each topic in a seminar format. The students have to prepare a term paper which is publishable in a conference.

Advanced Time Series Analysis (STA5036)

Recent advances in time series analysis such as high dimensional time series models and nonlinear time series models are discussed with latest research papers. This course requires paper readings, presentations and real data analysis projects.

AI Finance Seminar I (FIT5022)

This course focuses on examining various issues in finance using various ML techniques. One of the advantages of ML techniques is that the high-dimensional nature of the methods enhance their flexibility relative to more traditional econometric prediction techniques. ML may better approximate the unknown and likely complex data processes in asset returns or corporate financial decisions. In particular, we will use machine learning techniques to estimate asset premiums, to classify various classes of companies and stocks, to check extra alphas from ML-based portfolio formation, to detect new types of risk factors, to identify stock market anomalies and suspicious trading patterns, and to extract new variables from various texts, voices and images data. We will first review the existing related finance literature for your solid theoretical background, and then apply ML techniques to those problems to see whether ML techniques can expand our understandings of traditional finance.