Curriculum: Master of Science in Finance

This course educates students in the fundamentals of finance and accounting. The methods covered are used extensively throughout the MBA program. Topics include: the accounting process that results in the preparation of financial statements for external users, techniques for analyzing a basic set of financial statements, using accounting information to support management decisions, and using time value of money techniques to evaluate capital asset decisions. (MS Accounting students cannot earn credit by taking this course.) This course cannot be used as an elective course for Lindner College of Business Master's programs.

This course provides an introduction to the fundamentals of economics at the graduate level for students without previous economics coursework. Students will be exposed to the essentials of both microeconomics and macroeconomics. Microeconomics topics to be discussed include the supply and demand mechanism,how markets are affected by regulation and taxation, costs of production, and how market structure affects outcomes. Macroeconomic topics to be discussed include the fundamental measures of the aggregate economy, the sources of economic growth, explaining short-run fluctuations in economic activity, and how government policies can affect these fluctuations. A particular focus will be to understand how fundamental economic principles at both the micro and macro level can affect companies, investments, industries, and national economies. This course may not be used as an elective course for Lindner College of Business Master's programs.

Introduction to data analysis and statistical methods with focus on practical decisions using quantitative models in a spreadsheet environment. Topics include sources of data, descriptive and graphical statistical methods, probability, distributions, sampling and sampling distributions, estimation, confidence intervals, and hypothesis testing. This course may not be used as an elective course for Lindner College of Business Master's programs.

The purpose of this course is to provide students with a foundation in Marketing. Concepts such as segmentation, targeting, positioning, customer and market analysis, and basic marketing planning will be introduced. This course cannot be used as an elective for Linder College of Business master's students.

The purpose of this course is to provide students with a foundation in the study of Organizations (Management) in preparation for the MBA or MS program. The goal is to provide students with an introduction to the study of organizations (strategy, structure, design, and context) to help students navigate through the advanced graduate course work and to become a more effective manager. This entails understanding how organizations work as well as developing requisite personal skills in problem analysis and writing. This course cannot be used for an elective course for Lindner College of Business master's programs.

This course helps develop knowledge of the basic principles for operations and supply chain management (OSCM) through simulations and case studies. The focus is on decisions and activities involving the effective management of resources including, process improvement, supply management, logistics, and supply chain coordination

This course introduces students to applied research and applications in quantitative equity investing. First, students will learn about the empirical evidence related to prominent equity factors including size, value, and momentum. Second, students will learn how to construct and backtest factor strategies using real data. Finally, students will be exposed to real-world applications of factor investing in quantitative asset management via case studies. The goal of the course is to equip students with necessary knowledge and skills for applied research in quantitative equity management. More broadly, this course can be a useful part of training for students who are interested in a career in financial data analytics.

This course is about the analysis of financial information--particularly, but not limited to, a firm's financial statements--for making decisions about investing in a business. The primary focus is on equity (share) valuation, with some attention given to credit evaluation and the valuation of debt. The methods of fundamental analysis will be examined in detail.

This course is a continuation of FIN7035 (Financial Information and Valuation) with more emphasis on cases and managerial accounting. This course is therefore about the analysis of financial information--particularly, but not limited to, a firm's financial statements--for making decisions about investing in a business. The primary focus is on equity (share) valuation, with some attention given to credit evaluation and the valuation of debt. The methods of fundamental analysis will be examined in detail.

This course examines fixed-income markets, with an emphasis on the pricing and risk of fixed income securities, derivatives, and portfolios. Bond immunization and trading strategies will be discussed with an in-depth coverage of both Treasury and Corporate Debt Securities. We will explain how Federal Reserve uses monetary policy to influence the term structure of interest rates. This course helps students to establish a solid foundation in understanding fixed-income securities and furthermore to apply such knowledge to real-world investment decisions in bond markets.

This overview of investment analysis presents a comprehensive study of the theory and practice relevant to investment management. The course details the structure of markets, asset classes, and types of investment companies. Portfolio theory and asset pricing are a major focus of the course. A broad range of securities are analyzed including equities, bonds, and options; but for most topics the main focus will be on equity, leaving advanced fixed Income and derivatives topics to other courses. Many of the concepts covered are included in the body of knowledge leading to the CFA designation.

The principal objective of this course is to provide a detailed examination of options, futures, forwards, and swaps. By the end of the course students will have a good knowledge of how these contracts work, how they are traded, how they are used, and how they are priced. A major emphasis in the class will be on how derivative instruments are used by financial institutions in light of recent economic events.

This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

This course stresses the application of finance theory and methods to real business situations. While the focus of the project is finance related, a multi-disciplinary approach is desirable. A secondary objective of this course is to gain experience in collaborative teamwork to solve a business problem. All work in this course is team-based. You are required to work collaboratively and creatively with your team members to address the challenge presented. Peer evaluation and industry project coach’s evaluation of the team’s work is an important measure of your performance in this course.

This is an introductory masters level course in econometrics emphasizing econometrics foundations and financial data analysis. The course covers topics in time series analysis with an emphasis on applications rather than econometric theory. The course is designed to enable students to perform independently comprehensive financial data analysis using statistical software packages.

Analysis of financial data is a core component of investment management. You need to be comfortable and adept at sampling, modeling, regression analysis, and hypothesis design and testing in order to be an effective financial analyst. In this course we will cover many of the basic statistical and probability concepts that are central to financial analysis. Along the way we will touch on various finance concepts and terms, so, in part, this course will provide you with a conceptual introduction to various investment topics.

This course presents the mainstream and alternate view of portfolio management using research papers, articles, and materials from academics and the markets. Many of the concepts covered are covered in the body of knowledge leading to the CFA designation.

An in-depth analysis of capital budgeting decisions. Topics covered include: estimation of the cost-of capital, issues in forecasting and valuing cash flows from projects, and the applications of real options to corporate capital budgeting decisions.

The main objective of the course is to introduce students to fintech and cryptocurrency. The goal is to understand the fundamental concepts underlying financial technologies and their applications. Examples of these include artificial intelligence, machine learning, and blockchain in financial markets, such as business activities, financing, and investments. The course consists of four parts. The first part introduces the status quo and fundamentals of financial technologies (fintech) as well as their main applications, including artificial intelligence, payments, robo-advising, insure-tech, and blockchain. The second part covers the mechanisms and applications of artificial intelligence and machine learning by focusing on the use of natural language processing (NLP) and large language models (LLM). The third part focuses on blockchain and cryptocurrencies, discussing Bitcoin, Ethereum, stablecoin, and NFTs. The last part of the course focuses on cryptocurrency markets and portfolios. The cryptocurrency market comprises exchanges (both on-chain and off-chain) and spot and derivative contracts. Cryptocurrency portfolio analysis studies the risk and return tradeoff of such portfolios. The course combines lectures, class discussions, and case study analyses.

This course provides a comprehensive treatment of the fundamental principles required to design and implement algorithmic trading models in financial markets. The course will introduce the best practices and the formal process of generating trading ideas, the differences between low-frequency and high-frequency trading signals, back-testing and its associated biases, optimization techniques, and industry metrics for evaluating algorithmic trading models’ performance. Students will have the opportunity to implement basic algorithms in well-known paper-trading platforms.

In-depth study of special topics in Finance.