Supervised experiences involving the application of concepts and skills in an employment situation. Repeatable.

Grading Basis: Satisfactory/Unsatisfactory

Repeatable. Max Credits: 9.

This course provides a statistical foundation for subsequent courses in the Master of Science in Finance program. Major topics include descriptive statistics, probability theory, statistical estimation and inference and regression analysis. The emphasis is on finance applications, such as risk measurement, for portfolio diversification and the "market model". In addition, students develop competence in the use of statistical software packages. This course provides preparation for the statistical portions of the Certified Financial Analyst professional examinations.

Grading Basis: Letter Grade

Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Covers the U.S. financial system in the global economy. Specific topics include financial institutions, money creation and monetary policy; the Federal Reserve System and its operation; the international financial system; interest rate determination, yield curves, and their relation to fiscal policy; the role of households and business in financial markets; stock markets; and money markets and instruments. (Required for the M.S. in Finance degree.)

Grading Basis: Letter Grade

Coreq: BUSN 6620 Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School

In this course students will learn investment theories and how to apply them to portfolio management. Topics covered include asset allocation, security markets, the analysis and use of investment information, risk analysis and security valuation. This course is required for the M.S. in Finance degree.

Grading Basis: Letter Grade

Prereq: BUSN 6640 and BUSN 6620 with a grade of C (2.0) or higher Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Overview of financial institutions and their risk management/financial performance management issues such as: management of operational, credit, liquidity, interest-rate, capital, off-balance sheet, and environmental risks; Uniform Bank Performance Report (UBPR) risk/performance analysis, hedging techniques and regulations/performance/risk.

Grading Basis: Letter Grade

Prereq: BUSN 6640 with a grade of C (2.0) or higher Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School

Addresses financial management in an international context that considers international capital movements and foreign exchange problems, and international operations as they affect financial functions. It reviews foreign and international institutions and the foreign exchange process and considers financial requirements, problems, sources and policies of firms doing business internationally. Cross-listed with INTB 6372.

Grading Basis: Letter Grade

Prereq: BUSN 6640 with a grade of C (2.0) or higher Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School

This course introduces forward contracts, used in price risk management for millennia. We cover the properties of forward/futures contracts, structure of the markets and strategic implications for speculation and hedging. We price forwards from spot price, and introduce convenience yield. Options used for insurance purpose (think of your car insurance as a put option) is a more expensive way to manage risk; we cover option strategies and basic pricing. The course concludes with swaps, credit derivatives and structured products. Asset classes covered are equity, fixed income, currency, agriculture, energy (oil/gas and electricity) and metal/mining.

Grading Basis: Letter Grade

Prereq: BUSN 6640 with a C or higher Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Over the past several decades, the field of finance has developed a successful paradigm based on the notions that investors and managers were generally rational and the prices of securities were generally “efficient.” In recent years, however, anecdotal evidence as well as theoretical and empirical research has shown this paradigm to be insufficient to describe various features of actual financial markets. In this course we examine how the insights of behavioral finance complements the traditional paradigm and sheds light on the behavior of asset prices, corporate finance, and various Wall Street institutions and practices.

Grading Basis: Letter Grade

Prereq: BUSN 6640

Develops and implements financial models for purposes of financial planning and decision making. This course is intended to allow the student to increase her or his knowledge and skill in the development of various types of computer-based financial planning models. The students are exposed to the uses of a variety of computer software packages that can be used for modeling financial planning problems.

Grading Basis: Letter Grade

Prereq: BUSN 6640 with a C or higher Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This is a co-listed class with the J.P. Morgan Center for Commodities and the Finance Department. This course focuses on how securities and futures contracts are designed and traded including trading exchange operations, regulation, trading mechanisms and processes. Students will learn the theory and practice of securities and futures contract trading with a focus on hands-on trading experience using industry software (CQG and Bloomberg) as well use of data sources (Morningstar). In this course, we will review the origins of liquidity, volatility, price efficiency, and trading profits. Next we will cover a host of topics concerning equity and commodity trade execution strategies, such as why and how investors trade, what and when investors profit from investing and speculating, the key principles of high-frequency trading and investor’s overconfidence, why market institutions are organized as they are, and the role of public policy in the markets. Cross-listed with CMDT 4490, CMDT 6490 and FNCE 4490.

Grading Basis: Letter Grade

This course introduces students to the process of business intelligence in finance, focusing on using Python for financial modeling, data analysis, and decision support. Key financial concepts (e.g., the Time Value of Money, Rate of Return, and Effective Annual Return, among others) will be explored through Python coding examples to simulate financial behaviors. Additionally, students will learn to collect, process, and visualize financial data from various sources, as well as interpret analytical results to support financial decision-making. By the end of the course, students will have developed practical skills in Python for financial modeling and data analysis, and will be able to leverage these analyses to inform business decisions in finance. Cross-listed with ISMG 6820

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, graduate majors within the College of Engineering, Design and Computing, and PHCS PhD majors.

Experimental course offered irregularly for the purpose of presenting new subject matter in finance. Prerequisites vary depending upon topics covered. (Consult the 'Schedule Planner' for semester offerings.) Repeatable.

Grading Basis: Letter Grade

Repeatable. Max Credits: 9.

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Typically Offered: Fall, Spring, Summer.

Instructor approval required. Allowed only under special and unusual circumstances. Regularly scheduled courses cannot be taken as independent study. Repeatable.

Grading Basis: Letter Grade

Repeatable. Max Credits: 8.

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This class presents introduction to blockchain includes blockchain innovation tools, protocols and techniques and its application in the newly emerge cryptocurrency asset class. We then analyze the need and motives for the crypto, and how it disrupts the current and existing payment system. The final theme of the class focuses on the optimal cryptocurrency investment and presents robust trading strategy. Cross-listed with FNCE 3850.

Grading Basis: Letter Grade

Typically Offered: Fall, Spring, Summer.

The Payments and Digital Banking course offers an in-depth exploration of the rapidly evolving landscape of financial technology within the realm of payments and digital banking. Students will gain a comprehensive understanding of the various digital payment methods, mobile banking solutions, and emerging technologies shaping the financial services industry. The course delves into the intricacies of payment ecosystems, examining the role of blockchain, cryptocurrencies, and digital wallets. Additionally, students will explore regulatory frameworks, security considerations, and the impact of innovative technologies on traditional banking models. Through case studies, discussions, and hands-on projects, participants will develop practical insights into designing and managing digital banking solutions, preparing them for the dynamic and transformative nature of modern financial services. Cross-listed with FNCE 3860

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This course provides students with a comprehensive understanding of the applications of Artificial Intelligence (AI) and Machine Learning (ML) in the rapidly evolving field of Financial Technology (FinTech). Students will explore the foundational concepts of AI and ML and examine how these technologies are reshaping various aspects of the financial industry. Topics include predictive analytics, algorithmic trading, risk management, fraud detection, and customer experience enhancement. Through practical examples and real-world case studies, students will gain hands-on experience in applying AI and ML techniques to FinTech challenges. The course aims to equip students with the knowledge and skills needed to navigate the intersection of AI, ML, and FinTech, preparing them for roles in this dynamic and innovative sector. Cross-listed with FNCE 3870.

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This course provides an in-depth exploration of critical software tools essential for professionals in the field of Financial Technology (FinTech). Students will have the opportunity to choose and master three software applications from a curated list of six, gaining practical skills that are highly relevant in the FinTech industry. The course emphasizes hands-on experience, practical applications, and the integration of software tools in various FinTech domains. Cross-listed with FNCE 3880.

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This course introduces quantum computing and its applications in financial technology (FinTech). The first half of the course covers foundational quantum concepts, including qubits, superposition, entanglement, and quantum algorithms, with an emphasis on financial applications such as portfolio optimization, risk modeling, and algorithmic trading. The second half focuses on hands-on Python programming for financial data analysis, simulations, and quantum-inspired modeling. Students will gain both theoretical knowledge and practical coding skills, preparing them to apply quantum and classical computational techniques to real-world financial problems. Cross-listed with FNCE 3890

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

In this course, students will extend their knowledge of options from FNCE 6382. Students will learn about options market and microstructure, the use of options in risk management, e.g. in portfolio hedging. They will be able to set up different trading strategies, e.g. straddle, strangle, bull- and bear- spreads, covered calls and protective puts, butterfly options etc., and payoffs from and risks associated with these trading strategies. Students will be able to price options using tracking portfolio techniques, risk-neutral valuation, binomial method of options pricing -- and its application for real options used in corporate strategy --, and Black-Scholes method and its application. We will end the course with the Greeks and the Employees Stock Option Plans (ESOP).

Grading Basis: Letter Grade

Requires prerequisite course of BUSN 6640 (minimum grade C). Restricted to graduate majors and NDGR majors with a sub-plan of NBA or NBD within the Business School.

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Supports development of a dissertation in conjunction with a student's advisor. Repeatable.

Grading Basis: Letter Grade with IP

Repeatable. Max Credits: 15.

Additional Information: Report as Full Time.