Course listings | Case Western Reserve University

Term: Spring 2024

FNCE 403: Corporate Financial Technology (100/1238)

R 06:00-08:45 PM Jan 16-Apr 29

Harmon, G

This course is focused on the many aspects of the development in Financial Technology from recent notable successes to the current edge and thoughts about the future. Topics covered will include "FinTech" Applications, Incubators and Angels, Block Chains, Crypto-currencies, Crowdfunding, and Payment Schemes. Topics can change from semester to semester, in tune with changing technology. Offered as BAFI 403, FNCE 403 and FTEC 403.

FNCE 412: Algorithmic Trading (100/1373)

T 03:00-05:45 PM Jan 16-Apr 29

Noh, J

Finding the right algorithm to automatically and successfully trade in financial markets is the holy grail in finance. Not too long ago, Algorithmic Trading was only available for institutional players with deep pockets and lots of assets under management. Recent developments in the areas of open source, open data, cloud computing and storage, as well as online trading platforms, have leveled the playing field for smaller institutions and individual traders - making it possible to get started in this fascinating discipline being equipped with a modern notebook and an Internet connection only. Coding experience required or consent of instructor required. This course aims to: - Trace the evolution of quantitative trading strategies, - Expose students to the landscape of different market datasets (e.g., Alpha Vantage), - Discuss linear regression models and logistic regression models, - Introduce select machine learning techniques, - Evaluate the performance of these trading strategies, - Provide an introduction to textual analysis Offered as BTEC 412, FNCE 412, and FTEC 412.

FNCE 428: Financial Strategy and Value Creation (100/1239)

T 06:00-08:45 PM Jan 16-Apr 29

Fine, S

The intersection between the theory of perfect markets and the reality of market imperfections provides the basis for the exploration of value creation in this course. Opportunities in both product and financial markets are explored using case studies to develop a framework for strategic financial decisions.

FNCE 429: Investment Management (100/1261)

M 09:00-11:45 AM Jan 16-Apr 29

Noh, J

This course explores the characteristics of financial investments and markets and develops modern techniques of investment analysis and management. The goal is to help students develop a level of analytical skill and institutional knowledge sufficient to make sensible investment decisions. Topics include: an overview of stock, debt and derivative asset markets, practical applications of modern portfolio theory, equilibrium and arbitrage-based approaches to capital market pricing, the debate over market efficiency, the term structure of interest rates, bond portfolio management, and uses of derivative assets in investment portfolios.

FNCE 429: Investment Management (101/1262)

M 03:00-05:45 PM Jan 16-Apr 29

Noh, J

FNCE 431: Fixed Income Markets and Their Derivatives (101/11254)

R 03:00-05:45 PM Jan 16-Apr 29

Bae, J

This class is concerned with fixed income securities, interest rate risk management, and credit risk. Fixed income securities account for about two thirds of the market value of all outstanding securities, and hence this topic is important. The course covers the basic products of fixed income markets including treasury and LIBOR products, such as interest rate swaps. Risk management and hedging strategies are covered as well as selected topics in credit risk models and mortgage-backed securities.

FNCE 431: Fixed Income Markets and Their Derivatives (102/11255)

R 08:30-11:15 AM Jan 16-Apr 29

Bae, J

FNCE 433: Quantitative Risk Modeling (100/1265)

M 06:00-08:30 PM Jan 16-Apr 29

Martinez, E

This course is designed to help students learn quantitative models for estimating risk in various financial settings for different types of financial institutions (banks, hedge funds, and others). It is a very hands-on course where students will become familiar with several state-of-the-art quantitative risk models as well as their detailed implementation procedure in the real world. The course uses several in-class Excel exercises to illustrate the models as well as their practical implementation using real financial data. Offered as BAFI 433 and FNCE 433 and FTEC 433

FNCE 436B: Individual, Team and Career Development (100/1296)

F 09:00-11:15 AM Jan 19-Jan 19, F 09:00-11:15 AM Feb 02-Feb 02, F 09:00-11:15 AM Apr 05-Apr 05, F 09:00-11:15 AM Apr 12-Apr 12

Chaudhari, H; Mishra, S

This course is designed to focus on three areas of development critical to students' personal and professional success: 1) Individual; 2) Team; and 3) Career. The individual and team aspects include developing self and other awareness through exploration of learning styles, process skills, and building communication and presentation competencies. Career development includes a focus on strategies for success such as networking, resume building, and learning from executives through intensive and interactive seminars. The course involves use of assessments, group discussions, presentations and experiential activities.

FNCE 436B: Individual, Team and Career Development (101/1297)

F 09:00-11:15 AM Jan 26-Jan 26, F 09:00-11:15 AM Feb 09-Feb 09, F 09:00-11:15 AM Apr 19-Apr 19, F 09:00-11:15 AM Apr 26-Apr 26

Chaudhari, H; Mishra, S

FNCE 436D: Individual, Team, and Career Development (800/1323)

Keeler, M

This course is designed to focus on three areas of development critical to a student's personal and professional success: individual, team, and career development. This will be accomplished through a project with a company so that students get a real-life experience related to their field of study. This experience provides students with the opportunity to explore their career interests while applying knowledge learned in the classroom in a real-life setting. The experience also helps students build their professional networks and be part of a team assigned to work on the corporate project.

FNCE 440: Financial Decisions Modeling and Analytics (100/10935)

MW 12:45-02:00 PM Jan 16-Apr 29

Krishnan, C

The firm is a nexus of contracts among its various stakeholders (e.g., managers, shareholders, debt holders). In this course, we will examine Valuation, Quantitative Analysis of Real Options, Asymmetric Information, Agency Cost, Incentive Contracts and Performance Metrics, Regulation and Reputation. The takeaway learnings from this course are: (a) Understanding how value can be created or destroyed, (b) Measuring/quantifying value using financial big data, (c) Understanding the links between capital structure and asymmetric information, market reactions and signaling, agency and management incentives, taxes and shareholder, bondholder conflicts, (d) Understanding the links between payout policy and informational content, market reaction, stock returns and signaling, and clientele effects, and (e) design of Performance Metrics. We will download corporate financial data (financial big data) from research databases, and conduct empirical analysis to understand the value implications of financial decisions. Excel/SAS will be used. We will analyze case studies and real-world events.

FNCE 470: Financial Models Using Big Data (102/11253)

W 09:00-11:45 AM Jan 16-Apr 29

Bae, J

This course is focused on developing models in investments using financial big data. A strong theoretical base will be developed and then relevant empirical analyses using real data will be used for testing models, via individual assignments and group projects. In the projects, groups of students will be immersed in collecting, analyzing, and interpreting financial big data sets.

FNCE 480: Global Banking & Capital Markets (100/1268)

W 03:00-05:45 PM Jan 16-Apr 29

Balasubramanyan, L

This course will expose students to Banking and Capital Market Structure, Practices, and Regulations in North America, Europe, as well as Asia. Students will learn about structure of the financial services industry in different parts of the world, the history and evolution of the regulatory frameworks in this industry, and its consequent impact on financial and economic development as well as risk. Several case studies are used to expose students to different issues and questions that arise in the day-to-day jobs of financial managers in this industry. Offered as BAFI 480 and FNCE 480.

FNCE 494: Artificial Intelligence for Financial Modeling (100/1293)

F 11:30-02:15 PM Jan 16-Apr 29

Vaidya, J

This is a hands-on course on Artificial Intelligence (A.I.) where the emphasis is not only on understanding the theoretical underpinnings of various AI models but also on building, evaluating, and critiquing A.I. models as they apply to the finance industry. This course begins with an introduction of Machine Learning models; various key ideas such as bias-variance tradeoff, cross-validation, regularization techniques are introduced with relevant examples from Finance. The course then proceeds to discuss Artificial Neural Networks and its relevance to Deep Learning. Foundational ideas such as back-propagation are discussed in sufficient detail; we also lay a lot of emphasis on evaluating the performance of all these models. A key objective of this course is help students build cutting-edge A.I. models that are ready for prime time, i.e., real-life applications. Fittingly, we work with several real-life datasets and case studies from banking and finance. We will work with three case studies, each of which span multiple sessions. -In the first case study, students use Machine Learning algorithms to understand how imbalanced datasets are handled in real-life. -In the second study, students use time series data and learn not only about the power of regularization techniques but also to highlight the prominence of A.I. in financial markets. -In the third case study, students learn how to use cutting-edge Deep Learning models to extract sentiments from disparate news sources; these are in turn used to generate trading strategies. By contrasting the effort that goes into and the payoff obtained from Machine Learning and Deep Learning models, students gain an intuitive appreciation of both these classes of models. Offered as BTEC 494, FNCE 494 and FTEC 494.