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This is a doctoral level seminar in finance designed to acquaint students with the research in the area of market microstructure. The course evaluates the main theoretical models and their applications. Increasingly issues in microstructure are empirical in nature, and we will spend time on the empirical approaches used in such research. We will discuss a variety of current market microstructure topics including high-frequency trading and OTC markets. Because information and liquidity issues are fundamental to many of the issues covered in microstructure, this course should be of interest to students in a wide range of disciplines.

This course teaches the fundamentals of non-cooperative game theory and classic applications used in applied work in economics and related fields such as finance, marketing, operations, and accounting. The course begins with a brief primer on non-cooperative game theory that covers pure versus mixed strategies, Nash equilibrium, and various equilibrium refinements. Coverage then turns to basic frameworks that utilize game theory to model a wide range of settings in economics and related fields. These include agency analysis, classic asymmetric information models such as adverse selection and signaling, time inconsistency, and repeated games and reputation.

Accounting and finance are inextricably intertwined. For example, key roles for corporate financial reporting include assisting firms in raising debt and equity capital, improving the efficiency of the capital markets, and facilitating the effective monitoring of corporate managers' investing and financing decisions. This seminar provides a rigorous and integrative exposure to research that addresses economic issues of interest to both the accounting and finance research communities. Topics may include; an introduction to research methods, capital markets research in accounting, and experimental research in accounting.

Johnson PhD Seminar-Content changes regularly.

This is a thorough doctoral level class on corporate finance, covering modern theoretical and empirical work, with an emphasis on the latter category. The course is designed to help students understand how to operationalize empirical research on topics that are central to corporate finance, such as capital structure, product market behavior, the limits of the firm, financial constraints, investment behavior, liquidity management, and more. The course will also look at the interface between corporate finance and other research areas, such as banking and industrial organization. The overarching goal is to expose students to state-of-the-art research in the field corporate finance and prepare them to conduct their own work using new methods and tools.

In this special topics course, we will delve into several literatures involving the self and the buying, consuming, or producing of products and services. Some topics we will touch upon are Belk's concept of products as extended self, consumption and social status, dehumanization and objectification of consumers and producers, identity salience, moral identity, the influence of advertising and social media on consumer self-esteem, the influence of self-esteem on consumer behavior, gender relationships in consumer households, and the self in one's native versus a non-native language. In addition to the content of the articles and as opportunities arise, time discussing methodological and general research issues, such as how to triage ideas, how to do ethical and replicable research, and other topics related to becoming a productive experimental researcher. During this course, we will discuss a number of articles. Some articles will be chosen by the instructor. Others will be selected by the students. Students will be assigned to lead the discussion of specific papers.

This research-based graduate seminar involves a critical review of theory and research in Micro- and Meso-Organizational Behavior - research related to individual and group behavior in an organizational context. We will read both classic and current articles in the field, and students will be responsible for helping to shape the reading list and guide the class discussions. Due to the nature of the class it is critical that all students read the assigned materials before class and be prepared to contribute to a lively intellectual discussion about the work. Upon completion of this course, students should be able to: 1) Discuss and critique theory and research in major topic areas of Organizational Behavior, 2) integrate material within and across topics, and 3) propose new research questions that will extend management scholarship.

This sequence of two half-semester courses is designed to introduce students to the key questions and quantitative models and estimation methods commonly used in marketing. During the fall semester, the course is focused on reduced-form estimation approaches. It has two main objectives: (1) to acquaint students with the state-of-the-art reduced-form approaches, and (2) to enable students to build such models to address original research questions. Class will be a combination of lectures, discussion of assigned articles, data analysis, and presentations of research proposals.The spring section of this course is focused on structural models. It will introduce students to a number of classic and emerging structural econometric models that have become prevalent in quantitative marketing research. The course will include dynamic demand-side models, learning models, models with social effects and interactions, dynamic supply-side models, platform and network models, and structural behavioral models.

Research seminar for PhD students stressing current and classic research articles.

Johnson PhD Seminar-Content changes regularly.

This course is an introductory Ph.D.-level course on the basic theories of asset pricing. It consists of three parts. The first part covers arbitrage pricing theory, including the Black Scholes Merton, the Heath Jarrow Morton, and reduced form credit risk models. The second part covers portfolio theory, in both complete and incomplete markets. The third part studies equilibrium pricing models, both complete and incomplete markets, including the notion of market efficiency. This course emphasizes continuous time models and it is based on the use of martingales to understand asset pricing theory. Included in the course is the extensions of the standard theories to include asset price bubbles.

This course will familiarize students with the classic foundational work in operations management. The focus of this course will be on demand forecasting and inventory theory - the time series forecast models and the stochastic inventory models. Theory and empirical studies of the information distortion phenomenon along the supply chain (the bullwhip effect) will also be discussed. In addition, advanced models with optimal control and demand learning will be briefly discussed. Pedagogically, the course will include a combination of lectures, homework problems and coding assignments.

This is a doctoral course in operations management offered by the Cornell SC Johnson College of Business. The course will provide students a rigorous introduction to empirical and data-driven research in Operations Management. It will include application areas, types of data sets and methodologies, and current research directions. Students will be expected to critically study published papers and work with data. Examples of application areas that we will study include supply chain management, services operations, retailing, e-commerce, and revenue management. The course is required for first and second year doctoral students in the OTIM area in the Cornell SC Johnson College of Business. Doctoral students outside the college of business are welcome to enroll, but require instructor permission to assess suitability.

PhD Seminar-content changes regularly.

This course will focus on methods of descriptive, predictive, and prescriptive analytics that are being applied in operations management. Some of the research in this area is theoretical, such as on dynamic assortment optimization. Other research is based on machine learning to apply text analysis, feature detection, and community learning methods to large-scale prediction and optimization problems in OM. The course will combine both these types of approaches.

PhD Seminar-content changes reqularly.

This seminar will provide a tour of the burgeoning literature on human-AI dialogues. We will particularly focus on the effect these dialogues can have on humans' beliefs, attitudes and preferences. The seminar will involve reading and presentation of primary literature, as well as developing and critiquing ideas for research projects.