Bryan Seegmiller
Bryan Seegmiller is an Assistant Professor of Finance at the Kellogg School of Management. He joined Kellogg in July 2022 after completing his Ph.D. in financial economics at the Massachusetts Institute of Technology. His research interests are in labor and finance, technological innovation, and asset pricing. Bryan’s current projects examine how labor market power affects firms’ profitability and labor shares; the ways new technologies shape the tasks that workers perform and the risks that workers face in the labor market; the impact of artificial intelligence on labor demand; and how frictions in the financial sector are transmitted into stock price movements.
Finance I (FINCM-430-0)
Finance I (FINC-430-0)
Finance 1 answers managers' and investors' most fundamental finance question: how should a project or an asset be valued? Managers must determine the value of building a factory, entering a new market, or purchasing an entire firm when deciding in which projects to invest. Similarly, individuals must assess the value of financial securities to decide how to invest their wealth. Using a combination of lectures and business cases, Finance 1 teaches the discounted cash flow and multiples methods to value projects or assets. These valuation tools lay the foundation for all work in capital markets and corporate finance.
Prerequisite: Business Analytics I (DECS-430-5)
Corequisite/Prerequisite: Accounting for Decision Making (ACCT-430) and Business Analytics II (DECS 431-0)
AI Foundations for Managers - Finance (AIML-901FI-5)
Advancements in artificial intelligence technologies are quickly changing the landscape of financial markets. This course takes a hands-on approach to teaching students about how financial firms employ AI technologies, with an emphasis on using and understanding modern generative and agentic AI tools. Students will learn practical skills to implement AI in various financial contexts, as well as how AI/ML algorithms commonly used in finance work. Students will also develop critical thinking skills regarding the appropriate implementation and impact of these technologies in financial markets. Specific applications include valuation, automated financial reports, financial forecasting, robo-advising, credit risk modeling, fraud detection, textual analysis of financial documents using generative AI, and more. The course will also touch upon how financial markets value firms that develop and implement AI; the unique risks and ethical considerations inherent to AI's use in the finance industry; and organizational hurdles associated with AI adoption. No prior programming experience is required.
