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)
Corporate Finance (BUS_INST-304-0)
Effects of time and uncertainty on valuation and decision making. Discounting techniques, stock and bond valuation, capital budgeting, firm valuation, capital asset pricing model, financial options. May not receive credit for both this course and ECON 360-1. Not for students who have previously taken KELLG_FE 310-0. Prerequisites: ECON 201-0 and ECON 202-0; STAT 210-0 or equivalent; MATH 218-1 or MATH 220-1; and BUS_INST 301-0.
AI Foundations for Managers - Finance (AIML-901FI-5)
Advancements in artificial intelligence technologies are quickly changing the landscape of financial markets. This course offers a lens into of the myriad ways financial firms are employing AI technologies. Students will learn to understand how AI algorithms commonly used in finance work, as well as specific applications of these algorithms, including in valuation, financial forecasting, robo-advising/automated trading, credit risk modeling, fraud detection, textual analysis of financial documents using generative AI, and more. The course will also discuss how financial markets value AI-using firms, and the unique risks and ethical considerations inherent to AI's use in the finance industry. The material will be covered primarily at a high level, though students will be provided with opportunities to engage in hands-on applications of some of the AI methods covered in the class. No prior programming experience is required.
