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Fall 2026

Larry Schrenk, Associate Professor of Finance

Department of Finance

Winona State University

Winona, MN 55987-3384

507-457-2388, lschrenk@winona.edu

Larry Schrenk is Associate Professor of Finance and has taught at Winona State since 2016. He previously taught at the Robert Kogod School of Business at American University and the Robert Merrick School of Business at the University of Baltimore. His teaching focuses on investments, security selection, and portfolio management as well as the financial applications of artificial intelligence, especially fine-tuning (Unsloth) and agent orchestration (Hermes Agent). In particular, he teaches the Student Managed Investment Fund in which advanced finance students help manage part of the WSU Foundation’s portfolio. He also teaches a general education course, Financial Literacy (goals 5 and 9), which introduces students to broad issues in economics and finance in a conceptual way. His research involves corporate finance, investments, financial ethics, and finance & artificial intelligence, especially fine-tuning.

AI Use Statement: I use artificial intelligence (AI) tools extensively in my work. Because this use is collaborative and iterative, AI-assisted and original contributions cannot be precisely separated. Regardless of the extent of AI involvement, I take full responsibility for the accuracy, integrity, and originality of all work. All content reflects my considered judgment, and I vouch for the work in its entirety.

AI Teaching Statement: In all my courses, I treat artificial intelligence (AI) as a collaborator amplifying human capability, not as a substitute for thinking. Effective AI users stay actively involved: they direct the work, decide which tasks belong to AI and which to themselves, interrogate the results, and make all final judgments. Because productive AI use is iterative and closely tied to your own reasoning, AI-assisted and original contributions cannot always be precisely separated. Whatever role AI plays in your work, hold yourself to three standards:

Active Engagement – Think critically throughout the process and never rely on output you do not understand.

Critical Verification – Question assumptions, check facts and figures against reliable sources, and evaluate AI output as rigorously as you would any other source.

Full Accountability – You remain responsible for the accuracy, integrity, and originality of everything you submit.

For full information, see the AI policies for individual courses.