Date of Award
Spring 5-31-2026
Document Type
Open Access Honors Thesis
Degree Name
Bachelor of Science (BS)
Department
Management
Advisor
Aditya Kashikar
Director
Leonard von Morze
Subject Categories
Business | Business Administration, Management, and Operations | Business Intelligence | Corporate Finance | Finance and Financial Management | Human-Computer Interaction | Management Information Systems | Technology and Innovation
Abstract
This study examines the level of agreement and performance between artificial intelligence (AI) generated investment recommendations and human analyst recommendations across U.S. publicly traded firms. Using a sample of twelve companies categorized by firm size (large, mid, and small), the study collects buy, hold, or sell recommendations from generative AI systems and human analysts. Agreement between AI-to-AI and AI-to-human recommendations is measured using Cohen’s Kappa agreement. Portfolio performance is evaluated by constructing equal-weighted portfolios for each recommendation source and size category. Risk-adjusted returns are measured using the Sharpe ratio over 1-, 2-, and 3-month periods. Furthermore, the study tests whether agreement between AI-to-human recommendations is lessened among smaller firms, where information asymmetry and limited analyst coverage may increase uncertainty. Through agreement analysis and portfolio performance evaluation, this research aims to evaluate whether AI systems mirror, diverge from, or outperform traditional human analysts in short-term investment contexts.
Recommended Citation
Chen, Crystal, "AI-Augmented Financial Advisors: Comparing AI and Human Analyst Investment Recommendations in Agreement, Performance, and Firm Size Effects" (2026). Honors College Theses. 43.
https://scholarworks.umb.edu/honors_theses/43
Included in
Business Administration, Management, and Operations Commons, Business Intelligence Commons, Corporate Finance Commons, Finance and Financial Management Commons, Human-Computer Interaction Commons, Management Information Systems Commons, Technology and Innovation Commons
Comments
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