AI Investment Ratings: ChatGPT’s Analysis of 1Q2024 Earnings Call TranscriptsBurke, Thomas Artificial IntelligenceAIChatGPTEarnings Call TranscriptsRussell 2000 IndexSentiment AnalysisMarket EffciencyOpenAIAPIsFinanceB SThe rapid pace of innovation in artificial intelligence continues to prove beneficial across various business domains. Recent research studies highlight ChatGPT’s robust ability to interpret text and conduct sentiment analysis. For instance, Lopez-Lira & Tang (2023) demonstrated ChatGPT’s proficiency by observing cumulative returns of 500% over a 14-month period through its analysis of news announcements. Similarly, Pelster & Val (2024) identified a strong positive correlation between ChatGPT’s assessment of web articles for investment attractiveness and the subsequent returns of S&P 500 constituents. The existing body of literature suggests a prevalent phenomenon known as post-earnings announcement drift (PEAD), challenging the notion of market efficiency. According to Fink (2021), there exists an inverse correlation between PEAD and the size of firms, attributed possibly to the heightened trading frictions and worse information environments experienced by smaller companies. This study examines the efficacy of ChatGPT in analyzing earnings call transcripts from companies within the Russell 2000 index, particularly focusing on smaller firms prone to earnings drift. The investment recommendations derived from the analysis serve as a basis for portfolio construction, followed by the calculation of returns. The increasing integration of AI in finance carries significant implications for further enhancing the efficiency of public markets. This trend is likely to accelerate with widespread adoption of APIs provided by prominent financial software companies such as FactSet, Bloomberg, and S&P Capital IQ.Brian Spangler Davis, Thesis SupervisorBrian Spangler Davis, Thesis Honors Advisoropen_access2024-04-02T19:58:59Zhttps://honors.libraries.psu.edu/catalog/9137tjb6403