Artificial Intelligence Narratives in Earnings Calls and Their Effect on Market Dynamics: A Study of Volume and Volatility in Leading Tech Stocks - Kanade, Akshay Digamber (Digital Repository of Theses, 2026)
The thesis titled "Artificial Intelligence Narratives in Earnings Calls and Their Effect on Market Dynamics: A Study of Volume and Volatility in Leading Tech Stocks," explores the short term financial impact of AI related corporate narratives presented during the quarterly earnings calls by the five leading NASDAQ listed technology companies (Google, Microsoft, Meta, Apple and Nvidia). Earning calls have become a tactical means to influence the expectations of investors using a purposefully crafted language as artificial intelligence has taken stage. This research centers on the impact of specific AI themed narratives ranging from optimism and uncertainty to investment and ethical risk on immediate market behavior, specifically, the fluctuations in trading volume and volatility. The motivation behind this research comes from the recent recognition that qualitative disclosures now weigh equally, if not more compared to the traditional financial metrics in shaping the investor sentiment. Quantitative metrics such as earnings per share and financial ratios do remain core components of financial analysis. But the language used by executives -especially concerning emerging technologies like AI – can often serve as a signal of strategic intent, competitive positioning, and anticipated risks the business is surrounded by. Yet, existing financial analysis tools such as including sentiment lexicons, AI generated summaries, and predefined models, do not have the thematic specificity and transparency needed to capture the evolving vocabulary and contextual subtleties of AI related communication. To fill this gap, the research suggests a systematic methodology that involves building a custom AI lexicon, thematically analyzing earnings call transcripts, and empirically connecting these narrative categories to short term market indicators. It uses text mining, winsorization, and regression analysis to evaluate the extent to which AI narratives correlate with abnormal trading activity and volatility changes in the short term. This includes metrics such as the VIX or VXN. The study also provides a replicable and interpretable approach to narrative finance by comparing custom lexicon to generic sentiment model. This research interlinks multiple disciplines of financial verbiage, AI communication and movements in the financial market. The study focuses on some practical insights of analyzing the impact of AI narratives through the dissection of the language used on earnings calls which would help the investors interpret the cues into trading strategies. This research addresses a new gap in the literature and equips the financial wizards to better assess and respond to the narratives around Artificial Intelligence in the financial aspect. Keywords: AI Narratives, Earnings Calls, Financial Linguistics, Market Dynamics, Custom Lexicon, NLP