Can large language models (LLMs) generate enhanced returns by analyzing earnings releases? This thesis explores the intersection of AI-powered language models and earnings trading strategies. In a structured experiment, earnings releases from five American blue-chip stocks are evaluated by a selected LLM, classifying sentiment as positive, neutral, or negative, across 18 quarters from Q1 2019 to Q2 2023.
The theoretical foundation covers earnings trading strategies, the characteristics of American blue-chip stocks, and the core concepts of artificial intelligence, machine learning, deep learning, and LLMs. Three LLMs are assessed for suitability, and the most fitting model is selected for the experiment.
The LLM's sentiment outputs are then compared against actual stock price movements to determine whether AI-driven earnings analysis can meaningfully improve investment decisions. The findings are relevant to both retail traders seeking a competitive edge and institutional investors exploring scalable, AI-based approaches to market analysis.
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- Timon Ortwein (Autor:in), 2024, Assessment of Earnings Releases with a Large Language Model for Enhanced Returns with Five American Blue-Chip Stocks, München, GRIN Verlag, https://www.grin.com/document/1718356
