Can artificial intelligence (AI) in the form of large language models (LLM) improve upon existing methods to explain stock returns around earnings announcements? In the June 2026 version of their paper entitled "Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing", Ralph Koijen and Bradford Levy employ a a real-time, out-of-sample benchmark for evaluating optimized LLMs while avoiding lookahead bias and market adaptation effects. The benchmark measures how well AI systems explain stock returns around earnings announcements using only information available at announcement time (especially the announcement text). Optimized means experimenting with LLM prompts to improve results, such as guiding LLMs to assess earnings relative to expectations. Using earnings call transcripts, analyst consensus earnings and associated daily returns for U.S. stocks during the fourth quarter of 2025 (1,849 earnings announcements), they find that:
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