AI in Finance and Information Overload

Abstract

Artificial intelligence is reshaping financial markets, yet the limits to its rationality remain underexplored. This paper documents information overload in Large Language Models applied to financial analysis. Using earnings forecasts from corporate calls and market reaction predictions from news, we show that predictive accuracy follows an inverted U-shaped pattern, where excessive context degrades performance. Larger LLMs mitigate this effect, increasing the optimal context length. Our findings underscore a fundamental limitation of AI-driven finance: more data is not always better, necessitating empirical tuning to determine the right amount of context for each task.