Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively …

attention capabilities cs.cl cs.lg decision deep learning generate generative human language language models large language large language models llms making predictions process q-fin.st solution stock