Learning to Generate Explainable Stock Predictions using Large Language Models

The author argues that traditional deep learning models struggle with explaining stock predictions, while Large Language Models (LLMs) offer a solution by generating human-readable explanations. The proposed Summarize-Explain-Predict (SEP) framework utilizes self-reflection and reinforcement learning to teach LLMs how to make explainable stock predictions autonomously.