  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 harder with the introduction of
the explanation component, which requires LLMs to explain verbally why certain
factors are more important than the others. On the other hand, to fine-tune
LLMs for such a task, one would need expert-annotated samples of explanation
for every stock movement in the training set, which is expensive and
impractical to scale. To tackle these issues, we propose our
Summarize-Explain-Predict (SEP) framework, which utilizes a self-reflective
agent and Proximal Policy Optimization (PPO) to let a LLM teach itself how to
generate explainable stock predictions in a fully autonomous manner. The
reflective agent learns how to explain past stock movements through
self-reasoning, while the PPO trainer trains the model to generate the most
likely explanations from input texts. The training samples for the PPO trainer
are also the responses generated during the reflective process, which
eliminates the need for human annotators. Using our SEP framework, we fine-tune
a LLM that can outperform both traditional deep-learning and LLM methods in
prediction accuracy and Matthews correlation coefficient for the stock
classification task. To justify the generalization capability of our framework,
we further test it on the portfolio construction task, and demonstrate its
effectiveness through various portfolio metrics.
