Deng, S., Huang, X., Zhu, Y., Su, Z., Fu, Z. and Shimada, T. (2023) Stock Index Direction Forecasting Using an Explainable Extreme Gradient Boosting and Investor Sentiments. The North American Journal of Economics and Finance, 64, Article 101848.
https://doi.org/10.1016/j.najef.2022.101848

has been cited by the following article:

• TITLE: A Two-Stage Framework for Stock Price Prediction: LLM-Based Forecasting with Risk-Aware PPO Adjustment

  AUTHORS: Qizhao Chen

  KEYWORDS: LLM, Risk-Adjusted Return, Stock Price Prediction, Proximal Policy Optimization

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.4, April 27, 2025

  ABSTRACT: Accurate prediction of stock prices remains a fundamental challenge in financial markets, with substantial implications for investment strategies and decision making. Although machine learning and deep learning models have significantly advanced the prediction of stock price movements, they often overlook the critical aspect of financial risk. This research proposes a novel framework that integrates Large Language Models (LLMs) with Proximal Policy Optimization (PPO), a reinforcement learning technique, to improve stock price predictions while incorporating risk-adjusted mechanisms. The LLM provides initial predictions based on historical stock data and financial news sentiment, while PPO refines these predictions by adjusting them according to financial risk metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). The framework aims to improve prediction accuracy and provide more reliable forecasts that account for market volatility. Experimental results demonstrate that the proposed LLM-PPO framework outperforms traditional prediction models in terms of both prediction accuracy and risk-adjusted performance, offering a more robust tool for financial decision making in uncertain market environments.