Abstract
Financial sentiment analysis plays a crucial role in informing investment decisions, assessing market risk, and predicting stock price trends. Existing works in financial sentiment analysis have not considered the impact of stock prices or market feedback on sentiment analysis. In this paper, we propose an adaptive framework that integrates large language models (LLMs) with real-world stock market feedback to improve sentiment classification in the context of the Indian stock market. The proposed methodology fine-tunes the LLaMA 3.2 3B model using instruction-based learning on the SentiFin dataset. To enhance sentiment predictions, a retrieval-augmented generation (RAG) pipeline is employed that dynamically selects multi-source contextual information based on the cosine similarity of the sentence embeddings. Furthermore, a feedback-driven module is introduced that adjusts the reliability of the source by comparing predicted sentiment with actual next-day stock returns, allowing the system to iteratively adapt to market behavior. To generalize this adaptive mechanism across temporal data, a reinforcement learning agent trained using proximal policy optimization (PPO) is incorporated. The PPO agent learns to optimize source weighting policies based on cumulative reward signals from sentiment-return alignment. Experimental results on NIFTY 50 news headlines collected from 2024 to 2025 demonstrate that the proposed system significantly improves classification accuracy, F1-score, and market alignment over baseline models and static retrieval methods. The results validate the potential of combining instruction-tuned LLMs with dynamic feedback and reinforcement learning for robust, market-aware financial sentiment modeling.
Abstract (translated)
金融情感分析在指导投资决策、评估市场风险和预测股价走势方面发挥着关键作用。现有研究中，金融情感分析尚未充分考虑股票价格或市场反馈对情感分析的影响。本文提出了一种自适应框架，该框架将大型语言模型（LLMs）与现实世界的股市反馈相结合，以提升印度股市背景下的情感分类准确性。 所提出的方案采用基于指令的学习方法，在SentiFin数据集上微调LLaMA 3.2 3B模型。为了增强情感预测的准确性，采用了检索增强生成（RAG）管道，该管道动态选择多源上下文信息，并根据句子嵌入之间的余弦相似度来筛选。 此外，我们引入了一个反馈驱动模块，通过比较预测的情感与实际第二天股票回报率，调整来源的可信度。这使得系统能够逐步适应市场行为的变化。为了在时间序列数据中推广这种自适应机制，我们融入了使用近端策略优化（PPO）训练的强化学习代理。该PPO代理学会了根据情感与回报的一致性累积奖励信号来优化源权重策略。 实验结果表明，在2024年至2025年间收集的NIFTY 50新闻头条上，所提出的系统在分类准确性、F1分数和市场一致性方面显著优于基准模型和静态检索方法。这些结果显示了结合指令微调的LLMs与动态反馈及强化学习来构建稳健且具有市场意识的情感分析模型的潜力。
URL
https://arxiv.org/abs/2512.20082
