用AI情感分析增强随机森林，提升股市走势预测准确率。
SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest
- 将FinGPT生成的金融情绪特征融入随机森林模型
- 平均预测准确率提升9.23%，误差低于传统LSTM模型
- 适合关注量化投资与情绪驱动建模的研究者
股票趋势预测是金融领域中的难题，涉及大量数据与相关指标。仅依赖经验分析往往导致结果不可持续且无效。机器学习研究表明，随机森林算法可有效提升此类预测效果，成为辅助股市走势预测的重要工具。本文提出一种新方法，将基于FinGPT生成式AI的情绪分析与传统随机森林模型结合，构建名为‘情感增强随机森林’（SARF）的新框架，通过引入细粒度金融情绪特征优化股价预测。实验表明，SARF在预测股市走势方面优于传统随机森林与LSTM模型，平均准确率提升9.23%，且预测误差更低。
原文摘要 · Abstract (English)
Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called "Sentiment-Augmented Random Forest" (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements.
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