SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest

TL;DR

This paper introduces SARF, a novel method combining sentiment analysis from FinGPT with Random Forest to improve stock market prediction accuracy, outperforming traditional models.

Contribution

The paper presents SARF, a new sentiment-augmented approach that integrates AI-driven sentiment analysis into Random Forest for enhanced stock trend forecasting.

Findings

SARF achieves 9.23% higher accuracy than baseline models.

SARF reduces prediction errors in stock movement forecasting.

Sentiment features significantly improve prediction performance.

Abstract

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.