Research Article
SARF: Stock Market Prediction with Sentiment-Augmented Random Forest
@INPROCEEDINGS{10.1007/978-3-031-72393-3_5, author={Saber Talazadeh and Dragan Peraković}, title={SARF: Stock Market Prediction with Sentiment-Augmented Random Forest}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 8th EAI International Conference, FABULOUS 2024, Zagreb, Croatia, May 9--10, 2024, Proceedings}, proceedings_a={FABULOUS}, year={2024}, month={10}, keywords={Machine Learning Large Language Model Random Forest Sentiment Analysis Natural Language Processing Stock Price Prediction}, doi={10.1007/978-3-031-72393-3_5} }- Saber Talazadeh
Dragan Peraković
Year: 2024
SARF: Stock Market Prediction with Sentiment-Augmented Random Forest
FABULOUS
Springer
DOI: 10.1007/978-3-031-72393-3_5
Abstract
Stock trend forecasting, a challenging problem in the financial domain, involves extensive 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 incorporates 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 predicting stock market movements.
