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

arXiv: https://arxiv.org/abs/2410.07143 · HTML: https://mlsanity.com/paper/2410.07143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions](https://mlsanity.com/paper/1801.07047/) (2018)
- [Artificial Counselor System for Stock Investment](https://mlsanity.com/paper/1903.00955/) (2019)
- [Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets](https://mlsanity.com/paper/2204.05783/) (2022)

## Built on by (most cited first, 1 of 1)

- [Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?](https://mlsanity.com/paper/2503.04873/) (2025)

## Abstract (arXiv)

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.