Enhancing Stock Price Forecasting with Social Media Text: A Comparative Study of Machine Learning Approaches

Bingchang Li

2024

Abstract

With the power to influence investment decisions and provide market stability, stock price forecasting is essential to financial research. The use of machine learning techniques for prediction has gained popularity as technology has progressed. Researchers have proposed incorporating social media textual data to improve prediction accuracy. However, the efficacy of this strategy is still debatable because different kinds of textual information might produce varied results. Certain texts cause predictability to rise dramatically, while others cause it to fall. This research explores the use of machine learning techniques to predict stock prices using text data from social media platforms. Using Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) text representations, it assesses the effectiveness of Random Forest and Multinomial Naive Bayes classifiers. According to the investigation, Random Forest surpasses Multinomial Naive Bayes in terms of accuracy and robustness across a variety of datasets and text volumes, whereas TF-IDF consistently exceeds BOW. The analysis also reveals that Reddit's social media data has the most predictive value. These findings emphasize how important data quality and advanced text representation are to enhancing stock price forecasting models.

Paper Citation

in Harvard Style

Li B. (2024). Enhancing Stock Price Forecasting with Social Media Text: A Comparative Study of Machine Learning Approaches. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 331-336. DOI: 10.5220/0013225200004568

in Bibtex Style

@conference{ecai24,
author={Bingchang Li},
title={Enhancing Stock Price Forecasting with Social Media Text: A Comparative Study of Machine Learning Approaches},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={331-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013225200004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Enhancing Stock Price Forecasting with Social Media Text: A Comparative Study of Machine Learning Approaches
SN - 978-989-758-726-9
AU - Li B.
PY - 2024
SP - 331
EP - 336
DO - 10.5220/0013225200004568
PB - SciTePress