Multimodal Stock Price Prediction

Furkan Karadaş, Bahaeddin Eravcı, Ahmet Murat Özbayoğlu

2025

Abstract

In an era where financial markets are heavily influenced by many static and dynamic factors, it has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction. This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles. We capture real-time market dynamics and investor mood through sentiment analysis on these textual data using both ChatGPT-4o and FinBERT models. We look at how these integrated data streams augment predictions made with a standard Long Short-Term Memory (LSTM model) to illustrate the extent of performance gains. Our study's results indicate that incorporating the mentioned data sources considerably increases the forecast effectiveness of the reference model by up to 5%. We also provide insights into the individual and combined predictive capacities of these modalities, highlighting the substantial impact of incorporating sentiment analysis from tweets and news articles. This research offers a systematic and effective framework for applying multimodal data analytics techniques in financial time series forecasting that provides a new perspective for investors to leverage data for decision-making.

Paper Citation

in Harvard Style

Karadaş F., Eravcı B. and Özbayoğlu A. (2025). Multimodal Stock Price Prediction. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 687-694. DOI: 10.5220/0013174500003890

in Bibtex Style

@conference{icaart25,
author={Furkan Karadaş and Bahaeddin Eravcı and Ahmet Özbayoğlu},
title={Multimodal Stock Price Prediction},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={687-694},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013174500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Multimodal Stock Price Prediction
SN - 978-989-758-737-5
AU - Karadaş F.
AU - Eravcı B.
AU - Özbayoğlu A.
PY - 2025
SP - 687
EP - 694
DO - 10.5220/0013174500003890
PB - SciTePress