Predicting Stock Market Movements with Social Media and Machine Learning

Paraskevas Koukaras, Vasiliki Tsichli, Christos Tjortjis

2021

Abstract

Microblogging data analysis and sentiment extraction has become a popular approach for market prediction. However, this kind of data contain noise and it is difficult to distinguish truly valid information. In this work we collected 782.459 tweets starting from 2018/11/01 until 2019/31/07. For each day, we create a graph (271 graphs in total) describing users and their followers. We utilize each graph to obtain a PageRank score which is multiplied with sentiment data. Findings indicate that using an importance-based measure, such as PageRank, can improve the scoring ability of the applied prediction models. This approach is validated utilizing three datasets (PageRank, economic and sentiment). On average, the PageRank dataset achieved a lower mean squared error than the economic dataset and the sentiment dataset. Finally, we tested multiple machine learning models, showing that XGBoost is the best model, with the random forest being the second best and LSTM being the worst.

Paper Citation

in Harvard Style

Koukaras P., Tsichli V. and Tjortjis C. (2021). Predicting Stock Market Movements with Social Media and Machine Learning. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 436-443. DOI: 10.5220/0010712600003058

in Bibtex Style

@conference{webist21,
author={Paraskevas Koukaras and Vasiliki Tsichli and Christos Tjortjis},
title={Predicting Stock Market Movements with Social Media and Machine Learning},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={436-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010712600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Predicting Stock Market Movements with Social Media and Machine Learning
SN - 978-989-758-536-4
AU - Koukaras P.
AU - Tsichli V.
AU - Tjortjis C.
PY - 2021
SP - 436
EP - 443
DO - 10.5220/0010712600003058