Predicting Stock Market Movement: An Evolutionary Approach

Salah Bouktif, Mamoun Adel Awad

2015

Abstract

Social Networks are becoming very popular sources of all kind of data. They allow a wide range of users to interact, socialize and express spontaneous opinions. The overwhelming amount of exchanged data on businesses, companies and governments make it possible to perform predictions and discover trends in many domains. In this paper we propose a new prediction model for the stock market movement problem based on collective classification. The model is using a number of public mood states as inputs to predict Up and Down movement of stock market. The proposed approach to build such a model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. A particular implementation of our approach is based on Ant Colony Optimization algorithm and customized for individual Bayesian classifiers. Our approach is validated with data collected from social media on the stock of a prestigious company. Promising results of our approach are compared with four alternative prediction methods namely, bagging, Adaboost, best expert, and expert trained on all the available data.

Paper Citation

in Harvard Style

Bouktif S. and Adel Awad M. (2015). Predicting Stock Market Movement: An Evolutionary Approach . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 159-167. DOI: 10.5220/0005578401590167

in Bibtex Style

@conference{kdir15,
author={Salah Bouktif and Mamoun Adel Awad},
title={Predicting Stock Market Movement: An Evolutionary Approach},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={159-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005578401590167},
isbn={978-989-758-158-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Predicting Stock Market Movement: An Evolutionary Approach
SN - 978-989-758-158-8
AU - Bouktif S.
AU - Adel Awad M.
PY - 2015
SP - 159
EP - 167
DO - 10.5220/0005578401590167