TY - GEN
T1 - Predicting stock market movement
T2 - 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
AU - Bouktif, Salah
AU - Awad, Mamoun Adel
N1 - Publisher Copyright:
© 2015 by SCITEPRESS - Science and Technology Publications, Lda.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Ant colony optimization
KW - Bayesian classifiers
KW - Data mining
KW - Stock market
UR - http://www.scopus.com/inward/record.url?scp=84960844394&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960844394&partnerID=8YFLogxK
U2 - 10.5220/0005578401590167
DO - 10.5220/0005578401590167
M3 - Conference contribution
AN - SCOPUS:84960844394
T3 - IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
SP - 159
EP - 167
BT - KDIR
A2 - Fred, Ana
A2 - Dietz, Jan
A2 - Aveiro, David
A2 - Liu, Kecheng
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
PB - SciTePress
Y2 - 12 November 2015 through 14 November 2015
ER -