Authors:
Salah Bouktif
and
Mamoun Adel Awad
Affiliation:
UAE University, United Arab Emirates
Keyword(s):
Stock Market, Data Mining, Ant Colony Optimization, Bayesian Classifiers.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
Web Mining
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 resul
ts of our approach are compared with four alternative prediction
methods namely, bagging, Adaboost, best expert, and expert trained on all the available data.
(More)