Predicting stock market movement: An evolutionary approach

Salah Bouktif, Mamoun Adel Awad

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationKDIR
    EditorsAna Fred, Jan Dietz, David Aveiro, Kecheng Liu, Joaquim Filipe, Joaquim Filipe
    PublisherSciTePress
    Pages159-167
    Number of pages9
    ISBN (Electronic)9789897581588
    DOIs
    Publication statusPublished - 2015
    Event7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal
    Duration: Nov 12 2015Nov 14 2015

    Publication series

    NameIC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
    Volume1

    Other

    Other7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
    Country/TerritoryPortugal
    CityLisbon
    Period11/12/1511/14/15

    Keywords

    • Ant colony optimization
    • Bayesian classifiers
    • Data mining
    • Stock market

    ASJC Scopus subject areas

    • Software

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