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    Machine learning techniques and use of event information for stock market prediction: a survey and evaluation

    Yoo, Paul and Kim, M.H. and Jan, T. (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: UNSPECIFIED (ed.) CIMCA 2005: International Conference on Computational Intelligence for Modelling Control and Automation. IEEE Computer Society, pp. 835-841. ISBN 9780769525040.

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    Abstract

    This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. In this paper, we present recent developments in stock market prediction models, and discuss their advantages and disadvantages. In addition, we investigate various global events and their issues on predicting stock markets. From this survey, we found that incorporating event information with prediction model plays very important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required to provide better performance in financial time series prediction

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 Oct 2021 15:57
    Last Modified: 25 Oct 2021 15:57
    URI: https://eprints.bbk.ac.uk/id/eprint/46474

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