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    A scalable multi-step hybrid model for stock index prediction

    Dassanayake, Wajira

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    Wajira_Dassanayake_Scalable_Hybrid_Algorithm_for_Stock_Market_Index_Prediction.pdf (874.3Kb)
    Date
    2017-10-05
    Citation:
    Dassanayake, W. (2017, October). A scalable multi-step hybrid model for stock index prediction. Paper presented at the Unitec Research Symposium, 2017, Unitec Institute of Technology, New Zealand.
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/4333
    Abstract
    My research intends to derive an intelligent multistep scalable hybrid model Integrating technical, fundamental and textual analyses. This dynamic multistep model would link the existing segregated models in the literature. The literature survey reveals that the existing models formulate marginally segregated subsets in the area of stock market price prediction. This research will be the first scientific exploration to combine the effects of historical factors, macroeconomic determinants and spontaneous events in a single multistep model to forecast the stock market prices.
    Keywords:
    stock markets, stock movement. prediction, correlation analysis, stock price analysis
    ANZSRC Field of Research:
    1502 Other Banking, Finance and Investment, 080109 Pattern Recognition and Data Mining
    Copyright Holder:
    Author
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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