• Login
    View Item 
    •   Home
    • Research from April 2016
    • Computing
    • View Item
    •   Home
    • Research from April 2016
    • Computing
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UOBREPCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartmentThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartment

    My Account

    LoginRegister

    About

    AboutLearning ResourcesResearch Graduate SchoolResearch InstitutesUniversity Website

    Statistics

    Display statistics

    Using autoregressive modelling and machine learning for stock market prediction and trading

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Hushani, Phillip
    Affiliation
    University of Bedfordshire
    Issue Date
    2018-09-29
    Subjects
    autoregressive modelling
    stock market prediction
    machine learning
    
    Metadata
    Show full item record
    Other Titles
    Third International Congress on Information and Communication Technology
    Abstract
    Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. In this paper, we compare four forecasting models: autoregressive integrated moving average (ARIMA), vector autoregression (VAR), long short-term memory (LSTM) and nonlinear autoregressive Exogenous (NARX). The results of predictive modelling are analysed and compared in terms of prediction accuracy. The research aims to develop a new profitable trading strategy. Our findings are: (i) the NARX model has provided accurate short-term predictions but failed long forecasts, and (ii) the VAR model can form a good trend line required for trading. Thus, the profitable trading strategy can combine the machine learning predictive modelling and technical analysis.
    Citation
    Hushani P (2019) 'Using autoregressive modelling and machine learning for stock market prediction and trading', Third International Congress on Information and Communication Technology ICICT 2018 - London, Springer.
    Publisher
    Springer
    Journal
    Advances in Intelligent Systems and Computing
    URI
    http://hdl.handle.net/10547/624182
    DOI
    10.1007/978-981-13-1165-9_70
    Additional Links
    https://link.springer.com/chapter/10.1007/978-981-13-1165-9_70
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISSN
    2194-5357
    EISSN
    2194-5365
    ISBN
    9789811311659
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-981-13-1165-9_70
    Scopus Count
    Collections
    Computing

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.