Stock Market Movement Prediction using Disparate Text Features with Machine Learning

Salah Bouktif, Ali Fiaz, Mamoun Awad

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

    2 Citations (Scopus)

    Abstract

    Forecasting stock market movement is a widely researched topic both in academia and industry. Accurate forecast of stock direction can help investors to acquire opportunities for gaining profit in the stock exchange. Predicting stock market due to its dynamic, non-linear and complex nature is inherently difficult. One of the weaknesses of existing stock movement prediction research is that using only sentiment-based features extracted from social media do not completely harness underlying stock behaviour.Finding out which factors are the most significant presents a monumental challenge. Thus, in this research, we will integrate several factors that can affect the stock prices by integrating sentiment analysis with important textual features with relevant lags with the aim to construct more reliable and realistic sentiment representation. To evaluate the performance of our approach, we present a case study based on the AMZN NASDAQ stocks. The experiment results show that random forest model with important features was able to predict the AMZN stock movement direction and to outperform other prediction methods.

    Original languageEnglish
    Title of host publication2019 3rd International Conference on Intelligent Computing in Data Sciences, ICDS 2019
    EditorsPlamen Angelov, Jaouad Boumhidi, Hani Hagras, El Habib Nfaoui, Youness Oubenaalla, Chakir Loqman, Mohammed Mestari, Hajar Mousannif
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728100036
    DOIs
    Publication statusPublished - Oct 2019
    Event3rd International Conference on Intelligent Computing in Data Sciences, ICDS 2019 - Marrakech, Morocco
    Duration: Oct 28 2019Oct 30 2019

    Publication series

    Name2019 3rd International Conference on Intelligent Computing in Data Sciences, ICDS 2019

    Conference

    Conference3rd International Conference on Intelligent Computing in Data Sciences, ICDS 2019
    Country/TerritoryMorocco
    CityMarrakech
    Period10/28/1910/30/19

    Keywords

    • data mining
    • prediction
    • sentiment analysis
    • stock market direction
    • text feature

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Modelling and Simulation
    • Control and Optimization

    Fingerprint

    Dive into the research topics of 'Stock Market Movement Prediction using Disparate Text Features with Machine Learning'. Together they form a unique fingerprint.

    Cite this