IDEAS home Printed from https://ideas.repec.org/a/rbs/ijfbss/v8y2019i2p57-69.html
   My bibliography  Save this article

Trading volume as a predictor of market movement: An application of Logistic regression in the R environment

Author

Listed:
  • Edson Kambeu

    (Department of Business Management, BAISAGO University, Francistown, Botswana)

Abstract

A Logistic regression model become a popular model because of its ability to predict, classify and draw relationships between a dichotomous independent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

Suggested Citation

  • Edson Kambeu, 2019. "Trading volume as a predictor of market movement: An application of Logistic regression in the R environment," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(2), pages 57-69, April.
    • Handle: RePEc:rbs:ijfbss:v:8:y:2019:i:2:p:57-69
    as

    Download full text from publisher

    File URL: https://www.ssbfnet.com/ojs/index.php/ijfbs/article/view/177/289
    Download Restriction: no
    File URL: https://www.ssbfnet.com/ojs/index.php/ijfbs/article/view/177
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    2. Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akhilesh Prasad & Priti Bakhshi, 2022. "Role of the Global Volatility Indices in Predicting the Volatility Index of the Indian Economy," Risks, MDPI, vol. 10(12), pages 1-18, November.
    2. Akhilesh Prasad & Arumugam Seetharaman, 2021. "Importance of Machine Learning in Making Investment Decision in Stock Market," Vikalpa: The Journal for Decision Makers, , vol. 46(4), pages 209-222, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
    2. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    3. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    4. Sugai Han & Ansheng Li & Hongchao Wang & Xiaoyun Gong & Liangwen Wang & Yixiang Huang & Yanming Li & Wenliao Du, 2020. "A health management system for large vertical mill," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
    5. Pegah Eslamieh & Mehdi Shajari & Ahmad Nickabadi, 2023. "User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks," Mathematics, MDPI, vol. 11(13), pages 1-26, July.
    6. Hyungjin Ko & Jaewook Lee & Junyoung Byun & Bumho Son & Saerom Park, 2019. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    7. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
    9. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    10. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    11. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    12. Ehsan Hoseinzade & Saman Haratizadeh, 2018. "CNNPred: CNN-based stock market prediction using several data sources," Papers 1810.08923, arXiv.org.
    13. Catullo, Ermanno & Gallegati, Mauro & Russo, Alberto, 2022. "Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    14. Po Yun & Chen Zhang & Yaqi Wu & Xianzi Yang & Zulfiqar Ali Wagan, 2020. "A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    15. Dinesh K. Sharma & H. S. Hota & Kate Brown & Richa Handa, 2022. "Integration of genetic algorithm with artificial neural network for stock market forecasting," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 828-841, June.
    16. Jia LU & Noor Muhammad SHAZEMEEN & Raimonda MARTINKUTE-KAULIENE, 2020. "Portfolio Decision Using Time Series Prediction and Multi-objective Optimization," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 118-130, December.
    17. Jakub Frankowski & Maciej Zaborowicz & Jacek Dach & Wojciech Czekała & Jacek Przybył, 2020. "Biological Waste Management in the Case of a Pandemic Emergency and Other Natural Disasters. Determination of Bioenergy Production from Floricultural Waste and Modeling of Methane Production Using Dee," Energies, MDPI, vol. 13(11), pages 1-15, June.
    18. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
    19. I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.
    20. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbs:ijfbss:v:8:y:2019:i:2:p:57-69. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Hasan Dincer (email available below). General contact details of provider: https://edirc.repec.org/data/ssbffea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.