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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Gil Cohen, 2024. "Polynomial Moving Regression Band Stocks Trading System," Risks, MDPI, vol. 12(10), pages 1-15, October.
    2. 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.
    3. 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.

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