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

Edson Kambeu
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Edson Kambeu: Department of Business Management, BAISAGO University, Francistown, Botswana

International Journal of Finance & Banking Studies, 2019, vol. 8, issue 2, 57-69

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.

Keywords: Stock exchange; Market movement; Logistic regression; R programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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