Trading Volume as a Predictor of Market Movement

An Application of Logistic Regression in the R environment

  • Edson Kambeu Lecturer
Keywords: Stock exchange, Market movement, Logistic regression, R programming


A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent 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.


Alrasheedi, M (2012). Predicting Up/Down Direction using Linear Discriminant Analysis and Logit Model: The Case of SABIC Price Index. Research Journal of Business Management, 6 (4), 121-133. DOI: 10.3923/rjbm.2012.121.133.

Brooks, C. (1998). Predicting stock index volatility: Can market volume help? Journal of Forecasting, 17(1), 59-80.<59::AID-FOR676>3.0.CO;2-H

Choi, K.H; Kang, S.H & Yoon, S.M (2013). Relationship between Stock Returns and Trading Volume: Domestic and Cross-Country. Evidence in Asian Stock Markets. Proceedings of the 2013 International Conference on Economics and Business Administration, 33-39

Ciner, C (2003). Dynamic Linkages Between Trading Volume and Price Movements: Evidence for Small Firm. The Journal of Entrepreneurial Finance Stocks, 8(1), 87-102.

Dutta, A; Bandopadhyay, G & Sengupta, S (2012). Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression. International Journal of Business and Information, 7(1), 105-136.

Grigoryan, H (2015). Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock. Database Systems Journal, 6(2), 14-23.

Habib, N. M (2011). Trade Volume and Returns in Emerging Stock Markets. An Empirical Study: The Egyptian Market International Journal of Humanities and Social Science, 1(19).

Huang, W; Nakamori, Y & Wang, S (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32, 2513- 2522.

Hussain, S; Jamil, H; Javed, M & Ahmed, W (2014). Analysis of Relationship between Stock Return, Trade Volume and Volatility: Evidences from the Banking Sector of Pakistani Market. European Journal of Business and Management, 6(20),57-61.

Khaidem L., Saha S. & Dey S.R (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003

Kim, M & Sayama, H (2017). Predicting stock market movements using network science: An information theoretic approach. Applied Network Science , 2-35.

Pagolu, V. S ; Challa, K.N.R & Panda, G(2016).Sentiment Analysis of Twitter Data for Predicting Stock Market Movements. Proceedings of the International Conference on Signal Processing, Communication,Power and Embedded System, 28 October 2016

Qiu, M & Song Y (2016). Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLoS ONE, 11(5): e0155133, 1-11. pone.01551

Rajput, V and Bobde, S (2016). Stock market forecasting techniques: Literature survey. International Journal of Computer Science and Mobile Computing, 5(6), 500-506.

Remorov, R (2014). Stock Price and Trading Volume during Market Crashes. International Journal of Marketing Studies, 6(1), 21-30.

Tehranchian, A.M; Behravesh, M & Hadinia, S (2014). On the Relationship between Stock Returns and Trading Volume: A Case Study. European Online Journal of Natural and Social Sciences, 3(3),425-431.

Wang, Y (2014). Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HIS. International Journal of Business Intelligence and Data Mining, 9(2), 145-160. .

How to Cite
Kambeu, E. (2019). Trading Volume as a Predictor of Market Movement. International Journal of Finance & Banking Studies (2147-4486), 8(2), 57-69.