Abstract
This paper investigates the dynamic association between daily stock index returns and percentage trading volume changes. To proceed with this, linear and nonlinear Granger causality tests are applied to the Karachi Stock Exchange (KSE) data. The analysis covers the span of about 5 years with 1266 daily observations. The same methodology is employed for two non-overlapping sub-periods to examine the robustness of the results. Unidirectional linear Granger causality from stock returns to trading volume is observed for the entire sample period and for both the sub-periods as well. The null hypothesis of linear Granger noncausality from percentage volume changes to stock returns is rejected only in optimal lag length for the second sub-period. Regarding nonlinear Granger causality, the modified Baek and Brock's test (l992a) for nonlinear Granger causality provides evidence of significant unidirectional nonlinear Granger causality from percentage volume changes to stock returns in both the sub-periods for all the common lag lengths used but not for vice versa. The analysis exposed that volume has significant nonlinear explanatory power for stock returns, whereas stock returns have linear explanatory power for trading volume.
Highlights
Mostof the empirical work on stock market has focused traditionally on whether future stock prices moments can be projected or not
The core purpose of this study is to examine whether the information on trading volume can be used to predict the changes in stock prices
As a first step to investigate the interactions between stock returns and percentage volume changes, cross-correlation coefficients, CCs'P
Summary
Mostof the empirical work on stock market has focused traditionally on whether future stock prices moments can be projected or not. The first one considers those studies that tested the Market Efficient Hypothesis (MEH) or/and Random Walk Hypothesis (RWH) for stock markets. The second mass of studies on stock market has focused primarily on to identify the financial and socio-economic factors that have significant associations with stock prices. These studies have generally used OLS regression analysis, co integration and causality techniques to explore the relationship between stock prices and the other variables such as profits, book-to-market value, term premium, dividend yields, exchange rates, interest rates, inflation, money supply, output growth, etc
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