In this paper, we have examined the long memory property of Indian stock market by analyzing the trading volume series. Given the absence of trading volume index data, we have constructed trading volume series for the Indian stock market. We used maximum likelihood method to analyze the constructed trading volume index. The estimation of ARFIMA model, obtained a signicant parameter for the order of fractional integration, and this could be consistent with the long autocorrelations observed in the trading volume series. The ndings that stock trading volume is a long memory process is robust, given dierent estimating methods, different subsamples, temporal aggregation and tests on individual stocks. Because of the conditional heteroscedasticity in the series, we have also carried out ARFIMAGARCH procedures to check whether long persistence were robust in the presence of conditional heteroscedasticity.