Stock Return Volatility postulates the direction and the likelihood of the performance. The greater volatility means the greater risk premiums resulting from the investment. The research objective is to assess the effect trading volume volatility of the stock returns of the firms listed at NSE. The research was reinforced by three theories which include; signaling theory, Efficient Market Hypothesis and prospect theory. Moreover, the internal factors such as trading volume and trading size were critical stock return volatility as well as performance and leverage. The study maximizes descriptive research design as a tenet of this research since it reinforces the inference and interpretation. This research targeted 64 companies listed in NSE under the interval of 6years. Additionally, secondary data utilized spanned from 2016 to 2021. The data computation was accomplished via SPSS techniques. The study maximized the multiple regression analysis. The data normality test was undertaken to assess if the data follow the normal distribution pattern. Furthermore, autocorrelation and multicollinearity were determined through the use of Durbin Watson and Variance Inflation Factor respectively. Autocorrelation value was within the normal accepted range. The condition here is that if the p value of both test in every variable is below 0.05 hence postulates that data was normal distribution. The principle here was that if the VIF values obtain are below 10 and the tolerance values bigger than 0.2 concludes the non-presence of multicollinearity. The dataset was relevant and crucial for investigation and interpretation. From the training it is very imperative to postulate that trading size was highly fluctuating followed by stock return volatility. Nonetheless, ROA was smallest fluctuating among the variables prioritized in the computation. The R which is coefficient of determination is 0.615. Additionally, this value 0.615 implies a strong correlation among the variables under the assessment. The R square, Coefficient of determination stipulated by 0.378. Therefore, this indicates that 37.8% of change in variation of stock return volatility is caused by the predictor variables captured in this study. These explanatory variables incorporate; trading volume, trade size, ROA and leverage. The remaining 62.2% of change in variation is caused by factors not captured in the above. A unit change in trading volume results to a negative effect of 0.129 on Stock return volatility when all other factors are held at constant. A single unit change in trade size results to a negative effect of 0.008 on stock return volatility. A single unit increment in ROA triggers an increase in stock return volatility by 0.710 when all variables are kept constant. Finally, an increase of leverage triggers movement to the same direction (positive effect) on Stock Return Volatility of 1.039 respectively when all other factors are kept constant. The researcher recommends for examination of macroeconomic determinants verse the stock returns. The sectorial research can be enhanced to increase the knowledge and understanding. In a nutshell, specific variables such as the nature of economic variables, inflation, and fragility index should studied in conjunction to stock return volatility.

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