The Impact of Trading Volume on Price Volatility | ||||
| مجلة البحوث الإدارية | ||||
| Volume 43, Issue 2, April 2025 PDF (938.45 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/jso.2025.374516.1389 | ||||
 View on SCiNiTO | ||||
| Author | ||||
ياسين شريف نظمي  
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| الجامعه الحديثه للتكنولوجيا و المعلومات(M.T.I) | ||||
| Abstract | ||||
| The research investigates the trading volume's influence on price volatility among EGX 30 index-listed companies within the Egyptian stock market to help investors educate their decisions. The selected research method includes quantitative analysis of data obtained from the Egyptian Stock Exchange (EGX 30) for a period spanning 2003 to 2024. The research measures trading volume through log turnover (dividing trading volume by shares outstanding) and applies standard deviation alongside the GARCH model for price volatility assessment. The analysis using Stata v12 software implemented horizontal and vertical methods together with Residuals Heteroskedasticity Test and Normality of Residuals Test alongside Multi-Linear Correlation Analysis and Multicollinearity Diagnostic tests and Autocorrelation methods to verify whether trading volume positively influences price volatility under different firm conditions. The research data demonstrates that trading volume creates significant price volatility. The results show that stock price change increases by 0.34 points upon each turnover unit increase and 0.46 points through each trading volume increase with statistical significance at 5%. The study shows that firm age together with size influence volatility in a positive direction while leverage relations negatively with volatility. Statistical results indicate that the proposed model successfully assesses 72.2% of stock price change variation based on the hypothesis regarding heightened trading volume resulting in amplified price volatility. The research adds to existing knowledge through its examination of the Egyptian stock market from 2003 to 2024 in this emerging market context. The research design combines turnover data alongside raw trading volume to analyze volume-volatility dynamics through a powerful | ||||
| Keywords | ||||
| price volatility; trading volume; GARCH | ||||
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