Abstract This study investigates how algorithmic trading intensity and retail investor participation influence market qualityin emerging equity markets during the period 2020–2025. The research is motivated by structural changes thataccelerated after the pandemic, including the adoption of automated execution systems and rapid growth in retailtrading through low-cost digital platforms. Using secondary datasets obtained from exchange reports and marketdata providers, four core variables are analyzed: algorithmic trading intensity, retail participation, volatility, andliquidity. The study adopts a quantitative and explanatory design, applying descriptive statistics, correlationanalysis, and econometric modeling. Multiple regression models test the direct effects of algorithmic trading onvolatility and liquidity, and moderated regression models examine the interaction between algorithmic tradingand retail participation. Additional diagnostics, including GARCH specifications, are employed to assessvolatility persistence. The empirical findings show that algorithmic trading intensity increased notably over thesample period, coinciding with reduced annualized volatility and improved liquidity conditions reflected innarrower bid–ask spreads, higher turnover, and deeper order books. Retail participation also grew substantiallyand moderated the effects of automation, slightly attenuating volatility reductions while reinforcing liquidityimprovements. These results suggest that automation has supported market efficiency without destabilizing pricedynamics, even in settings characterized by rising retail activity. The study contributes to market microstructureliterature by introducing retail participation as a moderating variable and offers policy insight for regulators andexchanges engaged in balancing innovation with market integrity.