The determinants of IPO initial returns in emerging markets: a quantile regression

Purpose - This article investigates the determinants of cross-section variation of initial public offerings' (IPOs) first-day returns in a sample of 710 issues across seven emerging markets between 2013 and 2017. Design/methodology/approach - Ordinary least squares regression (OLS) and the semi-parametric quantile regression (QR) technique are employed. QR enables to analyse beyond the explanatory variables' relative mean effect at various points in the endogenous variable distribution. Furthermore, parameter estimates under QR are robust to the existence of outliers and long tails in the data distribution. Findings - Underpricing varies across countries with an average of 78%. According to the OLS results, independent variables explain 26% of the variation of IPOs' first-day returns. Findings show that employing QR is important, given the non-normality of the data and because each quantile is associated with a different effect of explanatory variables. Originality/value - In addition to firm-specific, market-specific and issue-specific factors, the paper extends IPOs' underpricing literature through studying the impact of country-specific characteristics, largely neglected by literature, on IPO underpricing.