Initial public offerings (IPO) tend to generate first-day abnormal returns compared to the rest of the market, and thus creating the well-documented IPO underpricing phenomenon. This thesis employs both the ordinary least squares (OLS) method and the quantile regression (QR) technique to investigate the relevance of information asymmetry theories, behavioural models and institutional explanations to cross-section variation of IPOs’ first-day returns in a sample of 710 issues across seven emerging markets between 2013 and 2017. Determinants of IPO underpricing include firm, issue, market, and country-specific variables. Understanding influences of IPO underpricing across countries and firms has important implications for issuing companies, investors, and policymakers. Underpricing varies across the employed countries with a positive average of 78%, with $238 billion money left on the table. According to the OLS results, independent variables explain 26% of the variation of IPOs’ first-day returns. Findings show that employing QR is crucial given the non-normality of the data and because each quantile is associated with a different effect of explanatory variables. Findings reveal the importance of country-specific variables (e.g., corruption control and the protection of private property) in explaining variation of cross-sectional IPO underpricing and, hence, confirming the institutional explanations of underpricing. The pre-IPO market sentiment significantly affects IPOs’ first-day returns, confirming the investor sentiment hypothesis. The certification hypothesis, suggesting that employing prestigious underwriters are associated with smaller levels of underpricing, is not validated. Moreover, the ex-ante uncertainty hypothesis holds true, to some extent, in explaining underpricing in the employed sample. |
