Bursa Malaysia IPO data 2004-2021
Description
The dataset covers a timeframe spanning from January 2004 to December 2021, with an initial sample of 493 Initial Public Offerings (IPOs). Several exclusion criteria were applied to ensure the study's focus remains aligned with its objectives. First, since the study exclusively examines IPOs that utilize the fixed-price method, IPOs employing the book-building method were excluded, resulting in the removal of 39 cases from the original sample. Furthermore, the study investigates explicitly investor behavior within the IPO market, focusing on public issues, offer-for-sale transactions, or hybrid forms of these two categories. Other offerings, such as restricted and special issues, were excluded to avoid generating less meaningful results (Albada et al., 2019a). This step led to the removal of an additional subset of IPOs. Additionally, Real Estate Investment Trusts (REITs) were excluded from the final sample due to their distinct financial statement presentation formats, which differ significantly from those of traditional IPOs. This exclusion resulted in the removal of another 104 IPOs. After applying these rigorous selection criteria, the final study sample comprises 350 IPOs. The data were meticulously collected from multiple sources to explore the signaling role of various ex-ante information factors on IPO initial returns, investor demand, and the information gap. This dataset provides valuable insights into how potential investors can enhance their decision-making processes when evaluating IPOs under conditions of pronounced information asymmetry. Additionally, the dataset offers opportunities for future research, including cross-country analyses that explicitly examine the influence of country-specific factors on underpricing outcomes. The data presented in this article are linked to several research studies, including “An Insight into the Signaling Role of Sharia Status: A Case from an Emerging IPO Market” (Albada, 2024), “Machine Learning Insights: Probing the Variable Importance of Ex-Ante Information” (Albada et al., 2025), and “Determinants of Investor Opinion Gap Around IPOs: A Machine Learning Approach” (Albada et al., 2024).