Bursa Malaysia IPO data 2004-2021

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).

本数据集的时间跨度为2004年1月至2021年12月，初始样本包含493宗首次公开发行（Initial Public Offerings, IPO）。为确保研究主题契合研究目标，本研究设置了多维度筛选标准。首先，由于本研究仅聚焦于采用固定定价法（fixed-price method）的IPO，因此剔除了采用簿记定价法（book-building method）的39宗初始样本。此外，本研究明确探究IPO市场中的投资者行为，仅纳入公开发售、售股发行或上述两类的混合形式的IPO；为避免得出无显著意义的研究结果，剔除了受限发行与特殊发行等其他类型的IPO（Albada等，2019a），此步骤进一步剔除了若干样本。另外，由于房地产投资信托（Real Estate Investment Trusts, REITs）的财务报表披露格式与传统IPO存在显著差异，因此将其从最终样本中剔除，本次共剔除104宗IPO。经上述严格筛选流程后，本研究最终样本共计350宗IPO。本数据集的多源数据均经细致采集，旨在探究各类事前信息（ex-ante information）因素对IPO初始收益率、投资者需求与信息缺口的信号传递作用。本数据集可为潜在投资者在高信息不对称情境下评估IPO时优化决策流程提供极具价值的参考视角。此外，本数据集还为后续研究提供了方向，例如可开展跨国分析，专门探究国别特质因素对IPO抑价（underpricing）结果的影响。本文所呈现的数据关联了多项研究成果，包括《关于伊斯兰教法合规地位（Sharia Status）的信号传递作用洞察：来自新兴IPO市场的案例》（Albada，2024）、《机器学习洞察：探究事前信息的变量重要性》（Albada等，2025）以及《IPO市场投资者意见缺口的决定因素：一种机器学习方法》（Albada等，2024）。

搜集汇总

背景与挑战

背景概述

该数据集涵盖2004年至2021年马来西亚的IPO数据，经过严格筛选（排除簿记建档、非公共发行和REITs等）后包含350个样本，专注于研究信息不对称下ex-ante因素对IPO表现的影响，并为投资者决策和跨国家分析提供基础。