本研究以 COVID-19 疫情為系統性衝擊斷點，探討 2010 年至 2025 年間台灣 IPO 市場在疫情前（2010–2019）、疫情中（2020–2022）及疫情後（2023–2025）三個子期間之承銷折價行為與長期績效是否發生結構性變遷，並進一步分析科技/電子產業與傳統產業之間之異質性效果。本研究以台灣經濟新報資料庫（TEJ）之 823 家上市櫃 IPO 公司為樣本，採用 Welch t 檢定、Wilcoxon 等級和檢定、OLS 複迴歸（HC1 穩健標準誤）、分量迴歸（Quantile Regression）及期間與產業交叉分析等多重實證方法進行檢驗。
實證結果可歸納為三點。第一，在短期定價效率方面，期間虛擬變數於平均（OLS）估計下並不顯著，但分量迴歸顯示，疫情期間之衝擊主要集中於原本就傾向高折價之 IPO 案件，亦即事前不確定性較高之新股，其折價幅度於高折價分量上顯著擴大。此一結果呼應 Beatty and Ritter (1986) 之事前不確定性假說，亦凸顯平均估計可能掩蓋之分量異質效應。第二，在長期績效方面，於疫情期間上市之 IPO 與疫情前並無顯著差異，但於疫情後上市之公司，其長期績效出現顯著且穩健之衰退，上市後一年與兩年之累積報酬皆明顯低於市場大盤，且此一衰退於分量迴歸之各分量上一致成立，支持 Ritter (1991) 之熱潮假說；尤其「疫情期間不顯著、疫情後始顯著」之時序錯位現象，為本研究最具差異化之觀察。第三，在產業異質性方面，疫情後科技／電子產業 IPO 之長期績效衰退幅度顯著小於傳統產業，兩者差距於疫情後明顯擴大，反映 COVID-19 加速數位轉型下科技產業所展現之結構性韌性。
本研究之貢獻有三：實證上首次以 2010–2025 年完整序列在台灣 IPO 樣本系統性區分疫情前、中、後三期間，填補蔡季衡 (2025) 之研究缺口；方法論上將分量迴歸應用於疫情衝擊之多期間分析，揭示 OLS 平均估計可能掩蓋之異質效應；理論上同時驗證事前不確定性假說、熱潮假說與產業異質性觀察於台灣後疫情樣本中之適用性，並透過時序錯位現象之揭示，深化熱潮假說之動態理論意涵。

This study examines whether Taiwan's IPO market experienced structural changes in underpricing and long-run performance across three sub-periods defined by the COVID-19 pandemic: pre-pandemic (2010-2019), during-pandemic (2020-2022), and post-pandemic (2023-2025), with particular attention to heterogeneity between technology and traditional industries. Using 823 IPO firms from the TEJ database, it applies difference tests, OLS regressions with robust standard errors, quantile regressions, and period-by-industry analyses.
Three findings emerge. First, the pandemic's effect on short-run underpricing is insignificant on average but concentrated among firms already prone to high underpricing, widening significantly at the upper quantiles, consistent with the ex-ante uncertainty hypothesis. Second, IPOs listed during the pandemic do not differ from pre-pandemic ones, whereas post-pandemic IPOs show a robust long-run deterioration; this temporal mismatch, insignificant during the pandemic yet significant afterward, is the study's most distinctive observation. Third, the post-pandemic decline is significantly milder for technology IPOs, with the industry gap widening.
The study's contributions are threefold: it is the first to partition the pandemic into three sub-periods using complete 2010-2025 Taiwanese IPO data, addressing the gap identified by Tsai (2025); it extends quantile regression to a multi-period shock framework that reveals effects masked by OLS; and it jointly validates the ex-ante uncertainty, hot-issue market, and industry-heterogeneity perspectives in the post-pandemic Taiwanese context.

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