Information Production by Institutions and Information Extraction by Underwriters in Hybrid IPO Auctions

We analyze the informational properties of hybrid IPO auctions using a large and unique database of institutional bids from Chinese IPO auctions. We find strong evidence of information production by institutions about the intrinsic values of IPO firms and of underwriters extracting and using this information in IPO pricing. The IPO offer price is more sensitive to bids from institutions able to produce more precise information. In particular, the offer price is more sensitive to bids from domestic institutions, compared to bids from foreign institutions who likely have less knowledge or experience about Chinese firms and the Chinese financial market due to geographic, legal, and cultural distances. Institutional bidding information also has predictive power for IPO initial returns and long-run post-IPO stock returns. Finally, institutions are compensated for their information production: institutions able to produce more precise information get significantly larger allocations in better performing IPOs; this difference in IPO allocations between better and worse performing IPOs is greater for institutions able to produce more precise information. Overall, “dirty” uniform price hybrid IPO auctions appear to be a reasonably effective alternative to the traditional book-building IPO mechanism, so that our analysis has important implications for IPO mechanism reform in developing countries.

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Institutions

Publication Details

Journal
Review of Corporate Finance
Published
2025-07-14
DOI
https://doi.org/10.1561/114.00000076
Citations
1
Primary Topic
Auction Theory and Applications
Type
article
Field-Weighted Citation Impact
2.11
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article

Information Production by Institutions and Information Extraction by Underwriters in Hybrid IPO Auctions

Chaopeng Wu, Qianqian Yu, Pengfei Ma, Thomas J. Chemmanur
1 citations
Review of Corporate Finance
Auction Theory and Applications
2.11
article

Information Production by Institutions and Information Extraction by Underwriters in Hybrid IPO Auctions

Chaopeng Wu, Qianqian Yu, Pengfei Ma, Thomas J. Chemmanur
article en
1 citations

Abstract

We analyze the informational properties of hybrid IPO auctions using a large and unique database of institutional bids from Chinese IPO auctions. We find strong evidence of information production by institutions about the intrinsic values of IPO firms and of underwriters extracting and using this information in IPO pricing. The IPO offer price is more sensitive to bids from institutions able to produce more precise information. In particular, the offer price is more sensitive to bids from domestic institutions, compared to bids from foreign institutions who likely have less knowledge or experience about Chinese firms and the Chinese financial market due to geographic, legal, and cultural distances. Institutional bidding information also has predictive power for IPO initial returns and long-run post-IPO stock returns. Finally, institutions are compensated for their information production: institutions able to produce more precise information get significantly larger allocations in better performing IPOs; this difference in IPO allocations between better and worse performing IPOs is greater for institutions able to produce more precise information. Overall, “dirty” uniform price hybrid IPO auctions appear to be a reasonably effective alternative to the traditional book-building IPO mechanism, so that our analysis has important implications for IPO mechanism reform in developing countries.

Review of Corporate FinanceVol. 5(3-4)
Boston College (US), Lehigh University (US), Xiamen University (CN), Xiamen University of Technology (CN), Singapore Management University (SG)
Decent work and economic growth
Openalex Percentile: Top 12%
Auction Theory and Applications
2.11
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Information Production by Institutions and Information Extraction by Underwriters in Hybrid IPO Auctions — Chaopeng Wu, Qianqian Yu, et al. · Review of Corporate Finance (2025) | TGRS Research Map | TGRS