2009Research in financeRequires access

Competition in IPO underwriting: Time series evidence

Mukesh Bajaj, Andrew H. Chen, Sumon C. Mazumdar

Open publisher page 2 citations

Abstract

Chen and Ritter (2000) documented that underwriter spreads for recent US initial public offerings (IPOs) in $20 million range as well as much larger IPOs in the $80 million range are clustered at 7%. This observation has led to a Department of Justice (DOJ) enquiry into potential price fixing by underwriters. We demonstrate through a times series analysis that IPOs have tripled in size and become much riskier over time. A pooled data analysis can therefore mask evidence of competition in the market. We find that spread clustering is not a recent phenomenon. Over time, clustering at 7% has increased as clustering above 7% has declined. IPO spreads have declined significantly over time as the firms going public more recently are riskier, underwriting efforts have increased and recent IPOs are much larger than IPOs in the past. Controlling for time trends, larger IPOs have lower average spreads. The market for underwriting IPOs seems to be competitive with entry of new firms during the hot markets.

About this research paper

What this paper is about

Chen and Ritter (2000) documented that underwriter spreads for recent US initial public offerings (IPOs) in $20 million range as well as much larger IPOs in the $80 million range are clustered at 7%. This observation has led to a Department of Justice (DOJ) enquiry into potential price fixing by underwriters. We demonstrate through a times series analysis that IPOs have tripled in size and become much riskier over time. A pooled data analysis can therefore mask evidence of competition in the market. We find that spread clustering is not a recent phenomenon. Over time, clustering at 7% has increased as clustering above 7% has declined. IPO spreads have declined significantly over time as the firms going public more recently are riskier, underwriting efforts have increased and recent IPOs are much larger than IPOs in the past. Controlling for time trends, larger IPOs have lower average spreads. The market for underwriting IPOs seems to be competitive with entry of new firms during the hot markets.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Chen and Ritter (2000) documented that underwriter spreads for recent US initial public offerings (IPOs) in $20 million range as well as much larger IPOs in the $80 million range are clustered at 7%. This observation has led to a Department of Justice (DOJ) enquiry into potential price fixing by underwriters. We demonstrate through a times series analysis that IPOs have tripled in size and become much riskier over time. A pooled data analysis can therefore mask evidence of competition in the market. We find that spread clustering is not a recent phenomenon. Over time, clustering at 7% has increased as clustering above 7% has declined. IPO spreads have declined significantly over time as the firms going public more recently are riskier, underwriting efforts have increased and recent IPOs are much larger than IPOs in the past. Controlling for time trends, larger IPOs have lower average spreads. The market for underwriting IPOs seems to be competitive with entry of new firms during the hot markets.

Key concepts: Underwriting, Initial public offering, Competition (biology), Business, Monetary economics, Finance, Economics, Biology

Related papers

Back to paper searchBrowse research topicsOriginal source
Competition in IPO underwriting: Time series evidence — Research Paper | ScholarLens