Why does IPO volume fluctuate so much?☆
Introduction
Both the number of initial public offerings (IPOs) and the total proceeds raised in these offerings vary substantially over time. Fig. 1 illustrates the extreme fluctuations in IPO volume over 37 years. For example, between 1973 and 1979 only 329 firms went public. In comparison, 2,644 firms went public in the seven years preceding 1973, and 3,805 firms went public during the seven years after 1979. Notably, this variation is far in excess of the variation in capital expenditures, suggesting that factors other than financing requirements have a substantial effect on the timing of a firm's IPO. The objective of this paper is to explore the underlying causes of this time-series variation in IPO volume and to determine whether the observed fluctuations are consistent with efficient markets.
While there exists a considerable body of literature on IPOs, the variation in IPO volume has received relatively little attention, and our understanding of these fluctuations is limited. Ibbotson and Jaffe (1975) and Ibbotson 1988, Ibbotson 1994 show the substantial fluctuations in IPO volume, but these studies do not examine the underlying cause of this variation. Lowry and Schwert (2002) note that IPO volume tends to be higher following periods of especially high initial returns, and their findings suggest that this relation is driven by information learned during the registration period. Specifically, more positive information leads to higher initial returns for those offerings and more companies filing to go public soon thereafter. The findings of Rajan and Servaes 1997, Rajan and Servaes 2003, Lee et al. (1991), Lerner (1994), and Pagano et al. (1998) suggest that IPO volume is related to various forms of market irrationality. Consistent with this finding, Lerner et al.'s (2003) results suggest that periods of low IPO volume represent times when private firms “can not” access the equity markets on favorable terms, thus forcing them to enter into less favorable financing arrangements. Specifically, they find that during periods of low equity issuance the agreements signed between small biotechnology firms and major corporations are less successful and more likely to be renegotiated, compared to those agreements signed during periods of higher equity issuance.
Notably, only Pagano et al. (1998) systematically test the relative power of several potential determinants of IPO volume, and their study focuses on the Italian market over an 11-year period, during which only 139 firms went public on the Milan Stock Exchange. They find that companies are more likely to have IPOs when the average market-to-book (MB) ratio of public firms in their industry is higher. Further, they note that the high MB ratio does not seem to reflect investment opportunities, as companies tend to go public following (rather than prior to) periods of high investment. They interpret their findings as indicating that companies time their IPOs to take advantage of industry-wide overvaluations, rather than to finance future growth.
The current study fills a gap in the literature by employing a 37-year time series of U.S. IPO volume and investigating the extent to which efficient- versus inefficient-market factors can explain the observed fluctuations. My primary objective is to explore three potential explanations for the variation in IPO volume. One common perception regarding IPO volume is that it simply varies with the business cycle. During economic expansions, economy-wide demand for capital is higher and more firms therefore go public. A second widely held viewpoint is that the variation in IPO volume is primarily driven by changes in investor optimism. The popular press contains many examples of this viewpoint. For example, “The [current] rule in the IPO market seems to be: Buy it at any price” (Wall Street Journal, May 20, 1996, p. C2), and “When [investors] get bearish, you can't go public. But when they go bullish, just about anyone can go public.” (Wall Street Journal, April 19, 1999, p. C1.) Finally, the lower numbers of IPOs during periods of high uncertainty potentially reflect a lemons problem, and this is a third explanation of the observed fluctuations in IPO volume. Variation in investors’ uncertainty regarding the true value of firms may cause the adverse-selection costs and therefore IPO volume to fluctuate over time. It is possible that more than one of these factors are important determinants of IPO volume, and I examine all three factors in my analysis.
The investigation of these factors is formalized into three hypotheses, which are developed in more detail in the following section. An examination of the time-series as well as cross-sectional patterns in the data indicates that industry dynamics play an important role in firms’ decisions to go public. Consequently, empirical tests are conducted at both the aggregate level and at the industry level. The specific tests used to discriminate between the importance of capital demands, adverse selection costs, and investor sentiment are described below.
As a first test of the three proposed explanations, I develop proxies for private firms’ aggregate capital demands, the adverse-selection costs of issuing equity, and the level of investor optimism. I then investigate the ability of these proxies to explain IPO volume. Results indicate that all three factors contribute to the observed fluctuations in the number of firms going public over time, with capital demands and investor sentiment being the most important.
It is possible that the importance of adverse selection is camouflaged in the aggregate analysis. More generally, any of the above factors may play a larger role at the industry level. However, inferences from the industry analysis are similar to those from the aggregate-level data. Empirical tests again provide some support for all three factors, but firms’ demands for capital and investor sentiment are the most highly significant.
The final set of tests focuses on post-IPO stock returns. This analysis has the advantage of not relying on proxies. If the number of IPOs is affected by changes in the level of investor sentiment, then post-IPO returns will be lowest following the high optimism, high IPO volume periods, when investors overpay the most. While I do not find a significant relation between abnormal IPO returns and IPO volume, results do show that IPO volume is significantly negatively related to both raw IPO post-issue returns and to post-issue market returns. Firms seem to successfully go public when a broad class of firms, often the entire market, is valued especially highly.
In summary, results indicate that changes in firms’ demands for capital and changes in the level of investor optimism explain a substantial portion of the variation in IPO volume. Adverse selection costs are marginally significant and appear to be of secondary importance. Notably, the prior literature has focused on only one potential determinant of IPO volume, investor sentiment. While sentiment does appear to affect the timing of IPOs, evidence indicates that it is not the only relevant factor.
Section 2 develops the hypotheses in more detail. Section 3 describes the data and analyzes the time-series properties of IPO volume. Section 4 examines the relation between the observed fluctuations in IPO volume and time-series variation in private firms’ demands for capital, the adverse-selection costs of issuing public equity, and the level of investor optimism. Section 5 investigates the relation between IPO volume and post-issue stock returns. Finally, Section 6 concludes.
Section snippets
Hypotheses
This section discusses in more detail the three proposed explanations for variation in aggregate IPO volume: the capital demands hypothesis, the information asymmetry hypothesis, and the investor sentiment hypothesis.
The capital demands hypothesis says that variation in IPO volume is caused by changes in private firms’ aggregate demand for capital. General economic conditions vary over time. When conditions are better and expected growth in the economy is higher, companies tend to have higher
Data and test specification
Between 1960 and 1996, 12,821 companies went public.2 Fig. 1 shows the variation in the number of IPOs over this period, as a fraction of the total number of public firms (in thousands) at the end of the previous quarter. This 37-year time series enables me to examine the intertemporal relation between the number of companies going public and variation in market conditions. Ending the
Time-series tests
This section investigates the timing of IPOs, using proxies for firms’ demands for capital, information asymmetry, and investor sentiment. I first describe each set of proxies and then discuss the empirical results. Descriptive statistics on each of the proxies are shown in Table 2. Because the majority of quarterly regressions are based on 1972–1996 and annual regressions on 1961–1996, the quarterly and annual statistics in Table 2 are calculated over these two separate time periods.
Post-IPO stock returns
This section investigates post-IPO stock returns as an additional means of gaining insight on the determinants of IPO volume. Unlike the time-series regressions, this analysis has the advantage of not relying on proxies. If the fluctuations in IPO volume are driven by changes in investor sentiment, then post-IPO returns will be lowest following the high optimism, high IPO volume periods, when investors overpay the most. While the level of post-IPO abnormal stock returns has received
Conclusions
Despite the large literature on IPOs, we still have relatively little understanding of why the number of IPOs fluctuates so substantially over time. This paper seeks to shed light on this issue. Specifically, I investigate the factors that lead so many companies to have IPOs during some periods, versus so few during other times. Results indicate that companies’ demand for capital and the level of investor sentiment explain a significant amount of the variation in IPO volume. In economic terms,
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I gratefully acknowledge the guidance and advise of my dissertation committee: G. William Schwert (chairman), Michael Barclay, and Jay Shanken. Helpful comments and suggestions were also received from George Benston, James Brickley, Harry DeAngelo, Laura Field, Andreas Gintschel, Christoph Hinkelmann, S.P. Kothari, Ken Kotz, Tim Loughran, Ronald Masulis, Harold Mulherin, Kevin J. Murphy, Jay Ritter, Richard Sansing, Susan Shu, Clifford Smith, René Stulz (the editor), Denis Suvorov, Paula Tkac, Jerry Zimmerman, an anonymous referee, and seminar participants at the University of Rochester, the University of Texas at Austin, the University of Southern California, Harvard, Dartmouth, Emory, Vanderbilt, Notre Dame, Pittsburgh, Penn State, and UCLA.