When are announced Initial Public Offerings completed?
Marie-Claude Beaulieu
and
William R. Sodjahin
2008 Annual Conference on Real Options Rio de Janeiro
Agenda
1. 2. 3. Motivation and literature Research questions The model Optimal timing for IPO completion Underpricing Empirical Analysis Determinants of the IPO waiting period Implications of the length of the IPO waiting period Conclusion
4.
5.
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Motivation and literature
Firms intent on going public face a timing issue.
Firms tend to undertake IPOs when market conditions are favorable (see Ritter, 1984 JB and Lowry, 2003 JFE among others),
This leads to fluctuations in IPOs volume over time (i.e., clustering of IPOs).
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Motivation and literature
When information asymmetry is high, firms are more likely to delay their IPOs and choose alternative types of financing (Lowry, 2003 JFE). The timing of IPOs is also linked with product market competition (see Maksimovic and Pichler, 2001 RFS; among others)
There may be an incentive to delay the offerings since some competitors that are not public yet can benefit from information produced.
→
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Motivation and literature
Logue (1973) positively related underpricing to the market return during the waiting period. Others papers study the partial adjustments of IPO prices to private and public information learned during the waiting period (e.g., Hanley, 1989; Edelen and Kadlec, 2005). However, these authors did not tackle the determinants of the length of the waiting period itself and its implications. Why is it interesting to study the IPO waiting period? Information asymmetry and adverse selection risk are prominent in the IPO market, The investigation of the information content of the IPO waiting period length will contribute to alleviating these uncertainties. The external preparation delay is important because a long waiting period can alleviate the concerns of public investors regarding the issuer’s financial health.
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Research Questions
This paper seeks to find answers to two fundamental research questions: What are the factors that explain the length of the IPO waiting period? Who benefits from the IPO waiting period length?
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The model
Formalizing the optimal timing for IPO completion requires taking into account three key issues:
The role play by the underwriting syndicate size The product market competition and its possible impact on the IPO timing The particularity of firms such as high-tech firms which bet more on the future and place greater value on future cash flows than present cash flows.
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The model
Our model captures these issues by introducing in a general real option framework for IPOs used by authors like Draho (2000) and Brada and Ma (2007):
Underwriting syndicate size → IPO cost According to Corwin and Schultz (2005) it is not always costless to add additional comanagers to the IPO syndicate.
A downward jump → Dynamics of the profit of the private firm The risk of profit drop due to product market competition can affect the IPO timing (see Maksimovic and Pichler, 2001 RFS, among others). Time inconsistency preference or patience factor → discount function To account for the particularity of firms such as high-tech firms which value more future cash flows than present cash flows (see Loughran and Ritter, 2004 among others)
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The model
Time is divided in two: the present period and all future periods. The intertemporal discount function
e ( s t ) if s t , T D (t , s ) i ( s T ) if s T , , e
i
(T t ) has an exponential distribution with mean
1 .
1,1
The dynamics of the profit of private firm follows a mixed diffusion process
d t
The firm value
t
dt dz dq
( t ) ET E e
i T
i ( s t )
t
s ds e
T
i ( s T )
i t s ds , i i i p, m
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The model
Then net proceeds to the private firm from the IPO, ( t ) , are
m t ( t ) 1 ( S ) C. m m
The entrepreneur chose the time τ(π*) that maximize the value:
F ( t ) E t
t ( *)
s e
p
( s t )
ds e ( *)( *) | t .
i
The trigger point * is critical profit level at which the firm goes public and ( *) is the first time the process t reaches * .
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The model
Proposition 1: The value of the firm to the entrepreneur prior to the IPO realization is
p F ( ) A , p p
where 1 and solution to the nonlinear equation:
1 2 ( 1) ( p p ) ( p ) (1 ) 0 2
The trigger point * is given by
*
1
C
m 1 ( S ) m m
p
p
p
1
.
where is the
The entrepreneur chooses to execute the IPO at ( *) inf s t \ t *, announcement date.
t
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The model
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The model
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The model
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The model
Underpricing The underpricing is derived under the assumption that the price offer is the price as estimated by the entrepreneur. We define the initial underpricing return UP as the difference, at the trigger point * , between the market value which is the net value of the firm to the public investors n ( *) and the offer value which is the net value of the firm to the entrepreneur n ( *) , normalized by the latter.
m p
m n m ( *) n p ( *) 1 ( S ) ( *) C UP 1. n p ( *) 1 ( S ) p ( *) C
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The model
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Empirical Analysis
Data Databases: CRSP, COMPUSTAT, ISS (Institutional Shareholder Services ) and Jay Ritter’s website. Sample: Our primary sample is composed of 690 IPOs that occurred on NASDAQ, NYSE and AMEX between January 2003 and December 2005. The sample begins in January 2003 because of ISS data availability
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Empirical Analysis
Variable description
IPO waiting period, calendar days
Mean
113.48
Median
95
Standard Deviation
79.72
First quartile
71
Third quartile
131
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Empirical Analysis
Question 1: What factors determine the IPO waiting period?
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Empirical Analysis
Two-stage least-squares (2SLS) regressions explaining syndicate size and waiting period
We conduct a two-stage least squares (2SLS) regression of the waiting period determinants, allowing the choice of syndicate size to be endogenous. In fact, while the waiting period may depend on syndicate size, the choice of syndicate size may also depend on how long firms are willing to wait.
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Table III: Two-stage least squares (2SLS) regressions explaining syndicate size and the waiting period Panel A: Univariate evidence Syndicate size Mean No. lead managers =1 No. lead managers >1 p-Value (comparison tests)
Panel B: Multivariate evidence
IPO waiting period Median 91.5 99.5 0.004*** No. of observations 424 266
106.43 124.73 0.005***
First stage Syndicate size ( S ) Log( S ) -0.661*** (0.000) 0.129*** (0.000) -0.015 (0.785)
Second stage IPO waiting period ( * ) Log(1+ * ) (1) 4.974*** (0.000) Log(1+ * ) (2) 4.848*** (0.000)
Intercept Offering characteristics Offering size Gross spread Underwriter prestige Underwriter rank Price revision Upward price revision Variables of the model Syndicate size ( S ) (Instrument) Downward jump risk ( ) High-tech ( ) Cash pressure and investment Cash/Sales Investment Leverage Leverage Firm size Ln(assets) Market NYSE/AMEX listing
-0.033 (0.509) 0.059 (0.603) -0.039* (0.086) -0.027 (0.124)
0.035*** (0.001) -0.060* (0.086)
-0.017 (0.797) 0.241*** (0.003) -1.001 (0.855) 0.173** (0.047) -0.181** (0.050)
0.0004*** (0.005) -1.061*** (0.000) -0.0001*** (0.000)
0.020* (0.083) 0.168*** (0.000)
-0.009 (0.672)
0.028 (0.702)
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Empirical Analysis
IPO waiting period and post-IPO managers’ and directors’ incentives
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IPO waiting period and Post-IPO managerial and directors’ incentives Panel A: Univariate evidence IPO Waiting period Mean Median 150 89 118.08 102 133.93 115 114.85 99 252.71 229 115.89 101 133.82 111 104.65 92 131.53 110 105.24 92 Diff. mean p-value 0.514 0.052* 0.043** 0.000*** 0.000***
Directors ownership Directors and officers ownership (5%-30%) Stock option plan Loan for option exercise Overall Incentives
Yes No Yes No Yes No Yes No Yes No
Panel B: Multivariate evidence Dependent Variable =log(1+IPO waiting period) Model 1 Intercept Marketing Timing Hot market dummy Cold market dummy Market conditions-3months Post IPO performance Aftermarket performance Post IPO incentives Dirsubstock Dirownership Stockplan Loansoption Overall Incentives 0.152 0.475 0.068 0.305 0.594*** 0.002 0.251*** 0.001 0.249 *** 0.001 0.234 0.644 0.136 0.790 -0.285*** 0.000 -0.083 0.352 -0.021 0.690 -0.234*** 0.006 -0.120 0.184 -0.021 0.683 4.540*** 0.000 Model 2 4.564 *** 0.000
Diagnostic R2 F-Statistic (coeff.=0) No. of observations 9.91% 7.420*** 0.000 359 6.33% 7.610*** 0.000 366
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Empirical Analysis
Question 2: Who benefit from this period?
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Table V: IPO waiting period and underpricing Panel A: Univariate evidence IPO waiting period Mean IPO waiting period ? median IPO waiting period < median p-Value (comparison tests) Panel B: Multivariate evidence Underpricing (UP) (UP1) Intercept Offering characteristics Offering size Gross spread Underwriter prestige Underwriter rank Price revision Upward price revision Variables of the model IPO waiting period (Log(1+ * )) Syndicate size ( S ) Downward jump risk ( ) High-tech ( ) Leverage Leverage Firm size Ln(assets) -0.0005* (0.064) -1.230** (0.010) 23.94% 16.57*** (0.000) 471 -0.0005** (0.048) -1.678*** (0.001) 24.49% 15.00*** (0.000) 471 0.244 (0.809) -0.858 (0.315) 225.71** (0.050) 0.306 (0.854) 0.285 (0.779) -1.501 (0.120) 261.70** (0.029) 0.088 (0.957) 9.565*** (0.000) 9.420*** (0.000) 0.642 (0.220) 0.451 (0.393) 11.061*** (0.000) 1.995** (0.031) 10.255*** (0.000) -7.911 (0.226) (UP2) -12.496* (0.069) 10.32 7.83 (0.149) Underpricing Median 4.00 0.36 (0.019)**
Diagnostics R-squares F-statistic No. of observations
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Empirical Analysis
Effects of IPO waiting period on belief heterogeneity, pricing and Money Left on The table (MLT)
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Turnover, Pricing, Money Left on The table (MLT) and IPO Waiting period Estimation method Dependant Variable Intercept Offerings size IPO waiting period Market conditions-3months Ln(assets) High-Tech STD return EPS-1*0.01 Diagnostics R2 (pseudo for Logit) F-Statistic (Wald test: coeff.=0)
2
OLS Turnover 0.222 0.119 0.105*** 0.000 -0.081** 0.046 0.077 0.712 -0.029*** 0.000 0.070 0.144 1.292 0.423 0.002*** 0.000
Logit Pricing at the Upper limit -2.329 0.041 0.751*** 0.000 -0.177 0.272 -1.696 0.418 -0.213** 0.014 -0.054 0.827 1.851 0.824 -0.190 0.611
**
OLS MLT -120.27*** 0.001 30.99*** 0.000 1.054 0.804 -17.40 0.583 -4.824*** 0.000 4.259 0.417 466.12** 0.044 -0.059 0.789
No. of observations
9.04% 12.58*** 0.000 452
4.87% 12.58*** 0.002 437
21.09% 5.100*** 0.000 451
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Empirical Analysis
Does the IPO waiting period affect the probability of changing underwriter in a subsequent SEO?
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Does IPO waiting period affect the probability of switching underwriter in a subsequent SEO? Dependent Variable= 1 if issuer changes lead underwriter in a subsequent SEO Model 1 Intercept IPO waiting period Size of the SEO -0.625 0.763 0.668** 0.021 -0.888*** 0.000 -0.579** 0.027 0.718** 0.049 Model 2 -0.604 0.771 0.665** 0.022 -0.855*** 0.001 -0.611** 0.026 0.140* 0.051 -0.005 0.401 Model 3 -6.146* 0.069 0.608** 0.043 -0.931*** 0.000 -0.533** 0.026 0.644** 0.044 1.127** 0.013
IPO underwriter rank SEO underwriter rank Underpricing Log (days from IPO to SEO announcement)
Diagnostics Pseudo R2 Wald test coeff.=0
2
No. of observations
13.86% 24.47*** 0.000 180
13.76% 24.53*** 0.000 179
17.68% 31.70*** 0.000 180
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Empirical Analysis
Probability of switching syndicate size between the IPO and the first SEO
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Multinomial Logistic Regressions modeling Syndicate Size Switching Decision
Scenario1 (1)
Offerings characteristics Underpricing Log IPO size Log SEO size IPO gross spread SEO gross spread Syndicate characteristics IPO syndicate size IPO underwriter rank SEO underwriter rank Underwriter switch =1 if switch =1 if for higher rkd Waiting periods IPO waiting period Log (days from IPO to SEO ann) SEO waiting period Intercept Diagnostics McFadden pseudo-R2 Wald test (all coeff.=0)
2
Scenario2 (3) (1) (2) (3) (1)
Scenario3 (2) (3)
(2)
-0.037 0.222 -1.994*** 0.002
-0.066 0.122 -2.259*** 0.004
-0.058 0.144 -2.433** 0.021
0.00005 0.994 0.107 0.709
0.002 0.789 0.357 0.258
-0.005 0.536 0.794** 0.044
0.037 0.228 2.101*** 0.003
0.068 0.114 2.617*** 0.001
0.053 0.193 2.433*** 0.003
0.987 0.537
1.166 0.522
3.210** 0.019 4.513*** 0.000 0.247 0.693
-0.657 0.359
-0.752 0.304
-1.051* 0.079 -1.747*** 0.003 0.551** 0.044
-1.643 0.334
-1.918 0.309
-3.210*** 0.004 -4.513*** 0.000 0.304 0.651
3.975*** 0.000 0.208 0.617
5.017*** 0.000 -0.175 0.758 2.090** 0.033
-1.065** 0.016 -0.066 0.582
-1.421*** 0.003 0.405** 0.049 1.636*** 0.000
-5.041*** 0.000 -0.274 0.519
-6.439*** 0.000 0.581 0.325 -0.453 0.662
2.126* 0.070 2.340** 0.046 0.247 0.693 -1.494*** 0.006 -10.516 0.289 44.87% 129.32*** 173
3.484*** 0.000 -0.200 0.615 -0.500 0.373 0.095 0.748 -3.493 0.371 44.87% 129.32*** 173
1.358 0.274 -2.540** 0.036 -0.126 0.917 1.589*** 0.008 7.023 0.499 44.87% 129.32*** 173
-0.273 0.761 -1.206*** 0.000 2.041 0.769 30.17% 100.25*** 188
-0.082 0.941 -1.361*** 0.001 2.583 0.760 39.00% 127.99*** 187
0.190 0.647 -0.279 0.113 0.775 0.792 30.17% 100.25*** 188
-0.282 0.554 -0.259 0.192 -1.902 0.574 39.00% 127.99*** 187
0.463 0.629 0.927** 0.010 -1.266 0.863 30.17% 100.25*** 188
-0.200 0.863 1.102** 0.010 -4.485 0.610 39.00% 127.99*** 187
No. of observations
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Empirical Analysis
Does the market react at the subsequent SEO to syndicate size switch and SEO waiting period?
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Does the market react to the subsequent SEO syndicate size change and SEO waiting period? Dependent Variable: Market adjusted SEO first-day return Model 1
Offerings characteristics Log SEO size SEO gross spread Syndicate characteristics SEO syndicate size SEO underwriter rank Waiting periods SEO waiting period Risk STD return Prospect indicator Market-to-book Syndicate size and underwriter switch =1 if reduce syndicate size =1 if switch for lower ranked Intercept Diagnostics R2 F-Statistic No. of observations -0.010 0.598 5.39% 2.97*** 169 0.009*** 0.007 -0.253** 0.022 0.0002 0.420 -0.004 0.338 0.005 0.437 0.003 0.480 -0.001 0.800 0.008** 0.013 -0.252** 0.020 0.0002 0.438 -0.003 0.749 0.001 0.897 -0.005 0.786 5.26% 2.39** 168 -0.010 0.606 5.42% 2.56** 169 0.009*** 0.007 -0.255** 0.019 0.0002 0.437
Model 2
-0.003 0.569 0.004 0.485
Model 3
-0.004 0.352 0.005 0.440 0.003 0.496
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Conclusion
All in all,
Our findings suggest that issuers and underwriters benefit from shorter IPO waiting period because of cash pressure, investment and leverage for the issuers and the chances to be maintained for a subsequent SEO for the underwriters. Contrary, investors benefit from longer waiting periods since a longer IPO waiting period reduces beliefs’ heterogeneity which means that the information asymmetry that could be detrimental to investors is reduced. Furthermore, this is also illustrated by the fact that the market reacts positively to a long SEO waiting period.
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MERCI!!!
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