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06.11.2023

IPO price formation and analyst coverage

verfasst von: Joseph Weber, Michael Willenborg, Biyu Wu, Yanhua Sunny Yang

Erschienen in: Review of Accounting Studies

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Abstract

Given important market and regulatory changes over the past two decades, we re-examine the relation between IPO pricing and coverage by sell-side stock analysts. Our design builds on the well-documented finding of the partial adjustment phenomenon, in that the IPO offer price revision following the road show is highly predictive of the first-day return. We provide new insights to the literature on IPO analyst coverage/recommendations. With respect to coverage, we find no evidence to suggest that the decisions of affiliated analysts vary with IPO pricing. As for unaffiliated analysts, our results indicate that their coverage decisions are driven much more so by the offer price revision than by the return on the first-trading day. We interpret this as consistent with the view that unaffiliated analysts consider institutional investor demand revealed during book-building as an important input into their decision of whether to cover an IPO issuer. With respect to recommendations, our findings suggest that, for IPOs after the JOBS Act of 2012, lead underwriters appear to bring on co-managers that seemingly pre-commit to provide favorable recommendations, particularly for IPOs that do not experience an increase in their offer price after the road show or an increase in their share price on the first trading day.

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Fußnoten
1
For example, in contrast to a 24% frequency in Aggarwal et al. (2002), just 6% of our sample classify as extra-hot IPOs (i.e., defined by Aggarwal et al. as an initial return greater than 60% using the opening price on the first day).
 
2
Put another way, while most literature (e.g., Rajan and Servaes 1997) suggests that an IPO issuer that goes public at $20 per share and ends the first trading day at a price of, say, $28 will receive higher analyst coverage than if the closing price is, say, $21, we show that it is more important to understand how the issue arrives at the $20 IPO price (i.e., whether the offer price moves up or down from the IPR mid-point and, if so, by what extent).
 
3
We examine the market reaction to the initiation of analyst coverage for our sample, which we partition by whether the IPO experiences an upward price revision. The results (not tabulated) indicate that the positive market reaction when analysts initiate favorable coverage is damped for IPOs with a positive revision. This is consistent with the view that aspects of the higher institutional investor demand have already been priced.
 
4
The latter distinction is important because the early 2000s regulatory events pertain to affiliated analysts.
 
5
Das, Guo and Zhang (2006) examine a sample of 1986–2000 IPOs and also report a positive association between IPO underpricing and total analyst coverage within the first six months.
 
6
This interpretation is relatively common in the literature. For example, per Mola, Rau and Khorana (2013, p. 668) “Firms value analyst coverage. CEOs spend time and resources attempting to obtain and maintain coverage from sell-side analysts, who typically work for a brokerage house and provide investment research to their employer’s clients. Rajan and Servaes (1997) and Cliff and Denis (2004) show that firms pay for the extent and quality of analyst coverage by underpricing their initial public offerings (IPOs).”
 
7
O’Brien and Tan (2015) study the effect of geographic proximity on analyst decisions to provide coverage within two years of a company’s IPO. They control for underpricing and report that it has a positive association with total analyst coverage. However, because their sample spans 1996–2009, it is unclear whether this relation is evident for the later periods of their sample or if, as with Bradley et al. (2008) and Liu and Ritter (2011), it is no longer evident.
 
8
Title V of the Sarbanes-Oxley Act of 2002 addresses analyst conflicts of interest and specifies similar restrictions.
 
9
Of these, the ability to attend pitch meetings may be particularly problematic. Per Lattman and Craig (2013, p. B1), “some analysts say Wall Street is slipping back into its old ways. Today, companies routinely interview analysts when selecting bankers to underwrite their IPOs. During these meetings, the analysts say, they increasingly feel pressure to say the right things to curry favor with a company’s management and owners. They also see themselves as participating in their banks’ efforts to win business, a potential breach of government regulations.”
 
10
Per Ritter (2011, p.356), “When an IPO uses bookbuilding, the single variable that has the greatest explanatory power for first-day returns is the revision in the offer price from the midpoint of the original file price range … If the offer price is revised down, on average there is very little underpricing. But if the offer price is revised upwards, there is on average fairly severe underpricing. Thus, the adjustment of the offer price can be used to forecast the first-day return, a pattern that is known as the partial adjustment phenomenon.” This is also consistent with the earlier view of Ritter and Welch (2002, p. 1803) that “the solution to the underpricing puzzle has to lie in focusing on the setting of the offer price, where the normal interplay of supply and demand is suppressed by the underwriter.”
 
11
Per Qian et al. (2022, p. 16), “Price revision is naturally a proxy for investor demand when the direct demand information is not publicly available for bookbuilding IPOs. There is evidence that price revision becomes insignificant when demand information is directly controlled for (see Cornelli and Goldreich (2003) for a sample of international bookbuilding IPOs underwritten by European banks; Qian et al. (2014) for a sample of U.S. bookbuilding IPOs).” In addition, see Ljungqvist and Wilhelm (2003, p. 724-725) and Loughran and Ritter (2002, p. 414). There is anecdotal support of price revision as a proxy for institutional investor demand. On his website (https://​site.​warrington.​ufl.​edu/​ritter/​ipo-data/​), Professor Ritter provides institutional investor data for eight book-built IPOs from 1997–2000. Using these data, we calculate a variable equal to the number of shares demanded by institutional investors divided by the number of shares offered in the prospectus disclosing the initial price range. The Pearson correlation between this variable and the offer price revision is 0.92 (p-value < 0.00).
 
12
Because of this, we cannot reliably determine the issuer’s age. In addition, many of these issuers present financial statements that pertain to successor and predecessor entities, and we cannot reliably compute certain ratios.
 
13
For example, Bradley et al. (2003) study the initiation of analyst coverage at expiration of the quiet period and report the percentage of IPOs with analyst coverage increases from 57.9% in 1996 to 95.6% in 2000.
 
14
For our primary analysis, we define the HighTech variable as per SDC. However, our results are essentially identical to those we table when we use Loughran and Ritter’s (2004) high-technology industry definition.
 
15
We specify the natural logarithm of Valuation (the mid-point of the IPR times the number of post-IPO shares outstanding) because most of the literature controls for the natural logarithm of post-IPO market value of equity as a determinant of coverage (e.g., Rajan and Servaes 1997; Aggarwal et al. 2002; Bradley et al. 2008). Alternatively, Cliff and Denis (2004) specify the natural logarithm of IPO proceeds, though it is insignificant in their estimations. Our findings and inferences are unchanged if we substitute LnProceeds in place of LnValuation.
 
16
In addition to the number of post-IPO institutional investors, some papers (e.g., O’Brien and Tan 2015) control for the percentage of post-IPO institutional ownership. For two reasons, we do not also specify a covariate for the percentage of institutional ownership as of three months post-IPO. One, at 0.57, it has a high Pearson correlation with LnInst-3mo (the natural logarithm of the number of institutional owners per the issuer’s first Form 13F), to the extent that specifying it engenders frequent sign flips to a significant negative coefficient. Two, it has weaker correlations with our coverage dependent variables (0.22, 0.34, and 0.05 with Analysts-Total, Analysts-Affiliated and Analysts-UnAffiliated, respectively; versus 0.42, 0.45 and 0.25 for LnInst#-3mo, respectively). Also, in contrast to LnInst#-3mo, which has correlations of 0.26 with InitialReturn and 0.40 with PriceRevision, the percentage of institutional ownership three months post IPO has correlations of 0.08 and 0.15 with InitialReturn and PriceRevision, respectively. As such, controlling for the number of institutional investors (i.e., instead of the percentage of institutional ownership) biases against results for InitialReturn and PriceRevision as determinants of post-IPO analyst coverage.
 
17
PriceRevision also has a high correlation with LnVolume-3mo (0.45), which, has a high correlation with Analysts-Affiliated and Analysts-UnAffiliated (0.65 and 0.47, respectively). See Sect. 4.7, where we document a positive relation from PriceRevision to Analysts-UnAffiliated via an indirect path involving LnVolume-3mo.
 
18
For example, quartile 1 includes Lumber Liquidators (PriceRevision –15.38%, InitialReturn –11.27%) and Facebook (PriceRevision + 20.63%, InitialReturn + 0.61%); and quartile 4 includes Kintera (PriceRevision –22.22%, InitialReturn + 47.14%) and Noodles & Co. (PriceRevision + 28.57%, InitialReturn + 104.17%).
 
19
As we state in Section 3.2, because less than 1% of our sample IPOs have no analyst coverage within one year post IPO, we do not estimate a first-stage regression to address instances of no coverage.
 
20
Because our analyst coverage variables are non-negative integers that exhibit considerable over-dispersion (e.g., Analysts-Total variance of 23.88 (not tabulated) exceeds its mean of 7.30), we use count data econometrics for estimation; specifically negative binomial regression (Rock, Sedo and Willenborg 2001; Bradley et al. 2008).
 
21
To facilitate convergence of our regressions, we do not specify fixed effects for those FF49 industries with three or fewer observations (i.e., for the full sample, these FF49 industries are: 03, 04, 05, 16, 20, 24, 25, 39 and 40).
 
22
We report McFadden’s (1973) pseudo-R2, a goodness-of-fit measure commonly reported with count models. It is defined as 1 – (the loglikelihood of the full model ÷ the loglikelihood of the intercept-only model). McFadden (1978, p. 307) states that the values of Pseudo-R2 “tend to be considerably lower than those of the R2 index and should not be judged by the standards for a ‘good fit’ in ordinary regression analysis. For example, values of 0.2 to 0.4 for [Pseudo-R2] represent an excellent fit.” For an improvement in model fit, we use the Akaike Information Criterion (Akaike 1973). Given our sample size, a reduction in Akaike Information Criterion of 2.5 or more indicates an improvement in fit (Hilbe 2011). We conclude that Eq. (1b) has greater explanatory power than Eq. (1a).
 
23
InitialReturn’s coefficient is also insignificant when we estimate Eq. (2b) with either of these subsamples (i.e., for the 1,167 IPOs with a lead or co-manager sanctioned by the Global Settlement, InitialReturn’s coefficient is 0.0004 (z-statistic 0.94) and for the remaining 387 IPOs InitialReturn’s coefficient is –0.0008 (z-statistic –1.64)).
 
24
Consistent with most literature, we measure coverage as the number of analysts that issue a recommendation within 12 months of the IPO. This is arguably a long window if coverage arises from the price revision after the roadshow or the return on the first day of trading. That is, if, say, it takes 11 months for an unaffiliated analyst to initiate coverage, it seems reasonable to think something else (other than IPO pricing) is driving their decision. To examine this, we estimate Eq. (3a)–(3d) as of each post-IPO quarter (i.e., instead of 12 months post-IPO, we measure unaffiliated coverage as of the three-, six- and nine-month marks). The results (not tabulated) resemble those in columns 9–12 in panel A of Table 4 (e.g., when we estimate Eq. (3d) as of the three-month mark, the coefficient on PriceRevision is 0.0088 (z-statistic 2.81) and the coefficient on UE(InitialReturn) is 0.0031 (z-statistic 2.05)).
 
25
See the notes to Table 4 for complete definitions of these four indicator variables.
 
26
We also find no evidence of a relation between the price revision and affiliated coverage (which, per Table 4, is strongly positively associated with Mgrs-Affiliated, the number of lead managers and co-managers). Along these lines, Aggarwal et al. (2002, p. 120) examine the implication that “greater first-day underpricing of an IPO generates momentum (in the form of higher levels of research coverage)” and report a positive association between initial return and the number of recommendations by nonlead analysts but no evidence of a positive association between initial return and the number of recommendations by lead analysts. In discussing this latter (null) result, Aggarwal et al. (2002, p. 128-129) state “The number of lead analyst recommendations is unrelated to the level of underpricing, suggesting that underpricing the IPO is not necessary to attract the attention of the lead underwriters’ analysts. This is consistent with the view that lead analyst recommendations are simply part of the service provided by the underwriter to firms going public.” Following this, one implication of our lack of findings regarding the price revision is that affiliated analysts underweight institutional investor demand when making their coverage decisions because such coverage is “simply part of the service provided by the underwriter to firms going public.”
 
27
When we follow the 461 IPOs as of three months post IPO through 12-months post IPO, the mean value of Recommend-UnAffiliated of 2.24 (per panel A of Table 5) decreases slightly to 2.21.
 
28
A test of the difference in mean recommendations between the 93 IPOs with InitialReturn of zero or less versus the 368 with InitialReturn greater than zero is significant at the 1% level.
 
29
For example, the difference in mean recommendations between the 118 IPOs with a revised price below the IPR minimum versus the 184 with a revised price above the IPR maximum is significant beyond the 1% level.
 
30
Similar to the difference in recommendations between lead manager analysts with versus without affiliation to a Global Settlement sanctioned bank (e.g., at three months post IPO, 2.21 versus 1.69, respectively – not tabulated), analysts with co-manager affiliation at sanctioned banks also provide less-favorable recommendations than analysts with co-manager affiliation at unsanctioned banks (e.g., at three months post IPO, 2.44 versus 1.75, respectively –not tabulated). It is simply that, in contrast to lead manager analysts, it is uncommon for a co-manager analyst to work at a sanctioned bank.
 
31
A test of the change in UE(InitialReturn)’s coefficient in the Eq. (3d) estimations, from pre JOBS of 0.0020 to post JOBS of 0.0040, yields a chi-square of 1.09 (p-value 0.30).
 
32
As in panel B of Table 5 for the overall sample, we also compare lead versus co-manager recommendations for pre-/post-JOBS EGC issuers. The findings (not tabulated) indicate that co-manager affiliated EGC analysts provide more favorable recommendations than their lead-manager counterparts for both pre- and post-JOBS periods. In addition, while both types of analysts increase their favorableness post JOBS, this increase is greater for co-manager analysts. As for unaffiliated analysts, we find they provide less favorable recommendations post JOBS.
 
33
Our six-month post-IPO results are most comparable to Dambra et al. (2018), which focuses on earnings forecasts within 180 days of IPO. Along these lines, we find that affiliated analyst recommendations become more favorable after JOBS (i.e., a test of the decrease from mean Recommend-Affiliated at the six-month post-IPO mark of 2.07 pre JOBS to 1.98 post JOBS is significant beyond 1%; whereas Recommend-UnAffiliated increases from 2.08 to 2.11).
 
34
The statistical significance of PriceRevision’s coefficient in the lower off-diagonal estimations (i.e., with 41 IPOs) is not robust to classifying affiliated analysts as those employed by any underwriter in the IPO syndicate (i.e., lead-managers, or co-managers, or nonmanagers). All other findings are robust to this alternative classification.
 
35
The coefficient on E(PriceRevision), while positive and marginally significant in the Eq. (3d´) estimation, is affected by its collinearity with the covariates that we also specify in the Appendix Table 13 PriceRevision regression. If we exclude UW, VC, LnAge and HighTech from Eq. (3c´), then E(PriceRevision)’s coefficient increases from 0.0032 (z-statistic 1.02) in Table 9 to 0.0111 (z-statistic 3.33) in Eq. (3c´). And, if we exclude these same four covariates from Eq. (3d´), then E(PriceRevision)’s coefficient increases from 0.0058 (z-statistic 1.76) in Table 9 to 0.0139 (z-statistic 4.13) in Eq. (3d´). Of important note, in either case, the coefficient on UE(PriceRevision) continues to be positive and significant beyond the 1% level.
 
36
Because we use OLS for the path analysis, we specify Ln(1 + Analysts-UnAffiliated) as the outcome variable.
 
37
UE(PriceRevision)’s indirect effect of 0.055 is the sum of the products of multiplying each mediating path coefficient with its respective mediating variable direct effect coefficient ((0.364 * 0.109) + (0.364 * 0.034 * 0.079) + (0.364 * 0.102 * 0.156) + (0.364 * 0.071 * –0.024) + (–0.080 * 0.079) + (0.130 * 0.156) + (0.190 * –0.024)) = 0.0397 + 0.0010 + 0.0058 – 0.0006 – 0.0063 + 0.0203 – 0.0046 = 0.0553).
 
38
Along these lines, it also seems that intentionally setting a low IPR would harm price efficiency.
 
39
We also proxy the issuer’s susceptibility to being low-balled by revising our computation of E(PriceRevision) to exclude those Appendix Table 13 determinants that are unavailable at the time the IPR is disclosed (i.e., PC1-LM, PC2-LM, RiskFactors, IndustryReturn, IndustryReturn+, and IPOReturn). When we sort this version of E(PriceRevision) into quintiles, the mean and median values of Analysts-UnAffiliated in quintile 5 are higher than all other quintiles (e.g., for quintile 5, the mean is 4.29 and the median is 3.00 versus a mean of 2.93 and a median of 2.00 for quintile 4).
 
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Metadaten
Titel
IPO price formation and analyst coverage
verfasst von
Joseph Weber
Michael Willenborg
Biyu Wu
Yanhua Sunny Yang
Publikationsdatum
06.11.2023
Verlag
Springer US
Erschienen in
Review of Accounting Studies
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-023-09808-2