IPO Waves, Information Spillovers, and Analyst Biases* Susan Christoffersena,b,c Amrita Nainb Ya Tangd September 2010 _________________________________ *Previously circulated under the title “IPO Cycles, Firm Characteristics, and the Role of Underwriters” a Rotman School of Management, University of Toronto, 105 St. George St., Toronto, ON, M5S 3E6 b Desautels Faculty of Management, McGill University, 1001 Sherbrooke St.West, Montreal, QC H3A 1G5 c Copenhagen Business School, Solbjerg Plads, DK-2000 Frederiksberg d Guanghua School of Management, Peking University, 5 Summer Place Road, Beijing, 100871, P.R. China Contact author: Amrita Nain, amrita.nain@mcgill.ca, 514-398-8440. Susan Christoffersen is grateful for support from SSHRC, IFM2, and the Leibovitch Award. Amrita Nain is grateful for support from SSHRC and FQRSC. The authors are grateful for helpful comments from Michelle Lowry, J. Ari Pandes, and participants of the Northern Finance Association Meetings, 2010. All remaining errors are our own. IPO Waves, Information spillovers, and Analyst Biases Abstract Existing research suggests the presence of learning and information spillovers in IPO waves. Consistent with information spillover models, we find that the level of information asymmetry surrounding IPO firms is significantly higher in the early stages of an IPO wave than in the late stages. Thus, the early stages of an IPO wave are characterized by high valuation levels as well as high valuation uncertainty. According to current thinking, private firms learn about IPO valuations by observing the price outcomes of recent IPOs. We propose a more direct method for underwriters to convey this information to potential issuers: affiliated analysts. We show that affiliated analysts issue excessively optimistic recommendations early in the wave, but not later in the wave. Institutional holdings are unaffected by the early-stage bias of affiliated analysts and the stock market appears to discount affiliated analysts in the early stages of a wave. We suggest that the affiliated-analyst bias in the early stages of an IPO wave is an attempt to convey positive demand information to private firms. 1. Introduction It is a well-established fact that there are pronounced cycles in IPO volume and initial returns.1 Recent theoretical and empirical work indicates the existence of learning and information spillovers during IPO waves. Lowry and Schwert (2002) find that more companies file IPOs following periods of high underpricing because high initial returns reflect positive information learned during the registration period. Evidence of information spillovers can be found in Benveniste, Ljungqvist, Wilhelm, and Yu (2003) (henceforth, Benveniste et. al. (2003)) who show that the decision to complete an IPO and the terms of the IPO depend on the experience of contemporaneous IPOs in the same industry. More recently, Alti (2005) presents a model of endogenous information spillovers in which firms with higher growth opportunities go public earlier in the wave when information asymmetry is high. A central tenet of the information spillover argument is that information asymmetry declines as an IPO wave progresses. The price outcome of each additional IPO reveals information about market demand and common valuation factors. Although several existing papers discuss the link between underpricing and information asymmetry, there is a dearth of evidence on the dynamics of information asymmetry within an IPO wave. 2 Thus, the first objective of this paper is to examine a key feature of the information spillover argument – does uncertainty surrounding IPO firms decline during the course of an IPO wave? Using different methods for identifying spurts in IPO volume 1 See, for example, Ibbotson and Jaffe (1975), Ibbotson, Sindelar, and Ritter (1988, 1994), Lowry (2003). 2 The positive correlation between underpricing and information asymmetry is discussed in Beatty and Ritter (1986), Rock (1986), Grinblatt and Hwang (1989), Benveniste and Spindt (1989), Michaely and Shaw (1994), and Lowry, Officer, and Schwert (2010). 1 and several different proxies of valuation uncertainty, we show that valuation uncertainty is higher in the early stages of an IPO wave than in the late stages. Having provided evidence that the informational environment is opaque at the beginning of an IPO wave, we examine the mechanism of information spillovers. Like other papers, we find extensive empirical evidence that price realizations early on in the wave provide information to the market about valuations and demand. However, we explore an alternative and more direct method for investment banks to communicate their private information to firms: affiliated analysts. During the road show, issuing firms and underwriters obtain information about investor demand and valuations. Underwriters would like to convey positive information gleaned during the registration period to private firms in order to build up the supply of IPOs. Investment banks can signal high market valuations by encouraging their analysts to provide favorable views of IPO performance. Accordingly, we expect the views of affiliated analysts to be biased early in the IPO wave when investment banks are responding to new demand information and building up the supply of IPO offerings. Consistent with this idea, we find strong evidence of an affiliated analyst bias early in the wave, but not later on. Specifically, we show that in the early stages of an IPO wave, when valuations are high but information quality is poor, analysts affiliated with underwriters provide disproportionately more positive recommendations to issuing firms as compared with unaffiliated analysts. Later in the wave, when valuations and information asymmetry are much lower, the recommendations of affiliated and unaffiliated analysts are similar. In a related test, we divided IPOs into two mutually exclusive groups - IPOs that receive a stronger recommendation from affiliated analysts 2 than from unaffiliated analysts (termed Affiliated Favored IPOs), and IPOs that receive a stronger recommendation from unaffiliated analysts (termed Unaffiliated Favored IPOs). We find that there are significantly more Affiliated Favored IPOs in the early stages of an IPO wave than in the late stages. It is well accepted that analysts affiliated with underwriters issue more favorable recommendations. 3 This bias is generally viewed in a negative light as an attempt by underwriters to appease issuing clients and institutional investors who are long the stock. In contrast, we suggest that the early-stage analyst bias is an attempt by underwriters to convey positive demand information to potential issuers during periods of high information asymmetry. However, we consider two potential alternative explanations for the affiliated-analyst bias in the early stages of an IPO wave. First, we explore whether affiliated analysts provide more favorable recommendations to early issuers than unaffiliated analysts because they have private information about the higher quality of early issuers. However, we find no significant differences in profitability, sales growth, cash holdings, liquidity, and abnormal stock returns of early and late issuers two years after issue date. Thus, there are no notable quality differences that would justify the more favorable outlook of affiliated analysts early in an IPO wave. The second alternative explanation we explore is that the analyst bias early in the wave is a mechanism to attract institutional demand rather than an attempt to signal positive valuations to private firms. To test this alternative explanation, we investigate how institutional holdings respond to analyst recommendations. Institutional investors need to trade-off two features of affiliated analysts. On the one hand, affiliated analysts 3 See, for example, Michaely and Womack (1999) and Bradley, Jordan, and Ritter (2008). 3 are likely to have an informational advantage over unaffiliated analysts. On the other hand, their recommendations may be biased due to pressure from the investment banking divisions. Our tests reveal that institutional investors optimally balance these competing factors and do not respond to the affiliated analysts bias early in the wave. Specifically, we find that in the early stages of an IPO wave, when affiliated analysts provide biased recommendations, institutional holdings of Affiliated and Unaffiliated Favored IPOs are similar. Later in the wave, when the analyst bias is absent, institutional holdings are significantly higher in Affiliated Favored IPOs (38.9%) than in Unaffiliated Favored IPOs (30.69%). Thus, institutions pay more attention to affiliated analysts later in the wave, when they are not biased and the recommendations reflect private information about the firms. We also find that within the subset of Unaffiliated Favored IPOs, institutions hold 39.3% of early issuers and 30.69% of late issuers. Institutional investors are, therefore, more likely to follow the recommendations of unaffiliated analysts early in the wave because affiliated analysts are overly optimistic at that time. Since institutional investors appear to be unmoved by the affiliated-analyst bias early in the wave, we also explore the possibility that the analyst bias is targeted to influence uninformed investors. We follow existing literature and calculate cumulative abnormal returns (CARs) at the announcement of strong-buy recommendations. We find that the market reacts positively to strong-buy recommendations from both affiliated and unaffiliated analysts. However, the CARs of early issuers are higher when unaffiliated analysts issue strong-buy recommendations than when affiliated analysts issue strong buy recommendations. In contrast, CARs of late issuers are higher when affiliated analysts 4 issue strong-buy recommendations than when unaffiliated analysts issue strong buy recommendations. These stock market reactions indicate that the market pays more attention to affiliated analysts’ opinions of late issuers and to unaffiliated analysts’ opinions of early issuers. Overall, the affiliated analyst bias early on in the wave is not successful in convincing institutions to increase their holdings of early issuers and appears to be discounted by the market as a whole. Since investors appear to be unmoved by the earlystage bias of affiliated analysts, the bias is possibly targeted at a different audience. We propose that the affiliated analyst bias serves as a mechanism of conveying information learned by underwriters during the book building process to the pool of private firms that are considering an IPO but are uninformed about IPO demand and valuations. Our paper makes several contributions to existing literature. Recent theory suggests that firms issuing early in a wave are likely to be different from firms that issue late in a wave. Ours is one of the first papers to show empirically that issuer characteristics proxying for valuation certainty change during the course of an IPO wave. We are also the first to demonstrate that the bias of affiliated analysts dissipates as an IPO wave progresses and that sophisticated investors like institutions know when to ignore affiliated analysts and when to follow their advice. Our results suggest that the analyst bias plays a positive role of disseminating information to the supply side of the IPO market. Finally, given the high correlation between IPO volume and IPO initial returns, existing literature tends to use high volume and high initial returns as substitute measures of IPO heat. Our results show that IPO wave periods are not uniformly hot and that 5 treating all issuers in an IPO wave as a homogenous group can result in a significant loss of information. Our paper adds to a small set of empirical studies that take into account the order of moves within corporate event waves. Benveniste, et. al. (2003) provide evidence that information spillovers influence issuers’ decision on whether to complete an IPO and how to price it. They examine industry IPO waves and show that information spillovers are stronger among pioneers (first movers) and early followers in an industry than among late followers. Çolak and Günay (2010) find that the average quality of issuing firms is lower in the early stages of rising IPO cycle. Bouwman, Fuller, and Nain (2009) examine mergers and acquisitions and show that acquirers that buy early in a merger wave perform worse than those that purchase later in a merger wave. The rest of the paper is organized as follows. Section 2 describes the data, Section 3 discusses IPO characteristics and valuation uncertainty, Section 4 examines the analyst bias, and Section 5 studies issuer quality. In Section 6, we present an analysis of institutional holdings and market reactions. Section 7 discusses robustness and Section 8 concludes. 2. Data Description 2.1. Sample To identify IPO waves, we use the 12,648 initial public offerings between January 1st, 1970 and December 31st, 2005 provided by Jay Ritter.4 To obtain offer-specific and 4 The data are available on Jay Ritter’s website at http://bear.warrington.ufl.edu/ritter/ipodata.htm 6 firm-specific information, we rely on Securities Data Company (SDC). This dataset include 11,490 firm-commitment IPOs between 1970 and 2005. We exclude unit offerings, real estate investment trusts (REITs), closed-end funds, and ADRs, and further restrict our analysis to stocks with information available on CRSP. If a firm’s stock price appears on CRSP more than 14 days after the IPO date, we drop it from the sample. This leaves us with a final sample of 7,043 IPOs. All the analyses in this paper are conducted on this sample of 7,043 IPOs. 2.2. Classification of IPO waves Our primary method of identifying IPO waves follows the simulation methodology of Harford (2005).5 Since each of the four decades in our sample period is characterized as a distinct era, we do the simulation separately for the 1970s, 1980s, 1990s and 2000s. From the sample of 12,648 IPOs, we take the total number of IPOs in each decade and create a simulated distribution by randomly assigning each actual IPO to a quarter within the decade. The probability of assignment is 1/40 for each quarter. We repeat this process 1000 times to generate 1,000 randomly drawn series of quarterly IPO volume. Any quarter where the actual number of IPOs exceeds the 95th percentile from the simulated distribution is designated as a high-volume quarter. Quarters where the actual number of IPOs lies below the 5th percentile are designated low-volume quarters. As robustness check, we also identify IPO waves using a methodology based on Helwege and Liang (2004). Details of this method and the robustness of our results are provided in Section 7. 5 7 This method gives us 38 high-volume quarters and 28 low-volume quarters. Since it takes time for a wave to form, peak, and disappear, we define an IPO wave as a period of three or more consecutive high-volume quarters. This method results in 6 IPO waves between January 1st, 1970 and December 31st, 2005. Out of the 38 high-volume quarters identified above, 5 are not included in our waves because the high volume period did not last long enough. Table 1 provides summary statistics of the 6 IPO waves. Panel A shows the start and end date of all six waves along with the total number of IPOs and the number of IPOs per quarter in each wave. A total of 3,052 IPOs occurred during IPO wave periods with an average of 99 IPOs per quarter. Panel B presents the number of IPOs, dollar volume of IPOs, initial returns and oversubscription in the first and last month within in each IPO wave. Initial returns, IR, are calculated as the first-day closing price less the offer price divided by the first-day closing price. First-day closing prices are collected from CRSP daily file and offer prices are obtained from SDC. The percentage of oversubscription, OVERSUBPT, is calculated as shares offered and oversold minus shares filed divided by shares filed. Panel B shows that IPO volume as measured by number of IPOs and dollar volume of IPOs is high in both the first and the last months of the six waves. This is to be expected since our classification of wave periods is based on the number of IPOs. However, underpricing and oversubscription is noticeably lower in the last month of the IPO waves than in the first month. Thus, by the end of a wave period, volume may still be high but IPO demand and underpricing have dropped significantly. This is consistent with Lowry and Schwert’s finding that initial returns lead volume by a few months. The low initial returns at the end of a wave period are indicative of the imminent drop in IPO activity. The difference in initial returns and 8 demand in the first and last month of the IPO wave provide a preview of how market conditions change within a high volume period. Since there is no set definition of what is ‘early’, we use three different classifications throughout the paper. In all the tests that follow, we define early (late) movers as the first (last) 10%, 15% or 20% of the IPOs in each wave. 3. IPO Characteristics 3.1. IPO Market Characteristics We begin by demonstrating that the state of the IPO market changes significantly during the course of an IPO wave. We compare initial returns, oversubscription, and the difference between the offer price and the original filing price of early and late issuers in a wave. Initial returns and oversubscription are calculated as described in Section 2.2. The variable HIGH_TO_OFFER is computed as highest original price filed less the offer price divided by highest original price filed. LOW_TO_OFFER is computed as the offer price less the lowest original price filed divided by lowest original price filed. Since the highest original price filed is fixed, smaller values of HIGH_TO_OFFER indicate better price outcomes for the issuing firms. Likewise, since the lowest original price filed is fixed, higher values of LOW_TO_OFFER indicate better price outcomes. Table 2 reports that for all three classifications of early/late movers, initial returns and oversubscription are significantly higher for early movers than for late movers. Likewise, for all three cut-offs, HIGH_TO_OFFER is lower and LOW_TO_OFFER 9 higher in the early stages of an IPO wave. Thus, all variables indicate that market conditions are significantly poorer in the late stages of an IPO wave than in the early stages. This is to be expected since IPO waves tend to die out after observing the unfavorable outcomes for late issuers. Thus, the pattern of oversubscription, initial returns, and offer prices within an IPO wave is consistent with existing evidence that volume responds to the experience of recent IPOs. The more interesting fact is that the differences between the early and late stages of an IPO wave are greater than the differences between IPO waves and low-volume periods. Table 2 also presents mean values of initial returns, oversubscription, HIGH_TO_OFFER and LOW_TO_OFFER for wave periods and low-volume periods. Recall that low-volume periods are quarters when IPO volume is lower than the 5th percentile of the simulated distribution. During lowvolume periods, each of the four variables takes a value that lies in between the extremes achieved during the early and late stages of an IPO wave. At first, it may seem counterintuitive that low-volume periods have stronger demand and valuations than the later stages of an IPO wave. However, low-volume periods are often periods of rising initial returns, which are then followed by an IPO wave (see Lowry and Schwert, 2002). In contrast, the coldest IPO market conditions are achieved shortly before the end of an IPO wave. The latter finding is consistent with existing theory suggesting that corporate event waves end after firms observe the poor outcomes of recent adopters (Persons and Warther, 1997; Rhodes-Kropf and Viswanathan, 2004). 3.2. Valuation Uncertainty 10 Information spillover arguments of Benveniste et. al. (2003) and Alti (2005) predict that valuation uncertainty is high at the beginning of an IPO wave but declines as the market learns about a common valuation factor from the outcomes of successive IPOs. Several existing papers have studied uncertainty surrounding IPO firms. For example, Lowry, Officer, and Schwert (2010) show that IPO valuation uncertainty varies over time and is highly correlated with the level of underpricing. The Yung, Çolak, and Wang (2008) model predicts greater dispersion in firm quality and greater information asymmetry during high IPO valuation periods because poorer quality firms pool with better quality firms when valuations are high. Since valuations are higher earlier in the wave than later in the wave, the Yung, Çolak, and Wang (2008) model would imply that information asymmetry is higher in the early stages of an IPO wave than in the late stages. Despite the recent discussion of valuation certainty in the literature, none of these papers directly examine whether information asymmetry declines during the course of an IPO wave as predicted by the information spillover models.6 Therefore, we use various measures of valuation uncertainty from each of aforementioned papers, and directly test for differences in valuation certainty in the early and late stages of an IPO wave Following Lowry, Officer, and Schwert (2010), we capture valuation uncertainty of issuing firms with the cross-sectional volatility of initial returns (IR_VOL). Panel A of Table 3 shows that the volatility of initial returns is higher for early movers than for late movers, regardless of the cut-off used to classify issuers as early or late. For comparison, the same table also presents volatility of initial returns for low-volume (non-wave) periods. We see that the extreme values of initial return volatility are achieved at the 6 One exception to this statement is the finding in Benveniste et. al. (2003) that IPO withdrawals are higher earlier in an industry wave than later. 11 beginning and the end of an IPO wave and not during low-volume periods consistent with an information spillover model. We also compare other proxies of information asymmetry like the tightness of the filing price range, the percentage of withdrawn IPOs, the percentage of NASDAQ stocks issued, and issue size. Since less information is available about smaller issues, these tend to be harder to value correctly. Similarly, technology stocks tend to be small, highgrowth stocks with greater valuation uncertainty. More uncertainty about the value of the issuer will lead to a wider filing price range and a higher incidence of withdrawn IPOs.7 We compare these issue characteristics for the early and late stages of an IPO wave and present the results in Table 3, Panel B. We find that the filing price range is wider, and the percentage of withdrawn IPOs higher in the early stages of an IPO wave. We also find that issue size is significantly smaller in the early stages of an IPO wave and that a larger fraction of early movers are NASDAQ firms as compared with late movers. Thus, stocks of more uncertain value go public earlier in the wave when market valuations are high. Finally, in the spirit of Yung, Çolak, and Wang (2008), we calculate the 12-month and 24-month buy-and-hold abnormal returns and measure dispersion of firm quality as the cross-sectional standard deviation of buy-and-hold abnormal returns (BHAR_VOL). Table 3, Panel A presents a comparison of BHAR_VOL for early issuers and late issuers. We see that volatility of buy-and-hold abnormal returns is higher for early IPOs than for late IPOs. 7 Benveniste et. al. (2002) suggest that IPO withdrawals capture firms which are exante difficult to value and are likely to decline as a wave progresses. Benveniste et. al. (2003) show that pioneers and early followers in an industry IPO wave are more likely to withdraw the offering than late followers. 12 In summary, the results in Table 3 provide robust evidence that early issuers are characterized by greater valuation uncertainty than late issuers. Interestingly, we find that the harder-to-value early movers (which tend to experience higher underpricing) use underwriters with a higher Carter-Manaster (1990) ranking score (Table 3, Panel B). Therefore, it seems that firms with high levels of uncertainty surrounding their valuation use more reputable investment banks to issue their stock. This result is consistent with existing literature suggesting that firms are willing to accept higher underpricing (notably early in a wave) in return for higher quality analyst coverage.8 4. Analyst Recommendations The results in the previous section show that firms that go public early in an IPO wave are of more uncertain value and display greater dispersion in quality than firms that go public later in the wave. These early movers use underwriters that are ranked higher than underwriters used by the late issuers. This result is intuitive insofar as riskier firms benefit more from the information produced by top-tier investment banks. One of the primary considerations in the choice of underwriters is analyst coverage. In light of this, we examine what kind of analyst coverage the hard-to-value early movers receive from the analysts of their reputable underwriters. We are particularly interested in examining whether investment banks use analyst coverage as a means to provide information to private firms considering an IPO. In the 8 See Rajan and Servaes (1997), Loughran and Ritter (2004), Cliff and Denis (2004), and Lowry, Officer, and Schwert (2010) 13 early price-discovery phase of an IPO wave, investment banks obtain positive demand information from institutional investors. While underpricing levels convey some of this information to potential issuers, we explore another, more direct method for investment banks to signal high demand and valuations to the pool of private firms. We predict that the early stages of an IPO wave, which are characterized by high valuations and high uncertainty, will also coincide with overly optimistic coverage by affiliated analysts. The affiliation of the analyst is important because it is through the affiliation with the underwriter that analysts obtain information about institutional demand. Hence, we do not expect the same optimism among unaffiliated analysts during the early stages of a wave. Although it is a well-established fact that affiliated analysts provide more positive recommendations to IPO stock than unaffiliated analysts, we propose that affiliated analyst bias is a feature of the early stages of an IPO market, when IPO demand and valuation uncertainty are high. 9 For each IPO firm, we calculate recommendation value, RECD, as mean value of I/B/E/S analyst recommendations in the first year after IPO. We calculate mean RECD received from affiliated analysts and unaffiliated analysts separately. I/B/E/S recommendations take a value between 1 and 5 for recommendations ranging for Strong Buy to Strong Sell. Thus, lower mean values of RECD variable indicate more positive analyst recommendations. We also calculate the percentage of positive recommendations, PER_RECD, as the number recommendation of buy or strong buy divided by the total 9 See, for example, Michaely and Womack (1999), Cliff and Denis (2003), James and Karceski (2006), Agrawal and Chen (2008), Bradley, Clarke, and Cooney (2009), and Ljungqvist, Marston, and Wilhelm (2009). 14 number of recommendations. Higher values of PER_RECD indicate more favorable coverage. Table 4 presents mean RECD and PER_RECD of affiliated and unaffiliated analysts for early and late IPOs within a wave. Panel A contains results at the 10% cutoff for early and late IPOs. Panel B presents the same results for the 15% and 20% cutoffs. In the early-mover subsample, we see that RECD is significantly lower and PER_RECD higher for affiliated analysts than for unaffiliated analysts. Thus, early movers receive more favorable recommendations from affiliated analysts than from unaffiliated analysts. In the late mover sub-group, RECD and PER_RECD are statistically indistinguishable. That is, late IPOs receive similar recommendations from affiliated and unaffiliated analysts. These results are robust for all three cutoffs for early and late. In Table 4, Panel C, we present more evidence of affiliated analyst bias in the early stages of an IPO wave. We define an IPO that receives more favorable recommendations on average from affiliated (unaffiliated) analysts as an Affiliated Favored (Unaffiliated Favored) IPO. We find that, at the 10% cutoff level, 53.4% of early movers are Affiliated Favored while 41.5% of late movers are Affiliated Favored. The difference in these percentages is statistically significant at the 95% confidence level. Similar evidence emerges if we use the 15% cutoff level. At the 20% cutoff there are still more Affiliated Favored IPOs in the early stages than in the late stages, but the difference is not statistically significant. The evidence for Unaffiliated Favored IPOs is the reverse, albeit weaker. The fraction of Unaffiliated Favored IPOs is higher in the later stages of the wave than in the earlier stages, and the difference is statistically significant at the 15% 15 cutoff. Together these results show that affiliated analysts issue disproportionately more favorable recommendations in the early stages of an IPO wave when firms of more uncertain value go public. Since early movers use better-ranked underwriters on average and experience higher underpricing, we control for these factors by examining analyst bias in a multivariate setting. Table 5 presents several regressions in which the dependent variable is the analyst recommendation value RECD. Each regression includes a dummy variable, AFF, which is equal to one if the recommendation is provided by an affiliated analyst and zero otherwise and a dummy variable, EARLY, which is equal to one if the IPO is an early issue and zero if it is a late issue. The cutoffs used to define early and late movers are 10%, 15% and 20% in columns 1, 2 and 3 respectively. In these three columns, the sample is restricted to early and late movers only. The key variable of interest in these three regressions is the interaction of the affiliated analyst dummy AFF and the early mover dummy EARLY. The interaction term is negative and statistically significant, confirming the univariate finding that affiliated analysts are biased in the early stages of an IPO wave. All of the aforementioned regressions include underpricing, the size of the issue, and reputation of underwriters as control variables. Previous research suggests a link between analyst coverage and underpricing. 10 Since underpricing is high during the early stages of a wave and low during the late stages, it could be argued that the difference in analyst bias between early and late movers is attributable to the difference in underpricing and not to the timing of the IPO 10 See, for example, Cliff and Denis (2004). 16 within the wave. We address this concern in two ways. First, as indicated above, we include the level of underpricing (IR) as a control variable in each regression. Despite controlling for underpricing, the early-mover dummy is still statistically significant. The coefficient on the underpricing variable is negative and statistically significant in all regressions. Thus, consistent with prior literature, we find that firms going public during periods of high underpricing receive more favorable recommendations on average (across both types of analysts). Second, in the last regression (column 4) we include the full sample of IPOs in order to distinguish between analyst recommendations issued during the early stages of an IPO wave and those issued during any period of high underpricing. In this regression, the dummy EARLY2 equals one if the IPO is an early mover in an IPO wave and zero for all other IPOs. HIGHIR is a dummy variable, which is equal to one for IPOs whose initial returns lie in the top quartile and zero otherwise. Both EARLY2 and HIGHIR are interacted with the affiliation dummy variable, AFF. As expected, the coefficient on HIGHIR is negative indicating that firms that go public during periods of very high underpricing receive more favorable recommendations. Similarly, the negative coefficient on AFF shows that, on average, affiliated analysts provide more favorable recommendations. More interestingly, the interaction off AFF and EARLY2 is negative and significant while the interaction of AFF and HIGHIR is statistically insignificant. Thus, we find evidence that affiliated analysts provide more favorable recommendations than unaffiliated analysts during the early stages of an IPO wave but not during all periods of high underpricing. On the basis of these results, we conclude that affiliated analysts provide biased recommendations specifically for IPOs that come out at the beginning of a wave when 17 volume, valuation uncertainty, underpricing, and demand are all simultaneously high. The findings are consistent with the view that analysts serve as a mechanism to signal high institutional demand to firms considering an IPO. However, there could be other explanations for the favorable outlook of affiliated analyst early on in the wave. First, the biased recommendations in the early stages of the IPO wave may be targeted towards institutional investors and not private firms. That is, the bias may be an attempt to ramp up demand for IPOs rather than encourage a greater supply of IPOs. Second, it could be argued that affiliated analysts are not biased, but have private information about better growth prospects for early issuers. In the next section, we explore these alternative explanations in more detail. 5. Issuer Quality Our finding that affiliated analysts give more favorable recommendations than unaffiliated analysts to early issuers is in itself not sufficient evidence that affiliated analysts are biased. Analysts affiliated with underwriters have an informational advantage relative to unaffiliated analysts. It could be argued that affiliated analysts give more favorable recommendations to early issuers because these firms have better growth prospects that unaffiliated analysts are not aware of. Therefore, we compare profitability, sales growth, change in market share, capital expenditures, and cash-to-net asset ratio of early and late issuers two years after IPO. Our measures of profitability are cash flow 18 margin, return on assets and asset turnover. Data for these variables are obtained from Compustat and, to reduce the impact of outliers, all variables are winsorized at the one percent and 99 percent level. Cash flow margin is calculated as operating income before depreciation divided by total sales. Return on assets is net income over total assets. Asset turnover is total sales over total assets. All three variables are adjusted for industry performance by subtracting the industry median, where an industry is defined as all firms in the same 3-digit SIC code. Table 6 shows that the differences in industry adjusted cash flow margin, return on assets and asset turnover of early and late issuers are mostly statistically insignificant. To compare growth potential of early and late issuers, we calculate realized growth in the two years following IPO. We calculate sales growth of an issuing firm as sales two years after issue date less sales in the IPO year divided by sales in the IPO year. The sales growth variable is adjusted for industry growth by subtracting the growth rate of the median firm in the three-digit SIC code. We also calculate the change in market share of an issuing firm over this two-year period as market share two years after IPO less market share in the IPO year divided by market share in the IPO year. Table 6 shows some differences in the sales growth and changes in market shares of early and late issuers, but these differences are not robust across different cutoffs used to define early and late issuers. At the 10% cutoff, early issuers have significantly slower growth than late issuers. At the 10% and 15% cutoff, early issuers have a significantly smaller increase in market share than late movers. Next, we test whether late issuers are more likely to sit on the cash raised during the IPO due to lack of investment opportunities. We compare capital expenditures and 19 cash-to-net asset ratio of early and late issuers. Capital expenditure ratio is calculated as the issuing firm’s capital expenditure divided by total assets. Cash-to-net asset ratio is calculated as cash and cash equivalents divided by total assets less cash and cash equivalents. Both variables are industry-adjusted. Table 6 shows that capital expenditure ratio and cash to net asset ratio of early and late issuers two years after IPO date are similar. Finally, we compare abnormal returns over the 2-years following IPO and find that the 24-month BHARs of early and late issuers are statistically indistinguishable. Thus, just as Helwege and Liang (2004) find no differences in firm quality between wave and non-wave period, we find no quality differences within the wave. In light of these results, we conclude that the disproportionately more favorable recommendations issued by affiliated analysts in the early stages of an IPO wave are not justified by the subsequent performance of early IPOs. 6. Market Reaction and Institutional Trading Results in Section 4 and 5 indicate that affiliated analysts do indeed provide excessively optimistic recommendations during the early stages of a wave. We have conjectured that the biased recommendations are the underwriter’s attempt to signal high valuations to private firms considering an IPO. However, we recognize that the affiliated-analyst bias may be an attempt by underwriters to build up institutional demand rather than a means to convey information to private firms. Therefore, our final set of tests explores this alternative explanation for the analyst bias. We investigate whether institutions respond to analyst bias early in the wave. 20 6.1 Institutional Response to Analyst Bias To examine if informed investors react to the biased recommendations of affiliated analyst, we examine secondary institutional trading activity in the post-IPO market. We collect information on institutional holdings from CDA/Spectrum from 1980 to 2005 (data prior to 1980 is sparse). For each IPO firm, institutional holding (HOLDING) is calculated as the shares held by institutional investors divided by total shares outstanding at the end the first year after the IPO. We define an institutional investor as a “buyer” (“seller”) if it’s holding at the 4th quarter after IPO is more (less) than its holding one quarter after IPO. Percentage of institutional buys (BUY) is defined as the number of shares bought by institutions divided by the total shares outstanding at the end of 4th quarter after the initial offering. Percentage of institutional sells (SELL) is defined as the number of shares sold by institutions divided by the total shares outstanding at the end of 4th quarter after the initial offering. Affiliated analysts may have better information about the issuing firms but they provide overly optimistic recommendations early in the wave. Do institutional investors know when to pay attention to affiliated analysts and when to listen to unaffiliated analysts? To answer this question, we identify IPOs for which there is a divergence between the recommendations of affiliated and unaffiliated analysts. Specifically, we divide early and late issuers into Affiliated-Favored and UnaffiliatedFavored IPOs. As described in Section 4 above, Affiliated Favored IPOs receive, on average, more favorable recommendations from affiliated analysts than from unaffiliated 21 analysts. On the other hand, Unaffiliated Favored IPOs receive more favorable recommendations from unaffiliated analysts. Table 7, Panel A presents institutional holdings of Affiliated Favored and Unaffiliated Favored IPOs one year after IPO. Results for early and late issuers are provided separately for all three cut-offs—10%, 15% and 20%. We see that, for the earlymover subgroup, institutions hold similar amounts of Affiliated Favored and Unaffiliated Favored IPOs. In the late-mover subgroup, institutions hold significantly more Affiliated Favored IPOs than Unaffiliated Favored IPOs. These findings are intuitive in light of two opposing factors – the informational advantage of affiliated analysts and their biased recommendations for early issuers. The informational advantage of affiliated analysts is likely to have a positive impact on how much Affiliated Favored stock institutions hold. In contrast, affiliated-analyst bias is likely to have a negative impact. Our finding that institutions hold similar amounts of Affiliated-Favored and Unaffiliated-Favored early issuers suggests that these two factors offset each other in the early stages of a wave. In the late stages of a wave, when the analyst bias is absent, the informational-advantage effect dominates and therefore, institutions hold more Affiliated-Favored late issuers than Unaffiliated-Favored late issuers. This pattern is consistent across all three early/late cutoffs and suggests that institutions are likely to listen to affiliated-analyst recommendations of late issuers and discount their recommendations of early issuers. Another interesting result emerges when we focus on the subset of Unaffiliated Favored IPOs in Table 7, Panel A. One year after issue date, institutions hold more Unaffiliated-Favored early issuers than Unaffiliated-Favored late issuers. This finding is also robust across all three cutoffs used to define early and late movers. It appears that 22 institutions give more weight to the optimistic opinion of unaffiliated analysts in the early stages of a wave than in the late stages. A potential explanation for this finding can be found in Table 4, which shows that unaffiliated analysts tend to be less optimistic about early issuers than late issuers. The results in Table 7 suggest that when unaffiliated analysts do issue more favorable recommendations than affiliated analysts to early movers, institutions appear to take it as a strong signal that the IPO stock is worth holding. A similar picture emerges when we look at the institutional buying variable (BUY). Table 7, Panel B shows that, during the year following the IPO, institutions increase their holdings of Affiliated-Favored and Unaffiliated-Favored early movers by similar amounts—26% and 28.8.3% respectively. However, institutional holdings in AffiliatedFavored late movers increase much more than institutional holdings of UnaffiliatedFavored late movers—38.7% and 23.3% respectively. In Panel B, we also see that within the subset of Affiliated Favored IPOs, institutional holdings increase more for late issuers than for early issuers. In stark contrast, we find that, within the subset of Unaffiliated Favored IPOs, institutional holdings increase much more for early issuers than for late issuers. Thus, institutions pay more attention to favorable recommendations by affiliated analysts in the later stages of a wave but listen more closely to favorable recommendations of unaffiliated analysts in the early stages of a wave – precisely when affiliated analysts are less reliable. These results provide compelling evidence that institutions know about the affiliated analyst bias in the early stages of a wave. Panel C presents the institutional selling variable (SELL). There are no discernable patterns in institutional selling which may reflect the fact that analysts rarely issue Sell or Strong Sell recommendations for IPOs. 23 In Table 8, we subject these findings to multivariate regressions that control for size of the issue, initial returns and underwriter rank. Panel A examines institutional holdings and Panel B examines institutional buys. Focusing on institutional holdings first (Panel A), we identify the sample of early issuers using the 10 % cutoff. We regress institutional holdings of early issuers one year after IPO date on a dummy variable AFF_FAVORED. AFF_FAVORED is equal to one if the IPO is Affiliated Favored and zero if it is Unaffiliated Favored. In column 1 we see that for the early-mover subsample, AFF_FAVORED is insignificant. Similarly, we identify the set of late issuers using the 10% cutoff and regress institutional ownership of late issuers one year after issue date on AFF_FAVORED. For the late-mover subsample in column 2, AFF_FAVORED is positive and significant at the 10 percent level. This confirms the univariate result that institutions holdings of late movers are positively related to favorable recommendations of affiliated analysts while institutional holdings of early movers are not. These results are robust to the 15% and 20% cutoff for identifying early/late movers (not shown). Next, we split the sample into Affiliated Favored and Unaffiliated Favored IPOs and regress institutional holdings in each group on a dummy variable EARLY, which is equal to one for early issuers and zero for late issuers. In the Affiliated Favored subsample (column 3, Panel A), the EARLY dummy is insignificant. However, in the Unaffiliated Favored Subsample, EARLY is positive and statistically significant (column 4, Panel A). Thus, institutions hold more Unaffiliated-Favored early issuers than Unaffiliated-Favored late issuers. This confirms the univariate test that institutions listen closely to positive recommendations given by unaffiliated analysts to early movers. Regressions of institutional buys shown in Table 8, Panel B present a similar picture. 24 Institutions pay more attention to favorable recommendations from affiliated analysts during the late stages of a wave, when the analyst bias is absent. They give more weight to favorable recommendations by unaffiliated analysts in the early stages of a wave since affiliated analysts provide biased forecasts at this stage. Consistently the results suggest that institutions do not react to analyst biases early on in the wave. Hence, we know that in equilibrium overly optimistic analyst biases cannot be targeted towards institutions as a mechanism to build institutional demand. However, affiliated analysts may try to influence uninformed investors since the uninformed are more likely to fall prey to the favorable recommendations issued by affiliated analysts. The next sub-section gauges the impact of the affiliated-analyst bias on uninformed investors by examining the overall market reaction to positive recommendations of affiliated analysts 6.2 Market Response to Analyst Bias To determine whether the market as a whole discounts affiliated analysts in the early stages of a wave, we follow past research and calculate the market’s reaction to strong-buy recommendations from affiliated and unaffiliated analysts separately. As before, we divide the IPO sample into firms that issue early in a wave and those that issue late in the wave. The market’s reaction is the cumulative abnormal return earned from one day before the recommendation date till one day after the recommendation date. Abnormal return is calculated as the firm’s return less the return on the CRSP valueweighted index. A univariate comparison of the mean CAR across all strong-buy 25 recommendations is presented in Table 9, Panel A. We see that the market reacts positively to strong-buy recommendations from affiliated and unaffiliated analysts both in the early and late stages of an IPO wave. However, the differences in mean CARs presented in the last row of Panel A show that in the early stages of a wave, CARs are significantly higher when unaffiliated analysts issue strong-buy recommendations than when affiliated analysts issue strong-buy recommendations. In contrast, later in the wave, CARs are significantly higher when affiliated analysts issue strong-buy recommendations. These results show that early in the wave, when affiliated analysts issue biased recommendations, the market gives more weight to unaffiliated analysts. Later in the wave, when the affiliated-analyst bias is absent, the market reacts more strongly to recommendations of affiliated analysts than to those of unaffiliated analysts. This comparison of mean CARs suggests that, early on in a wave, the market is relatively skeptical of affiliated analysts. The response of stock market prices is in line with the trading behavior of institutional analysts. We examine this issue in more detail using multivariate regressions presented in Table 9, Panel B. The dependent variable in Panel B is the issuer’s CAR at the announcement of a recommendation by an analyst. In both the regressions shown, we include initial returns (IR), underwriter rank (UWRANK), and issue size (LNSIZ) as control variables. In column 1, the variable of interest is the dummy variable AFF, which is equal to one if the recommendation is from an affiliated analyst and zero otherwise. The AFF dummy is negative and significant indicating that the market’s reaction to recommendations from affiliated analysts is, on average, more muted. In column 2, we include the dummy variable EARLY, which is equal to one if the issuer is an early mover 26 and zero if it is a late mover. Since the affiliated analyst bias exists only in the early stages of an IPO wave, we want to see if the market reacts differently to recommendations issued by affiliated analysts to early issuers. Therefore, we include an interaction of AFF and EARLY in the second column. The interaction term is negative and significant indicating that the market’s reaction is less positive when affiliated analysts give strong-buy recommendations to early issuers. Bradley, Jordan, and Ritter (2008) show that recommendations initiated immediately after the quiet period are different from those initiated later. In unreported tests, we divide the sample of strong-buy recommendations into quiet period and postquiet period recommendations. Quiet-period recommendations are those issued within 30 days of going public. Post-quiet period recommendations are those issued more than 30 days after going public.11 In the subsample of quiet-period recommendations, we again find that the market reacts more positively to unaffiliated (affiliated) analysts earlier (later) in the wave. This result also holds, although weakly, in the subsample of postquiet period recommendations. Overwhelmingly, the results of Section 6.1 and 6.2 show no reaction of either institutions or the overall market to affiliated analyst recommendations early in wave. This non-result provides strong evidence that the bias is not meant to build a demand for shares. We, therefore, argue that the bias exists as mechanism for underwriters to provide information to private firms deciding whether to go public. 7. Robustness During the quiet period, which lasts for 25 calendar days, the issuing firm and members of the underwriting syndicate are not allowed to issue opinions or recommendations about the issuing firm. 11 27 In this section, we discuss the robustness of our results. Our results are likely to be sensitive to how an IPO wave is classified. Our tests require us to identify sustained periods of high IPO volume. The main classification of IPO waves used in this paper relies on a simulation method similar to that used by Harford (2005) to identify merger waves. We also identify IPO waves following Helwege and Liang’s (2004) movingaverage procedure. For each decade, we calculate a three- quarter moving average of the number of IPOs per quarter. Periods of at least three consecutive quarters with a moving average in the top quartile are classified as an IPO wave. Quarters with a moving average lower than the bottom 30% of the quarterly moving average are considered low-volume periods. This method results in six IPO waves between January 1970 and December 2005. As before, we define early (late) movers as the first (last) 10%, 15% or 20% of issuers in a wave. Table 10 repeats the key tests of the paper using this alternative definition of IPO waves. Panels A and B present a comparison of the various measures of information uncertainty described in Section 3.2 above. We find that BHAR volatility and volatility of initial returns is higher for early issuers than for late issuers. Moreover, filing price ranges are wider for early movers than for late movers. Withdrawals are also higher for early movers than for late movers, although the difference is statistically insignificant. Panel C compares the recommendations of affiliated and unaffiliated analysts in the early and late stages of an IPO wave. As before, we find that affiliated analysts issue more favorable recommendations for early issuers than unaffiliated analysts. This difference does not exist in the late stages of an IPO wave. Panel D presents institutional buying of IPO stock and confirms that institutional analysts are cognizant of the trade-off 28 between the information advantage of affiliated analyst and their bias in the early stages of an IPO wave. Focusing on the subset of late issuers, we see that institutions buy more affiliated-favored late movers than unaffiliated-favored late movers. Since affiliated analysts do not provide biased forecasts for late issuers, this result suggests that the informational advantage of affiliated analysts dominates. However, institutions do not show a similar preference for affiliated-favored early movers. Thus, institutions appear to discount affiliated analyst views of early issuers. This result is also evident when we look within the subset of affiliated-favored IPOs. Institutions buy more affiliated-favored late movers as compared with affiliated-favored early issuers. Finally, to allow for the possibility that each IPO wave is different and that relationships between the main variables used in this paper may have changed over time, we run our tests for the 1970s, 1980s, 1990s and 2000s separately if data availability permits it. We find that early movers have higher IR volatility and higher BHAR volatility in all the decades, although the results are weaker for the 1990s. Thus, the decade-by-decade analysis confirms our finding that uncertainty is higher early in the wave than later. We examine the analyst bias and institutional trading for the 1990s and 2000s only because data availability prior to the 2000s is insufficient. We find that in both the 1990s and 2000s the analyst bias existed only in the early stages of an IPO wave and that institutional investors discounted the overly optimistic early-stage recommendations of affiliated analysts. Thus, our results are robust to alternative methods of classifying IPO waves and appear to be stable over time. 8. Conclusion 29 Fluctuations in IPO volume have received significant attention in the financial literature. Several recent papers suggest that learning and information spillovers occur during IPO waves. The outcome of recent and contemporaneous IPOs provides potential issuers with information on the price they can expect to receive for the IPO. A key implication of the information spillover arguments is that information asymmetry declines as an IPO wave progresses. We provide strong evidence in favor of information spillovers. Using several different proxies of valuation uncertainty, we show that the early stages of an IPO wave are characterized by much higher levels of information asymmetry than the late stages of an IPO wave. These hard-to-value early issuers receive unjustifiably positive recommendations from affiliated analysts. Interestingly, the affiliated-analyst bias is absent later in the wave when the value of issuing firms is more certain. Thus, affiliated analysts provide biased recommendations precisely when information asymmetry is the highest and investors’ need for information the greatest. We try to discern whom the affiliated analysts hope to influence with these biased forecasts: firms on the supply side or institutions on the demand side? We find strong evidence that institutional investors discount affiliated analysts when they are biased and listen to them closely when they are not. Hence, the bias does not seem successful in building a demand for securities but rather is targeted at building a supply of firms to take to market. We conclude that the affiliated-analyst bias serves as another source of information spillover – it provides a positive signal of institutional demand to private firms considering an IPO. 30 The difference in valuation uncertainty, analyst bias, and institutional holdings of early and late issuers indicates that firms that go public in an IPO wave are not a homogeneous group of firms. Moreover, the drastic change in IPO market conditions during a wave indicates that high-volume periods are not uniformly hot. Thus, financial researchers may learn more from deconstructing an IPO wave than from comparing IPO waves with periods of low IPO volume. 31 REFERENCES Agrawal, Anup and Mark A. Chen, 2008, Do Analyst Conflicts Matter? Evidence from Stock Recommendations,” Journal of Law and Economics, 51(3), 503-537. 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There are six IPO waves from January 1970 to December 2005 and 28 quarters are defined as a non-wave period. Within each IPO wave, early-movers and late-movers are defined as the first and last 10%, 15%, and 20%. Panel A reports the start and end time, the total number of IPOs and the average number of IPOs per quarter of each IPO wave. Panel B report the number of IPOs, the dollar volume, the underpricing level (IR) and the percentage of oversubscription (OVERSUBPT) in the first and last month of each IPO wave. IR is defined as the difference between the first-day close price and offer price over the first-day close price where first-day close prices are obtained from CRSP daily file and offer prices are obtained from the SDC. Percentage of over-subscription OVERSUBPT is calculated as shares offered and oversold minus shares filed divided by shares filed. Shares offered and oversold and shares filed are obtained from SDC. Panel A: IPO Waves from 1970 to 2005 70s Wave1 71Q1 72Q4 Total number of IPOs 522 80s Wave2 Wave3 Wave4 Wave5 Wave6 83Q2 85Q4 93Q4 95Q4 00Q1 84Q1 87Q3 94Q2 96Q4 00Q3 483 779 363 702 202 121 98 121 140 67 3052 99 90s 00s Start End All Number of IPOs per quarter 65 Panel B: Volume, Underpricing, and Oversubscription during IPO Waves Wave1 Wave2 Wave3 Wave4 Wave5 Wave6 Number of IPOs First month Last month 14 27 17 19 24 33 42 35 49 30 15 17 Dollar Volume First month Last month 60.554 221.175 438.378 161.313 645.794 1343.787 1582.050 1360.544 1534.218 1027.955 2429.280 1002.488 IR First month Last month 12.42 4.66 16.82 3.46 13.03 2.02 13.88 8.04 12.43 10.33 19.85 18.77 OVERSUBPT First month Last month N/A N/A 13.27 -2.78 7.28 3.29 12.22 -0.49 33.38 10.96 12.81 -3.47 35 Table 2 IPO Demand and Underpricing of Early and Late Issuers This table reports the underpricing level and market demand for early movers in the IPO waves, late movers in the IPO waves, IPOs in the wave periods, and IPOs in the cold issue periods, respectively. IPO waves are identified based on the simulation of aggregate initial offering activities in 1970s, 1980s, 1990s, and 2000s separately. There are six IPO waves from January 1970 to December 2005. Within each IPO wave, early-movers and late-movers are defined as the first and last 10%, 15%, and 20% of the firms go to public, respectively. Correspondingly, quarters which have actual number of IPOs below the 5 th percentile from the simulated distribution are identified as non-wave periods. Initial return IR is defined as the difference between the first-day close price and offer price over the first-day close price where first-day close prices are obtained from CRSP daily file and offer prices are obtained from the SDC. Percentage of over-subscription, OVERSUBPT is calculated as shares offered and oversold minus shares filed divided by shares filed where the shares offered and oversold and shares filed are obtained from SDC platinum. HIGH_TO_OFFER is calculated as difference between the highest original filed and offer scaled by the highest original price filed and LOW_TO_OFFER is calculated as difference between the lowest original filed and offer scaled by the lowest original price filed, where highest, lowest priced filed and offer price are obtained from SDC platinum. LOGSHARE is calculated as the logarithm of the number of shares (in millions) offered in the IPO where the number of shares offered is obtained from SDC platinum; For example, the IR value 20.29% reported in the 3 rd column and the 2nd row indicates that the first 10 percent of the firms go to public in the IPO waves has underpricing level as high as 20.29%. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively.. INITIAL RETURN (IR) OVERSUBPT HIGH_TO_OFFER LOW_TO_OFFER Cutoffs Early-Mover in the waves Late- Mover in the waves 10% 20.29 8.44 15% 22.68 9.02 20% 23.57 9.37 10% 11.67 2.81 15% 11.84 3.77 20% 11.68 4.86 10% 4.77 12.88 15% 4.06 12.33 20% 3.39 11.55 10% 10.35 -0.94 15% 11.23 0.06 20% 12.19 0.75 Diff (early late) 11.84*** (4.62) 13.66*** (5.80) 14.20*** (6.68) 8.86*** (6.66) 8.07*** (7.03) 6.83*** (7.27) -8.11*** (-6.14) -8.26*** (-7.57) -8.16*** (-8.50) 11.30*** (7.50) 11.17*** (8.88) 11.40*** (10.39) Wave Period IPOs Low Volume Period Diff (Wave – Low Vol) INITIAL RETURN (IR) 14.62 20.28 -5.66*** (-3.19) OVERSUBPT 7.96 6.02 1.94 (1.59) HIGH_TO_OFFER 8.29 6.93 1.36* (1.88) LOW_TO_OFFER 5.23 7.57 -2.05** (-2.25) Diff (Early – Low Vol) 0.01 (0.05) 2.40 (0.71) 3.29 (1.50) 5.65*** (3.64) 5.64*** (3.18) 5.66*** (2.95) -2.16** (-2.10) -2.87* (-1.92) -3.54** (-2.39) 2.78*** (-2.96) 3.66*** (3.16) 4.62** (2.46) Diff (Late – Low Vol -11.84*** (-3.72) -11.26*** (-3.66) -10.91*** (-3.69) -3.21** (-1.98) -2.25* (-1.81) -1.16 (-1.17) 5.95*** (4.91) 5.40*** (5.66) 4.62*** (4.68) -8.51*** (-5.46) -7.71*** (-6.24) -6.82*** (-6.41) 36 Table 3 Valuation Uncertainty of Early and Late Issuers This table reports measures of valuation uncertainty and dispersion of firm quality for firms that go public early in an IPO wave and firms that go public late in an IPO wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. We identify six IPO waves from January 1970 to December 2005. Within each IPO wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of the firms go to public. The table also summarizes valuation uncertainty variables for wave periods on average, and low-volume periods. Low-volume periods are quarters in which the actual number of IPOs lies below the 5th percentile of the simulated distribution. Variables shown in this table are as follows. In Panel A, IR_VOL is the volatility of initial returns and is calculated as standard deviation of IPO initial returns; 12m BHAR Volatility is the cross-sectional volatility of 12-month buy-and-hold abnormal returns (BHAR) where an issuers 12-month BHAR is calculated as the issuer’s buy-and-hold return less the buy-and-hold return of a portfolio of size-matched firms. Likewise, 24m BHAR volatility is the cross-sectional volatility of 24-month BHARs. In Panel B, PER_NASDAQ is the percentage of IPOs listed on NASDAQ. UWRANK is the average Carter-Manaster (1990) underwriter ranking score, as updated by Carter, Dark, and Singh (1998) and Loughran and Ritter (2004). LNSIZE is calculated as the logarithm of the number of shares (in millions) offered in the IPO where the number of shares offered is obtained from SDC platinum; RANGE is the difference between the maximum original price filed and the minimum original price filed. The values reported are the mean of each group. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. 37 PANEL A: IR and BHAR Volatility Early Late F-test (Prob >F) IRVOL: Volatility of Initial Returns 10% 15% 20% 0.658 0.637 0.610 0.231 0.273 0.272 <0.0001*** <0.0001*** <0.0001*** 12m BHAR Volatility 10% 15% 20% 0.796 0.776 0.740 0.683 0.605 0.718 0.008*** <0.0001*** 0.06* 10% 15% 20% 0.906 0.856 0.815 0.778 0.792 0.797 0.004*** 0.08* 0.15 24m BHAR Volatility Wave Periods LowVolume Periods F-test (Prob >F) 0.437 0.373 <0.0001*** 0.749 0.521 <0.0001*** 0.759 1.089 <0.0001*** PANEL B: Issue Size, Percentage NASDAQ, Price Range, and Percentage Withdrawals Cutoff LNSIZE: Issue Size PER_NASDAQ: Percentage of NASDQQ firms UWRANK: Underwriter Rank WITHDRAW: Percentage of Withdrawals RANGE: Filing Price Range Observations Early Late 10% 14.336 14.471 15% 14.381 14.489 20% 14.396 14.480 10% 49.39% 48.15% 15% 53.32% 48.94% 20% 53.22% 48.87% 10% 6.565 6.047 15% 6.453 6.209 20% 6.513 6.269 10% 23.89 19.81 15% 23.73 20% 22.82 20.71 10% 1.70 1.45 15% 1.69 1.52 20% 1.73 1.53 10% 15% 20% 316 467 622 324 472 620 20.86 Early Late -0.134** (-2.01) -0.108* (-1.77) -0.084* (-1.65) 1.24 (1.39) 4.38** (2.59) 4.35** (2.23) 0.527** (2.34) 0.243* (1.66) 0.244* (1.65) 4.07** (2.24) 2.87* (1.69) 2.11 (1.47) 0.25 (2.72)** 0.17 (2.27)* 0.20 (3.09)** Wave Period IPOs NonWave Period Diff (Wave – Non-Wave) LNSIZE: Issue Size 14.462 14.732 -0.267*** (-5.99) PER_NASDAQ: Percentage of NASDQQ firms 51.57% 55.92% -4.35** (-2.24) 6.341 6.432 -0.091 (-1.00) 20.48% 18.53% 1.95* (1.75) 1.62 1.40 0.22 (0.68) 3052 633 UWRANK: Underwriter Rank WITHDRAW: Percentage of Withdrawals RANGE: Filing Price Range Observations 38 Table 4 Analyst Recommendation for Early and Late Issuers This table reports the recommendations given by affiliated and unaffiliated analysts to firms that issue in the early and late phases of IPO waves. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Correspondingly, quarters which have actual number of IPOs below the 5th percentile from the simulated distribution are identified as non-wave periods. We identify six IPO waves from January 1970 to December 2005. Within each IPO wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of the firms go to public. For each IPO firm, we calculate the recommendation value, RECD, as the mean value of I/B/E/S recommendations from affiliated analysts or unaffiliated analysts in the first year after IPOs. For each issuing firm, the percentage of positive recommendations, PER_RECD, from affiliated (unaffiliated) analysts is calculated as the number buy or strong buy recommendation issued by affiliated (unaffiliated) analysts divided by the total number of recommendations issued by affiliated (unaffiliated) analysts. Panel A reports RECD and PER_RECD from affiliated and unaffiliated analysts for early and late moves using the 10 percent cutoff to define early and late movers. Panel B reports RECD and PER_RECD from affiliated and unaffiliated analysts for the 15 and 20 percent cutoffs. Panel C reports the fraction of Affiliated Favored and Unaffiliated Favored IPOs in the early and late stages of a wave. Affiliated Favored IPOs are IPOs that get a higher average recommendation from affiliated analysts than from unaffiliated analysts. Unaffiliated Favored IPOs are IPOs that get a lower average recommendation from affiliated analysts than from unaffiliated analysts. No difference IPOs are defined as those that get a same average recommendation from affiliated analysts as from unaffiliated analysts. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. 39 Panel A : Early (Late) movers defined as the first (last) 10% of IPOs in a wave Unaffiliated RECD Affiliated Diff (Unaff - Aff) Unaffiliated PER_RECD Affiliated Diff (Unaff - Aff) Observations Early Late 1.814 1.724 1.646 1.693 0.168** (2.28) 0.032 (0.39) 0.821 0.842 0.891 0.820 -0.070** (-2.38) 159 0.022 (0.62) 167 Wave Periods 0.090 (1.14) -0.046 (-0.60) 1.795 1.808 1.683 1.773 0.112*** (4.58) 0.035 (0.78) 0.824 0.809 0.870 0.828 -0.046*** (-4.33) 1752 -0.019 (-0.86) 439 -0.021 (-0.65) 0.071** (2.25) Diff (Wave – NonWave) -0.013 (-0.33) -0.089** (-2.27) Non-Wave Periods Early - Late 0.015 (0.84) 0.042** (2.46) Panel B: Early (Late) movers defined as the first (last) 15% or 20% of IPOs in a wave 15% cut-off 20% cut-off RECD PER_RECD Early Late Unaffiliated 1.859 1.751 Affiliated 1.690 1.674 Diff (Unaff - Aff) 0.169** (2.60) 0.077 (1.19) Unaffiliated 0.796 0.838 Affiliated 0.859 0.829 -0.063** (-2.32) 214 0.009 (0.30) 212 Diff (Unaff - Aff) Observations Diff (Early - Late) 0.108* (1.64) 0.016 (0.25) -0.042 (-1.43) 0.030 (1.06) Early Late 1.822 1.759 1.692 1.667 1.30** (2.31) 0.092 (1.62) 0.807 0.837 0.863 0.847 -0.057** (-2.37) 264 -0.011 (-0.42) 267 Diff (Early - Late) 0.063 (1.10) 0.023 (0.46) -0.030 (-1.16) 0.016 (0.66) Panel C: IPOs Favored by Affiliated and Unaffiliated Analysts Early 10% 15% 20% Late Affiliated Favored 53.38% 41.45% Unaffiliated Favored 34.75% 41.45% Affiliated Favored 52.11% 41.86% Unaffiliated Favored 34.98% 44.34% Affiliated Favored 52.96% 45.97% Unaffiliated Favored 36.86% 42.93% Diff (Early – Late) 12.07%** (1.97) -6.71% (1.47) 10.25%* (1.86) -9.36% (1.81) 6.99% (1.58) -6.07% (1.46) 40 Table 5 Multivariate Analysis of Analyst Recommendations This table reports a multivariate analysis of recommendations given by affiliated and unaffiliated analyst to issuing firms. In each regression, the dependent variable is the recommendation value, RECD, calculated as the mean value of I/B/E/S recommendations from affiliated analysts or unaffiliated analysts in the first year after IPO. We regress RECD on LNSIZE, UWRANK, IR, AFF, EARLY, AFF_EARLY. The variable LNSIZE is the coefficient of the logarithm of number of shares (in millions) offered in the IPO. UWRANK is the underwriter ranking scores (Loughran and Ritter,1994). Initial return, IR, is defined as the difference between the first-day close price and offer price over the first-day close price. AFF is a dummy variable that is equal to one if the recommendation is from the affiliated analysts and zero otherwise. EARLY is a dummy variable that is equal to one if the IPO is an early mover and zero if it is a late mover within an IPO wave. AFF_EARLY is the interaction of AFF and EARLY. In columns 1, 2, and 3 respectively, early movers are defined as the first 10%, 15%, or 20% of the firms to go public in an IPO wave. EARLY2 is a dummy variable equal to one if the issuer is an early mover in an IPO wave and zero otherwise. AFF_EARLY2 is the interaction of AFF and EARLY2. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Within each IPO wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of the firms go to public. t-statisics are provided in parenthesis. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively.. RECD (10%) RECD (15%) RECD (20%) RECD (10%) 1 2 3 4 Intercept 0.235 (0.42) 0.656 (1.22) 0.726 (1.35) 0.791*** (4.49) LNSIZE 0.075* (1.88) 0.059 (1.55) 0.048 (1.24) 0.056*** (4.18) UWRANK 0.031* (1.82) 0.036* (1.93) 0.034* (1.86) 0.022*** (3.90) IR -0.334*** (-3.92) -0.214*** (-2.84) -0.206*** (-2.91) -0.082*** (-4.59) AFF -0.091 (-1.27) -0.048 (-0.74) -0.079 (-1.05) -0.063** (-3.42) EARLY 0.133 (1.58) 0.085 (1.28) 0.097 (1.41) AFF_ EARLY -0.212** (-2.08) -0.185** (-2.02) -0.203** (-2.03) EARLY2 -0.009 (-0.17) AFF_EARLY2 -0.071* (-1.91) HIGHIR -0.057* (-1.75) AFF_HighIR -0.031 (-0.69) Ad_R (%) 3.90 3.84 3.87 2.82 Obs 542 706 902 4995 41 Table 6 Operating Performance and BHARs of Early and Late Issuers This table compares operating performance and abnormal stock returns of firms that go public early and firms that go public late in an IPO wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95 th percentile of the simulated distribution is classified as an IPO wave. We identify six IPO waves from January 1970 to December 2005. Within each IPO wave, early (late) movers are defined as the first (last) 10%, 15%, and 20% of the firms go to public. The following variables are calculated as of 2 years after IPO date. Cash Flow Margin is calculated as operating income before depreciation over net sales. Return on Assets is calculated as net income over Book Assets. Asset Turnover is calculated as net sales over total assets. Cash to Net Assets is cash and cash equivalents divided by total assets less cash and cash equivalents. Capital Expenditure ratio is calculated as the issuing firm’s capital expenditure divided by total assets. Sales Growth is calculated as firm as sales two years after issue date less sales in the IPO year divided by sales in the IPO year. Change in Market Share of an issuing firm over this two-year period is market share two years after IPO less market share in the IPO year divided by market share in the IPO year. Market share is firm sales over total sales of all firms in the same three-digit SIC code. For all aforementioned variables except Change in Market Share, an abnormal value is calculated by subtracting the median value of the 3-digit SIC industry. 24-month BHAR is the buy-and-hold-abnormal return 24 months after IPO date. It is calculated the buy-and-hold return of the issuing firm over the 24 months following IPO less the buy-andhold return of an size-matched portfolio over the same period. Since data availability for each variable differs, the table presents the minimum number of observations used in each subgroup. t-statistics presented in absolute values are in provided in parentheses. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. 42 Abnormal Cash-Flow Margin Abnormal Return on Assets Abnormal Asset Turnover Abnormal Sales Growth Change in Market Share Abnormal Cash to Net-Asset Ratio Abnormal Capital Expenditure Ratio Cutoff 10% Early -1.181 Late -0.488 15% -0.973 -0.615 20% -0.831 -0.543 10% -0.118 -0.068 15% -0.096 -0.082 20% -0.088 -0.094 10% 0.037 0.123 15% 0.069 0.101 20% 0.061 0.085 10% 2.685 6.500 15% 3.199 5.341 20% 3.844 4.763 10% 1.819 5.379 15% 2.133 4.405 20% 2.679 3.923 10% -0.367 -0.161 15% 0.515 -0.028 20% 0.081 -0.194 10% 0.0284 0.031 15% 0.031 0.029 20% 0.033 0.032 -0.129 -0.081 -0.113 -0.098 -0.111 -0.097 114 188 264 127 173 236 10% 24-Month BHAR 15% 20% Observations (min) 10% 15% 20% Early-Late -0.692* (1.83) -0.357 (1.17) -0.288 (1.19) -0.049 (1.33) -0.014 (0.47) 0.005 (0.21) -0.085 (1.28) -0.031 (0.57) -0.025 (0.52) -3.813** (2.05) -2.141 (1.61) -0.919 (0.78) -3.560** (2.43) -2.271** (2.17) -1.243 (1.35) -0.206 (0.47) 0.079 (0.22) 0.276 (0.92) -0.002 (0.35) 0.001 (0.22) -0.001 (0.11) -0.048 (1.21) -0.015 (0.41) -0.014 (0.40) 43 Table 7 Institutional Trading and Analyst Bias This table reports institutional holdings and institutional buying of early and late issuers depending on whether they are Affiliated Favored or Unaffiliated Favored IPOs. Early (late) issuers in an IPO wave are defined as the first (last) 10%, 15%, and 20% of the firms go to public in a wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Affiliated Favored IPOs are IPOs that get a higher average recommendation from affiliated analysts than from unaffiliated analysts; Unaffiliated Favored IPOs are IPOs that get a lower average recommendation from affiliated analysts than from unaffiliated analysts. For each IPO firm, institutional holding, HOLDING, is calculated as the shares held by institutional investors divided by total share outstanding at the end the 4 th quarter after IPO. We define an institutional investor as a “buyer” (“seller”) if its holding at the 4th quarterend after issue date is more (less) than its holding at 1st-quarter-end after issue date. For institutional “buyers”, the percentage of institutional buys, BUY, is defined as 4th quarter-end institutional holding less the 1st quarter-end institutional holding divided by the total shares outstanding at end of 4th quarter after IPO. For institutional “sellers”, the percentage of institutional sells, SELL, is defined as the 1st quarter-end institutional holding less the 4th quarter-end institutional holding divided by the total shares outstanding at end of 4th after IPO. Panel A reports HOLDING, Panel B reports BUY and Panel C reports SELL. Each panel presents results for all three early/late cutoff points: 10%, 15% and 20%. t-statistics are provided in parenthesis and the symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. 44 Panel A Institutional Holding 10% Early/Late Cutoff Affiliated Favored Unaffiliated Favored Difference 15% Early/Late Cutoff 20% Early/Late Cutoff Early Late Diff (Early-Late) Early Late Diff (Early-Late) Early Late Diff (Early-Late) 35.52 38.88 34.10 35.91 37.73 30.69 36.64 31.00 -1.82 (-0.68) 6.73* (1.72) 35.63 39.30 -3.36 (-1.08) 8.61* (1.75) 37.63 32.13 -2.10 (-0.90) 5.50* (1.64) -2.67 (-0.90) 8.19* (1.78) -2.75 (-0.94) 5.80* (1.64) -1.99 (-0.74) 5.61* (1.78) Panel B Institutional Buys 10% Early/Late Cutoff Affiliated Favored Unaffiliated Favored Difference 15% Early/Late Cutoff 20% Early/Late Cutoff Early Late Diff (Early-Late) Early Late Diff (Early-Late) Early Late Diff (Early-Late) 26.05 38.72 28.00 36.44 34.94 23.29 28.16 18.79 -8.44* (-1.94) 9.85** (2.28) 30.94 28.83 -12.67** (-2.31) 5.54 (1.23) 29.38 21.41 -3.39 (-1.00) 7.97* (1.95) -2.78 (-0.86) 15.34** (2.22) -0.65 (-0.32) 17.64*** (3.26) 1.56 (0.32) 12.93** (2.68) Panel C Institutional Sells 10% Early/Late Cutoff Affiliated Favored Unaffiliated Favored Difference 15% Early/Late Cutoff 20% Early/Late Cutoff Early Late Diff (Early-Late) Early Late Diff (Early-Late) Early Late Diff (Early-Late) 24.66 16.89 24.68 20.26 20.81 22.02 21.05 24.33 4.43 (1.27) -3.28 (-0.83) 24.62 17.06 7.77 (1.53) -4.96 (-1.11) 21.58 25.31 3.81 (1.16) -3.73 (-0.94) 7.60 (1.38) -5.13 (-1.11) 3.63 (0.83) -4.07 (-1.23) 3.04 (0.93) -4.50 (-1.36) 45 Table 8 Multivariate Analysis of Institutional Activities and Analyst Bias This table examines the relation between institutional holdings and analyst recommendations in a multivariate setting. In Panel A, the dependent variable is institutional holding (HOLDING) one year after issue date. In Panel B, the dependent variable is the percentage increase in institutional holdings (BUY) from the first quarter after issue date till the fourth quarter after issue date. Columns 1 and 2 of both panels restrict the sample to early issuers and late issuers respectively. Early (Late) issuers are defined as the first (last) 10% of issuers in an IPO wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Columns 3 and 4 of both panels restrict the sample to Affiliated Favored and Unaffiliated Favored issuers respectively. Affiliated Favored IPOs are IPOs that get a higher average recommendation from affiliated analysts than from unaffiliated analysts, Unaffiliated Favored IPOs are IPOs that get a lower average recommendation from affiliated analysts than from unaffiliated analysts. The variable LNSIZE is the logarithm of the number of shares(in million) offered in the IPO. IR is the initial return which defined as the difference between the first-day close price and offer price over the first-day close price. UWRANK is the underwriter ranking score (Loughran and Ritter,1994). AFF_FAVORED is a dummy variable equal to 1 if the issuer is Affiliated Favored and 0 if it is Unaffiliated Favored. EARLY is a dummy variable that is equal to one if the IPO is an early mover and zero if it is a late mover. t-statistics are in parenthesis and the symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. PANEL A: INSTITUTIONAL HOLDINGS Intercept LNSIZ IR UWRANK AFF_FAVORED PANEL B: INSTITUTIONAL BUYS 1 2 3 4 1 2 3 4 Early Late Late -1.287 (-1.26) 0.150* (1.67) -0.080 (-0.97) 0.081 (0.97) 0.129** (2.42) Unaffiliated Favored -0.545 (-0.84) 0.036 (0.72) 0.001 (0.01) 0.041 (1.54) Early 0.343 (0.64) 0.027 (0.77) 0.013 (0.07) 0.045* (1.69) 0.004 (0.07) Affiliated Favored -0.485 (-0.70) 0.048 (1.00) -0.069 (-0.21) 0.027 (0.82) 0.018 (0.04) 0.001 (0.01) -0.132 (-0.76) 0.032 (1.30) 0.011 (0.18) -1.536* (-1.78) 0.146* (1.87) 0.359 (0.64) 0.061 (1.96) 0.146* (1.84) Affiliated Favored -0.069 (-0.11) 0.022 (0.48) 0.351 (1.09) 0.011 (0.35) Unaffiliated Favored -0.581 (-1.09) 0.030 (0.73) -0.232 (-1.25) 0.041 (1.22) -0.023 (-0.26) 3.25 0.114* (1.85) 4.98 0.083* (1.82) 5.67 EARLY Ad_R (%) 3.19 8.11 Obs 249 237 226 207 5.52 10.38 -0.132 (-1.51) 3.48 139 137 104 107 46 Table 9 The Market’s Reaction to Analyst Recommendations This table presents the market’s reaction to strong-buy recommendation given to early and late issuers by affiliated and unaffiliated analysts. The market’s reaction is captured by the cumulative market adjusted return (CAR) over three days (-1,+1) centered on the recommendation date. Information on strong-buy recommendations and recommendation dates are obtained from I/B/E/S. An issuer’s CAR is calculated as its return cumulated over the three-day window less cumulated return of the CRSP value-weighted market index. Early (late) issuers in an IPO wave are defined as the first (last) 10%, 15%, or 20% of the firms go to public in a wave. IPO waves are identified by simulating aggregate IPO activity in 1970s, 1980s, 1990s, and 2000s separately. Any period of three or more consecutive quarters with actual IPO activity greater than the 95th percentile of the simulated distribution is classified as an IPO wave. Panel A presents mean CARs for early and late issuers split by analyst affiliation. A recommendation is defined as Affiliated if a lead manager or co-manager of the IPO employs the analyst; otherwise the recommendation is defined as Unaffiliated. Panel B presents a multivariate analysis of the abnormal returns. We regress the CAR on the following variables LNSIZE, UWRANK, IR, AFF, EARLY, AFF_EARLY. The variable LNSIZE is the logarithm of the number of shares(in million) offered in the IPO. UWRANK is the underwriter ranking scores (Loughran and Ritter,1994). IR is the initial return defined as the difference between the first-day close price and offer price divided by the first-day close price. AFF is equal to one if the recommendation is from the affiliated analysts and zero otherwise. EARLY is equal to one if the IPO is an early mover and zero if it is a late mover. AFF_EARLY is the interaction term of AFF and EARLY. Patell (1976) Z test statistics are reported in parenthesis and the symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. Panel A: Univariate analysis of CARs (All Recommendations) 10% 15% Early Late Early Late Affiliated 1.81* 2.19*** 2.92** 1.99*** RECD (1.60) (3.23) (1.63) (3.51) Unaffiliated 4.29*** 0.85* 3.96*** 0.72* RECD (4.99) (1.34) (6.56) (1.55) Diff -2.48*** 1.34* -1.04* 1.27 (Aff-Unaff) (-2.79) (1.68) (-1.68) (1.54) Early 2.26** (2.01) 3.68*** (7.22) -1.42** (-1.99) 20% Late 2.16*** (4.36) 1.01** (1.94) 1.15* (1.72) Panel A: Univariate analysis of CARs (All Recommendations) 1 2 2.539*** 2.531*** Intercept (4.59) (4.58) -0.181*** -0.175*** LNSIZE (-4.53) (-4.35) 0.038 0.037 UWRANK (1.55) (1.52) 0.021 0.020 IR (0.24) (0.23) -0.097* -0.128 AFF (-1.82) (-1.37) 0.073 EARLY (0.58) -0.164** AFF_EARLY (-2.00) 3.99 4.87 Adjusted R-square Obs 527 527 47 Table 10 Robustness to Moving Averages Classification of IPO Wave This table presents compares of valuation uncertainty, analyst recommendations, and institutional buys for early and late issuers in IPO waves. IPO waves are identified using 12,648 IPOs from January 1970 to December 2005 in U.S. market following the moving-average method of Helwege and Liang (2004). For each decade, we calculate three-quarter moving averages of the number of IPOs for each quarter. Periods with at least three consecutive quarters that have a moving average exceeding the top quartile of the quarterly moving average are labeled as wave periods, and those with a moving average lower than the bottom 30% of the quarterly moving average are considered non-wave periods. This method identifies six six IPO waves from January 1970 to December 2005. Within each IPO wave, early-movers and latemovers are defined as the first and last 10% firms go to public. Panels A and B compare measures of valuation uncertainty in the early and late stages of IPO waves. All variables in Panel A and B are calculated as defined in Table 3. Panel C compares recommendations issued by affiliated and unaffiliated analysts for early and late issuers. The variables RECD and PER_RECD are as defined in Table 4. Panel D compares institutional buying of early and late IPOs. Institutional buys, affiliated favored, and unaffiliated favored IPOs are calculated as defined in Table 7. The values reported are the mean of each group. The symbols, *, **, and ***, indicate significance at the 10%, 5%, and 1% levels, respectively. 48 Panel A: Uncertainty (IR and BHAR Volatility) Early Late F-test (Prob>F) IRVOL: Volatility of Initial Returns 0.440 0.283 <0.0001*** IRVOL: Volatility of Initial Returns 24m BHAR Volatility 0.990 0.762 <0.0001*** 12m BHAR Volatility Wave Periods 0.517 NonWave periods 0.358 0.757 1.021 F-test (Prob>F) <0.0001*** <0.0001*** PANEL B: Uncertainty (Issue Size, Percentage NASDAQ, Price Range, and Percentage Withdrawals) LNSIZ Issue Size PER_NASDAQ: Percentage of NASDAQ firms EarlyMover in the waves 14.425 Late Mover in the waves 14.480 Diff (early late) -0.055 (-1.09) 54.92% 8.65*** (2.69) 46.27% NonWave periods Wave Periods Diff (early late) LNSIZ Issue Size 14.461 14.634 -0.173*** (-4.43) PER_NASDAQ: Percentage of NASDAQ firms 55.88% 57.72% -1.84% (-1.62) UWRANK: Underwriter Rank 6.529 6.073 0.456** (2.43) UWRANK: Underwriter Rank 6.331 6.533 -0.202* (-1.72) WITHDRAW: Percentage of Withdrawal 21.13 20.00 1.13 (1.05) WITHDRAW: Percentage of Withdrawal 21.57 18.86 2.71* (1.90) RANGE: Filing Price Range 1.74 1.44 0.29*** (4.01) RANGE: Filing Price Range 1.59 1.51 0.08* (1.72) Observations 452 450 Observations 2261 519 Panel C: Analyst Recommendations RECD Unaffiliated Earlymovers in the waves 1.808 Latemovers in the waves 1.711 Affiliated 1.688 1.728 Diff (Unaff – Aff) 0.120* (1.91) -0.016 (-0.28) PER_RECD Diff (early – late) 0.097 (1.52) -0.040 (-0.70) Earlymovers in the waves 0.813 Latemovers in the waves 0.847 0.866 0.855 -0.053** (-2.09) -0.007 (-0.29) Diff (early – late) -0.035 (-1.29) 0.011 (0.46) Panel D: Institutional Buys (BUY) of IPOS Affiliated Favored Unaffiliated Favored Difference Early Mover Late Mover 29.53 36.81 33.67 28.21 -4.14 (-0.75) 10.14** (2.22) Diff (early – late) -7.28* (1.65) 5.48 (0.88) 49