Investor Demand in Bookbuilding IPOs: The US Evidence

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1 Investor Demand in Bookbuilding IPOs: The US Evidence Yiming Qian University of Iowa Jay Ritter University of Florida An Yan Fordham University August, 2014

2 Abstract Existing studies of auctioned IPOs show that individual investors demand are subject to sentiment. Using a proprietary US dataset, we examine institutional investor demand in bookbuilding IPOs. We find that investor demand in bookbuilding IPOs is positively related to public information variables such as VC backing and the firm s profitability, but does not depend on the recent market return or market sentiment. IPO demand is positively associated with the stock s first day return, but is not correlated with its long run returns. These results suggest that institutional investors in bookbuilding IPOs take into account valuerelevant information and do not appear to be subject to sentiment, which lends support to the fundamental assumption of the classic bookbuilding theory that IPO investors are informed investors.

3 1. Introduction Bookbuilding (or the US method) is the most popular method for initial public offerings (IPOs) internationally. The majority of the IPO literature has been devoted to this type of offerings. Yet little is known about a key and fundamental element of the important process, i.e., the demand of bookbuilding investors. What factors drive their demand? How does investor demand affect the offer price and the aftermarket prices and returns? We know little of the answers because of the lack of the data. In the US as well as in most other countries where bookbuilding is used as the IPO method, underwriters guard the demand information as top secrets. 1 We obtain access to the demand data of one prominent investment bank on the US market for the IPOs it underwrote during We attempt to answer some of the questions above. A few papers are able to study IPO demand of non bookbuilding investors. Derrien (2005) examines individual investors demand for a sample of French IPOs where a fraction of the offering is reserved for individual investors. Dorn (2009) also studies individual investors demand on Germany s when issued market for IPO shares. Both studies document that individual investors demand is positively related to market conditions, and that high demand leads to high initial returns and poor long run performance. Both therefore conclude that individual investors demand reflects sentiment. 2 Chiang, Sherman, and Qian (2010) examine Taiwanese IPO auctions where individual investors are the dominant class of investors. They find that individual investors are return chasers, 1 As rare exceptions, Cornelli and Goldreich (2001, 2003) and Jenkinson and Jones (2004) each study books (containing demand and allocation data) of a European investment bank. But they examine very different questions from this study. They focus on what type of bids and investors tend to receive favored allocation. 2 Cornelli, Goldreich and Ljungqvist (2006) use prices from European when issued market as a proxy for individual investors sentiment and also find that high sentiment leads to high initial returns and low long run returns. 1

4 i.e., there are more likely to bid in an IPO if recent IPO returns are high, consistent with the notion that they are sentiment investors. 3 Bookbuilding IPOs differ from other IPO methods such as auctions and fixed price offerings in two important aspects. First, underwriters enjoy the most discretion in bookbuilding offerings, over both pricing and allocating the shares. Second, the investor clienteles are very different. Across countries, fixed price offerings cater to individual investors either by explicit restrictions, or by restricting the maximum possible allocation size to a small number. Auctions can be open to both institutional and individual investors, but are often dominated by individual investors. In contrast, investors participating in bookbuilding IPOs are mostly institutional investors. The papers discussed above examine individual investors demand and its impact under other IPO methods. It remains to be seen, however, for most IPOs in the world, whether (institutional) investor demand is also driven by sentiment and hence has similar impact on short run and long run returns. We try to fill this void in the literature. We examine three research questions: (1) Is investor demand in bookbuilding IPOs rational, i.e., is demand driven by fundamentals or sentiment, or both? (2) How does investor demand affect the initial returns? (3) How does investor demand affect the long run returns? On the second question, both rational theories based on information asymmetry and behavioral theories of investor sentiment predict a positive relationship between initial returns and investor demand. Under the sentiment hypothesis, an IPO may be overpriced relative to its fundamental value but will still be underpriced relative to what sentiment investors are willing to pay (hence positive initial returns) either because sentiment investors can t absorb all the IPO shares (Ljungqvist, Nanda and Singh, 2004), or because underwriters need to hedge the risk of costly price support in case of 3 Other papers have examined the dispersion of orders in IPO auctions. See Kandel, Sarig, and Wohl (1999), Liu, Wei, and Liaw (2001), DeGeorge, Derrien, and Womack (2009). 2

5 weak aftermarket prices (Derrien, 2005). If higher demand indicates higher sentiment, then there is a positive relation between demand and initial return. Under the asymmetric information based theories, demand may very well be driven by private information, and the IPO pricing will only partially adjust to the positive information revealed in the demand. That is, the more positive the information, the higher the initial return. 4, 5 The two types of theories, however, have very different predictions on IPO firms long run stock performance. Given that a stock s price converges to its fundamental value eventually, if it is initially overpriced as predicted by the sentiment hypothesis, then we expect to see underperformance in the long run. Ljungqvist, Nanda and Singh (2004) further point out that the long run underperformance should hold not only relative to the first trading day price, but also to the offer price, since the theory predicts that the offer price will also be higher than the fundamental value albeit lower than what the sentiment investors are willing to pay. If higher sentiment leads to more initial overpricing, then higher sentiment is associated with more underperformance in the long run. Under the rational theories, the aftermarket price is efficient and offer price is on average lower than the fundament value, hence there will be no long run underperformance either relative to the aftermarket first day price, or relative to the offer price. Our results show that investor demand in bookbuilding IPOs is positively related to public information variables such as whether the firm is backed by a VC, and whether the firm already generates positive profits. Demand does not significantly depend on the recent market return or market sentiment. The results are therefore in contrast to previous evidence on individual investors 4 For the classic bookbuilding theory, see Benveniste and Spindt (1989), Beveniste and Wilhelm (1990) and Spatt and Srivastava (1991). For costly information production theories, see Sherman and Titman (2002), Sherman (2005), and Benveniste, Busaba and Wilhelm (2002). 5 In addition to the two types of theories based on information asymmetry or sentiment, Loughran and Ritter (2003) argue that self interested underwriters want to underprice IPO shares and use prospect theory to explain why issuers are fine with that. The theory predicts that the IPO pricing will partially adjust to both private and public information. Based on this theory, even if investor demand reflects only public information, it will also be associated with higher initial return. This theory predicts the offer price is underpriced and assumes the aftermarket price is efficient, hence it does not predict long run underperformance. 3

6 demand. It is consistent with the notion that institutional investors are a class of more rational and more sophisticated investors. Similar as previous papers, we find that high investor demand strongly predict high initial returns. In fact, demand alone has more explanatory power than all other determinants together we use that are known to have an impact on initial returns. This evidence itself, as discussed before, is consistent with both the rational asymmetric information theories and the sentiment hypothesis. The results on long run returns support the notion that institutional demand is not driven by sentiment. Long run returns, either relative to the immediate after market price or the offer price, are not significantly related to investor demand in the bookbuilding process. Instead, we find that long run returns are related to information publicly available at the time of IPO such as the initial price range, and whether the firm already generates positive profits, which is consistent with the findings of Field and Lowry (2009). In addition, long run returns are negatively related with market sentiment at the time of IPO, although there is no evidence sentiment has bigger impact on IPO firms than on firms in general. Overall, we find institutional demand in bookbuilding IPOs take into account value relevant information and find no evidence that their demand is inflicted with sentiment, which lends support to the important assumption of the classic bookbuilding theory that IPO investors are informed investors. The findings can also contribute to the debate on IPO method. It has been shown that powerful underwriters tend to abuse the bookbuilding system (e.g., see Reuter, 2006 and Liu and Ritter, 2010). Interestingly, there has been an international trend to converge to this IPO method once it is allowed with no additional regulatory restrictions. 6 If the merit of the bookbuilding method largely lies with 6 See Jagannathan, Jirnyi and Sherman (2011). 4

7 the rationality and sophistication of its investor clientele, issuers and regulators can consider using other methods but restricting IPO access to such investors as well. It is important to point out that our results do not disapprove the possibility that the aftermarket prices or the offer price of IPO shares can be affected by sentiment. Even if bookbuilding investors are rational, anticipating they can sell to sentiment investors when the aftermarket prices are too high, the issuer may be able to negotiate a higher offer price compared to the situation without sentiment investors on the after market (Ljungqvist, Nanda and Singh (2004)). In fact, we do find evidence that high sentiment at the time of IPO is associated with low long run returns. But such sentiment is not reflected in investor demand in bookbuilding IPOs. The rest of the paper is organized as follows. In section 2 we describe our sample and data. Section 3 examines what factors drive investor demand. Section 4 studies the relationship between investor demand and initial returns. Section 5 investigates the impact of investor demand on longrun returns, either relative the first trading day closing price, or relative to the offer price. Section 6 concludes. 2. Sample and variables We obtain the demand data from one investment bank for IPOs it underwrote during The bank is active in both the US and international markets. Considering that different markets may have different institutions and different clienteles of investors, we focus on its US offerings. To make sure the demand data is reliable and complete, we also require that the bank act as a bookrunner for an IPO to be included in the sample. Our final sample includes 48 US offerings during (the last IPO was in June 2008). Table 1 Panel A shows the numbers of IPOs by year. 5

8 We then match the sample to SDC and Compustat to retrieve data on its IPO and firm characteristics. When data is missing, we manually collect information from the firm s IPO prospectus. The return data are obtained from CRSP. Table 1 Panel A presents the summary statistics of the firm and IPO characteristics. The average firm in our sample has asset value of $1 billion in the fiscal year prior to its IPO. Thirty eight percent of them are backed by venture capital. Sixty three percent of them already show positive EBITDA (earnings before interest, tax and depreciation and amortization). Field and Lowry (2009) find evidence that institutional investors tend to make better use of such public information in determining their holdings soon after the IPO. The average IPO raises $220 million in the offering. The price range, which is the ratio of the higher bound of the initial price range over the lower bound of the price range minus one, has an average of 11%. The offer price has a range of $6 $29 and an average of $13. We define price revision as the ratio of the offer price over the midpoint of the initial price range minus one. Hanley (1993) documents this variable is positively related to the first day initial return, which is the well known partial adjustment phenomenon. In our sample, price revision has a slightly negative mean of 3.5% and a zero median. The key variables for this study measure investor demand for IPO shares. We use two variables: subscription ratio and dollar demand. Subscription ratio is the total investor demand over the number of shares offered in the IPO excluding the overallotment. Dollar demand is the total investor demand times the offer price. The average IPO has a subscription ratio of 5.7 times, and a dollar demand of $1.7 billion (in comparison, the average IPO proceeds is $220 million). All but two offerings are oversubscribed (i.e., with a subscription ratio larger than one), half of the sample IPOs have a subscription ratio of at least 2, and 9 of the offerings have a subscription ratio of at least 10. 6

9 Table 1 Panel C presents the market condition and after market returns of the IPOs. For market conditions, we look at two measures: the market return 3 months before the IPO, and the market sentiment index based on Baker and Wurgler (2006, 2007) for the month of the IPO. Baker and Wurgler (2006, 2007) sentiment index is the first principal component of six standardized sentiment proxies, each proxy first orthogonalized with respect to a set of macroeconomic conditions. The six sentiment proxies are average discount on closed end mutual funds, detrended natural log turnover of NYSE firms, number of initial public offerings, average first day returns of initial public offerings, the ratio of equity issuance to total equity and debt issuance, and the dividend premium (i.e. log ratio of average market to book ratios of dividend payers and nonpayers). The set of macroeconomic variables are the growth in industrial production, the growth in durable, nondurable and services consumption, the growth in employment and a NBER recession dummy. The sentiment index is standardized to have zero mean and unit variance for the construction period (i.e. July 1965 December 2010 for the data we use). In our sample, the average 3 month market return before IPO is 4.7%. The market sentiment ranges from 0.28 in July 2005 to 0.34 in June The average market sentiment is 6%. On average, the market conditions seem to be good but not overheated. We look at both the first day initial return and the long run returns after the IPOs. The initial return is the ratio of the closing price of the first trading day over the offer price minus one. The 6 month (18 month, 3 year) return is the buy and hold return 6 months (18 months, or 3 years) after the first trading day of the IPO. Abnormal returns are adjusted of the CRSP value weighted market return. The average initial return is 8.6%. The average long run raw and abnormal returns are all insignificantly different from zero at the conventional significance levels except for the 3 year return of 23.3%, which is significant at 1% level. 3. Determinants of Investor Demand 7

10 We examine what determine investor demand for the bookbuilding IPOs in our sample. Specifically, we are interested to see whether the demand depends on market conditions or sentiment, similar to individual investors demand as previously documented. We use a direct measure of market sentiment based on Baker and Wurgler (2006, 2007) and use the market return prior to the IPO as another measure for market conditions. We examine the relationship between investor demand and prior market returns in Table 2. In Panel A, we divide the sample into terciles based on the 3 month market returns prior to the IPO. We then look at the mean and median subscription ratio and natural log dollar demand in each tercile. The mean and median subscription ratios increase from market return tercile one to tercile 3, but not in a monotonic way. We perform t test for the means and Wilcoxon Mann Whitney test for the medians between terciles one and three. Neither of the differences is statistically significant. Results are similar for natural log of dollar demand. Using natural log smoothes out the distribution of dollar demand and reduces the impact of outliers. Using the dollar demand itself largely increases its mean value in the first tercile of market return, and does not change the result that demand in the first and third terciles are not significantly different. We then estimate a multivariate regression of investor demand on prior market returns, controlling for other firm and IPO characteristics. We include the following control variables: firm size measured by assets, an indicator whether the firm is backed by VC, an indicator whether the firm already has positive EBITDA, and the initial price range (i.e., the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound). Field and Lowry (2009) show that institutional holding shortly after the IPO is positively related to the VC dummy and the profitability dummy and argue that their holding decisions make use of such publicly available information. By the same token, institutional investors IPO demand may depend on these variables 8

11 as well. We also include price range as a wider initial range may indicate higher uncertainty of the valuation of the firm, which may in turn affect investor demand. Table 2 Panel B reports the regression results. We find that subscription ratio is significantly positively related to the VC dummy and the positive EBITDA dummy. This supports the notion that institutional demand takes into account publicly available information. Subscription ratio is negatively related to price range but the coefficient is not statistically significant. We are most interested in the coefficient on market return 3 months prior to the IPO. We find that the coefficient is positive but insignificant, suggesting that after controlling after fundamentals, investor demand does not significantly depend on how bullish the stock market is. Results are similar when we measure investor demand by natural log of dollar demand, except that the coefficient on the VC dummy becomes insignificant. We then perform similar tests using the sentiment index instead of prior market return. Results are reported in Table 3. In Panel A, we divide the sample into terciles based on the sentiment index and look at the means and medians of subscription ratio and log dollar demand in each tercile. Investor demand is not significantly higher in the third tercile (when market sentiment is high) than the first tercile (when market sentiment is low). Regression results reported in Panel B conveys the same message. Investor demand is higher if the firm is backed by venture capital and/or if the firm is already profitable. But the demand is not significant related to market sentiment per se. The results therefore contrast with those of Derrien (2005) and Dorn (2009) who find that individual investors demand for IPO shares increase with market conditions/sentiment. Consistent with prior literature, our results suggest that institutional investors are a class of more rational and sophisticated investors, whose IPO investments are based on information which may include both 9

12 public and private information. 7 We add to the literature, however, by providing direct evidence that institutional demand is not subject to sentiment, but takes into account of public information that is useful in predicting future stock performance. 4. Initial Return and Investor Demand In this section, we examine how demand affects initial return of the IPO investors. Table 4 reports the results of regressions of initial return on investor demand. Panel A uses subscription ratio as the demand variable and Panel B uses dollar demand. We present three regressions in each panel. In Panel A Column (1), we estimate a univariate regression of initial return on subscription. As expected, initial return is significantly positively related to subscription. Moreover, subscription alone explains 74% of the variation of initial return. In terms of economic significance, one standard deviation increase in subscription ratio (7.3) increases the initial return by 20%. Panel A Column (2) estimates a regression of initial return on other firm and IPO characteristics without subscription. The variables include natural log of assets, a VC dummy, a positive EBITDA dummy, initial price range and sentiment. In addition, we also control for the price revision relative to the midpoint of the initial price range, which Hanley (1993) shows to positively predict the initial return and the effect has been repeatedly confirmed in the literature. Consistent with the literature, we find initial return is positively related to the VC dummy and the price revision. It is also positively related to price range, which is consistent with the notion that higher uncertainty leads to more underpricing. The coefficients on positive EBITDA dummy and sentiment are positive but insignificant. In terms of economic significance, firms backed by VC have a higher expected initial return than those 7 For past studies that document institutional versus individual performance in IPOs, see Hanley and Wilhelm (1995), Aggarwal, Prabhala, and Puri (2002), Boehmer, Boehmer, and Fishe (2006), Field and Lowry (2009), Chiang, Qian and Sherman (2010), and Chiang, Hirshleifer, Qian and Sherman (2011). 10

13 without VC backing, by 22%. One standard deviation increase in price range (7.2%) increases the initial return by 9%. One standard deviation increase in price revision (17.4%) increases the initial return by 10%. What is striking, however, is that all these variables together explain a lower proportion of the variation of initial return compared to subscription ratio alone(r square is 63% vs. 74% in Column (1)). In Panel A Column (3), we include subscription as well as the other determinants of initial returns. Again, it is striking to see that once subscription ratio is included, the coefficients on all other variables (except for the constant) become insignificant. In addition, the magnitude of each of these coefficients decreases to a large degree. In particular, the coefficient on price revision changes from 0.56 in Column (2) to In other words, the well known and very robust effect of price revision is completely subsumed once subscription is controlled. On the other hand, the coefficient on subscription ratio does not change much. The result does not mean that partial adjustment to private information does not hold any more. More likely it suggests subscription ratio is a better measure for private information. (In fact, the correlation between the two variables is 66%. In absence of the demand data, price revision is a good proxy for demand and private information.) In addition, results in Table 2 show that demand also incorporates public information. Once controlling for demand, the public information variables have no additional explanatory power for initial return. Panel B uses log dollar demand instead of subscription ratio. In the univariate regression in Column (1), the coefficient on log dollar demand is also positive and highly significant. One standarddeviation increase in the variable (1.5) increases the initial return by 16%. The R squared is 45%, lower than the explanatory power of subscription ratio. Column (2) is the same as that in Panel A, looking at the impact of other determinants of initial returns. Column (3) includes the variables in Column (2) as well as log dollar demand. Similar as the results in Panel A Column (3), the coefficient on the demand measure does not change much and remains highly significant; and the effect of price revision 11

14 is completely subsumed. Unlike those in Panel A Column (3), the coefficients on the VC dummy and price range remain significantly positive. In summary, investor demand incorporates both public and private information and is the strongest predictor for initial return. Between subscription ratio and dollar demand, subscription has stronger explanatory power. 5. Long Run Stock Returns and Investor Demand All the existing theories predict a positive relationship between investor demand and initial returns, for which we show confirming evidence in the previous section. The sentiment theory and the information asymmetry theory, however, have very different predictions on the long run stock performance of IPO stocks. If investor demand is driven by sentiment, then the higher the demand, the more overpriced of the offer price and the immediate aftermarket price, and therefore the more negative the long run stock performance will be. The information asymmetry theory predicts no such negative relationship between demand and long run stock returns. We examine the relationship between long run stock returns and investor demand in this section. Results are reported in Tables 5 and 6. Table 5 estimates regressions of buy and hold long run returns on subscription (Panel A) or log dollar demand (Panel B), controlling for other firm and IPO characteristics. In the first 3 columns, the dependent variables are 6 month, 18 month and 3 year returns relative to the closing price on the first trading day. In the last 3 columns, the dependent variables are 6 month, 18 month and 3 year returns relative to the offer price. For all the 12 regressions in both Panels A and B, the coefficients on demand measures are insignificant. That is, higher IPO demand is not associated with more negative long run stock returns. Thus, higher IPO demand is not associated with more overpricing for either the immediate aftermarket price or the offer price. 12

15 In contrast, we find evidence that 6 month and 18 month returns relative to the first trading day price are negatively related to the market sentiment during the IPO month. So is the 18 month return relative to the offer price. For example, for Panel A Column (1), one standard deviation increase in sentiment (15.6%) leads to a decrease of 10 percentage point in the 6 month return. This is consistent with the sentiment hypothesis in that higher sentiment is associated with more negative long run return. However sentiment is not reflected in bookbuilding investor demand. We also find that the dummy of positive EBITDA has a positive impact on the 6 month return (both relative to the first trading day price and to the offer price), which is consistent with Field and Lowry s (2009) finding. There is some evidence the initial price range has a negative relationship with long run returns. Table 6 replicates the regressions of Table 5 using buy and hold abnormal returns as the dependent variables. Abnormal returns are calculated as the sample firm s buy and hold raw returns minus the buy and hold CRSP value weighted market index returns. Main results are similar to those in Table 5: the coefficients on the demand measures are all statistically insignificant. That is, higher IPO demand is not associated with long run underperformance, either relative to the first trading day price or the offer price. Also similar to Table 5, there is evidence that long run abnormal returns are positively related to the dummy of positive EBITDA, and are negatively related to the initial price range. Unlike the results in Table 5, the coefficients on sentiment are now all insignificant. This suggests that in our sample period, the impact of market sentiment is similar on IPO stocks to that on stocks in general. Hence we observe negative effects of sentiment on raw returns, but not on market adjusted returns. In short, the results suggest that IPO demand in the bookbuilding process is not associated with negative long run stock performance and hence is not associated with IPO overpricing. In contrast to 13

16 (but not inconsistent with) previous studies that use individual investors demand for IPO shares as a proxy for sentiment, we find no evidence that institutional demand for IPO shares is inflicted with sentiment. 6. Conclusions (to be completed) References (to be completed) 14

17 Table 1: Summary Statistics The sample contains 48 US offerings during VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Proceeds is total capital raised in the IPO (in millions of dollars), excluding the overallotment. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. Price revision is offer price minus the midpoint of the initial price range, divided by the midpoint. Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). Sentiment is the sentiment index based on Baker and Wurgler (2006), which is the first principal component of six (standardized) sentiment proxies, each proxy first orthogonalized with respect to a set of macroeconomic conditions. Initial return is the closing price on the first trading date divided by the offer price, minus one. 6 month (18 month, 3 year) return is the buy and hold return 6 months (18 months, or 3 years) after the first trading day of the IPO. Abnormal returns are adjusted of the CRSP value weighted market return. Panel A: # of IPOs by year Year # of observations Panel B: Firm and IPO Characteristics Mean Std Dev 25th Pctl 50th Pctl 75th Pctl Assets ($MM) VC dummy Positive EBITDA Proceeds ($MM) Price range Offer price ($) Price revision Subscription Dollar demand ($MM)

18 Panel C: Market condition and Aftermarket Returns Mean Std Dev 25th Pctl 50th Pctl 75th Pctl Last 3 month market return Sentiment Initial return month return month return year return month abnormal return month abnormal return year abnormal return

19 Table 2: Prior Market Returns and Investor Demand The sample contains 48 US offerings during Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. In Panel A, the p values for means are based on t tests and those for medians are based on Wilcoxon Mann Whitney tests. In Panel B, t statistics adjusted for heteroskedasticity are in parentheses. ***, **, and * denote significance at 1%, 5% and 10% level, respectively. Panel A: Investor demand by terciles of market return Subscription Log Dollar Demand Tercile of market return 3 months before IPO N Mean Median Mean Median 1 (low) (high) p value of diff b/w terciles 1 and Panel B: Regressions Subscription Log Dollar Demand Market return 3 months before IPO Log (Assets) VC 8.520* Price range Positive EBITDA 6.903*** 1.451*** Intercept ** Industry dummies Yes Yes R square

20 Table 3: Market Sentiment and Investor Demand The sample contains 48 US offerings during Sentiment is the sentiment index based on Baker and Wurgler (2006), which is the first principal component of six (standardized) sentiment proxies, each proxy first orthogonalized with respect to a set of macroeconomic conditions. Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. In Panel A, the p values for means are based on t tests and those for medians are based on Wilcoxon Mann Whitney tests. In Panel B, t statistics adjusted for heteroskedasticity are in parentheses. ***, **, and * denote significance at 1%, 5% and 10% level, respectively. Panel A: Investor demand by terciles of market sentiment Subscription Log Dollar Demand Sentiment Tercile N Mean Median Mean Median 1 (low) (high) p value of diff b/w terciles 1 and Panel B: Regressions Subscription Log Dollar Demand Sentiment Log (Assets) VC * Price range Positive EBITDA 6.594*** 1.422*** Intercept * Industry dummies Yes Yes R square

21 19

22 Table 4: Initial Return and Investor Demand The sample contains 48 US offerings during The dependent variable is Initial return, i.e. the closing price on the first trading date divided by the offer price, minus one. Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. Price revision is offer price minus the midpoint of the initial price range, divided by the midpoint. Sentiment is the sentiment index based on Baker and Wurgler (2006), which is the first principal component of six (standardized) sentiment proxies, each proxy first orthogonalized with respect to a set of macroeconomic conditions. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). t statistics adjusted for heteroskedasticity are in parentheses. ***, **, and * denote significance at 1%, 5% and 10% level, respectively. Panel A: Investor demand measured by subscription (1) (2) (3) Subscription 0.028*** 0.024*** log (Assets) VC 0.223** Price range 1.309* Price revision 0.557*** Positive EBITDA Sentiment Intercept 0.073*** 0.644** 0.436*** Industry dummies No Yes Yes R square

23 Panel B: Investor demand measured by dollar demand (1) (2) (3) Log dollar demand 0.105*** 0.096*** log (Assets) VC 0.223** 0.188*** Price range 1.309* 1.180** Price revision 0.557*** Positive EBITDA Sentiment Intercept 0.567*** 0.644** 1.077*** Industry dummies No Yes Yes R square

24 Table 5: Long Run Returns and Investor Demand The sample contains 48 US offerings during month (18 month, 3 year) return is the buy and hold return 6 months (18 months, or 3 years) after the IPO. Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. Price revision is offer price minus the midpoint of the initial price range, divided by the midpoint. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). t statistics adjusted for heteroskedasticity are in parentheses. ***, **, and * denote significance at 1%, 5% and 10% level, respectively. Panel A: Investor demand measured by subscription Raw return from 1st trading day Raw return relative to offer price 6 month return 18 month return 3 year return 6 month return 18 month return 3 year return Subscription log (Assets) VC Price range ** * Price revision Positive EBITDA 0.405** ** Sentiment 0.627* 1.427*** *** Intercept ** * Industry dummies Yes Yes Yes Yes Yes Yes R square

25 Panel B: Investor demand measured by dollar demand Raw return from 1st trading day Raw return relative to offer price 6 month return 18 month return 3 year return 6 month return 18 month return 3 year return Log dollar demand log (Assets) VC Price range 2.628* 4.879** Price revision Positive EBITDA 0.390** ** Sentiment 0.695** 1.455*** *** Intercept 0.702** 1.693** Industry dummies Yes Yes Yes Yes Yes Yes R square

26 Table 6: Long Run Abnormal Returns and Investor Demand The sample contains 48 US offerings during month (18 month, 3 year) return is the buy and hold return 6 months (18 months, or 3 years) after the IPO. Abnormal returns are adjusted of the CRSP value weighted market return. Subscription is the demand of the IPO shares divided by the number of shares offered in the IPO excluding the overallotment. VC is a dummy equal to one if the firm is backed by venture capital and zero otherwise. Positive EBITDA is a dummy equal to one if the firm has positive EBITDA in the fiscal year prior to the IPO. Price range is the difference between the higher bound and the lower bound of the initial price range, divided by the lower bound. Price revision is offer price minus the midpoint of the initial price range, divided by the midpoint. Dollar demand is the demand of the IPO shares times the offer price (in millions of dollars). t statistics adjusted for heteroskedasticity are in parentheses. ***, **, and * denote significance at 1%, 5% and 10% level, respectively. Panel A: Investor demand measured by subscription Abnormal return from 1st trading day Abnormal return relative to offer price 6 month return 18 month return 3 year return 6 month return 18 month return 3 year return Subscription log (Assets) VC Price range 2.753* 5.350** ** Price revision Positive EBITDA 0.401** 0.533* ** Sentiment Intercept * Industry dummies Yes Yes Yes Yes Yes Yes R square

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