Investor Sentiment and IPO Pricing during Pre-Market and Aftermarket Periods Li Jiang a, * , Gao Li a a School of Accounting and Finance, Hong Kong Polytechnic University, Hong Kong, China This version: January 14, 2012 Abstract In this study, we separately measure pre-market and aftermarket investor sentiment and investigate their impact on IPO pricing in a two-stage framework. We find that compensation for institutional investors is associated with their fractional allocation and the risk they bear. This helps explain the partial adjustment of offer prices to pre-market sentiment. We also show that pre-market sentiment tends to spill over to the aftermarket period, and aftermarket sentiment causes a further price run-up in the secondary market. Overall, our findings suggest that institutional investors play an important role in re-distributing shares in the secondary market and the momentum in investor sentiment makes it possible for underwriters to implement the staged distribution strategy. JEL Classification Code: G02; G14 Keywords: Pre-market sentiment; Aftermarket sentiment; IPO pricing; Institutional investors * Corresponding author. Tel.: +852 2766 4672; Fax: +852 2330 9845 E-mail addresses: [email protected](L. Jiang); [email protected](G. Li)
36
Embed
Investor Sentiment and IPO Pricing during Pre-Market and ... ANNUAL MEETINGS/2012-Barcel… · IPO underpricing phenomenon has been studied extensively in finance literature. So far,
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Investor Sentiment and IPO Pricing during Pre-Market and Aftermarket Periods
Li Jianga, *, Gao Lia
a School of Accounting and Finance, Hong Kong Polytechnic University, Hong Kong, China
This version: January 14, 2012
Abstract
In this study, we separately measure pre-market and aftermarket investor sentiment and investigate their impact on IPO pricing in a two-stage framework. We find that compensation for institutional investors is associated with their fractional allocation and the risk they bear. This helps explain the partial adjustment of offer prices to pre-market sentiment. We also show that pre-market sentiment tends to spill over to the aftermarket period, and aftermarket sentiment causes a further price run-up in the secondary market. Overall, our findings suggest that institutional investors play an important role in re-distributing shares in the secondary market and the momentum in investor sentiment makes it possible for underwriters to implement the staged distribution strategy.
In this study, we examine the impact of investor sentiment over pre-market and
aftermarket stages on IPO pricing. By allowing sentiment to evolve over the transition from
primary market to secondary market trading, we are able to show empirically how institutional
investors are compensated for re-distributing new shares and who are leaving the money on the
table. Our findings can be summarized as follows: Underwriter capitalizes on pre-market
investor sentiment by revising the offer price upward. Investor attention appears to drive pre-
market retail demand for IPO. The offer price revision is only partial in the sense that pre-market
sentiment still affects offer-to-open return. We empirically confirm the prediction by Ljungqvist
et al. (2006) that compensation for regular investors is one of the main reasons for partial
adjustment.
We also shed lights on why sentiment investors are willing to participate in the IPOs by
paying an even higher price than the offer price in the secondary market. We show that pre-
market sentiment tends to spill over to aftermarket period, suggesting that additional sentiment
investors may arrive in the aftermarket stage. The aftermarket sentiment pushes up the stock
price further, making those sentiment investors who participate in the early stage benefit from
the sequential arrival of sentiment investors. The long-term underperformance confirms that
sentiment eventually fades away and overpricing is corrected over time. However, the presence
22
of investor sentiment and its momentum make it possible for underwriter to successfully
implement the staged distribution strategy.
23
References
Agarwal, S., Liu C., Rhee S. G., 2008, Investor demand for IPOs and aftermarket performance: Evidence from the Hong Kong stock market, International Financial Market, Institutions & Money 18, 176-190.
Baker, M., Stein, J. C., 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets 7, 271-299.
Barber, B. M., Odean T., 2008, All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Review of Financial Studies 21, 785 – 818.
Benveniste, L. M., Spindt P. A., 1989, How investment bankers determine the offer price and allocation of new issues? Journal of Financial Economics 24, 343-361.
Bradley, D. J., Gonas, J. S., Highfield, M. J., Roskelley, K. D., 2009. An examination of IPO secondary market returns. Journal of Corporate Finance 15, 316-330.
Bradley, D. J., Jordan, B. D., 2002. Partial adjustment to public information and IPO underpricing. Journal of Financial and Quantitative Analysis 37, 595 – 616.
Chan, Y. C., 2010. Retail trading and IPO returns in the aftermarket, Financial Management 1475-1495.
Chen, Z., Wilhelm, W., 2008. A theory of the transition to secondary market trading of IPOs. Journal of Financial Economics 90, 219-236.
Cook, D., Kieschnick, R., Van Ness, R., 2006. On the marketing of IPOs. Journal of Financial Economics 82, 35-61.
Cornelli, F., Goldreich, D., Ljungqvist, A., 2006. Investor sentiment and pre-IPO markets. Journal of Finance 61, 1187-1216.
Da, Z., Engelberg, J., Gao, P., 2011. In search of attention. Journal of Finance 66, 1461-1499.
Daniel, K., 2002. Discussion of “Why don’t issuers get upset about leaving money on the table in IPOs?”. Review of Financial Studies 15, 445-454.
Derrien, F., 2005. IPO pricing in “hot” market conditions: Who leaves money on the table? Journal of Finance 60, 487-520.
Dorn, D., 2009, Does sentiment drive the retail demand for IPOs? Journal of Financial and Quantitative Analysis 44, 85-108.
24
Edelen, R. M., Kadlec, G. B., 2005, Issuer surplus and the partial adjustment of IPO prices to public information. Journal of Financial Economics 77, 347-373.
Espinasee, P., 2011, IPO: A Global Guide. The Hong Kong University Press.
Falconieri, S., Murphy, A., Weaver, D., 2009. Underpricing and ex post value uncertainty. Financial Management 285-300.
Hanley, K. W., 1993. The underpricing of initial public offerings and the partial adjustment phenomenon, Journal of Financial Economics 34, 231-250.
Huang, Z., Heian, J.B., Zhang, T., 2011. Differences of opinions, overconfidence, and the high-volume premium. Journal of Financial Research 34, 1-25.
Kaustia, M., Knupfer, S., 2008. Do investors overweight personal experience? Evidence from IPO subscriptions. Journal of Finance 63, 2679 – 2702.
Kumar, A., Lee, M.C., 2006. Retail investor sentiment and return comovements. Journal of Finance 61, 2451-2486.
Kutsuna, K., Smith, J. K., Smith, R. L., 2009. Public information, IPO price formation, and long-run returns: Japanese evidence. Journal of Finance 64, 505-546.
Lee, M. C., Ready, M., 1991. Inferring trade direction from intraday data. Journal of Finance 2, 733-746.
Ljungqvist, A., Nanda, V., Singh, R., 2006. Hot markets, investor sentiment and IPO pricing. Journal of Business 79, 1667-2702.
Loughran, T., Ritter, J. R., 2002. Why don’t issuers get upset about leaving money on the table in IPOs? Review of Financial Studies 15, 75-109.
Lowry, M., Schwert, G. W., 2004. Is the IPO pricing process efficient? Journal of Financial Economics 71, 3-26.
Miller, E. M., 1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151-1168.
Ofek, E., Richardson, M., 2003. Dot-com mania: The rise and fall of Internet stock prices. Journal of Finance 58, 1113-1138.
Ritter, J. R., Welch, I., 2002. A review of IPO activity, pricing and allocations. Journal of Finance 57, 1795-1828.
25
Table 1 Descriptive Statistics
The full sample consists of 316 IPOs in Hong Kong from 1999 to 2009. The sample is restricted to IPOs using bookbuilding method. We exclude IPOs of closed-end funds, REITs, unit offerings, and companies switching from growth market. OFFER_PRICE is the offer price after price revision. PLACING is the portion of shares allocated to the Placing Tranche in an IPO. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. PROCEEDS is the amount raised in millions of HK dollars. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. LARGENET is the buyer-initiated large trades minus seller-initiated large trades divided by the total dollar trading volume on the first trading day. ADJOTC is the market-adjusted open-to-close return. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. COMPEN is the compensation for institutional investors and calculated as the offer to open return times the number of shares allocated to institutional investors scaled by total asset. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. 6MADJRTN, 1YADJRTN, and 18NADJRTN are 6-month, 1-year and 18-month buy-and-hold market-adjusted returns from close price on the first trading day. Panel A: Full Sample Variable N Mean Median Maximum Minimum Std Dev
Table 3 Pre-Market Sentiment and Offer Price Revision
This table tests of Hypothesis 1. The dependant variable, REVISION, is the offer price divided by the midpoint of initial price range minus one. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Dependent Variable: Offer price revision (REVISION) Variables Model 1 Model 2
SUBRATE 0.015***
(6.10) ASVI 0.028*** (2.88)
PRE_IPO_RTN 0.154*** 0.201*** (4.31) (3.96) RANGE -0.193** -0.215*
Table 4 Partial Adjustment of Offer Price to Pre-Market Sentiment
This table tests Hypothesis 2. The dependant variable, OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
This table tests Hypothesis 3. In models 3 to 6, the full sample is divided into “Cold” and “Hot” IPO subsamples based on the sign of REVISION. The dependent variable, COMPEN, is the compensation for institutional investors, defined as the offer to open return times the number of shares allocated to institutional investors scaled by total asset. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. R_PLACING is the residuals from regressing PLACING on SUBRATE. RANGE is the price range announced in its prospectus divided by its midpoint price. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. SIZE is the logarithm of total asset. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level. Dependent Variable: Compensation for institutional investors (COMPEN) Full Sample Hot IPOs s Cold IPOs s
This table tests Hypothesis 4. The dependant variables, TURNOVER and SMALLNET, are defined as follows: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. Explanatory variables: SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. .REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Panel A Panel B Dependant Variables: TURNOVER TURNOVER SMALLNET SMALLNET Model 1 Model 2 Model 3 Model 4
Table 7 Aftermarket Sentiment and Open-to-Close Return
This table tests Hypothesis 5. The dependant variable, ADJOTC, is the market-adjusted open-to-close return. Explanatory variables: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. R_PLACING is the residual from regressing PLACING on SUBRATE. LARGENET is the buyer-initiated large trades minus seller-initiated large trades divided by the total dollar trading volume on the first trading day. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. SIZE is the logarithm of total asset. RANGE is the price range announced in its prospectus divided by its midpoint price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. REVISION is the offer price divided by the midpoint of initial price range minus one. TOP is a dummy variable equals 1 when offer price is set at the upper bound of the price range. OTO is the offer to open return, that is, the open price on the first trading day divided by the offer price minus one. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
Table 8 Investor Attention and Pre-Market Retail Demand
This table examines the relation between oversubscription rate and investor attention during the pre-market period. The dependant variable, SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. Explanatory variables: ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. SIZE is the logarithm of total asset. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level.
This table shows the IPO long-run returns. Raw Return is calculated as the percentage difference between the close prices of 6 months, 12 months, and 18 months after the listing, and the close price of the listing day. Benchmark Return is the contemporaneous market return. The Adjusted Return is the difference between the corresponding Raw Return and the Benchmark Return. Variables 6 Months 12 Months 18 Months Raw Return (%) Mean 2.14 6.87 9.83
Median -2.71 -5.11 -11.29
Benchmark Return (%) Mean 4.40 8.25 10.08
Median 5.07 11.34 13.61
Adjusted Return (%) Mean -2.13 -1.10 0.35
Median -5.58 -11.52 -16.90
Observations 316 316 316
35
Table 10 Investor Sentiment and Long-run Underperformance
This table examines the determinants of long-run IPO underperformance. The dependant variable, 1YADJRTN, is the 1-year buy-and-hold market-adjusted return from the closing price on the first trading day. Explanatory variables: TURNOVER is the total trading volume divided by the number of shares outstanding on the first-trading day. SMALLNET is the buyer-initiated small trades minus seller-initiated small trades divided by the total dollar trading volume on the first trading day. SUBRATE is the number of shares subscribed by individual investors divided by the number of shares assigned to the Public tranche. ASVI is the abnormal Google search volume, defined as the search volume index during the bookbuilding week minus the median of search volume index in the previous eight weeks. PRE_IPO_RTN is the average initial return of five latest IPOs before the IPO. RANGE is the price range announced in its prospectus divided by its midpoint price. UWREP is a dummy variable which equals one if one of sponsors is among the top ten based on the underwriting market share ranking. SIZE is the logarithm of total asset. VOLATILITY is the standard deviation of intraday prices on first trading day normalized by the offer price. Robust t-statistics are in parentheses. *, ** and *** represent significance at the 10%, 5% and 1% level. Dependant Variable: One-year market-adjusted buy-and-hold return (1YADJRTN) Variables Model 1 Model 2 Model 3 Model 4 TURNOVER -11.850** -11.950** (-2.20) (-2.23) SMALLNET -0.196** -0.174* (-2.01) (-1.79) SUBRATE -0.016 -0.026 (-0.75) (-1.30) ASVI -0.050 -0.061 (-1.33) (-1.55) PRE_IPO_RTN -0.543* -0.504 -0.548* -0.503 (-1.92) (-1.11) (-1.94) (-1.11) RANGE -0.415 -0.599 -0.499 -0.647 (-1.04) (-1.01) (-1.24) (-1.07) UWREP -12.34 -17.10 -13.33 -16.46 (-1.19) (-0.93) (-1.28) (-0.90) SIZE 7.182*** 8.554** 7.423*** 8.852** (2.80) (2.24) (2.90) (2.30) VOLATILITY -0.108 -1.275 -0.719 -2.501 (-0.06) (-0.73) (-0.43) (-1.40) Constant -8.733 -13.17 -3.485 -7.751 (-0.34) (-0.32) (-0.14) (-0.18) Observations 316 157 316 157 R-squared 0.057 0.080 0.054 0.057