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Review of Quantitative Finance and Accounting

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01.08.2016 | Original Research

The pricing of first day opening price returns for ChiNext IPOs

verfasst von: Qi Deng, Zhong-guo Zhou

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2016

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Abstract

We study the listing day opening price return and compare it with the closing price return for ChiNext IPOs after the China Securities Regulatory Commission (CSRC) adopted a new “Chinese-style” bookbuilding process. We start from a traditional OLS model by screening a set of potential variables, characterized in the existing literature. Through a variable reduction process, we identify 7 significant variables for each of the return series. We further utilize a GARCH-M model with an ARMA(1,1) adjustment in the residuals to correct possible autocorrelation in the returns and cross-correlation between the return and its conditional variance to improve the model. We find that the opening price synthesizes the overall market demand for new shares from institutional and individual investors as the subscription ratios from both sectors are highly significant (oversubscriptions). In addition, the market condition over the past 21 trading days prior to listing a ChiNext IPO (market momentum), offer size (size effect), and conditional return variance (asymmetric information) are also significant. We find similar significant variables that affect the closing price return except the subscription ratio from individual investors. Overall, the opening price return contains valuable information to predict the closing price return.

Vorheriger Artikel Do liquidity and idiosyncratic risk matter? Evidence from the European mutual fund market

Nächster Artikel Market share and risk taking: the role of collateral asset managers in the collapse of the arbitrage CDO market

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There are numerous studies covering IPO underpricing around the world. For example, see Mok and Hui (1998), Su and Fleisher (1999), Ritter and Welch (2002), Gu (2003), Guo and Brooks (2008), Li et al. (2008), Lowry et al. (2010), Zhou and Zhou (2010), Guo et al. (2011), Chang et al. (2013), Deng and Zhou (2014), Hussein and Zhou (2014), and among others.

Barry and Jennings (1993) find a similar result, using the US IPO data with a small sample.

All the ChiNext IPOs are initiated at the SZSE. The business hours at the SZSE are 9:15–9:25 AM for the pre-open session, 9:30–11:30 AM for the morning session, 13:00–14:57 PM for the afternoon session and 14:57–15:00 PM for the closing call auction.

We select the dataset based on the availability when we start the project. The CSRS suspended all IPO activities temporarily from early 2013 to reform the Chinese IPO markets. After a year of overhaul, the CSRS reopened the Chinese IPO markets in early 2014.

Interested readers can consult the SPSS user manual for technical details on the procedure, using linear multivariate models with specific Enter, Remove, Backward, Forward and Stepwise features and their applications in selecting the significant variables (or in reducing the insignificant variables by one at a time in each regression).

There are 16 broad industry sectors in China listed in Table 3. The industry sector is treated as a nominal variable numbered from 1 to 16. A better way to distinguish the possible difference in the opening price return across different industries is to use industry dummies. As the industry dummies contribute little towards the open price return and they turn out to be insignificant in the final GARCH-M model we do not repeat the analysis using the industry dummies. The same logic and procedure apply to the closing price return.

Since the standardized beta measures the importance of an independent variable towards the dependent variable after adjusting for different units in a regression it provides additional information about the contribution from each independent variable towards the overall model fitness.

We perform the Box and Jenkins (1976) procedure on the first day open price returns for 352 ChiNext IPOs. We conduct various tests and try different combinations of the ARMA(p,q) procedures and find that an ARMA(1,1) process best fits the data. The detailed analysis is available from the authors upon request.

The average subscription ratio from institutional investors is 39.38 with a standard deviation of 33.74 while the average subscription ratio from individual investors is 135.65 with a standard deviation of 74.68. From those statistics we can find how severe the oversubscriptions are from institutional investors in a competitive market and from individual investors in a noncompetitive market.

The average offer size, measured in the logarithm of the offer price times the number of shares offered, is 20.13. The standard deviation of the offer size is 0.54.

We also compare the determinants for the opening and closing price returns from the OLS models after variable reduction and find that all the significant variables are the same except one variable. For the opening price return, the pre-issue profit growth is significant; while for the closing price return, the pre-issue P/E ratio is significant. Both of them become insignificant in the final GARCH-M models with an ARMA(1,1) process in the residuals, suggesting the ARMA(1,1) process and conditional return variance can pick up the effects from those two variables.

Before April 28, 2012 only the shares allocated to individual investors can be traded on the listing days while the shares allocated to institutional investors are subject to a 3-month lockup period. Therefore, we know that the opening price must be initiated by individual investors before April 28, 2012. After April 28, 2012, all the shares allocated to both institutional and individual investors are floating on the listing days. As a result, it is difficult to tell who actually initiates the opening price on the listing days.

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Titel: The pricing of first day opening price returns for ChiNext IPOs
verfasst von: Qi Deng
Zhong-guo Zhou
Publikationsdatum: 01.08.2016
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 2/2016
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-015-0500-x

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