Maps of Bounded Ratonality - A Perspective on Intuitive Judgment and Choice
How to Make a Good Choice on IPO? from Perspective of ...
Transcript of How to Make a Good Choice on IPO? from Perspective of ...
EF 5070
Econometrics
Academic Year 2007-2008
Project
How to Make a Good Choice on IPO?
from Perspective of Corporate Governance and Market Effect
Instructor: Dr. Isabel YAN
Student Name:
Kendrick CHAN Ka Ho
Esther CHOI Sin Man
Eric DAI Man Fan
Date: December 8, 2007
Table of Contents:
1. Introduction ......................................................................................................................2 2. The Data ...........................................................................................................................4 3. The Regression Model .....................................................................................................8 4. Empirical Results ...........................................................................................................10
Descriptive Statistics .....................................................................................................10 Regression Results .......................................................................................................10 Extended Regression Results .....................................................................................12
5. Hypothesis Tests and Results........................................................................................14 6. Conclusion ......................................................................................................................16 7. Appendices......................................................................................................................17
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1. Introduction
Since the second half of year 2003, the number of initial public offering (“IPO”) activities on
the Hong Kong Stock Exchange has increased significantly and on average there were over
50 IPOs every year since 2004. Other than the increase in frequency of IPOs in these few
years, the size of these IPOs is also in an increasing trend, especially the IPOs of those giant
state-owned enterprises in the People’s Republic of China, which we usually regard as
H-shares if they are established in China, or else we usually regard as Red Chips if they are
incorporated overseas (including Hong Kong for this purpose), they all contribute to the
increase in IPO activities on Hong Kong Stock Exchange and also those long queues for
public offers outside those designated bank branches. We can read this kind of press
coverage on local newspapers nearly every few days.
With such an increasing trend of IPO as background, IPO subscription has become one of the
most popular and common phenomenon among the individual investors of Hong Kong
domestic market. We have also read from local newspaper that there are local investors who
will apply for all IPO subscriptions in the market in order to benefit from the raise in share
price after listings of the shares. It was also reported that there are subscribers with
substantial financing in order to increase the chance of successful subscription. This may be
kind of successful strategy for certain period of time but we should know that a stock price
increase after IPO is not guaranteed and this can be shown in some of the IPOs recently in
November 2007.
In this study we would like to investigate how can we use econometrics model to help us to
have a good pick in IPOs. How can we choose a good IPO from such a large amount of
IPOs in a year? In order to help us to benefit from an IPO, we would like to pick those
companies which would try to lower the share prices in a public offering such that we can
have a better chance to earn money from a price increase after IPO. That is, can we observe
any factors which are related to companies likely to under-price their shares in a public
offering?
In analyzing these factors which may affect a company to price their shares in an IPO, we
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would like to separate these factors into two different points of views, one is from the
perspective of corporate governance, and another one is the effect of the market.
From the perspective of corporate governance, we will investigate the effect of under-pricing
if the company under IPO is a family business or not. We will also try to find out the effect
if the company is an H-share company and a Red-Chip. Also we will focus on whether the
financial information has been audited by one of the Big Four CPA firm (or to be exact, the
accountant’s report is prepared by one of the Big Four CPA firm).
We would also like to know the effect of market on IPOs and such effect on the price increase
after listing of shares. In our study we will focus on the offered price of the IPO, the
percentage for public offering available to retail investors (other than those offerings
designated to international corporate investors and also the subscription multiple during the
public offering.
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2. The Data
Our sample covers the IPOs on the Main Board in the SEHK over three-year period between
2004 and 2006. There are 158 IPOs in the period, as extracted from the Bloomberg. Some
IPOs are excluded under below conditions:
a) REITs: Since the business nature of REITs is different from common stocks and there
are only 5 REITs were listed in the three years which is not sufficient for the
regression sampling.
b) Incomplete information.
c) The IPOs are for placing only.
There are 147 samples for the regression analysis. Except the price data of stock and the
Hang Seng Index, which are obtained from Bloomberg and Datastream. The majority of the
items are hand collected from prospectuses, IPO allotment results and the new listing reports
from the website of Hong Kong Exchange and Clearing Limited. The classification of
H-shares and Red Chip firms follow that as prescribed by the SEHK. The details of the data
sources are as tabulated.
Raw Data Source Stock Code Bloomberg Name Bloomberg Listing Date Bloomberg Initial Offer Price (Lower Range) Prospectus Initial Offer Price (Upper Range) Prospectus Final Offer Price IPO Allotment Result Details of Directors and senior management Prospectus Auditor Prospectus Sponsors Prospectus Subscription_Multiple IPO Allotment Result Number of Shares offered in IPOs Prospectus Number of IPO Shares allocated to Placing IPO Allotment Result Total shares Outstanding Prospectus Hang Seng Index Price Bloomberg Stock Price DataStream
Several factors related to the corporate governance and market effect have been analyzed.
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Two periods, 1 day and 1 week, of returns are calculated as the dependent variables. On the
independent variables, four independent variables and four dummy variables are derived.
Definition of variables
Variable Name Definition Calculation
Dependent Variables
Return_1D Return after 1 day (IPO closing price after 1 day - IPO offer price )/ IPO offer price
Return_1W Return after 1 week (IPO closing price after 1 week - IPO offer price )/ IPO offer price
Excess_Return_1D Excess Return over HSI return after 1 day
Return after 1 day - HSI Return after 1 day
Independent Variables
Offer Price Offer price The final price of the offer
P_Retained Percentage of shares retained by the substantial shareholders
1 - The number of IPO offer shares / total number of share outstanding
P_Retail Percentage of shares eligible for retail investors subscription
(The number of IPO offer shares - the number of shares allocated to placing) / total number of share outstanding
Sub_Multi Subscription Multiple Subscription multiple by the retail investors
Dummy
Family_Company Family Company Flag of whether the company is family company
UP_Offer Upper Price offer / Single price offer
Flag of whether the offer price is one-priced or upper price of initial offer
Is_China_Stock Red Chips / H Shares Flag of whether the IPO is red chips or H shares
Is_Auditor_Big_Four Auditor is big four CPA firm Flag of whether the auditor is big-four CPA firm
Both Return_1D and Excess_Return_1D are calculated as the measure of the firm’s IPO
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underpricing. Excess return is taken as the measure in order to eliminate the effect of the
market volatility. The one-day excess return is our primary measure of the IPO underpricing.
P_Retained is the percentage of shares retained by the substantial shareholders. As shown in
the Retain Control theory, the control right of family owners are so important and they do not
want to be monitored by outside block shareholders. For companies retained more shares,
they may prefer underpricing to ensure oversubscription such that the share allocation process
would help to effectively reduce the block size of new shareholdings. P_Retained is expected
to be positively correlated with the underpricing.
P_Retail is the percentage of shares eligible for retail investor subscription and Sub_Multi
represents the subscription Multiple. Under limited supply, it would lead to the opportunity of
oversubscription with the overwhelming demand from the retail investors. The promoted
oversubscription would provide higher liquidity in the secondary market. Under this
argument, the P_retail as expected to be negatively correlated with the underpricing. Counter
arguments also exist, for more percentage allocated to retail investors, press coverage would
be higher since less stocks are allocated to placement, which is allocated to insiders or
institutional investors. It also implies the management has the confidence on the public
acceptance. With more “noise” in the market, together with the support of overscription, the
degree of underpricing is higher. Under this arguement, both P_Retail and Sub_Multi are
expected to be positively correlated with the underpricing.
Family_Company is the dummy variable for family company, 1 = family firm and 0 =
entrepreneur firm. The definition of family firms varies in different hypothesis. Anderson and
Reeb (2003) suggested that the family firms are defined as those in which the founding
individuals and/or family members are members in the board of directors / block shareholders.
CK Low and X. Yu (2007) refines the definition of family firms if two generations of the
family are represented on its board of directors, or if siblings or cousins serve on the board,
which implies a higher likelihood of control rights transferred within the family. CK Low and
X. Yu opined that family firm has a higher level of IPO underpricing that it dominates the
agency conflict effect. In this study, we refer to family firm defined by CK Low and X. Yu.
UP_Offer is the dummy variable for whether the offer price is one-priced or upper price of
initial offer, 1 = the final offer price is set at the same as the upper range of initial offer price 6
or the price is set at single price; 0 = else. The firm decision on the final offer price implies
the confidence of the stock market performance. Hence, it is expected that the underpricing
would be higher for the offer price being set at the upper range of the initial offer price.
Is_China_Stock is the dummy variable for whether the IPO is red chips or H-shares, 1 = the
firm is red-chips or H-shares and 0 = else. Both red chips and H shares are state-owned
companies. H-shares are the shares of companies operating within different legal and
regulatory framework. CK Low and X. Yu suggested that higher degree of IPO underpricing
is resulted, due to the negative perception of weaker corporate governance, legal protection
and asymmetric information, in order to compensate the higher degree of risk. In addition, as
the IPO induces separation between the ownership and control, agency problems may exists
such that the IPO underpricing is more significant.
Is_Auditor_Big_Four is the dummy variable for whether the firm is audited by the Big-four
CPA firms. It is expected that the presence of the Big-Four CPA firms in auditing the
financial statements would reduce the underpricing since the professionalism of Big-Four
CPA firm implies better auditing quality.
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3. The Regression Model
We start our regression model as below:
<Model 1>
εβββββββββ
+++++++++=
)_ln(________)_ln(_1__
87654
3210
MultisubretailPretainPFourBigAuditorIsStockChinaIsOfferUPpriceofferCompanyFamilyDreturnExcess
As illustrated in table 1 in the appendix, the variable ln_OP has the highest p-value. We reject
this variable in the list of explanatory variables.
Then we run the regression again with:
<Model 2>
εββββββββ
++++++++=
)_ln(_________1__
7654
3210
MultisubretailPretainPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess
As illustrated in table 2 in the appendix, the variable P_retain has the p-value higher than 0.4.
We reject this variable in the list of explanatory variables.
<Model 3>
εβββββββ
+++++++=
)_ln(________1__
654
3210
MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess
As illustrated in table 3 in the appendix, despite the p-value for the variable
Family_Company is higher than 0.1, in considering family firm is one of our key objectives
of analysis and it also passes the one-tailed test (as explained in later session) at 10%
significant level, we accept this model as our final regression model for further analysis.
Besides 1-day excess return, we also regress the 1-day return and 1-week return on the six
explanatory variables in model 3. Table 4 and table 5 show the result of regression models
<Model 4> (dependent variable = 1-day return) and <Model 5> (dependent variable = 1-week
return).
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As explained in section 2, most Red-chips and H-shares are mainland state-owned enterprise.
We also run the regression model, same as model 3, for stocks which are neither Red-chips
nor H-shares <Model 6>.
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4. Empirical Results
Descriptive Statistics
Figure 1 presents the descriptive statistics of the dependent variables, based on the results, the
mean of the overall first day excess return and overall first day return are equal to 11.319%
and 11.380% respectively. The mean of the first day excess return and first day return are
much smaller if the China stocks were excluded, which are 8.294% and 8.271% respectively.
On the other hand, the standard deviations of the overall first day returns increased when
the China stocks were included.
Figure 1
Descriptive Statistics of the Dependent Variables for Regression Models
Number of Observations Dependent Mean
Dependent Standard Deviation
Overall 147
1 Day Excess Return 0.11319 0.20083
1 Day Return 0.11380 0.20087
Non-China Stocks 107
1 Day Excess Return 0.08294 0.16507
1 Day Return 0.08271 0.16529
Regression Results
Figure 2 presents the regression results for the three different models with different number of
independent variables. From the results, the R-Square values are near to 0.39 for both
models, which mean about 39% of total variations are explained by the models. The
adjusted R-Square values are near to 0.36 for both models too. It can easily be seen that the
goodness of fit for these three different models is nearly the same, and found that the effect
on offer price and percentage of shares retained is not significant; we can prove this argument
from p values in the latter chapter.
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In all the regression models, there is a positive relationship between family company dummy
variable and the return, which estimate that the first day excess return increases by about 4%
for a family company’s IPO, holding other variables constant. There is also a positive
relationship between the upper price offered dummy variable and the return, which estimate
that the first day excess returns increase by about 6% when an IPO with upper price offered,
holding other variables constant.
It also estimates that the first day excess returns increase by about 11% when the IPO is a
China stock, holding other variables constant. And the first day excess returns increase by
about 1% and 2% when the retail proportion increase by 1% and when the subscription
multiple increase by 1% respectively, holding other variables constant.
Figure 2
Regression Results (coefficients of the explanatory variables) for the models with different
number of independent variables
1 Day Excess Return (Model 1)
1 Day Excess Return (Model 2)
1 Day Excess Return (Model 3)
R-Square 0.3910 0.3908 0.3882
Adj R-Sq 0.3557 0.3601 0.3619
Intercept -0.19109 -0.19193 -0.03396
Family_Company 0.03662 0.03810 0.04058
ln_OP 0.00487 - -
UP_Offer 0.05931 0.05977 0.06461
Is_China_Stock 0.10909 0.11166 0.10764
Is_Auditor_Big_Four -0.08853 -0.08545 -0.08487
P_Retained 0.00212 0.00211 -
P_Retail 0.01071 0.01073 0.00806
ln_Sub 0.01953 0.01974 0.02414
Finally, the models estimate that first day excess returns reduce by about 8.5% when the
auditor is a big four CPA firm, holding other variables constant. Therefore, the auditor
quality is negatively related to first day excess return in all the models, showing that auditors
with a higher perception of quality help to reduce pre-IPO information asymmetry which in
turn reduces under-pricing.
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Extended Regression Results
We further analyze the data with the first day return and the first week return compared with
the first day excess return. Figure 3 presents the first day return and the first week return are
only taken the return to the IPO offer price; the means are lightly larger than the mean for the
first day excess return, showing that the IPO has lightly better performance from the effect of
the market volatility.
Figure 3
Regression Results (coefficients of the explanatory variables) for the models
1 Day Excess Return (Model 3)
1 Day Return (Model 4)
1 Week Return (Model 5)
Dependent Mean 0.11319 0.11380 0.11378
R-Square 0.3882 0.3876 0.3383
Adj R-Sq 0.3619 0.3614 0.3099
Intercept -0.03396 -0.03180 -0.11409
Family_Company 0.04058 0.04053 0.04368
UP_Offer 0.06461 0.06441 0.06471
Is_China_Stock 0.10764 0.11184 0.15836
Is_Auditor_Big_Four -0.08487 -0.08624 -0.01948
P_Retail 0.00806 0.00845 0.01336
ln_Sub 0.02414 0.02285 0.01333
The estimated values estimate that the first week return increase from 11% to about 16%
when the IPO is a China stock, compared with the first day excess return, holding other
variables constant. China stock will provides better performance after one week of listing.
It also estimates that if the auditor is a big four company, the first week return tends to zero,
holding other variables constant, showing that the auditors with a higher perception of quality
help to reduce the incorrect IPO pricing. It also shows lesser effect to the first week return
when subscription multiple, holding other variables constant.
We also analyze first day excess return between overall IPO and non-China stocks’ IPO.
Figure 4 presents the mean of the first day excess return drops from 11.319% to 8.294% for
non-China stocks’ IPO, showing that non-China stocks’ IPO have lower degree of IPO
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under-pricing in the measure of the mean comparison.
Figure 4
Regression Results (coefficients of the explanatory variables) for the models
1 Day Excess Return (Model 3)
1 Day Excess Return for Is_China_Stock = 0 (Model 6)
Dependent Mean 0.11319 0.08294
R-Square 0.3882 0.3022
Adj R-Sq 0.3619 0.2677
Intercept -0.03396 0.00307
Family_Company 0.04058 0.03894
UP_Offer 0.06461 0.01628
Is_China_Stock 0.10764 -
Is_Auditor_Big_Four -0.08487 -0.08641
P_Retail 0.00806 0.00743
ln_Sub 0.02414 0.02120
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5. Hypothesis Tests and Results
We continue to analyze the results with hypothesis tests. Figure 5 presents the p values for
each regression results for all models. The p value will provide information of whether we
reject the null hypothesis of the estimated value for the variable equal to zero at 5% or 10%
significant level.
Figure 5
Regression Results for the models
1 Day Excess Return
(Model 1)
1 Day Excess Return
(Model 2)
1 Day Excess Return
(Model 3)
1 Day Return
(Model 4)
1 Week Return
(Model 5)
1 Day Excess
Return for Is_China_ Stock = 0 (Model 6)
R-Square 0.3910 0.3908 0.3882 0.3876 0.3383 0.3022
Adj R-Sq 0.3557 0.3601 0.3619 0.3614 0.3099 0.2677
Intercept -0.19109 (0.3721)
-0.19193 (0.3684)
-0.03396 (0.5499)
-0.03180 (0.5757)
-0.11409 (0.1022)
0.00307 (0.9538)
Family_ Company
0.03662 (0.2404)
0.03810 (0.2108)
0.04058 (0.1797)
0.04053 (0.1806)
0.04368 (0.2379)
0.03894 (0.1761)
Ln_OP 0.00487 (0.8129)
- - - - -
UP_Offer 0.05931
(0.0620)# 0.05977
(0.0587)# 0.06461
(0.0370)* 0.06441
(0.0377)* 0.06471
(0.0871)# 0.01628 (0.5980)
Is_China_Stock 0.10909
(0.0038)* 0.11166
(0.0019)* 0.10764
(0.0024)* 0.11184
(0.0017)* 0.15836
(0.0003)* -
Is_Auditor_ Big_Four
-0.08853 (0.0871)#
-0.08545 (0.0867)#
-0.08487 (0.0883)#
-0.08624 (0.0835)#
-0.01948 (0.7481)
-0.08641 (0.0502)#
P_Retained 0.00212 (0.4412)
0.00211 (0.4422)
- - - -
P_Retail 0.01071
(0.0478)* 0.01073
(0.0466)* 0.00806
(0.0495)* 0.00845
(0.0398)* 0.01336
(0.0082)* 0.00743 (0.1277)
Ln_Sub 0.01953 (0.1225)
0.01974 (0.1163)
0.02414 (0.0312)*
0.02285 (0.0414)*
0.01333 (0.3282)
0.02120 (0.0916)#
Note: the p value is in parentheses; * reject null hypothesis H0: βi = 0 at 5% significant level;
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# reject null hypothesis H0: βi = 0 at 10% significant level
First, the p value for the estimated value of “ln_OP” is 0.8129 in model 1, we do not reject
the null hypothesis H0 : the coeff of ln_OP = 0 and conclude that the change of offer price
does not significantly affect the underpricing. Furthermore, the p value for the estimated
value of “P_Retained” is 0.4422 in model 2, we do not reject the null hypothesis H0:
coefficient of P_Retained = 0 and conclude that for the change in the percentage of shares
retained by the substantial shareholders does not affect the underpricing too. Therefore, we
reject these two dependent variables for the further analysis.
We now use the model 3 to further analyze the results. If we do right tailed hypothesis test
on the “Family_Company” variable, we found the p value is 0.1797/2 = 0.08985, which is
smaller than 10%, therefore we can reject the null hypothesis H0 : the coefficient of
Family_Company (β3) ≤ 0 at 10% significant level. We can conclude that the coefficient
for the “Family_Company” variable is significantly larger than zero at 10% significant level.
In the other words, a family company would increase the underpricing, holding other
variables constant
We can also conclude from the results that the coefficients for the “UP_Offer”,
“Is_China_Stock”, “P_Retail” and “ln_Sub” variables are all significantly different from
zero at 5% significant level. Similar arguments hold on the coefficients for the “UP_Offer”,
“Is_China_Stock”, “P_Retail” and “ln_Sub” variables are greater than zero as seen in the
right-tailed test. It implies the upper price range offer, China stock, more percentage allocated
to retail investors and more subscription multiples would increase the degree of underpricing,
holding other variables constant.
For the effect of “auditor being Big Four CPA Firm”, we can conclude from a left tailed test
on the “Is_Auditor_Big_Four” variable that the coefficient is significantly smaller than zero
at 5% significant level as its p value is 0.0883/2 = 0.04415.
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6. Conclusion
As we have stated in our objective, we would like to find out from our study which kind of
company under an IPO will tend to offer its shares at a lower price and thus we can have
better chance to have better earnings after listing of shares.
From our econometrics model as listed above and also our test results, we can see that
corporate governance is one of the major concerns of the general investors. Thus companies
will usually be more conservative when setting their offer prices if there is a general
perception that these companies are less concerned about corporate governance. Therefore,
a family company, which is generally regard as less sophisticated in terms of corporate
governance when compared with an entrepreneur firm, will usually under-price their shares
during IPO. Such result also applies to those H-share and Red-chip companies which again
will generally be regarded as less sophisticated in corporate governance. We can also
observe that IPOs with one of the Big Four CPA firms as reporting accountant can have a
higher offering price as these Big Four CPA firms are perceived to have higher professional
standard in preparing the accountant’s report.
From the perspective of market effect, we can observe that IPOs with higher offering
percentage to public investors and have a higher subscription multiple will have a higher
chance of under-pricing of their shares. This also applies to those companies offer their
shares at the upper limit of the office price range. Although there may be critics that some
of these data may not be available until the closing of IPO subscription, usually there will be
press coverage on the market response on IPOs in the early stage of the subscription period
and thus we consider that investors can make use of such publicly available information to
make their IPO investment judgments.
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7. Appendices
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Appendix A - Regression Model Result
1. Table 1 – Regression Model 1
εβββββββββ
+++++++++=
)_ln(________)_ln(_1__
87654
3210
MultisubretailPretainPFourBigAuditorIsStockChinaIsOfferUPpriceofferCompanyFamilyDreturnExcess
Number of Observations Read 147
Number of Observations Used 147
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 8 2.30254 0.28782 11.08 <.0001
Error 138 3.58611 0.02599
Corrected Total 146 5.88864
Root MSE 0.16120 R-Square 0.3910
Dependent Mean 0.11319 Adj R-Sq 0.3557
Coeff Var 142.41319
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -0.19109 0.21343 -0.90 0.3721
Family_Company 1 0.03662 0.03106 1.18 0.2404
ln_OP 1 0.00487 0.02066 0.24 0.8139
UP_Offer 1 0.05931 0.03152 1.88 0.0620
Is_China_Stock 1 0.10909 0.03704 2.94 0.0038
Is_Auditor_Big_Four 1 -0.08853 0.05138 -1.72 0.0871
P_Retained 1 0.00212 0.00274 0.77 0.4412
P_Retail 1 0.01071 0.00536 2.00 0.0478
ln_Sub 1 0.01953 0.01257 1.55 0.1225
18
2. Table 2 – Regression Model 2
εββββββββ
++++++++=
)_ln(_________1__
7654
3210
MultisubretailPretainPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess
Number of Observations Read 147
Number of Observations Used 147
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 7 2.30109 0.32873 12.74 <.0001
Error 139 3.58755 0.02581
Corrected Total 146 5.88864
Root MSE 0.16065 R-Square 0.3908
Dependent Mean 0.11319 Adj R-Sq 0.3601
Coeff Var 141.92858
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -0.19193 0.21267 -0.90 0.3684
Family_Company 1 0.03810 0.03031 1.26 0.2108
UP_Offer 1 0.05977 0.03135 1.91 0.0587
Is_China_Stock 1 0.11166 0.03529 3.16 0.0019
Is_Auditor_Big_Four 1 -0.08545 0.04952 -1.73 0.0867
P_Retained 1 0.00211 0.00273 0.77 0.4422
P_Retail 1 0.01073 0.00534 2.01 0.0466
ln_Sub 1 0.01974 0.01249 1.58 0.1163
19
3. Table 3 – Regression Model 3
εβββββββ
+++++++=
)_ln(________1__
654
3210
MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess
Number of Observations Read 147
Number of Observations Used 147
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 6 2.28576 0.38096 14.80 <.0001
Error 140 3.60288 0.02573
Corrected Total 146 5.88864
Root MSE 0.16042 R-Square 0.3882
Dependent Mean 0.11319 Adj R-Sq 0.3619
Coeff Var 141.72264
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -0.03396 0.05665 -0.60 0.5499
Family_Company 1 0.04058 0.03010 1.35 0.1797
UP_Offer 1 0.06461 0.03067 2.11 0.0370
Is_China_Stock 1 0.10764 0.03485 3.09 0.0024
Is_Auditor_Big_Four 1 -0.08487 0.04945 -1.72 0.0883
P_Retail 1 0.00806 0.00407 1.98 0.0495
ln_Sub 1 0.02414 0.01109 2.18 0.0312
20
4. Table 4 – Regression Model 4
εβββββββ
+++++++=
)_ln(________1_
654
3210
MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturn
Number of Observations Read 147
Number of Observations Used 147
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 6 2.28346 0.38058 14.77 <.0001
Error 140 3.60764 0.02577
Corrected Total 146 5.89110
Root MSE 0.16053 R-Square 0.3876
Dependent Mean 0.11380 Adj R-Sq 0.3614
Coeff Var 141.06394
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -0.03180 0.05669 -0.56 0.5757
Family_Company 1 0.04053 0.03012 1.35 0.1806
UP_Offer 1 0.06441 0.03069 2.10 0.0377
Is_China_Stock 1 0.11184 0.03488 3.21 0.0017
Is_Auditor_Big_Four 1 -0.08624 0.04948 -1.74 0.0835
P_Retail 1 0.00845 0.00407 2.08 0.0398
ln_Sub 1 0.02285 0.01110 2.06 0.0414
21
5. Table 5 – Regression Model 5
εβββββββ
+++++++=
)_ln(________1_
654
3210
MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyWreturn
Number of Observations Read 147
Number of Observations Used 147
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 6 2.76064 0.46011 11.93 <.0001
Error 140 5.40058 0.03858
Corrected Total 146 8.16122
Root MSE 0.19641 R-Square 0.3383
Dependent Mean 0.11378 Adj R-Sq 0.3099
Coeff Var 172.61889
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -0.11409 0.06936 -1.64 0.1022
Family_Company 1 0.04368 0.03685 1.19 0.2379
UP_Offer 1 0.06471 0.03755 1.72 0.0871
Is_China_Stock 1 0.15836 0.04267 3.71 0.0003
Is_Auditor_Big_Four 1 -0.01948 0.06054 -0.32 0.7481
P_Retail 1 0.01336 0.00498 2.68 0.0082
ln_Sub 1 0.01333 0.01358 0.98 0.3282
22
6. Table 6 – Regression Model 6 for Is_China_stock = 0
εβββββββ
+++++++=
)_ln(________1__
654
3210
MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess
Number of Observations Read 107
Number of Observations Used 107
Analysis of Variance
Source DF Sum of Squares
Mean Square
F Value Pr > F
Model 5 0.87291 0.17458 8.75 <.0001
Error 101 2.01546 0.01996
Corrected Total 106 2.88837
Root MSE 0.14126 R-Square 0.3022
Dependent Mean 0.08294 Adj R-Sq 0.2677
Coeff Var 170.31172
Is_China_Stock = 0
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 0.00307 0.05292 0.06 0.9538
Family_Company 1 0.03894 0.02858 1.36 0.1761
UP_Offer 1 0.01628 0.03078 0.53 0.5980
Is_Auditor_Big_Four 1 -0.08641 0.04359 -1.98 0.0502
P_Retail 1 0.00743 0.00484 1.54 0.1277
ln_Sub 1 0.02120 0.01245 1.70 0.0916
23
Appendix B - SAS Code
proc import datafile="F:\project\SAS Data\sample.csv" out=IPO dbms=csv replace; getnames=yes;
quit; data IPO2; set IPO; Return_1D = first_day_closing / final_OP -1; Return_1W = first_day_closing / final_OP * AP_1w/First_day_AP -1; Return_1M = first_day_closing / final_OP * AP_1M/First_day_AP -1; Return_3M = first_day_closing / final_OP * AP_3M/First_day_AP -1; Excess_Return_1D = Return_1D - HSI_1D_Return; ln_OP = log(Final_OP); ln_PE = log(PE); ln_Sub = log(Sub_Multi); ln_CR = log(Capital_Raise); year_listing = year(listing_date); run; Proc REG DATA=ipo2 OUTEST=param_est_adj_raw tableout alpha = 0.05; TITLE 'Modeling (adjusted) - ALL Factor'; MODEL excess_return_1D=family_Company ln_OP UP_offer is_China_stock is_Auditor_big_four P_Retained P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit; Proc REG DATA=ipo2 OUTEST=param_est_adj_raw2 tableout alpha = 0.05; TITLE 'Modeling (adjusted) - ALL Factor - Remove Price'; MODEL excess_return_1D=family_Company UP_offer is_China_stock is_Auditor_big_four P_Retained P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit; Proc REG DATA=ipo2 OUTEST=param_est_adj_all tableout alpha = 0.05; TITLE 'Modeling (adjusted)'; MODEL excess_return_1D=family_Company UP_offer is_China_stock is_Auditor_big_four P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit; Proc REG DATA=ipo2 OUTEST=param_est_adj_hk tableout alpha = 0.05; where is_china_stock= 0 ; TITLE 'Modeling (adjusted), HK'; MODEL excess_return_1D=family_Company UP_offer is_China_stock is_Auditor_big_four P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit; Proc REG DATA=ipo2 OUTEST=param_est_unadj_all tableout alpha = 0.05; TITLE 'Modeling (unadjusted)'; MODEL return_1D=family_Company UP_offer is_China_stock is_Auditor_big_four P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit; Proc REG DATA=ipo2 OUTEST=param_est_unadj_1W tableout alpha = 0.05; TITLE 'Modeling (unadjusted) - 1 Week'; MODEL Return_1W =family_Company UP_offer is_China_stock is_Auditor_big_four P_Retail ln_Sub /p r rsquare adjrsq; OUTPUT OUT=out_est P=pred R=ehat; quit;
24
Appendix C – Dataset
Sample.csv
25
26
References
Chee Keong Low, Xin Yu (2007), Do Family Firms Leave Money on the Table During Initial
Public Offerings in Hong Kong