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

1

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

2

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.

3

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.

4

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

5

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.

7

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).

8

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>.

9

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.

10

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)



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.

11

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 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

12

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 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

13

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;

14

# 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.

15

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.

16

7. Appendices

17

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


εββββββββ

++++++++=

)_ln(_________1__

7654

3210

MultisubretailPretainPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturnExcess





Mean Square

F Value Pr > F

Model 7 2.30109 0.32873 12.74 <.0001

Error 139 3.58755 0.02581




Coeff Var 141.92858

Parameter Estimates


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


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


εβββββββ

+++++++=

)_ln(________1__

654

3210






Mean Square

F Value Pr > F

Model 6 2.28576 0.38096 14.80 <.0001

Error 140 3.60288 0.02573




Coeff Var 141.72264

Parameter Estimates


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


P_Retail 1 0.00806 0.00407 1.98 0.0495

ln_Sub 1 0.02414 0.01109 2.18 0.0312

20


εβββββββ

+++++++=

)_ln(________1_

654

3210

MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyDreturn





Mean Square

F Value Pr > F

Model 6 2.28346 0.38058 14.77 <.0001

Error 140 3.60764 0.02577




Coeff Var 141.06394

Parameter Estimates


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


P_Retail 1 0.00845 0.00407 2.08 0.0398

ln_Sub 1 0.02285 0.01110 2.06 0.0414

21


εβββββββ

+++++++=

)_ln(________1_

654

3210

MultisubretailPFourBigAuditorIsStockChinaIsOfferUPCompanyFamilyWreturn





Mean Square

F Value Pr > F

Model 6 2.76064 0.46011 11.93 <.0001

Error 140 5.40058 0.03858




Coeff Var 172.61889

Parameter Estimates


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


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






Mean Square

F Value Pr > F

Model 5 0.87291 0.17458 8.75 <.0001

Error 101 2.01546 0.01996




Coeff Var 170.31172

Is_China_Stock = 0

Parameter Estimates


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


P_Retail 1 0.00743 0.00484 1.54 0.1277

ln_Sub 1 0.02120 0.01245 1.70 0.0916

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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;

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Appendix C – Dataset

Sample.csv

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References

Chee Keong Low, Xin Yu (2007), Do Family Firms Leave Money on the Table During Initial

Public Offerings in Hong Kong

Documents

How to Make a Good Choice on IPO? from Perspective of