This document is a thesis submitted by Nguyen Le Ngoc Khoa for the degree of Master of Arts in Development Economics at the University of Economics in Ho Chi Minh City, Vietnam. The thesis examines the aftermarket returns of initial public offerings on the Vietnamese stock market from 2001-2013. It aims to investigate short-term and long-term stock performance after IPOs, and determine which factors most impact performance. Literature on IPO underpricing and market efficiency is reviewed. Research methodology uses abnormal returns and regression models to analyze IPO data from the Ho Chi Minh City and Hanoi stock exchanges.

1. 1
UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIET NAM
INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS
VIET NAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
-------------------------------------------
AFTERMARKET RETURNS OF INITIAL PUBLIC OFFERING
THE CASE OF VIETNAM
By
NGUYEN LE NGOC KHOA
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
Ho Chi Minh City, July 2014

2. 2
UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIET NAM
INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS
VIET NAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
-------------------------------------------
AFTERMARKET RETURNS OF INITIAL PUBLIC OFFERING
THE CASE OF VIETNAM
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
NGUYEN LE NGOC KHOA
Academic supervisor
Dr. TRUONG DANG THUY
Ho Chi Minh City, July 2014

3. 1
ACKNOWLEDGEMENTS
This paper has could not be started and completed without the help of several
individuals who supported me directly and indirectly. First of all, I appreciate my
supervisor Dr. Nguyen Dang Thuy so much for his enthusiastic assistance. He has
not only given me intellectual guidance in academy but also encouraged me a lot
through the analysis process. It is so hard for me to complete this research without
his profound advices. I am also thankful to Dr. Nguyen Trong Hoai and Dr. Pham
Khanh Nam for sharing his knowledge and practice experiences in researching
which are very useful for this study. I also thank my colleague, Ms. Ngo Thi Kim
Thanh for sharing her suggestion on the ideas to this thesis as well as econometric
techniques.

4. 2
Abstract
Initial public offerings (or IPO) are usually hot topic in financial world. This thesis
is to examine return behavior after IPOs in short-run and long-run on Vietnam stock
market by using abnormal returns to measure the stocks return. Market Efficiency
hypothesis is applied to test the long-run performance. Furthermore, given a
regression model, the thesis also aims to determine which factors most impact on
the stock’s performance aftermarket. The thesis uses IPO price and trading price
data from listed companies on Ho Chi Minh City Stock Exchange (HSX) and Hanoi
Stock Exchange (HNX) in the period 2001 – 2013. The results showed that most of
these companies are undervalued average of 63.5% in the first trading day after IPO
events. Then, stock returns are negative due to investors’ taking profit. In the long-
term, average rate of return of stocks are higher than the Vietnam’s benchmarks
(VN – Index and HNX Index) within one year, two years and three years after the
IPO. In addition, the study shows that in short-term, the abnormal return of IPO
events in the first trading day affected by firm size, listing exchange and industry.
But in the long-term, one year, two years, and three years after the IPO, there are no
variables have statistical significance. It implies that the accumulated abnormal
returns were not affected by the model’ factors.
Keywords: Stock market, underpricing

5. 3
Contents
CHAPTER I: INTRODUCTION ............................................................................... 5
1.1. Problem statement ................................................................................. 5
1.2. Research objectives................................................................................ 6
1.3. Scope of study......................................................................................... 7
CHAPTER II: LITERATURE REVIEW.................................................................. 8
2.1. Theoretical studies ................................................................................. 8
2.2. Empirical studies.................................................................................... 8
2.3. Conceptual framework........................................................................ 13
CHAPTER III: RESAERCH METHODOLOGY AND DATA............................ 14
3.1. Research methodology......................................................................... 14
3.2. Research data ....................................................................................... 15
3.3. Variables description........................................................................... 19
3.4. Testing methods ................................................................................... 22
CHAPTER IV: RESEARCH RESULT ................................................................... 25
4.1. Variables descriptive statistics............................................................ 25
4.2. Multicollinearity testing ...................................................................... 26
4.3. Multivariate regression model............................................................ 27
CHAPTER V: CONCLUSION AND POLICY RECOMMENDATION............. 32
5.1. Conclusion ............................................................................................ 32
5.2. Policy recommendation ....................................................................... 33
REFERENCE ............................................................................................................. 35
APPENDICES.............................................................................................................. 38

6. 4
Table of charts and figures
Figure 1: VN Index ...................................................................................................16
Figure 2: HNX Index ................................................................................................16
Figure 3: No. of IPO events during period 2001 - 2010 ...........................................17
Table 1: IPO events by stock exchange ....................................................................17
Table 2: IPO events by industry................................................................................18
Table 3: IPO events by underwriters.........................................................................18
Table 4: Descriptive statistics all variables...............................................................25
Table 5: Correlation matrix.......................................................................................26
Table 6: Variance inflation factor test.......................................................................27
Table 7: Mean cumulative abnormal returns ............................................................27
Table 8:Short-term regressions .................................................................................28
Table 9: Underpricing regression model...................................................................28
Table 10: White test result ........................................................................................30
Table 11: Skewness/kurtosis tests for Normality......................................................30
Table 12: Underpricing regression model after data trimming.................................30
Table 13: Regression models in long-term ...............................................................31
Table 14: Simple t-test to dependent variables .........................................................38
Table 15: Descriptive statistics detail to all variables...............................................40
Table 16: Underpricing regression model.................................................................46
Table 17: CAR4D regression model.........................................................................46
Table 18: CAR5D regression model.........................................................................47
Table 19: CAR6M regression model ........................................................................47
Table 20: CAR6MN regression model .....................................................................48
Table 21: CAR1Y regression model.........................................................................48
Table 22: CAR2Y regression model.........................................................................49
Table 23: CAR3Y regression model.........................................................................49

7. 5
CHAPTER I: INTRODUCTION
1.1. Problem statement
Initial public offering (IPO) plays an important role in development of issuers and
investors. Firstly, IPO helps to attract more investment capital for the issuers’
development in long-terms and improve its image in investor’s eyes (Ritter and
Welch, 2002). Secondly, IPO of large and potential companies has positive effect in
catching the investors’ attention and then increase the market’s liquidity. In
addition, success of IPO depends on participation of investors who always want to
look for large profit from the stock market. It means that investors should be known
that they would buy a good bargain in the IPO events and then would receive the
profits as selling stocks in the market. Therefore, this obviously motivates so-called
“underpircing” in IPO to make it to be more interesting for investment (Rock,
1986). However, a drawback to profit-seekers from IPO events is Efficient Market
Hypothesis (EMH) (Fama, 1970 and 1997). Under the hypothesis, investors seem
hard to seek more profit aftermarket, especially in long-term. The daily trading
price will fully reflect available information and thus there will be no more
abnormal return. Nevertheless, EMH, itself, could not explain convincingly many
extraordinary phenomena in the financial history. For instance, the Black Monday
on October 19th
in 1987 indicated a great decline of 30% within a day in the U.S.
stock market and the other international stock markets in the world. They stabilized
and recovered quickly not long after that and generate a huge profit or abnormal
returns to investors.
In Vietnam, the stock market has experienced ups and downs since its start in 2001.
When Vietnam had been going to join WTO, along with outbreak of VN-Index, IPO
issuance also broke out in 2006–2007 period. Most of investors wanted to jump in

8. 6
the IPO events to seek abnormal returns. A large cash flow was poured into the IPO
events due to lacking of investment opportunities at that time and they hoped to
earn profits as selling. Since end 2008, because of influence of economic recession,
Vietnamese corporation’ IPOs have not usually succeeded. Thanks to positive signs
for the economy, Vietnam is urging to speed up equitization process. However, such
one of the youngest stock markets over the world as Vietnam, whether investors can
earn profit from IPOs, at least in short-term, or not and how about in long-run?
Many theories and studies have explained underpricing in various ways using
different data from different countries and in different time periods in the world.
However, there are very few empirical studies on this issue in Vietnam. Several
studies have tried to explain underpricing but just in terms of descriptive statistics.
My goal is to use the updated data available with a longer event study to examine
the IPO underpricing in Vietnam. If there are more evidences indicate existing
profitable of investment in IPOs or investors can score a success for their
investment decisions, it can be more interesting for investors. That is the reason for
this research “Aftermarket return of Initial Public Offering – The case of Vietnam”.
1.2. Research objectives
Firstly, the research aims to investigate aftermarket stocks’ performance of initial
public offerings in short-run and long-run. Data from hundreds companies went
public and listing on HSX and HNX after IPO events are used to demonstrate the
existing of abnormal returns. The hypothesis here is returns in short-run is different
from zero. Obviously, the returns are expected as large as possible. In the long-run,
under the EMH hypothesis, it is expected there is no existence of abnormal returns.

9. 7
Secondly, by using a regression model the thesis also is to determine which factors
can impact to IPOs’ performance aftermarket. There are many models are applied to
different period.
1.3. Scope of study
The research has studied on the IPO and listed events on Vietnam's stock market in
the period 2001 – 2013. For the IPO events in a certain year, (ex. 2001), the next
three years (ex: 2002, 2003, and 2004) will be considered as the first, second and
third year since the first listed year. Data will end in 2010 as the years 2011, 2012,
and 2013 are used for long-term analysis.

10. 8
CHAPTER II: LITERATURE REVIEW
2.1. Theoretical studies
Efficient Market Hypothesis (EMH) was developed by Eugene E. Fama (1970), in
which he gave three forms of the theory, including weak form, semi-strong form
and strong form. Two first forms have been accepted commonly than the last one.
The first form claims that the current price of securities fully reflects its past
information and investors cannot win the market given the past information. The
second form claims that securities’ current price “fully reflect all obviously publicly
available information” (Fama, 1998). By efficient, most proponents of the theory
mean that investors cannot earn above-average returns on the stock aftermarket. It
means that difference between stock returns and market returns equals zero. The
theory then has been challenged by behavior finance economists. However, in the
latest research on this theory in 1998, Fama reiterated his most important conclusion
related to abnormal return in long-run. He reckons that “apparent anomalies can be
due to methodology, most long-term return anomalies tend to disappear with
reasonable changes in technique”.
2.2. Empirical studies
Most research on the issue recognized the existence of underpricing as returns on
the first listing day is usually positive (Miller and Really, 1987; Allen and
Faulhaber, 1989; Rock, 1986; and Tinic, 1998). However, based on EMH, there are
unending discussion between its challengers and proponents about stocks
performance in aftermarket in long-term. The challengers said that IPO stocks often
underperformed the market returns at least in the period three-to-five years. The
proponents imply that there are no evidences for underperformance of IPO stocks in
long-term based on the EMH.
2.2.1. IPO stock returns versus the market returns in short-term

11. 9
Researchers found that the IPO corporations are often undervalued with supports of
underwriters. There are many studies to explain the causes of the market
outperformance in the short term. Below are summaries of causes.
a. Underpricing makes higher returns versus market return in the short-term.
Kevin Rock (1986) developed a model for the underpricing of IPO which that this
phenomenon is due to uncertainty about value of stock to be offered. This is caused
by asymmetric information. Therefore, in order to attract the participation of
common investors, the issuers’ stock often undervalued. Besides, Ogden et al.
(2003) suggested that another cause of underpricing is to reduce risks of litigation to
underwriters. Some other ideas including Allen and Faulhaber (1989), Grinblatt and
Hwang (1989) and Welch (1989) suggested that many managers volunteer in low
pricing to themselves companies. Sometimes, they associate analyst to create
positive information as trading on the secondary market.
In Vietnam, Ayi Gavriel Ayayi (2011) conducted the study, "Underpricing and
long-term performance of auctioned IPOs: the Case of Viet Nam" including a
sample of 206 companies auctioned from Feb 2005 to Jun 2007. The main findings
are: (1) the IPO events in Vietnam were dominated by large-cap enterprises (mostly
state-owned enterprises) which accounted for 98.5% of the IPOs. These enterprises
tried to choose the most appropriate time for their IPO event. On the other hands,
there are differences in the discriminatory auction mechanism Vietnamese firms use
to determine their prices results in comparison with auction-to-listing returns (-
93.63% to 1,182.68%). Moreover, the average return in the first trading day was
low at 0.58%.
b. Overoptimism of the market causes stock’s outperformance in the short-
term.
Purnanandam and Swaminathan (2004) in study “Are IPOs really underpriced?"
which used the basic valuation methods, such as price-to-sales, price-to-earnings,

12. 10
and price-to-EBITDA. The results showed that the IPO events have been profitable
in the first five trading days. However, median value indicated that IPO firms’ stock
were overvalued of 14% - 50% as using multiple valuations, depending on which
multiple used. These results showed that investors were often optimistic in
forecasting growth, but less attention to current profits in valuing the IPO firms.
Another study by Purnanandam, Swaminathan, and Steven X. Zheng (2007) showed
that in the short-term IPO firms desired a higher return than the market. However,
in the long-term, the IPO firms’ stock had no higher return versus the market in five
years after the IPOs.
c. Supply restrictions in the first trading day cause the stock’s outperformance.
Zheng (2007) showed supply limitation (measured by the percentage of number of
floating shares over number of initial outstanding shares) brought about
outperformance. This limitation is due to restriction regulation on transfer of shares
of founders, insider shareholders, and major shareholders within 180 days (6
months) from the offering day. Besides, according to the Ogden et al (2003), the
median of shares restricted rate in six months from 1991 to 2000 was 65%. At the
same time, short-sell transactions in the first trading day were. Therefore, because
of excessive number of bids led, investors have had to pay more for securities in the
IPO. An IPO event has excessive stock demand would lead to higher price level in
short-term after listing (Ellis, 2005 and Zheng, 2007).
d. High stock demand and trading volume are also push stocks price in the first
trading day.
According to Aggarwal (2003), 70% of number of stocks listed after IPO had
trading volume in the first trading day higher than the average accounted.
Furthermore, Geczy et al (2002), Ellis (2006) suggested that investors have looked
for profit from stocks price fluctuations would contribute the increase in trading
volume. Katrina Ellis (2006) in the study "Who trades IPOs? A close look at the

13. 11
first days of trading" found the first trading volume on stock exchanges has been
influenced by profit-seekers by trading and investors who transferred their stock
right after IPO events. For small-cap corporations, it should be noted that dealer
usually accumulate shares from IPO events to be a market-makers as they would be
listed. Ellis found that there was 25% of trading volume in the first listing day
traded by interdealer.
2.2.2. IPO stock return versus the market return in long-term
Most studies of Loughran , Ritter and Rydqvist (1994), Ogden et al (2003) , Zheng
(2007) have said that the IPO stocks have underperformed their benchmarks in
long-term. The main reasons include:
a. The excessive optimism and overreaction of investors
Based on a few successful IPO events, many investors have expected a high return
from the IPO. However, many failures have admitted that profit in short-term could
be lost in long-term. This phenomenon was explained by the gradual correction of
the market after hot period.
b. Agency costs related to cash flow from the IPOs
Zheng (2007) said that agency costs would affect potential cash flow received from
the IPO and would be transferred as overvaluation in short-term. It has led to
underperformance in long-term. Fluctuation of the agency costs have depended on
level of capital expenditure in new assets and bid-ask spread in the first trading day.
c. Uncertainty of value of the IPO firms
Houge , Loughran , Suchanek and Yan (2001) found a relationship between wide
opening spread, late opening trade, and a high flipping ratio and long-run returns.
These one suggests that IPOs can generate an overvaluation in short-term, but
underperformance in long-term.

14. 12
d. Restriction removal on transfer of IPO shares would increase supply in long-
term
After transfer restriction has been removed, outstanding shares would be added,
according to Zheng (2007), Ogden et al (2003). The supplement shares supply
would decompress the increase in stock price.
Probability of underperformance and outperformance in long-run is the same.
Many recent studies have disclaimed the idea of underperformance in long-run. The
researchers confirmed the market is perfect, therefore it is not able to predict stock
price in the future. There are two explanations for this idea (1) returns calculation
method, (2) characteristics of IPO companies.
Fama (1998) suggested that underperformance of IPO stocks could be eliminated as
using suitable adjusted techniques to maintain independence between
anomalousness and applied methods. He also demonstrated the efficiency of market
which has generated zero return to investors in long-run. However, if there have
been anomalous events, overoptimism could be occurred.
Gompers and Lerner (2003) studied the growth of overperformance of IPO events
in NASDAQ from 1935 to 1972. Abnormal returns are calculated by difference
between stock returns and benchmarks returns. The outcome showed the abnormal
return has depended on the measurement method used. They found that the Buy and
hold abnormal returns (BHAR) would generate underperformance. Conversely, the
Cumulative abnormal returns (CAR) could eliminate compounding effect of a
single year’s poor performance. Anomalous returns are calculated by subtracting the
market return from the IPO company’s return. CAR was computed by summing the
abnormal returns over three years or five years. The results in comparison with the
benchmark indices showed BHAR had a stronger fluctuation than CAR.

15. 13
Brav and Gompers (1997) demonstrated that the IPO firms’ characteristics affect
the return in future. They concluded that many small and same size IPO firms
underperformed. In long-term, underperformance has also happened to the small
firms.
The studies above of IPO events are mostly conducted in developed countries. For
Vietnam, there is few related studies can be found, such as Ayi Gavriel Ayayi
(2011), Ly, T.T.H. and Kha Duong (2013). While Ayi Gavriel Ayayi just used
descriptive analysis to generate his conclusion, the research of Ly, T.T.H and Kha
Duong used a small sample of 69 IPO events. So I conducted this thesis with a
wider sample to test two issues (1) Is there underpricing in IPO events in such
young market like Vietnam. (2) Which factors affect anomalous returns in short run
and long run as well? Based on previous studies, I determined CARs is better to
measure the abnormal returns. I also determined which explanatory variables should
be included in the regression model to detect the significant factors.
2.3. Conceptual framework

16. 14
CHAPTER III: RESAERCH METHODOLOGY AND DATA
3.1. Research methodology
The aim of the study is to detect stock underpricing as they are listed in the
aftermarket in short-run and long-run as well. Stock returns are expected to
outperform the benchmarks. Then, I also would like to determine which factors
affect the abnormal returns.
Abnormal return measurement. Difference between rate of return of stocks
affected by the IPO event and the market rate of return in the short-term and long-
term is considered as abnormal return. This return is normally measured by
cumulative abnormal return (CARs) method and buy and hold abnormal return
(BHAR) method. CAR method is selected based on the research of Fama (1998)
and Gompers and Lerner (2003). In these researchers view, CAR is better than
BHAR which can increase underperformance. The CAR measurement is described
as below:
CARi(t1,t2) = yε*
i
Where:
γ' is a vector of IPO i from position t1 to t2.
Sum of all ε*
i, and take average to calculate abnormal return for the IPO.
Multiple regression models. The model is used to measure the impact of
dependent variables, including firm size, free-float ratio, trading volume in the first

17. 15
day of listing, the 1-month market return before the IPO events, listing exchange
and underwriter to independent variable, abnormal return.
3.2. Research data
As mentioned, there are few researches related underpricing of IPO firms in
Vietnam. Therefore, this study was conducted to find evidences for the low
valuation phenomenon of in short–term. It is aim to find the explanatory factors for
this one given a data sample included 579 companies from both Ho Chi Minh Stock
Exchange (HSX) and Hanoi Stock Exchange (HNX) during the period 2001 – 2013.
They are collected from State Securities Committee. Although HSX operated since
2000, but there is not enough data in this year. So started time is 2001. In addition,
there are some IPO firms that cannot collect IPO data, so the first day listing prices
are used. Time frame used in this study is the period 2001 – 2010. For the IPO
events in a certain year, (ex. 2001), the next three years (ex: 2002, 2003, and 2004)
will be considered as the first, second and third year since the first listed year. Data
will end in 2010, correspondingly the years 2011, 2012, and 2013 are used for long-
term analysis.

18. 16
Figure 1: VN Index
Source: Hochiminh Stock Exchange
Figure 2: HNX Index
Source: Hanoi stock Exchange
The list of IPO companies was taken from Stockplus’s database. The market index
stocks price were obtained from HSX and HNX. The offer prices or listing price
were from the firms’ prospectus.
From figure 3, it was a significant increase in the number of IPO events for the
years 2006 and 2007 as the index climbed o record high. In this period, Vietnam
was going to be a member of World Trade Organization and the economy was
booming due to capital inflow from foreign investors as well as local investors. In
addition, in order to attract more firms joining the stock market, many encouraged
policies were effective, including corporate income tax remission, IPO processes
then have been slower due to decline of the market after the financial crisis in late
2008. It is consistent with the idea that IPO firms and underwriters have tried to
issue their shares in the booming market.
0
200
400
600
800
1,000
1,200
1,400
0
50
100
150
200
250
300
350
400
450
500

19. 17
Figure 3: No. of IPO events during period 2001 - 2010
Source: Stockplus
Number of firms listed in HNX has been higher than in HSX. It could be explained
that listing standards in HNX is easier than that one, so many firms can approach
without any difficulties. However, average issuing value in HNX has been much
lower than HSX. It is because
Table 1: IPO events by stock exchange
Industry No. of issuers Mean (VND mm)
Median (VND
mm)
Max(VND mm)
Min (VND
mm)
HNX 329 278,001 72,900 12,600,000 6,122
HSX 250 1,743,384 515,369 38,000,000 10,384
Total 579 910,723 155,191 38,000,000 6,122
Source: Stockplus
Although there were not much firms conducting IPO, Oil & gas and banking
industries had the most number of huge issuing events during this period. Industrials
has the most number of IPO firms of 248 companies, but their size were not large as
average market capitalization of just 321 VND bil.
0
10
20
30
40
50
60
70
80
90
0
200
400
600
800
1,000
1,200
07/31/01 07/31/02 07/31/03 07/31/04 07/31/05 07/31/06 07/31/07 07/31/08 07/31/09 07/31/10
No. of IPO events VN-index

20. 18
Table 2: IPO events by industry
Industry No. of issuers Mean (VND mm)
Median (VND
mm)
Max(VND mm)
Min (VND
mm)
Banks 7 9,375,635 11,550,489 16,481,828 551,800
Basic materials 64 1,401,731 301,250 38,000,000 19,487
Consumer goods 77 852,400 247,500 17,150,394 10,384
Consumer services 39 214,884 50,880 2,168,000 7,952
Financials 79 1,901,258 602,498 22,061,524 13,031
Healthcare 15 330,108 259,265 991,440 10,881
Industrials 248 321,268 82,410 6,912,000 6,122
Oil & Gas 4 4,183,888 1,982,024 12,600,000 171,504
Technology 20 996,677 67,358 9,729,637 11,260
Utilities 26 1,048,840 299,268 12,241,580 19,367
Total 579 920,268 157,735 38,000,000 6,122
Source: Stockplus
Bao Viet Securities Company and Saigon Securities Corporation have been the
biggest securities companies in Vietnam in terms of capital and reputation.
Therefore, they have been also the largest underwriters out of 53 ones on Vietnam
stock market. 173 out of 579 IPO events were conducted by the two firms during
period 2001 – 2010. The firms’ average market capitalizations of the deals were
also the highest. This showed that large companies have cHSXn the large consulting
organizations to conduct their IPO.
Table 3: IPO events by underwriters
Industry No. of issuers Mean (VND mm)
Median (VND
mm)
Max(VND mm)
Min (VND
mm)
BVSC 96 1,178,238 233,220 22,061,524 10,348
SSI 77 1,237,106 247,500 15,148,000 13,962
ThangLong SC/MBS 33 307,793 82,362 2,926,800 11,260
ACBS 31 750,425 75,900 11,550,489 12,773
BSC 30 580,213 89,585 9,729,637 14,099
VCBS 26 614,747 73,294 12,078,768 6,875

21. 19
VietinSC 26 830,197 48,923 12,241,580 7,194
KLS 21 990,678 151,872 4,928,000 21,591
SVS 12 367,956 143,129 1,845,000 32,693
DongASC 18 323,835 120,435 1,650,000 13,444
Others 209 921,272 176,150 38,000,000 6,122
Source: Stockplus
3.3. Variables description
Regression method was used to evaluate explanatory variables for underpricing.
Based on studies of Fama (1998) and Gompers and Lerner (2003), Ellis (2006),
Ogden et al (2003), together with a study of Belden Saro Fernando and Mohammad
Tayseer Chenine (2007), the following variables were included in the linear
regression with OLS method to determine degree of influence of explanatory factors
to the CARs in short-term and long term.
Ri = 0 + 1SIZEi + 2FREEFLOATi + 3VOLUMEi + 4MARKETPERFi +
5EXCHANGEi + 6UNDERWRITERi + 7INDUSTRYi +  (1)
Where, variables in (1) as defined as below:
Ri = CARi
SIZEi: the IPO firm size
FREEFLOATi: the floating shares ratio
VOLUMEi: the trading volume in the first listing day
MARKETPERFi: the marker returns one month before listing of stocks
EXCHANGEi: the stock exchanges
UNDERWRITERi: the underwriter for the issuer
INDUSTRYi: the business industry of IPO firm
Cumulative abnormal returns (CARs): In order to detect anomalous returns of
IPO events, CARs method is used to measure. It is selected based on the research of

22. 20
Fama (1998) and Gompers and Lerner (2003) which proposed a market model to
measure normal performance.
Rit = αi + βRmt + εit
Where (1) Rit is the stock return, (2) Rmt is the market returns, (3) εit is the anomalous
returns.
ε*it = Rit – αi - βRmt
ε*it = Rit – E[Rit | Ωit]
The simplest model is set to αi = 0 and βi = 1.
ε*it = Rit – Rmt
The anomalous returns are difference between daily stock returns and daily market
returns. Daily anomalous returns are cumulated for each period from t1 to t2.
CARi(t1,t2) = yε*
i
Where: γ' is a vector of IPO i from position t1 to t2.
Sum of all ε*
i, and take average to calculate abnormal return for the IPO.
Abnormal returns in short-term: one day, two days, three days after the listing
are considered as short-term, where rate of returns in the first day are measured by
difference between closed price and offer price defined by underwriters.
Abnormal returns in long-term: They are determined as anomalous returns within
one year, two years and three years after. This is to test the Efficient market theory
of Fama (1998) which reckon that rate of returns in long-term would be zero. It
means that there is no factors influence on the rate of returns.

23. 21
Natural logarithm of firm size: It is the companies’ capitalization value and
defined as the product of the initial outstanding shares and the offer price. The
larger the companies are expected to generate more outperformed versus the market
(Gompers and Lerner, 2003). Natural logarithm is to adjust the influence of
abnormal data.
Free-floating shares ratio: It is the ratio of free-floating shares divided by the
number of offered shares in the IPO events. The variable is expected to have two
influences (1) the low free-floating shares ratio has positive effect to stock price due
to limitation of supply. (2) However, if it is too low, liquidity can be impacted
(Ogden et al 2003).
The trading volume in the first trading day over number of offered shares: It is
expected to have directly proportional to IPO returns. The attractive IPO events
have a high return and large trading volume in the first listing day and vice versa
(Ellis 2005).
The market rate of return previous month before stock IPO events: According
to Ogden et al (2003), Ritter (2006) and Fama (1998) argued that IPO events timing
is very important to its successfulness. In the research, the market CAR one month
is used to measure this variable. Hypothesis assumes that the market returns and
tock returns vary direct proportion.
Exchange: According to Mohammad Tayseer Chenine (2007) showed that after
listing, the performance could be affected by exchanges where the IPO firms listed.
In Vietnam, there are two stock exchanges, including Ho Chi Minh Stock Exchange
- HSX and Hanoi Stock Exchange - HNX, in which HSX has higher standard for
listing companies. Better exchanges that the firms are listed, higher returns are
generated. These ones are dummy variables, where HSX is set as 1 and others are
set as 0.

24. 22
Underwriters: Fernando and Mohammad Tayseer Chenine Saro Belden (2007)
found that underwriting organizations affect rate of return. It is expected that issuers
are underwritten by large and reputation organization can generate a better profit.
This is also dummy variables where large underwriters are set as 1 and others are
set as 0. Large organizations are determined as ones have do consultancy work to
more than thirty IPO issuers.
3.4. Testing methods
Underpricing and EMH, how do we know?
In order to answer it, average CARs in various periods are used to measure degree
of underpricing. In short run, CARs is expected positive value, but in long run, it
should be close to zero under efficient market hypothesis. Its test statistic is shown
at once to define degree of significance of CARs in comparison them to the critical
values.
Correlation matrix to test multicollinearity.
Where the X variables are correlated with each other but not perfect as below:
λ1X1 + λ2X2 + …+ λkXk = 0
Where λ1 = λ2 = ... = λk are constants and not simultaneously equal to zero.
A rule of degree of correlation is that if it is higher than 0.8, then multicollinearity
becomes a significant problem. However, this should seem a necessary condition
for existence of multicollonearity. This statistical phenomenon may exist even
degree of correlation rather low (less than 0.5).
White test for heteroscedasticity
This test is to examine residual (ui) in population regression function. It is expected
to be homoscedasticity or have the same variance. If there is existence of
heteroscedasticity or changes in variance, they are not efficient anymore.
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25. 23
The regression equation residuals ui:
Null hypothesis H0: α2 = α3 = α4 = α5 = α6
It means that there are no changes in variance. If Chi-square value exceeds the
critical Chi- square at selected significant level, we may conclude existence of
changes in variance. The best linear unbiased estimator (BLUE) method is used to
correct the heteroskedasticity phenomenon.
In summary, the study used data sources IPO firms in the period 2001-2013 to study
the influences of the factors of company size, free-float ratio, market returns one
month before IPO firms, exchanges, and underwriters to CARs from the IPO events
in short-term and long-term. The multivariate regression model under OLS method
is used. Then, the tests are occurred to examine 1) whether the residual variance of a
variable in a regression model is constant (homoscedasticity) by the White test, 2)
multicollinearity by the correlation matrix and variance inflation factor test, and 3)
autocorrelation by Durbin – Watson test and then correct the autocorrelation by
Prais-Winston regression.
Jarque – Bera test to examine normality of residuals and censoring data
The Jarque – Bera (JB) test is to examine whether sample data follow normal
distribution or not. It is defined by the following formula.
Where n is the number of observations; S is the sample skewness, and K is the
sample kurtosis.
In case the residuals are not normality I will conduct Winsorization technique to
limit extreme values or untrustworthy in the data set and then the magnitude of the
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26. 24
deviation is reduced. According to W. J. Dixon (1960) and J. W. Tukey (1962),
outliers can influence the distribution of many statistics. A basic way is to transform
all outliers to a specified percentile of the data. The percentile of 90% is usually
used to trim the outliers. It means that I will trim 5% of the largest value and 5% of
the smallest value out of the data set. This percentile probably does not significantly
affect the accuracy of the P value.
After exclude the extreme value, I hope the residuals will become normality and the
models are also better.
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