How Vietnam Stock Returns Response to Events Announcement

VIETNAM STOCK MARKET: HOW STOCK RETURNS
RESPONSE TO EVENT ANNOUCEMENTS
By:
Vu Duy Bang
Nguyen Thanh Nam
Nguyen Quynh Anh
Mac Thi Huong
Intake: MEBF6-HCM
A Thesis Submitted to
CFVG
University Paris Dauphine
ESCP-EAP
In partial fulfillment of the requirements for the degree of
MASTER IN ECONOMICS OF BANKING AND FINANCE
Hochiminh City, June 2010

Authorization
We hereby declare that we are authors of the thesis.
We authorize CFVG to lend this thesis to
other institutions or individuals for the purpose of scholarly research.
We further authorize CFVG to reproduce
the thesis by photocopying or by other means, in total or in part, at the request of other
institutions or individuals for the purpose of scholarly research.
_________________________ _________________________
Vu Duy Bang Nguyen Thanh Nam
_________________________ _________________________
Mac Thi Huong Nguyen Quynh Anh

ABSTRACT
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
The Master in Economics of Banking and Finance to CFVG
Student’s Name:
Vu Duy Bang – Nguyen Thanh Nam
Nguyen Quynh Anh – Mac Thi Huong
Title:
Vietnam Stock Market:
How Stock Returns Response to Event Announcements
Date: 25 June 2010
This event study focuses on how Vietnamese stock price return responses to earning
announcement and right issue announcement. Market model is employed to extract
abnormal return during the pre and post period of the official announcement date. For
the total of 231 observations, we found that for positive earning and right issue, there
is meaningful abnormal return accumulated about 20 days before the official
announcement date. This shows information leakage as rumors is common in Vietnam
stock market. In contrast, negative earning event does not exhibit negative abnormal
return during pre period due to short selling is not allowed. Post abnormal return for
positive earnings is also more substantial than that of negative earnings. For right
issue, cash dividend shows higher abnormal return compared to that of share issue
except for share issue with high ratio.

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CONTENTS
INTRODUCTION ..................................................................................................................... 2
CHAPTER 1: BACKGROUND ................................................................................................ 3
1. Empirical theories .......................................................................................................... 3
2. Models for Measuring Normal Performance (3)
............................................................. 5
3. Market Efficiency in Capital Market ............................................................................. 8
4. Vietnam Stock Market Overview .................................................................................. 9
CHAPTER 2: STUDY METHODOLOGY AND DATA PROCESSING .............................. 16
1. Study methodology ...................................................................................................... 16
2. Events to be studied in this paper ................................................................................. 17
3. The Population and The Sample .................................................................................. 18
4. Define and categorize the events ................................................................................. 20
5. Sampling interval and time line ................................................................................... 22
6. Parameters computation ............................................................................................... 23
CHAPTER 3: FINDINGS AND ANALYSIS ......................................................................... 28
1. Aggregation of abnormal return over time – CAR ..................................................... 28
2. CAR sensitivity by compound ratios: .......................................................................... 33
3. Density chart of CAR over PRE period ....................................................................... 34
4. Density chart of CAR over POST period ..................................................................... 35
5. Density chart of PRE CAR by event type .................................................................... 37
6. Density chart of POST CAR by event type ................................................................. 39
7. Chart of distribution function of PRE CAR ................................................................. 41
8. Chart of distribution function of POST CAR .............................................................. 43
CHAPTER 4: CONCLUSION ................................................................................................ 45
APPENDIX I ........................................................................................................................... 46
APPENDIX II .......................................................................................................................... 47
APPENDIX III ......................................................................................................................... 67

Vietnam Stock Market: How Stock Returns Response to Event Announcements 2
INTRODUCTION
This paper examines the impact of selected events on stock prices in Vietnam stock
market.
Event studies have been considered as an essential part of empirical research in
finance and other areas. Regarding the relationship between share prices and
information release, there are various event studies have been taken over years for
both well-developed markets (the UK, the US etc) or emerging markets (India,
Malaysia, China).
We realize that there is no pertinent paper available for now that studies how stock
prices react to event announcements in Vietnam stock market after ten years of
operation. With limitation of data, our aim is to assess and discuss about the impact of
some economical events on stock prices using statistical survey method as well as
with theoretical assumptions in Vietnam market context.
The paper is organized as follows: chapter 1 mentions background of the study by
reviewing literature and Vietnam stock market overview, chapter 2 states study
methodology and data processing, chapter 3 presents our findings, chapter 4 is
conclusion for the study.

CHAPTER 1: BACKGROUND
1. Empirical theories
Event studies examined the behavior of stock prices on corporate events (1)
. Many
literatures of event studies was researched over the past several decades and become
an important part of financial economics. In a corporate context, the usefulness of
event studies from the fact that magnitude of abnormal movement at the time of
events provided a measure of the impact of these events on the wealth of the firm.
Event studies are the most successful uses of econometrics in policy analysis. The
methodology, which studies the movement of stock prices due to specific events were
originally developed to test the hypothesis that the stock market was efficient-that
publicly available information is impounded immediately into stock prices such that
an investor can not earn abnormal profits by trading on the information after its
release.
The event study methodology is well-accepted and extensively used in finance.
Events study results have been used in several hundreds scholarly articles in leading
(1)
We focus on event studies on the mean stock price effects. Others types of event
studies such as return variances (Beaver, 1968 and Patell, 1976), trading volume
(Beaver, 1968 and Campbell and Wesley, 1996), operational performance
(Barber and Lyon, 1996) are not mentioned on this research.

academic finance journals to analyze corporate finance issues, such as stock
repurchases and stock split and the relation between stock prices and accounting
information, by examining the impact of earnings release.
Event studies serve the important goal in capital market research as a way of testing
market efficiency. Systematically non-zero abnormal returns that persist after a
particular type of corporate events are in consistent with market efficiency.
Theories concerning to dividend payout (2)
were based on Miller and Modigliani
(1961) argued that the share price is independent of dividend policy – the value of a
share reflects both the future cash flow stream and future growth opportunities. MM
acknowledged the dividend changes influence stock price and attributed this
phenomenon to the “information content of dividends”. A stock price change resulting
from a change in dividend payout because of the informational content of dividends
represents differences in the private information known by corporate managers and
the information available to the public.
The results of early empirical attempts to support the information content of dividend
hypothesis are ambiguous. Separate studies by Fama (1969), Pettit (1972, 1976),
Griffen (1976) and Laub (1976) showed positive (negative) excess returns accruing
following unexpected dividend increases (decreases). Work by And (1975) and
Gonedes (1978) failed to support the premise, whereas Watts (1973) found that
transaction costs preclude excess return capture by market participants. Charest
(1978) reported that earnings announcement and dividend announcement effects are
confounded. Inconsistencies in the results can be traced to differences in data, sample
period, methods of analysis, and model misspecification.
(2)
George M. Frankfuter and Bob G. Wood with James Wansley, 2003, “Dividend
Policy Theory and Practice”, Elsevier Science

Supported by cash flow signaling theory, Bhattacharya, (1979, 1980) extended the
model to a two-period inter-temporal setting, whereas Talmor (1981) expanded it to a
multivariate model with several valuation parameters and signaling mechanisms.
In their extension of the model, Makhija and Thompson (1986) defined the least
profitable firm differently than Bhattacharya (1979). If all firms have nonzero
earnings, the dividend/earnings relation will be nonlinear. To ensure equilibrium
existence, the dividend policy of the most profitable firms must be constrained and
additional limiting conditions likely have to be imposed.
John and Williams (1985) developed a signaling model with multiple equilibriums
using the assumption that firms with unique private information will receive varied
marginal benefits following changes in dividend policy.
There are many theories about stock split such as such as Dolley (1933) examined the
price the price effects of stock splits, studying nominal price changes at the time of
the split. Myers and Bakay (1948), Barker (1956, 1957, 1958) and Ashley (1962) are
examined of studies during time period. In the late 1960s, seminal studies by Ball and
Brown (1968) and Fama, Fisher, Jensen and Ball and Brown considered the
information content of earnings.
2. Models for Measuring Normal Performance (3)
2.1 Statistical models
Model from statistical assumption concerning the behavior of asset returns and do not
depend on any economic arguments. In contrast, models in the second category rely
___________________________________________
(3)
John Y. Campbell/Andrew W. Lo/A. Craig MacKinlay 1997, “The econometrics of
Financial Markets”, Princeton University Press, Princeton, New Jersey

on assumptions concerning investors’ behavior and are not based solely on statistical
assumptions. It should, however, be noted that to use economic models in practice it
is necessary to add statistical assumptions.
Constant-Mean-Return Model
Let µi the ith
element of µ, the mean return for asset i. Then the Constant-Mean-
Return Model is:
Rit = µi + ξit
E[ξit ] = 0
Var [ξit ] = σξ
2
Where:
Rit : the ith
element of Rt, is the period-t return on security i
ξit : the disturbance term
σξ
2:
the (I,i) element of Ω
Although the constant-mean-return model is perhaps the simplest model, Brown and
Warner (1980, 1985) find it often yields results similar to those of more sophisticated
models. This lack of sensitivity to the model choice can be attributed to the fact that
the variance of the abnormal return is frequently not reduced much by choosing a
more sophisticated model.
Market Model
The market model is a statistical model which relates the return of any given security
to the return of the market portfolio. The model’s linear specification follows from the
assumed joint normality of asset returns. For any security i we have:

Rit = αi + βiRmt + Єit
E[Єit] = 0
Var[Єit] = σЄ
2
Where:
Rit, Rmt are the period-t returns on security i and the market portfolio.
Єit: the zero-mean disturbance term
αi, βi and σЄ
2:
the parameters of the market model
The market model represents a potential improvement over the constant-mean-return
model. By removing the portion of the return that is related to variation in the
market’s return, the variance of the abnormal return is reduced. This can lead to
increased ability to detect event effects. The benefit from using the market model will
depend upon the R2
of the market model regression.
Other statistical models
A number of other statistical models have been proposed for modeling the normal
return. A general type of statistical model is the factor model. Factor models
potentially provide the benefit of reducing the variance of the abnormal return by
explaining more of the variation in the normal return.
Sometimes limited data availability may dictate the use of a restricted model such as
the market-adjusted-return model. This model can be viewed as a restricted market
model with αi constrained to be 0 and βi constrained to be 1.
2.2 Economic models
Economic models restrict the parameters of statistical models to provide more
constrained normal return models. Two common economic models which provide

restrictions are the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing
Theory (APT). The CAPM was commonly use in event studies during the 1970s.
During the last ten years, however, deviations from the CAPM have been discovered
and this casts doubt on the validity of the restriction imposed by the CAPM on the
market model.
Some studies have used multifactor normal performance models motivated by the
APT. The APT can be made to fit the cross-section of mean returns, so chosen APT
model does not impose false restrictions on mean returns. On the other hand, the use
of the APT complicates the implementation of an event study and has little practical
advantage relative to the unrestricted market model. There seems to be no good
reason to use an economic model rather than a statistical model in an event study.
3. Market Efficiency in Capital Market
The simplest but economically reasonable statement of market efficiency hypothesis
is that security prices at any time fully reflect all available information to the level in
which the profits made based on the information do not exceed the cost of acting on
such information. The cost includes the price of acquiring the information and
transaction fees. When the price formation in equity market satisfies the statement,
market participants cannot earn unusual profits based on the available information.
The origin of the Efficient Market Hypothesis (“EMH”) was contributed by Bachelier
(1900) and the empirical research of Cowles (1933). The modern literature in
economics begins with Samuelson (1965), whose contribution is neatly summarized
by the title of his article: “Proof that Properly anticipated prices fluctuate randomly”.
This classical market efficiency definition was summarized by Fama (1970), and
developed at length by researchers in the field (4)
___________________________________________
(4)
Cheng-Few Lee, Alice C.Lee, 2006, “Encyclopedia of Finance”, Springer Science
- Business Media, Inc

Fama distinguished market into three forms:
Weak-form efficiency: The information set includes only the history of process or
returns itself.
Semi-strong form efficiency: The information set includes all information known to
all market participants (public available information)
Strong-form efficiency: The information set includes all information known to any
market participant (private information)
The Market Efficiency Model
Assumption that the condition of market equilibrium can be stated in terms of
expected returns. Although there exists diversified expected return theories, they can
in general be expressed as follows:
E( pi,t+1) = [1 + E(ri,t+1 | It)] x pi,t
Where:
E : the expected value operator
pi,t : the price of security i in period t
ri,t+1 : the one-period rate of return on security i in the period ending at t+1
E(ri,t+1|It): the expected rate of return conditional on information (i) available in period t
4. Vietnam Stock Market Overview
Introduction
Along with the implementation of the equalization plan of Vietnam (started from
1991), the stock market has been established, started by establishing of Vietnam State

Securities Commission (“SSC”) in 1996, followed by the Vietnam Securities
Depository and Hochiminh Stock Exchange (in 2000) and Hanoi Stock Exchange (in
2005).
The Vietnam stock market, formally known as the Securities Trading Centre (STC)
located in Ho Chi Minh City, was launched on July 28th 2000. At the opening trading
session, only two individual stocks with a total market capitalization of VND 444,000
million (about USD27.95 million) were traded on the market. Over ten years of
operation (at the end of June 2010), the number of listed companies have increased to
666 with a total market capitalization of VND703,692 billion (equal to USD37
billion), with two stocks exchanges are Hochiminh Stock Exchange (“HOSE”) and
Hanoi Stock Exchange (“HASTC”). Although the market has significantly grown
over the period, it is still rather thin, the market size for HOSE and HASTC is only
around 40%/GDP.
Organization and operation of the stock market
This section briefly introduces the organization and operation of the stock market in
Vietnam. Specifically, the section focuses on regulations regarding some
organizations involved in the market and how the market works.
The State Securities Commission (“SSC”)
The State Securities Committee, officially established in November 1996, is
responsible for the organization, development and supervision of the country’s
securities market. Before February 2004, the SSC had operated as an organ directly
belonging to the Prime Minister. During this period, the SSC could not well regulate
the market due to some structural weaknesses. Consequently, the Prime Minister
decided, on February 19th 2004, to hand over the task of managing the SSC to the
Ministry of Finance. The Government hopes that the transfer would help to improve
the performance of the market, which has not been performing well since its

establishment in July 2001.
Under the new model of operation, the main functions of the SSC are as follows:
Issuing, implementing and enforcing regulations and guidelines related to
securities and securities markets.
Organizing and managing stock trading centre in Vietnam.
Receiving feedbacks for securities companies, securities advisers, securities
investment funds, and securities depositaries & custodians.
Training the profession for the securities industry.
Stock Exchanges
The Stock Exchanges are the organization under the control of the SSC. There are two
stocks exchanges:
(1) Hochiminh Stock Exchange (“HOSE”): located in Hochiminh City, for
listing (but not limited to) the big firms. HOSE has the number of listed
companies of 246 with a total market capitalization of VND555,831 billion
(equal to USD29.2 billion)
(2) Hanoi Stock Exchange (“HASTC”): located in Hanoi, for managing the
listing (but not limited to) the small firms, the Over-The-Counter trading, and
the Specific Bond Market. HASTC has the number of listed companies of 305
with a total market capitalization of VND147,600 billion (equal to USD7.8
billion)
The Stock Exchanges assumes responsibilities of organizing, executing and
supervising securities trading activities on the Centre. Specifically, responsibilities
and rights of the STC include the followings:

Organizing, managing and supervising the trading of listed securities.
Managing the securities trading system.
Managing and supervising the listing of securities.
Managing and supervising the information disclosure activities of listed
companies.
Managing and supervising activities of the members of the Stock Exchanges.
Organizing, managing and conducting the market information disclosure.
Investment Banking Corporation
By regulation, Investment Banking Corporation can be established in either joint-
stock or limited liability ones. Moreover, the main businesses of the Investment
Banking Corporation could consist of brokerage, investment, asset management,
underwriting, and financial and securities investment advisory. Investment Banking
Corporation, which are licensed by the SSC as brokers or dealers, are eligible to
register as members of the stock exchanges HOSE and/or HASTC. Importantly, only
members of the stock exchange have been permitted to trade securities through the
trading system of the stock exchanges.
Listing requirements
To ensure the credibility and integrity of the stock exchanges, the Government has
placed special emphasis on the overall quality of listed companies by issuing the
criteria and regulations for listing. A company must comply with all of the listing
requirements prior to obtaining a listing license. The qualifications for listing are as
follows:
Being a joint-stock company with a minimum capital of VND80 billion (for
listing in HOSE) or 10billions (for listing in HASTC)
Having profits in the last two consecutive years before the year of applying for
listing.

Having the commitment made by members of the firm’s Board of Directors,
Board of Management, and Board of Supervisors to hold at least 50% of their
shares for six months from the date of listing.
Having at least 100 outside investors, holding at least 20%/share capital, as for
joint-stock company having share capital of VND100 billion or more, a rate of
15% is applied.
Information disclosures of listed companies
Listed companies are required to disclose publicly all information that is important for
investors' investment decisions. The stock exchange has implemented a full disclosure
policy, allowing investors to receive accurate, adequate and timely information in
order to ensure market transparency and integrity. Practically, the information
disclosure is conducted through the mass media or the Bulletin of the stock exchange.
Listed firms’ information, which is obligated to disclose can be classified into two
groups: regular and irregular information.
Regular information includes quarterly, semi-annual, and annual financial statements.
By regulation, within 10 days from the date of completing annual financial
statements, listed companies have to disclose publicly their audited financial
information on three consecutive issues of a national newspaper or a local newspaper
at the place where the head office of a listed company is located or on the Bulletin of
the Stock Exchanges. For the quarterly and semi-annual financial statements, listed
companies have to disclose them within five days from the date of completion on the
Bulletin of the Stock Exchanges.
Irregular information consists of any information related to events that happen
irregularly and should affect investors’ decisions. The listed companies are required to
disclose information within 24 hours from the occurrence of any of the following
events:

Having significant changes in conditions for its business activities.
Suffering from a loss equivalent to or more than 10 percent of its equity
The listed company, its member(s) of the Board of Directors, member(s) of the
Board of Management, Board of supervisors, and Chief Accountant being
prosecuted by the legal authority, being convicted by a court concerning
operations of the company; and violating tax laws as stated by the tax
authority
Having changes in business strategy and scope.
Having decisions on expanding its business activities, an investment worth 10
percent or more of another company’s equity, or buying or selling fixed assets
worth 10 percent or more of its equity.
Falling into bankruptcy situation, making a decision on corporate merge and
acquisition, split, and dissolution.
Signing a loan agreement or issuing bond, which worth 30% or more of its
equity.
Changing the Chairperson of the Board of Directors, or more than one-third of
the members of the Board of Directors, or Director (General Director);
approving of the resolutions of shareholders’ meeting.
Having other events that may considerably affect the share price or investors’
benefits.
Stock split, additional issuance to increase its share capital
Issuing bonus shares or share dividends, which is worth more than 10 percent
of the equity
Applying for de-listing.
Foreign participation
Foreign investors (institutions and individuals) can buy or sell shares on the Vietnam
Stock Exchanges through investment banking corporation. However, their ownership
(aggregation ownership of all foreign investors) in a listed firm is limited to 49
percent of the share capital, especially in case of banking sector this ratio is only 30

percent. In addition, foreign investors who wish to participate on the stock exchange
are required to register through a licensed custodian who holds shares on behalf of
foreign investors. Currently, three foreign banks (the Hong Kong and Shanghai
Banking Corporation, Deutsche Bank AG and Standard Chartered Bank) have
licensed by the SSC to provide custodian services for foreign investors. Once
registered, a securities transaction code is issued to the foreign investor who may then
open a trading account with one or more of the thirteen Investment Banking
Corporations for trading securities on the Stock Exchange. Moreover, foreign
securities business institutions are allowed to buy shares of securities and/or
investment fund management companies, or contribute capital to establish a newly
joint-venture securities and/or investment fund management companies with
Vietnamese partners. However, the proportion of capital contribution by foreign
partners in a joint-venture is not more than 49 percent of the firms’ share capital.
Vietnam stock market has its characteristics as following:
Market size is around 40% GDP.
Legal framework is not developed enough.
Derivatives are not allowed in Vietnam stock markets that lead to many
investors’ activities are performed not by laws.
Regulated band for maximum daily price change in trading, they are 5% and
7% for HOSE and HASTC respectively.

CHAPTER 2: STUDY METHODOLOGY AND DATA PROCESSING
1. Study methodology
Our target is to find the stock prices’ (or firm values’) behavior prior to and after
selected economic event announcements in Vietnam stock market. This can be
reflected through distribution of the sample data parameters and we can understand
that characteristic of the market in term of event announcements. Below process will
be gone through:
To define the event types to be studied and its reasons.
To scan data and determine criteria to select sample that presents the
population.
To categorize the event.
To determine sample interval and event window.
To compute necessary parameters of the sample with assumption of Vietnam
market model and use Visual Basic Application (VBA).
Results: descriptive analysis
1. Aggregation of abnormal returns over time: all event window and pre-
event versus post-event period
2. Cumulative abnormal returns’ sensitivity to compound ratios:
3. Density chart of aggregated abnormal returns over pre-event period
4. Density chart of aggregated abnormal returns over post-event period
5. Density chart of pre-event aggregated abnormal returns by event type
6. Density chart of post-event aggregated abnormal returns by event type
7. Distribution function chart of pre-event aggregated abnormal

8. Distribution function chart of post-event aggregated abnormal
Conclusion
2. Events to be studied in this paper
There are many event announcements may influence the company value. They can be
finance-related events such as earnings announcement, dividend payout, share split;
M&A; or firms’ activity-related events such as management team change, firms’ legal
case; or regulatory-related event such as changes in regulations, change in political
institutions.
In this paper our team limits the study in events that much relation to corporate
finance of listed firms including:
Financial reports announcement;
Dividend announcement;
Stock issuance to existing shareholder announcement:
Non-conditional: Stock issuance without additional capital contribution
Conditional: Stock issuance with additional cash contribution of VND10,000
per each
The reason for our choice is they are all economic events and are considered as firm’s
operation output, firm’s operational efficiency instead of social events that seems to
be uncontrollable. As a result we can use events to measure major change in
endogenous value of the listed firms.
Specific to Vietnam market, a listed company may discretionary announce cash
dividend payout, or share dividend right, or stock split right to existing shareholders
from time to time. For stock issuance to shareholder, there may be a condition of
additional pay at face value or not. We therefore standardize these kinds of

shareholder benefit to a comparable unit and treat relative events as a homogeneous
event type called “right issue”.
In summary our studied events are periodic earnings statement release and right issue
announcement.
3. The Population and The Sample
The population is above mentioned events occurred in Vietnam stock market that
leaded to changes in price of stocks listed in two stock exchanges HOSE and HASTC.
At the moment there are 666 stocks are listed in Vietnam stocks market. During
nearly ten years of operation to date, Vietnam market has 66 stocks being listed each
year in average.
To have a typical sample in Vietnam stock market environment, we have to find a
solution that enables us to choose stocks those:
are comparable in term of maximum daily price change (trading band);
are comparable in term of time horizon;
include both events being investigated;
represent all firms across over market capitalization size, business sectors;
have the high traded volume in average so stocks liquidity is considered
mainly decided by demand versus supply relationship.
The solution is setting some criteria as follows:
1. To avoid dispute on return computation by skipping the affect of different
daily price band between two floors: choose HOSE listed stocks only;

2. To choose long enough observation period: survey data over two-year time
from Mar 2008 to Mar 2010;
3. To choose stocks that have been listing for more than 3 years as at Mar 2010
so its sufficient to apply beta in computation of normal and abnormal return;
4. To choose high-liquid stocks to reduce manipulation impact;
5. To choose stocks across variance business sectors: manufacturing (FMCG,
steel etc), banking, services, real estate developer, infrastructure developer,
constructional materials, agricultural products processing, technology,
industrial property developer, oil, power, textile and garment, transportation,
assets management.
Data collection format:
SYMBOL DATE
CLOSING
PRICE VOLUME
ADJ CLOSING
PRICE
ANV 3/3/2008 69,000 66,340 60,610
ANV 3/4/2008 66,500 207,480 58,850
ANV 3/5/2008 63,500 213,560 56,190
ANV 3/6/2008 66,500 3,950 58,850
ANV 3/7/2008 69,500 173,780 61,510
ANV 3/10/2008 72,500 194,590 64,150
Source: HOSE
Adjusted closing price is the price that was adjusted from historical closing price by
dividing by compounded rate. This price was smooth and actually reflects stock price
change though time. All subsequent computations are based on adjusted closing price.

4. Define and categorize the events
4.1 Earnings events:
Each firm announces their earnings periodically. Four pieces of information are
collected: the expected earnings, the event date, the actual earnings and the event
definition.
We started with annual plan NOPAT of the firms that adopted by its Board of
Shareholders beginning of each year as the market’s expected earnings. The overview
of the data and sample shows that to clearly assess the impact of the earnings
announcement on the market valuation of the firm we need to add 25% deviation on
top of plan NOPAT to define whether the actual earnings are really good or bad
compared to expected earnings.
Event date is the date when the quarter financial report of the firm is released
officially, i.e. the report is submitted to Hochiminh Stock Exchange.
Actual earnings is reported earnings of the firm
In order to examine the impact of the earnings announcement, we assign earnings
announcements into two event categories: “positive earnings” for those exceed 125%
of market’s expected earnings and “negative earnings” for those behind 75% of
market’s expected earnings.
4.2 Right issue events:
We collect all announcements of selected 39 firms regarding:
Cash dividend payout

Share dividend
Share split
Share bonus
Additional share issuance with condition of additional capital contribution by
existing shareholders
Sometimes more than one benefit above is combined in a firm’s announcement.
Usually firm announces for shareholder benefit upon having a good earnings.
Three pieces of information are collected: the event date, the benefit rate for each
relative announcement, all benefits will be combined to find a compounded rate that
represent shareholder’s benefit in each announcement. Our group names this
compounded rate as “right issue”. In case of annual basis this compounded rate
represents dividend yield of the stock.
The event date is official date that firm announces the right issue.
The compounded rate is to standardize the right issue rate of stocks with different
trading price, different type of shareholders’ benefit announcement. Formula is:
Right issue rate Y = Pt-1 / [(Pt-1+SI*IP-FV*CD)/(1+SD+SB+SI)]
Where:
Y is standardized compounded rate. This is also the rate to use to adjust closing prices
of the initial data at of each relative right execution date
t is the date of right issue execution date, that is usually about 10-20 trading after
event date
Pt-1 is closing price of stock on the date (t-1), i.e. 1 day prior to right execution date
SI is rate in case share issuance to existing shareholders with additional capital
contribution request

IP is issuing price, i.e. the amount in VND shareholder has to pay for each share when
execute the right
FV is face value of the share equal to VND10,000 in Vietnam market
CD is the rate for cash dividend
SD is rate for share dividend
SB is the rate for share bonus or share split in case no additional capital contribution
request
4.3 Sample size
With mentioned assumption, our sample consists 231 events categorized into 3 types:
103 positive earnings, 78 negative earnings and 50 right issues events across over 39
selected stocks during two-year period that we strongly believe they represent
population characteristic.
5. Sampling interval and time line
From data collected, we set sampling interval to one day then daily return are used.
We investigate the stock return over:
21-day event window that comprises of 10 pre-event days, the event day and
10 post-event days.
41-day event window that comprises of 20 pre-event days, the event day, and
20 post-event days.
61-day event window that comprises of 30 pre-event days, the event day and
30 post-event days.

We don’t investigate longer event window to avoid overlap between two event
windows that leads to incorrect cumulated returns.
Event timeline can be illustrated as follow:
| T0 |—————| T1 |—————| T2 |
Where:
| T1 | is event date
| T0 | to | T1 | is pre-event period
| T1 | to | T2 | is post-event period
6. Parameters computation
6.1 Actual daily stock return
Ractual i, t = (Pi, t / Pi, t-1) - 1
Where:
Pi, t is adjusted closing price on date t
Pi, t-1 is adjusted closing price on date t-1
The numbers are pulled out from original data collected
6.2 Estimation of expected daily return - Normal daily stock return
There are some approaches to compute normal return of given stocks. For simplicity,
we use statistical market model as a basis to measure normal return with below
assumption for sample’s characteristics:
Firm stock returns are linear with VNIndex return at slope β;

Regardless the estimation window, the β is constant for each stock during
whole sample study period;
We all understand that it is much complicated to define parameter αi.
Particularly in Vietnam stock market context, there is no sufficient data to
compute α. In other word, limited data availability dictates the use of adjusted
market model with αi constrained to be zero;
If the event does not occur, ξit is considered zero too;
Firm’s market capitalization portion in sample is remained unchanged or small
enough that it has little effect on empirical work.
Normal return is considered as market’s expectation in case the event would not take
place.
Ri,t = αi + βiRm,t + ξi,t
Where:
Ri,t, Rm,t are the period-t return of stock i and of the market portfolio
ξI,t is zero mean disturbance term
αi, βi are parameters of market model
With above mentioned assumption of αi and ξit are zero, in this research we compute
normal return by formula:
Ri,t = βiRm,t
To be independent and with assumption of efficient market, in application a board-
based stock index is used for market portfolio return and βi is β of stock i that market
accepts as at March 2010. In this case they are VNIndex return and β gathered from
Bloomberg. For example the beta for stock symbol ITA is 1.139.

Associated with mentioned assumption, our formula has some limitations that may
lead to slight difference in the result.
Model parameters should depend on non-announcement period. In our
research we eliminate α and assume that β is fixed. Our dictation may create
correction between the event under analysis and the market return, for
example a part of price variation is already reflected in β;
The result of aggregation abnormal return varies according to sample size;
To allow for normal changes in firm’s stock price relative to the market only
whilst actually there are reasons for stock price movement such as firm
directors change, M&A activities, competition pressure and other
uncontrollable events;
The event actually may not be independent of the behavior of the stock price
in short, event date is endogenous
6.3 Estimation of Abnormal daily stock return due to event
To assess the event’s impact on the stock price, we need to measure the variation of
the return arising from event announcement over the event window. It interprets
abnormal return by taking actual return minus expected return during the event
window:
ARi,t = Ractual i, t - Ri,t
Where:
ARi,t is abnormal return of stock i at date-t

Ractual i, t is actual return of stock i at date-t
Ri,t is expected return of stock i at date-t
6.4 Aggregation abnormal returns
At this stage we have to assume that abnormal returns for all stocks are independent.
With this assumption, we compute abnormal return of given stocks and then
aggregate them in order to draw overall inferences for the event of interest.
The aggregation is along two dimensions: through time then across stocks.
The first is aggregation of abnormal return through time series for individual stock
over selected period within event window: this is Cumulative Abnormal Return
(CAR). Since the impacts of event on the stock price are sometimes not immediately,
so we must access CAR to get abnormal return over pre-defined period of time.
Pre-event CAR: CARi, t0,t1 = ∑ARi,t (t run T0 to T1)
Post-event CAR: CARi, t1,t2 = ∑ARi,t (t run from T1 to T2)
Event window CAR: CARi, t0,t2 = ∑ARi,t (t run from T0 to T2)
Where:
CARi is cumulated abnormal return of stock i over defined period
In this paper, we do aggregate across different time period to see if the effect develops
over time and to find meaningful/effective event window.
The second then is aggregation through time and cross-section, i.e. aggregation of
CARs of all stocks through selected period within event window: from T0 to T1 (Pre-

event CAR), from T1 to T2 (Post-event CAR), or from T0 to T2 (event window CAR)
then to get the Average CAR.
Average pre-CAR = ∑ CARi, t0,t1 / N (i run across 39 stocks)
Average post-CAR = ∑ CARi, t1,t2 / N (i run across 39 stocks)
Average event window CAR = ∑ CARi, t0,t2 / N (i run across 39 stocks)
Where N is the number of stocks employed in related event.
6.5 IT technique applied
Our group writes VBA codes to generate all desired CAR, aggregation CAR and
average aggregation CAR to demonstrate our findings (appendix II)
The data after processing is illustrated as follows:
SYMBOL DATE ADJ PRICE ACTUAL
RETURN
NORMAL
RETURN=Be
ABNORMAL
RETURN
PRECAR POSTCAR COMPOUNDED
RATE
EARNING
(P vs N)
ANV 7/24/2008 39,390 -2.86% -2.33% -0.53%
ANV 7/25/2008 38,230 -2.94% -1.54% -1.41%
ANV 7/28/2008 37,150 -2.83% 1.28% -4.10% -28.69% -1.17% Negative Earning
ANV 7/29/2008 36,710 -1.18% 2.38% -3.56%
CII 2/5/2009 14,720 -2.45% -3.17% 0.72%
CII 2/9/2009 16,010 3.56% 2.31% 1.25%
CII 2/10/2009 15,390 -3.87% -2.11% -1.76%
CII 2/11/2009 15,030 -2.34% -2.25% -0.09% 5.32% 16.16% 1.0474
CII 2/12/2009 14,850 -1.20% -0.05% -1.15%
CII 2/13/2009 14,780 -0.47% -0.63% 0.15%
CII 7/15/2009 23,580 -0.84% 2.22% -3.06%
CII 7/16/2009 24,480 3.82% 1.64% 2.17%
CII 7/17/2009 24,350 -0.53% -1.21% 0.68% -0.83% 6.93% Positive Earnings

CHAPTER 3: FINDINGS AND ANALYSIS
1. Aggregation of abnormal return over time – CAR
1.1 CAR over event window of 61 days
‐15.0%
‐10.0%
‐5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
‐30 ‐20 ‐10 0 10 20 30
CAR
Time
Figure 1. Average CAR over event window‐ grouped by event type
POSITIVE EARNINGS MEAN NEGATIVE EARNINGS MEAN RIGHT ISSUES MEAN
Trading day = 0: the announcement date
a) We aggregate abnormal return over time to see the effect of the events. An
event window of 61 trading days is used to calculate the over CAR. This
event window consists of 31 days PRE announcement period (including the
announcement date) and 30 days POST announcement period. Then the
computed CARs are averaged according to its event types.

b) Figure 1 shows that for positive earnings and right issues announcements, the
CAR start to increase about 20 days before the announcement date and
continue to increase for the next 20 days after the announcement and flat out
afterward.
c) The increased CAR in the PRE announcement period can be attributed to
information leakage. This truly reflects current situation information
asymmetry in Vietnamese stock market.
d) The process of this stock return behavior can be characterized as follows:
• Before the information is known to the public, the people who
knows the information first buy the stocks (or having their
relatives buy the stocks)
• Then they informally release the information to the public. This
information is rumors. The public usually follow the rumors since
they are proven to right. This is seen how the PRE CAR is
accelerated.
• When the information is officially announced, the market continues
to act on it and the effects is subsidized after about 20 trading days.
e) Returns behavior on negative earnings is quite different than the positive
earnings and can be summarized in the followings:
• There is no CAR movement during the PRE period. This is due to
short selling is not allowed, thus it is difficult to take advantage of
the information.

• The effect on post period is not as strong as compared to that of the
positive earnings. This can be explained by the following reasons:
Market in general focuses more on positive information
than negative information. As mentioned above, short sell is
not allowed so investors cannot make profit based on
negative information.
Overall, the degree of negative earnings is lesser than that
of the positive the positive one. Since the determination
whether the earnings is positive or negative is based on
comparing the actual earnings against the company’s
earnings forecast. The company management tends to be
more conservative so that it is easier to beat the expectation.
The motivation for beating the expectation is for stock price
increase, so as their bonuses and job security.
The major shareholders tend to fights against downward
price movements. They would use their private source of
money to buy the stock when there is price downward
pressure. This could be done through trading accounts
under different names so transactions on these accounts
would not need to notify with the exchange Committee.

1.2 CAR separated from PRE and POST period
‐4%
‐2%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0 5 10 15 20 25 30
CAR
Time
Figure 2.1 Positive Earning ‐ Average CAR (PRE period Vs POST period)
Average CAR over PRE period Average CAR over POST period
‐16%
‐14%
‐12%
‐10%
‐8%
‐6%
‐4%
‐2%
0%
2%
0 5 10 15 20 25 30
CAR
Time
Figure 2.2 Negative Earning ‐ Average CAR (PRE period Vs POST period)

0%
5%
10%
15%
20%
25%
0 5 10 15 20 25 30
CAR
Time
Figure 2.3 Right Issue‐ Average CAR (PRE period Vs POST period)
a) To see the effects of CAR over the PRE and POST period separately, CARs
for PRE period stops at the end of the announcement day. CAR for POST
period is calculated from the first day after the announcement day. Figure 2.1
and figure 2.3 show that CAR is stronger during post period than pre period
for positive earnings and right issue events, while the reverse is true for
negative earnings as shown in figure 2.2.
b) Strong POST CAR of positive earnings can be attributed to Post Earnings
Announcement Drift effect presented by Bernard and Thomas (1989).
c) Strong POST CAR of right issue can be partially explained that it would
compensate the time value of the capital that is “locked up” during the right
issue process. In other word, there is a share price adjustment (normally
adjusted down) associated with right issue thus existing shareholders will
loose the “time value” of the locked up capital.

2. CAR sensitivity by compound ratios:
‐10%
‐5%
0%
5%
10%
15%
20%
25%
30%
‐30 ‐20 ‐10 0 10 20 30
CAR
Time
Figure 3. CAR Sensitivity To Compounded Ratios
MEAN Ratio 1‐1.05 MEAN Ratio 1.05‐1.1 MEAN Ratio > 1.1
Figure 3 shows that right issues with low compounded ratio and high compounded
ratios have strong CAR while right issues with medium compounded ratio have
weaker CAR. This is due to right issue events with low compounded ratio consists of
mainly cash dividends while medium and high ratio consists of share issues (including
share dividend, share bonus, rights to buy). This show cash dividend is more
attractive than that of share issues unless share issues with high compounded ratio.
Despite the fact that cash dividend is not necessary beneficial to shareholders because
of the shareholder return might be higher average market return. In this case, the
company would rather not pay cash dividend since it will result a higher return than
the market return. For the case of share issues, it is also not necessary positive since
the effect of share dilution will result in a lower earnings per share in the future.
However, the investors’ perception in Vietnam about cash dividend and share issues
is strongly favorable.

3. Density chart of CAR over PRE period
.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
NUMBER  OF OBSERVATIONS
CAR
Figure 3.1 Density chart of CAR over PRE 11 period by event type
POSITIVE EARNINGS
Mean: 5.69%
Std. Deviation: 7.54%
Kurtosis:    1.4
Skewness: 0.9
NEGATIVE EARNINGS
Mean: (7.58%)
Kurtosis:    0.3
Skewness: 1.3
RIGHT ISSUE
Mean: 8.67%
Kurtosis:    1.3
Skewness: 1.6
.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
‐35% ‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
POSITIVE EARNINGS
Mean: 12.55%
Kurtosis:    4.8
Skewness: 2.2
NEGATIVE EARNINGS
Mean: (13.67%)
Kurtosis:    3.7
Skewness: 2.1
RIGHT ISSUE
Mean: 10.43%
Kurtosis:    4.6
Skewness: 2.2

.0
5.0
10.0
15.0
20.0
25.0
‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
POSITIVE EARNINGS
Mean: 11.37%
Kurtosis:    2.8
Skewness: 1.2
NEGATIVE EARNINGS
Mean: (15.24%)
Kurtosis:    2.0
Skewness: 0.9
RIGHT ISSUE
Mean: 11.75%
Kurtosis:    2.5
Skewness: 1.6
Figure 3.1 shows density chart of CAR over 11 days PRE period for positive earnings,
negative earnings and right issue events. Figure 3.2 and 3.3 is the same chart with
CAR over 21 and 31 PRE period respectively. For all three PRE period, positive
earnings event exhibits a more peak than that of the negative earnings and right issue
event suggesting positive earnings event has higher probability of having CAR fall
around its mean.
4. Density chart of CAR over POST period
.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30%
CAR
Figure 4.1 Density chart of CAR over POST 10 period by event type
POSITIVE EARNINGS
Mean: 0.24%
Kurtosis:    4.9
Skewness: 2.2
NEGATIVE EARNINGS
Mean: (1.27%)
Kurtosis:    3.8
Skewness: 2.1
RIGHT ISSUE
Mean: 4.18%
Kurtosis:    3.8
Skewness: 2.1

‐
5
10
15
20
25
30
‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
POSITIVE EARNINGS
Mean: 2.65%
Kurtosis:    6.0
Skewness: 2.3
NEGATIVE EARNINGS
Mean: (0.21%)
Kurtosis:    2.8
Skewness: 1.9
RIGHT ISSUE
Mean: 8.26%
Kurtosis:    0.7
Skewness: 1.3
.0
5.0
10.0
15.0
20.0
25.0
‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
RIGHT ISSUE
Mean: 8.22%
Kurtosis:    (0.2)
Skewness: 1.0
NEGATIVE EARNINGS
Mean: 2.24%
Kurtosis:    (0.1)
Skewness: 0.7
POSITIVE EARNINGS
Mean: 4.38%
Kurtosis:    0.8
Skewness: 1.4
Figure 4.1 to 4.3 are constructed the same as figure 3.1 to 3.3 but using POST period
of 10, 20 and 30 days. For all three period, positive earnings event again shows a
higher peak than negative and right issue. However, right issue event exhibits higher
mean overall.
The density chart of PRE CAR in general has higher peak than that of the POST
counterpart, which indicates the mean of the PRE is more meaningful than that of the

POST. The PRE observation has higher probably to fall near its mean compared to
the POST observation.
POST 20 and 30 also show a long tail on the right with CAR up to 45 – 50% suggests
there are some events exhibits strong abnormal returns in the post period.
5. Density chart of PRE CAR by event type
0
5
10
15
20
25
30
35
‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
Figure 5.1 Density chart of POSITIVE EARNING CAR over PRE period
PRE‐CAR11
Mean: 5.69%
Kurtosis:    1.2
Skewness: 1.3
PRE‐CAR21
Mean: 12.55%
Kurtosis:    2.3
Skewness: 1.6
PRE‐CAR31
Mean: 11.37%
Kurtosis:    0.8
Skewness: 1.2

0
5
10
15
20
25
30
35
‐40% ‐35% ‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15%
CAR
Figure 5.2 Density chart of NEGATIVE EARNING CAR over PRE period
PRE‐CAR11
Mean: (7.85%)
Kurtosis:    (1.6)
Skewness: 0.3
PRE‐CAR21
Mean: (13.67%)
Kurtosis:    0.8
Skewness: 1.4
PRE‐CAR31
Mean: (15.24%)
Std. Deviation: 12%
Kurtosis:    0.3
Skewness: 0.9

0
2
4
6
8
10
12
14
16
18
20
‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
Figure 5.3 Density chart of RIGHT ISSUE CAR over PRE period
PRE‐CAR11
Mean: 8.67%
Kurtosis:    1.7
Skewness: 1.7
PRE‐CAR21
Mean: 10.4%
Std. Deviation: 8,9%
Kurtosis:    3.4
Skewness: 1.9
PRE‐CAR31
Mean: 11.7%
Kurtosis:    2.5
Skewness: 1.6
Chart 5.1, 5.2 and 5.3 are constructed by drawing PRE CAR for positive earning,
negative earnings and right issue event respectively.
For positive and negative earnings events, kurtosis value of PRE 21 days are higher
than that of period 11 and 31 which means the distribution of PRE 21 is more peaked
than 11 and 31 period. This indicates that there are high numbers of observations
which have meaningful CAR starting from 21 trading days before the announcement
date. We can conclude that for a given type of event, there is abnormal return starting
from about 21 days before the official announcement date. This finding confirms with
the finding in section 1.
For right issue event, difference between 11,21 and 31 is not as clear as for positive
and negative earnings. It also exhibits a long tail of high CAR on the right.

6. Density chart of POST CAR by event type
0
5
10
15
20
25
30
35
‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
Figure 6.1 Density chart of POSITIVE EARNING CAR over POST period
POST‐CAR10
Mean: 0.24%
Kurtosis:    8.3
Skewness: 2.7
POST‐CAR20
Mean: 2.6%
Kurtosis:    3.8
Skewness: 2.1
POST‐CAR30
Mean: 4.4%
Kurtosis:    7.4
Skewness: 2.7
0
5
10
15
20
25
30
‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
CAR
Figure 6.2 Density chart of NEGATIVE EARNING CAR over POST period
POST‐CAR10
Mean: (1.27%)
Kurtosis:    3.8
Skewness: 2.1
POST‐CAR20
Mean: (0.21%)
Kurtosis:    2.8
Skewness: 1.9
POST‐CAR30
Mean: 2.2%
Kurtosis:    0.6
Skewness: 1.2

0
2
4
6
8
10
12
14
16
‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
Figure 6.3 Density chart of RIGHT ISSUE CAR over POST period
POST‐CAR10
Mean: 4.18%
Kurtosis:    3.2
Skewness: 1.9
POST‐CAR20
Mean: 8.2%
Kurtosis:    0.3
Skewness: 1.2
POST‐CAR30
Mean: 8.2%
Kurtosis:    0.2
Skewness: 1.1
Chart 6.1, 6.2 and 6.3 are POST CAR for positive earning, negative earnings and right
issue event respectively.
For positive earnings event, there is no clear difference between 10, 20 and 30 period.
The post mean is lower than the PRE mean suggesting the good news reflect on price
more on PRE period than post period.
For negative earnings event, post 10 show a highest peak. All the mean of all three
period falls around zero indicating that bad new already reflects on price on the PRE
period.
For right issue, post 10 exhibits highest peak, but it has lower mean than post 20 and
30.

7. Chart of distribution function of PRE CAR
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
ACCUMULATIVE  PROBABILITY
CAR
Figure 7.1 DISTRIBUTION  FUNCTION of POSITIVE EARNINGS over PRE period
PRE‐CAR11 PRE‐CAR21 PRE‐CAR31
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
‐40% ‐35% ‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20%
CAR
Figure 7.2 DISTRIBUTION  FUNCTION of NEGATIVE EARNINGS over PRE period
PRE CAR 11 PRE CAR 21 PRE CAR 31

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%
CAR
Figure 7.3 DISTRIBUTION FUNCTION of RIGHT ISSUE over PRE period
PRE‐CAR11 PRE‐CAR21 PRE‐CAR31
Figure 7.1 to 7.3 shows the accumulative distribution function of PRE CAR for
positive earnings, negative earnings and right issue event respectively.
For positive earnings (figure 7.1), PRE 11 shows about 50% of its sample having
negative CAR suggests there is profit taking within the last 11 days before the
announcement date. In other word, for about half of the observations, market tends to
buy around 21 days before and take profit during the last 11 day before the
announcement.
Figure 7.2 shows high probability of negative PRE CAR for negative earnings event.
For right issue event, figure 7.3 shows probability distribution is the same for PRE 11
and 21, while PRE 31 shows a higher probability for lower CAR and lower
probability for higher CAR comparing to PRE 11 and PRE 21.

8. Chart of distribution function of POST CAR

0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
CAR
Figure 8.1 DISTRIBUTION  FUNCTION of POSITIVE EARNINGS over POST period
POST‐CAR10 POST‐CAR20 POST‐CAR30
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
‐45% ‐40% ‐35% ‐30% ‐25% ‐20% ‐15% ‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
CAR
Figure 8.2 DISTRIBUTION  FUNCTION of NEGATIVE EARNINGS over POST period

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
‐30%‐25%‐20%‐15%‐10% ‐5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65%
CAR
Figure 8.3 DISTRIBUTION FUNCTION of RIGHT ISSUE over POST period
Series chart from 8.1 to 8.3 are the accumulative distribution functions of POST CAR
for positive earnings, negative earnings and right issue event respectively.

CHAPTER 4: CONCLUSION
CHAPTER 4: CONCLUSION
Evidence shows abnormal return exists about twenty days before the event official
announcement date due to information leakage which is channeled to the market as
rumors. This market characteristic is common in emerging market where investors
make their decision based on rumors which creates herd-style behavior in the market.
This certainly add a new dimension of risk as corporation’s fundamental is weighted
less than its deserved importance.

APPENDIX I
APPENDIX I
APPREVIATIONS
HOSE Hochiminh Stock Exchange
HASTC Hanoi Stock Exchange
SSC State Securities Committee
VBA Visual Basic for Applications
CAR Cumulative Abnormal Return
PRE Timeline before event date
POST Timeline after event date

APPENDIX II
APPENDIX II
VISUAL BASIC APPLICATION CODES
THIS PROGRAM CALCULATE CAR FOR OBSERVATION, PRECAR AND
POSTCAR OVER A SPECIFIED WINDOW, AND MEAN OF CAR GROUPED
BY EVENT TYPE AND COMPOUNDED RATIO
Sub Calculate_CAR()
'This sub calculate CARs for each observation for the specified window.
Dim window As Integer
'Event window
window = 10
Dim totalRows As Integer
Dim arrSymbol() As String
Dim arrDate() As String
Dim arrAbnormalReturn() As Double
Dim arrFinancialStatement() As Double
Dim arrRights() As Double
Dim posNoFiEvent As Integer
Dim negNoFiEvent As Integer
Dim noRightEvent As Integer
noRightEvent = 0
posNoFiEvent = 0
negNoFiEvent = 0
Dim indexPosF() As Integer ' read when (position) the event happen
Dim indexNegF() As Integer

APPENDIX II
Dim indexRightEvent() As Integer
' Read basic Data
Sheets("Data").Select
Cells(4, 1).Select
totalRows = Range(Selection, Selection.End(xlDown)).count
'MsgBox (totalRows)
ReDim arrSymbol(totalRows) As String
ReDim arrDate(totalRows) As String
ReDim arrAbnormalReturn(totalRows) As Double
ReDim arrFinancialStatement(totalRows) As Double
ReDim arrRights(totalRows) As Double
'read Data into these array
Dim i As Integer
For i = 4 To totalRows
arrSymbol(i) = Worksheets("Data").Cells(i, 1)
arrDate(i) = Worksheets("Data").Cells(i, 2)
arrAbnormalReturn(i) = Worksheets("Data").Cells(i, 10)
arrRights(i) = Worksheets("Data").Cells(i, 17)
arrFinancialStatement(i) = Worksheets("Data").Cells(i, 18)
'count good/bad BCTC
If (arrFinancialStatement(i) = 1) Then
posNoFiEvent = posNoFiEvent + 1
ElseIf (arrFinancialStatement(i) = -1) Then
negNoFiEvent = negNoFiEvent + 1
End If
'count Rights
If (arrRights(i) <> 0) Then
noRightEvent = noRightEvent + 1
End If
Next i
'MsgBox (noRightEvent)

APPENDIX II
ReDim indexPosF(posNoFiEvent) As Integer
ReDim indexNegF(negNoFiEvent) As Integer
ReDim indexRightEvent(noRightEvent) As Integer
Dim j, k, m As Integer
j = 0
k = 0
m = 0
'get the position of event
j = j + 1
indexPosF(j) = i 'position of event
k = k + 1
indexNegF(k) = i
End If
m = m + 1
indexRightEvent(m) = i
End If
Next i
'Calculate CAR based on estimate window - including the announment day
Dim PRECAR_FI_POS() As Double
Dim PRECAR_FI_NEG() As Double
Dim PRECAR_RIGHTS() As Double
ReDim PRECAR_FI_POS(posNoFiEvent) As Double
ReDim PRECAR_FI_NEG(negNoFiEvent) As Double
ReDim PRECAR_RIGHTS(noRightEvent) As Double
Dim POSTCAR_FI_POS() As Double
Dim POSTCAR_FI_NEG() As Double
Dim POSTCAR_RIGHTS() As Double

APPENDIX II
ReDim POSTCAR_FI_POS(posNoFiEvent) As Double
ReDim POSTCAR_FI_NEG(negNoFiEvent) As Double
ReDim POSTCAR_RIGHTS(noRightEvent) As Double
Worksheets("Result").Cells(1, 1) = "POSITIVE FINANCIAL STATEMENT"
Worksheets("Result").Cells(2, 1) = "SYMBOL"
Worksheets("Result").Cells(2, 2) = "DATE"
Worksheets("Result").Cells(2, 3) = "PRECAR20"
Worksheets("Result").Cells(2, 4) = "POSTCAR20"
'Worksheets("Result").Cells(2, 9) = "PRECAR50"
'Worksheets("Result").Cells(2, 10) = "POSTCAR50"
Worksheets("Result").Cells(1, 12) = "NEGATIVE FINANCIAL STATEMENT"
Worksheets("Result").Cells(1, 23) = "RIGHTS EVENT"
Worksheets("Result").Cells(2, 25) = "RATE"

APPENDIX II
'Worksheets("Result").Cells(2, 32) = "PRECAR50"
Dim posStep As Integer
posStep = 2
'POSITIVE EARNING
For i = 1 To posNoFiEvent ' go through number of event
For j = 1 To window
PRECAR_FI_POS(i) = PRECAR_FI_POS(i) +
arrAbnormalReturn(indexPosF(i) - j + 1)
POSTCAR_FI_POS(i) = POSTCAR_FI_POS(i) +
arrAbnormalReturn(indexPosF(i) + j)
Next j
'writeback CAR to Data sheet
Worksheets("Data").Cells(indexPosF(i), 11) = PRECAR_FI_POS(i)
Worksheets("Data").Cells(indexPosF(i), 12) = POSTCAR_FI_POS(i)
'write CAR to result sheet
posStep = posStep + 1
Worksheets("Result").Cells(posStep, 1) = arrSymbol(indexPosF(i))
Worksheets("Result").Cells(posStep, 2) = arrDate(indexPosF(i))
'to change when window change
'window 20
'Worksheets("Result").Cells(posStep, 3) = PRECAR_FI_POS(i)
'Worksheets("Result").Cells(posStep, 4) = POSTCAR_FI_POS(i)
'window 30
'Worksheets("Result").Cells(posStep, 5) = PRECAR_FI_POS(i)
'Worksheets("Result").Cells(posStep, 6) = POSTCAR_FI_POS(i)
'window 10

APPENDIX II
Worksheets("Result").Cells(posStep, 7) = PRECAR_FI_POS(i)
Worksheets("Result").Cells(posStep, 8) = POSTCAR_FI_POS(i)
Next i
Dim negStep As Integer
negStep = 2
'NEGATIVE EARNING
For i = 1 To negNoFiEvent ' go through event
For j = 1 To window
PRECAR_FI_NEG(i) = PRECAR_FI_NEG(i) +
arrAbnormalReturn(indexNegF(i) - j + 1)
POSTCAR_FI_NEG(i) = POSTCAR_FI_NEG(i) +
arrAbnormalReturn(indexNegF(i) + j)
Next j
'write CAR to Data sheet
Worksheets("Data").Cells(indexNegF(i), 11) = PRECAR_FI_NEG(i)
Worksheets("Data").Cells(indexNegF(i), 12) = POSTCAR_FI_NEG(i)
'write CAR to result sheet
negStep = negStep + 1
Worksheets("Result").Cells(negStep, 12) = arrSymbol(indexNegF(i))
Worksheets("Result").Cells(negStep, 13) = arrDate(indexNegF(i))
'to change when window change
'window 20
'Worksheets("Result").Cells(negStep, 14) = PRECAR_FI_NEG(i)
'Worksheets("Result").Cells(negStep, 15) = POSTCAR_FI_NEG(i)
'window 30
'Worksheets("Result").Cells(negStep, 16) = PRECAR_FI_NEG(i)
'Worksheets("Result").Cells(negStep, 17) = POSTCAR_FI_NEG(i)
'window 10
Worksheets("Result").Cells(negStep, 18) = PRECAR_FI_NEG(i)
Worksheets("Result").Cells(negStep, 19) = POSTCAR_FI_NEG(i)
Next i

APPENDIX II
Dim rightStep As Integer
rightStep = 2
'write Data to Result
For i = 1 To noRightEvent ' go through event
For j = 1 To window
PRECAR_RIGHTS(i) = PRECAR_RIGHTS(i) +
arrAbnormalReturn(indexRightEvent(i) - j + 1)
POSTCAR_RIGHTS(i) = POSTCAR_RIGHTS(i) +
arrAbnormalReturn(indexRightEvent(i) + j)
Next j
Worksheets("Data").Cells(indexRightEvent(i), 11) = PRECAR_RIGHTS(i)
Worksheets("Data").Cells(indexRightEvent(i), 12) = POSTCAR_RIGHTS(i)
rightStep = rightStep + 1
Worksheets("Result").Cells(rightStep, 23) = arrSymbol(indexRightEvent(i))
Worksheets("Result").Cells(rightStep, 24) = arrDate(indexRightEvent(i))
Worksheets("Result").Cells(rightStep, 25) = arrRights(indexRightEvent(i))
'window 20
'Worksheets("Result").Cells(rightStep, 26) = PRECAR_RIGHTS(i)
'Worksheets("Result").Cells(rightStep, 27) = POSTCAR_RIGHTS(i)
'window 30
'Worksheets("Result").Cells(rightStep, 28) = PRECAR_RIGHTS(i)
'Worksheets("Result").Cells(rightStep, 29) = POSTCAR_RIGHTS(i)
'window 10
Worksheets("Result").Cells(rightStep, 30) = PRECAR_RIGHTS(i)
Worksheets("Result").Cells(rightStep, 31) = POSTCAR_RIGHTS(i)
Next i
MsgBox ("finish processing data")
End Sub

APPENDIX II
'~~~~~~~~~~~~~~~~~~~~~~ handle continuous CAR
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Sub Calculate_PRECAR_POSTCAR()
'This sub calculate mean of PRECAR and POSTCAR seperately for each event type
'parameter: event window
window = 30
Dim posPreMean() As Double
Dim posPostMean() As Double
Dim negPreMean() As Double
Dim negPostMean() As Double
Dim rightPreMean() As Double
Dim rightPostMean() As Double
ReDim posPreMean(window) As Double
ReDim posPostMean(window) As Double
ReDim negPreMean(window) As Double
ReDim negPostMean(window) As Double
ReDim rightPreMean(window) As Double
ReDim rightPostMean(window) As Double
noRightEvent = 0
posNoFiEvent = 0
negNoFiEvent = 0

APPENDIX II
' Read basic Data
Cells(4, 1).Select
'MsgBox (totalRows)
Dim i As Integer
End If
'count Rights

APPENDIX II
End If
Next i
j = 0
k = 0
m = 0
j = j + 1
k = k + 1
indexNegF(k) = i
End If
m = m + 1
End If
Next i

APPENDIX II
Dim count As Integer
count = 1
Dim totalPositive As Double
Dim totalNegative As Double
Dim totalRight As Double
totalPositive = 0
totalNegative = 0
totalRight = 0
Dim PostPosMean As Double
Dim PostNegMean As Double
Dim PostRightMean As Double
Worksheets("Result2").Cells(count, 1) = "WINDOW"
Worksheets("Result2").Cells(count, 2) = "PF MEAN"
Worksheets("Result2").Cells(count, 3) = "NF MEAN"
Worksheets("Result2").Cells(count, 4) = "RIGHT MEAN"
'~~~~~~~~~~~~~~~~PRE CAR~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For j = window To 0 Step -1
'POSITIVE

APPENDIX II
'PRECAR_FI_POS(i) = PRECAR_FI_POS(i) +
posPreMean(j) = posPreMean(j) + arrAbnormalReturn(indexPosF(i) - j)
Next i
' Calculate Mean
totalPositive = totalPositive + posPreMean(j)
' totalPositive = totalPositive / posNoFiEvent
'NEGATIVE
For i = 1 To negNoFiEvent ' go through number of event
negPreMean(j) = negPreMean(j) + arrAbnormalReturn(indexNegF(i) - j)
Next i
' Calculate Mean
totalNegative = totalNegative + negPreMean(j)
' totalNegative = totalNegative / negNoFiEvent
'RIGHTS
For i = 1 To noRightEvent ' go through number of event
rightPreMean(j) = rightPreMean(j) + arrAbnormalReturn(indexRightEvent(i) - j)
Next i
' Calculate Mean
totalRight = totalRight + rightPreMean(j)
' totalRight = totalRight / noRightEvent
'write MEAN to result2 sheet
count = count + 1
Worksheets("Result2").Cells(count, 1) = -j
Worksheets("Result2").Cells(count, 2) = totalPositive / posNoFiEvent
Worksheets("Result2").Cells(count, 3) = totalNegative / negNoFiEvent
Worksheets("Result2").Cells(count, 4) = totalRight / noRightEvent
Next j

APPENDIX II
'get CAR from PRE
'PostPosMean = totalPositive
'PostNegMean = 0
'PostRightMean = 0
'~~~~~~~~~~~~~~~~POST CAR~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For j = 1 To window
'POSITIVE
posPreMean(j) = posPreMean(j) + arrAbnormalReturn(indexPosF(i) + j)
Next i
' Calculate Mean
totalPositive = totalPositive + posPreMean(j)
'NEGATIVE
negPreMean(j) = negPreMean(j) + arrAbnormalReturn(indexNegF(i) + j)
Next i
' Calculate Mean
totalNegative = totalNegative + negPreMean(j)
'RIGHTS
rightPreMean(j) = rightPreMean(j) + arrAbnormalReturn(indexRightEvent(i) +
j)
Next i
' Calculate Mean
totalRight = totalRight + rightPreMean(j)
'write MEAN to result2 sheet
count = count + 1
Worksheets("Result2").Cells(count, 1) = j
Worksheets("Result2").Cells(count, 2) = totalPositive / posNoFiEvent
Worksheets("Result2").Cells(count, 3) = totalNegative / negNoFiEvent

APPENDIX II
Worksheets("Result2").Cells(count, 4) = totalRight / noRightEvent
Next j
End Sub
Sub Calculate_Mean_CAR()
'This sub calculate mean CAR from -30 to 30 days for all three event type
window = 30
Dim posPreMean() As Double
Dim posPostMean() As Double
Dim negPreMean() As Double
Dim negPostMean() As Double
Dim rightPreMean() As Double
Dim rightPostMean() As Double
ReDim posPreMean(window) As Double
ReDim posPostMean(window) As Double
ReDim negPreMean(window) As Double
ReDim negPostMean(window) As Double
ReDim rightPreMean(window) As Double
ReDim rightPostMean(window) As Double

APPENDIX II
noRightEvent = 0
posNoFiEvent = 0
negNoFiEvent = 0
' Read basic Data
Cells(4, 1).Select
'MsgBox (totalRows)
Dim i As Integer

APPENDIX II
End If
'count Rights
End If
Next i
j = 0
k = 0
m = 0
j = j + 1
k = k + 1
indexNegF(k) = i
End If
m = m + 1
End If

APPENDIX II
Next i
Dim count As Integer
count = 1
Dim totalPositivePre As Double
Dim totalNegativePre As Double
Dim totalRightPre As Double
totalPositivePre = 0
totalNegativePre = 0
totalRightPre = 0
Dim totalPositivePost As Double
Dim totalNegativePost As Double
Dim totalRightPost As Double
totalPositivePost = 0
totalNegativePost = 0
totalRightPost = 0
Worksheets("Result3").Cells(count, 1) = "WINDOW"
Worksheets("Result3").Cells(count, 2) = "PF MEAN"

APPENDIX II
Worksheets("Result3").Cells(count, 3) = "NF MEAN"
Worksheets("Result3").Cells(count, 4) = "RIGHT MEAN"
'~~~~~~~~~~~~~~~~PRE CAR~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For j = window To 0 Step -1
'POSITIVE
'PRECAR_FI_POS(i) = PRECAR_FI_POS(i) +
posPreMean(j) = posPreMean(j) + arrAbnormalReturn(indexPosF(i) - j)
Next i
' Calculate Mean
totalPositivePre = totalPositivePre + posPreMean(j)
' totalPositivePre = totalPositivePre / posNoFiEvent
'NEGATIVE
negPreMean(j) = negPreMean(j) + arrAbnormalReturn(indexNegF(i) - j)
Next i
' Calculate Mean
totalNegativePre = totalNegativePre + negPreMean(j)
' totalNegativePre = totalNegativePre / negNoFiEvent
'RIGHTS
rightPreMean(j) = rightPreMean(j) + arrAbnormalReturn(indexRightEvent(i) - j)
Next i
' Calculate Mean
totalRightPre = totalRightPre + rightPreMean(j)
' totalRightPre = totalRightPre / noRightEvent
'write MEAN to Result3 sheet
count = count + 1

APPENDIX II
Worksheets("Result3").Cells(count, 1) = -j
Worksheets("Result3").Cells(count, 2) = totalPositivePre / posNoFiEvent
Worksheets("Result3").Cells(count, 3) = totalNegativePre / negNoFiEvent
Worksheets("Result3").Cells(count, 4) = totalRightPre / noRightEvent
Next j
'get CAR from PRE
'PostPosMean = totalPositivePre
'PostNegMean = 0
'PostRightMean = 0
'~~~~~~~~~~~~~~~~POST CAR~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For j = 1 To window
'POSITIVE
posPreMean(j) = posPreMean(j) + arrAbnormalReturn(indexPosF(i) + j)
Next i
' Calculate Mean
totalPositivePost = totalPositivePost + posPreMean(j)
'NEGATIVE
negPreMean(j) = negPreMean(j) + arrAbnormalReturn(indexNegF(i) + j)
Next i
' Calculate Mean
totalNegativePost = totalNegativePost + negPreMean(j)
'RIGHTS
rightPreMean(j) = rightPreMean(j) + arrAbnormalReturn(indexRightEvent(i) +
j)
Next i
' Calculate Mean
totalRightPost = totalRightPost + rightPreMean(j)

APPENDIX II
'write MEAN to Result3 sheet
count = count + 1
Worksheets("Result3").Cells(count, 1) = j
Worksheets("Result3").Cells(count, 2) = totalPositivePost / posNoFiEvent
Worksheets("Result3").Cells(count, 3) = totalNegativePost / negNoFiEvent
Worksheets("Result3").Cells(count, 4) = totalRightPost / noRightEvent
Next j
End Sub

APPENDIX III
APPENDIX III
REFERENCES
1. John Y. Campbell/Andrew W. Lo/A. Craig MacKinlay, 1997, “The
econometrics of Financial Markets”, Princeton University Press, Princeton,
New Jersey
2. Cheng-Few Lee, Alice C.Lee, 2006, “Encyclopedia of Finance”, Springer
Science - Business Media, Inc
http://books.google.com.vn/books?id=I6BH-
RKYVG4C&printsec=frontcover&dq=Encyclopedia+of+Finance&hl=vi&ei=qyw
sTMzYNYP-
8AaVoIGfDg&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCcQ6AEw
AA#v=onepage&q&f=false
3. Beta were collected from Bloomberg on 30th
March, 2010
www.bloomberg.com/
4. Stock prices and VN-index were collected from Hochiminh Stock Exchange
www.hsx.vn
5. B. Espen Eckbo, 2007, “Handbook of Corporate Finance”, North-Holland
http://books.google.com.vn/books?id=p7HcvlIAgdkC&printsec=frontcover#v=on
epage&q&f=false
6. Donald Getz, “Event Studies. Theory, Research and policy for plannes
events”, 2007, Butterworth-Heinemann

APPENDIX III
http://books.google.com.vn/books?id=g6uwI-
19chMC&printsec=frontcover&dq=event+studies&hl=vi&ei=7xcoTKbrH4OB8g
aSoKzfDw&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCwQ6AEwA
A#v=onepage&q&f=false
7. George M. Frankfuter and Bob G. Wood with James Wansley, 2003, “
Dividend Policy Theory and Practice”, Elsevier Science
http://books.google.com.vn/books?id=9G6H70WdBFoC&pg=PA97&dq=dividen
d+hypothesis&hl=vi&ei=vGMoTN6qJMmRnweQwYSpAQ&sa=X&oi=book_res
ult&ct=result&resnum=1&ved=0CCcQ6AEwAA#v=snippet&q=%22dividend%2
0hypothesis%22&f=false
8. A. Mitchell Polinsky, Steven Shavell, “Handbook of law and economics”
http://books.google.com.vn/books?id=ibCgJjch1lAC&pg=PA947&dq=%22event
+studies%22&hl=vi&ei=uQQqTJz1K9S_rAfmueV0&sa=X&oi=book_result&ct=
result&resnum=4&ved=0CDcQ6AEwAw#v=onepage&q&f=false

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