testing the weak-form market efficiency of the vietnamese stock market
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1
TESTING THE WEAK-FORM MARKET EFFICIENCY OF
THE VIETNAMESE STOCK MARKET
By
MY CHAU BUI
2006
A Dissertation presented in part consideration for the degree of the
MA. Finance and Investment
2
ABSTRACT
The main intention of this study is to test whether the Vietnamese stock market is weak-
form efficient. This is investigated by employing two different approaches, including tests
of randomness and tests of predictability through the examination of the applicability and
validity of technical analysis. In order to test for the first condition of weak-form market
efficiency, the portmanteau tests of autocorrelations, the unit root tests, and the Lo and
MacKinlay’s variance ratio test are applied on the series of weekly returns of the
Vietnamese price index. The results obtained from the three tests indicate significant
deviations from the random walk hypothesis of the stock returns in the Vietnam’s market.
Furthermore, tests of the applicability of technical trading rules reveal that stock price
changes in the Vietnamese stock market are predictable and can be profitably exploit net
of trading costs. The implication of these results is that the Vietnamese stock market is not
weak-form efficient.
3
TABLE OF CONTENT
CHAPTER 1 INTRODUCTION......................................................................................6
1.1 BACKGROUND AND OBJECTIVE ...................................................................6 1.2 RESEARCH METHODOLOGY ..........................................................................7 1.3 OUTLINE OF THE DISSERTATION..................................................................8
CHAPTER 2 OVERVIEW OF THE VIETNAMESE STOCK MARKET .................9
2.1 OVERVIEW..........................................................................................................9 2.2 BACKGROUND OF THE VIETNAM’S STOCK MARKET ............................10
2.2.1 The shareholding reforms................................................................................11 2.2.2 The Stock Exchanges .......................................................................................13 2.2.3 The Ho Chi Minh City Securities Trading Centre...........................................14
CHAPTER 3 BACKGROUND AND LITERATURE REVIEW ................................21
3.1 RANDOM WALK THEORY..............................................................................21 3.2 EFFICIENT MARKET HYPOTHESIS ..............................................................23 3.3 LITERATURE REVIEW ....................................................................................25
3.3.1 Testing for Market Efficiency ..........................................................................25 3.3.2 Empirical Evidence .........................................................................................32
CHAPTER 4 DATA AND METHODOLOGY .............................................................41
4.1 DATA AND DESCRIPTIVE STATISTICS .......................................................41 4.1.1 Data .................................................................................................................41 4.1.2 Descriptive Statistics of the Data ....................................................................44
4.2 METHODOLOGY ..............................................................................................48 4.2.1 Tests of Randomness........................................................................................49 4.2.2 Tests of Technical Analysis .............................................................................59
CHAPTER 5 EMPIRICAL RESULTS AND ANALYSIS ...........................................64
5.1 TESTS OF RANDOM WALK HYPOTHESIS...................................................64 5.1.1 Portmanteau test..............................................................................................64 5.1.2 Unit Root tests .................................................................................................65 5.1.3 Variance Ratio Test .........................................................................................66
5.2 TESTS OF TECHNICAL ANALYSIS ...............................................................69 5.2.1 VMA Results ....................................................................................................69 5.2.2 FMA Results ....................................................................................................72
5.3 ANALYSIS OF THE TESTS’ RESULTS...........................................................75 5.3.1 Summary of the tests’ results ...........................................................................75 5.3.2 Possible explanations of the market inefficiency in Vietnam ..........................77
CHAPTER 6 CONCLUSION .........................................................................................80
6.1 CONCLUDING REMARKS...............................................................................80 6.2 LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH ............81
REFERENCE ...................................................................................................................80
4
ACKNOWLEDGEMENT
First and foremost, I would like to thank my parents who inspired and motivated me and
have been a constant source of love and support throughout my studies.
Second, I would like to thank my supervisor, Professor Bob Berry for his precious
guidance during the preparation of this study.
My last special thanks should go to my sweetheart. Without his love, support, patience and
help, I would have never been completed this dissertation.
5
List of Tables
Table 2.1: Trading features promulgated on the HOSTC …………………………...16
Table 2.2: Price limitations on daily trading in the HOSTC …………………......... 17
Table 4.1: Descriptive Statistics for the VN-Index’s weekly returns
(28/07/2000- 31/07/2006)…………………………………………………………...45
Table 4.2: Descriptive statistics for the VN-Index’s daily returns
(01/03/2002 – 31/07/2006……………………………………………………….. ….47
Table 5.1: Ljung-Box test statistics…………………………………………… .. ….65
Table 5.2: Unit Root Tests……………………………………………………… … .66
Table 5.3: Variance-ratio estimates VR(q) and variance-ratio test statistics z(q) and
z*(q) for a one-week base observation period…………………………………........ 67
Table 5.4: Standard test results for the VMA rules (01/03/2002 – 31/07/2006)…… 70
Table 5.5: Standard test results for the FMA rules (01/03/2002 – 31/07/2006)… ….72
List of Figures
Figure 2.1: Number of listed company at 30/06/2006……….………………………15
Figure 2.2: The movements of the VN-Index and the trading volumes from 28/07/2000
to 30/06/2006………………………………………………………...........................18
Figure 2.3: Number of trading accounts……………………………………………..19
Figure 4.1: Histogram of weekly return data……………………………………. ….46
6
The Efficient Market Hypothesis is one of the most controversial and well-studied
proposition in economic and financial theories. Throughout the history of the literature, the
different levels of efficiency of various markets have long been of great interest to
researchers. While tests of semi-strong form and strong form market efficiency are rare,
especially in less developed market, weak-form tests are voluminous. Evidence obtained
from developed markets such as U.K and U.S suggests that stock markets in these
countries are efficient at least in the weak form. Nevertheless, there is a wide consensus
that stock markets in emerging and developing countries are neither semi-strong form nor
strong form efficient, and in many of the cases do not exhibit even weak form efficiency.
Vietnam is considered a developing country whose stock market possesses many
characteristics found in most developing and emerging stock market around the world. The
stock exchange in Vietnam has only been operating for six years since July 2000. Up to the
present time, there have only been 45 companies listed on the exchange. Most of these are
privatized companies which were originally state-owned enterprises. The newness and
underdevelopment of the market are characterized by thin trading with a large number of
inactive stocks, informational asymmetries, incomplete regulatory framework and
insufficient corporate governance system. Especially, investors in a newly emerged market
lack sufficient knowledge about the operations of the stock market and the listing
Chapter 1
INTRODUCTION
1.1 BACKGROUND AND OBJECTIVE
7
companies, thus often behave irrationally. Since there has not been any prior study which
investigates the level of efficiency of the stock market in Vietnam, the objective of this
dissertation is to examine whether the Vietnamese stock market is weak-form efficient or
not. The research is important as it may contribute to the literature of the stock market in
Vietnam.
The focus of this dissertation is to apply the tests of randomness and tests of predictability
in an attempt to investigate whether the weak-form efficient market hypothesis holds in the
Vietnamese stock market.
Market efficiency is commonly defined as the simple statement that stock prices fully
reflect all available information. However, this statement has been criticized since market
efficiency is conditional on some critical assumptions which are regarded as unrealistic.
Particularly, it assumes away the associated costs of trading securities, finding and
analyzing information. Hence, definition of market efficiency has been loosen to assume
that stock price reflect information to the point where the marginal benefits of acting on
information do not exceed the marginal costs. Therefore, besides testing for randomness as
the first proposition of weak-form market efficiency, the Variable Moving Average and
Fixed Moving Average technical trading rules will also be tested to explore whether future
movements of stock prices can be forecasted and profitably exploited in a costly
environment. The data to be tested is obtained from the databank of the State Securities
Committee of Vietnam.
1.2 RESEARCH METHODOLOGY
8
The dissertation will be organized as follows.
Chapter 1 is an introduction to the paper which specifies the objective and methodology of
the study. It also provides a brief outline of the dissertation.
Chapter 2 is an introductory chapter which provides an overview of the Vietnamese stock
market in terms of the background, development process, and the market features such as
the participants, listing and trading profiles and stock ownership structure.
Chapter 3 addresses the theories underpinning the Efficient Market Hypothesis (EMH)
with a focus on the weak-form of market efficiency. The chapter also provides readers
with a literature review of the empirical evidence of weak-form market efficiency in
emerging and developing stock markets.
Chapter 4 describes the research methodology adopted in this dissertation. It provides a
statistical description of the data, a brief overview of the methodology as well as details
regarding the specific tests chosen for the study.
Chapter 5 reports and analyzes the results of the empirical tests.
Chapter 6 concludes by summarizing the findings of the study.
1.3 OUTLINE OF THE DISSERTATION
9
On July 2000, Vietnam took a major step towards establishing a more robust market
economy and creating a new channel of capital mobilization for developing the economy
with the opening of its first functioning stock exchange in Ho Chi Minh City (HCMC), the
so-called economic centre of the country. After four years of preparation and numerous
delays, the communist governmental regime of Vietnam finally fulfilled its commitment to
the creation of a public securities market.
The setting up of a stock exchange has been the target of the Vietnamese government since
the early 1990s, marked by the establishment of a governmental special committee in
1993, specializing in researching and preparing strategic plans for the initiation of a stock
market. In a step towards materializing the plan, the State Securities Commission of
Vietnam (SSC) was established in 1996. The SSC is the highest governmental body, which
is responsible for the promulgation of laws and other regulations, organization and
management of the stock exchange, as well as supervision of all activities of relating
parties and individuals in the stock market.
Four years after the foundation of the SSC, it was not until July 28, 2000 did the Ho Chi
Minh City Securities Trading Centre open its door. On the first day of trading, the
exchange had only two listed companies. At the end of July 2006, the market had 45 listed
Chapter 2
OVERVIEW OF THE VIETNAMESE
STOCK MARKET
2.1 OVERVIEW
10
companies with capitalization worth about US$2.5 billion in total. The market
capitalization represents about 6.5% of total GDP, excluding the value of listing bonds
which accounts for 9% of the GDP. Given the fact that in other Southeast Asian countries
the average market capitalization to GDP is about 130%, Vietnam’s stock market can be
considered one of the smallest and least liquid markets in the Asian region. As compared
to its initial level, the Vietnamese stock market has an understandably low but steady
growth. The evolvement of the stock market is accompanied by a considerable economic
growth in Vietnam. In the last five years, there have seen rapid changes in the economy
that are generating 7%-8% GDP growth, annual FDI of US$5.0 billion, and US$4.0
billion of inward remittances. These achievements were underpinned by proactive
government policy. Besides, the privatization program that is underway has the potential to
meaningfully expand the breadth ad depth of the stock market over the next five years.
Therefore, Vietnam’s stock market, one of the last frontier emerging markets in the region,
may potentially attract serious investor attention over the next decade.
This chapter introduces the background of Vietnam’s stock market. Some descriptive
characteristics of the market are performed as well.
Unlike the typical development of the stock markets in many countries where the
unofficial markets and the OTC markets had existed for a certain period before the stock
exchanges were officially established, there was no such kinds of informal or OTC
markets in Vietnam before the establishment of the official stock market in 2000. The
formation of the stock market in Vietnam was based on the government’s awareness of the
2.2 BACKGROUND OF THE VIETNAM’S STOCK MARKET
11
necessity of a stock market for the development of the capital markets in particular and the
whole economy in general.
2.2.1 The shareholding reforms
The Vietnam economy is characterized by a long period of subsidization after the end of
the Vietnam War in 1975. During the post-war period from 1975 to 1986, hundreds and
thousands of state owned enterprises (SOEs) were founded by the government; private
enterprises were restricted. In 1985, Vietnam launched the ‘Doi Moi’- economic reforms-
a governmental initiative aimed at improving the country’s ailing economic conditions.
Restrictions on the establishment of private enterprises were relaxed. New laws were
passed in order to facilitate and encourage the necessary Foreign Indirect Investment (FDI)
that would help aid Vietnam’s feeble economy and allow it to prosper as the next Asian
economic power. In this process, many of the SOEs appeared to be highly inefficient and
could not compete with private and FDI enterprises. Besides the strategy of creating the
first functioning stock market in Vietnam, it was necessary that there be a sufficient
number of joint-stock companies to be listed on the stock exchange. This reason
incorporated with the need to reform the inefficient SOE system were two of the main
factors that led to the shareholding reforms called the privatization in the late 1990s.
In Vietnam, the privatization of SOEs is more commonly referred to as equitization: the
conversion of an SOE into a joint stock company. In 1998, the government issued its first
regulations on the procedures for equitizing SOEs. Additional regulations have been issued
regarding the method of selecting SOEs to be equitized and the way that SOEs should be
valued before equitization. After the equitization process begins, usually with a limited
public offering and an allocation of shares to employees that is typically anything between
12
20%-30% of the total shares in issue. In the first instance, these trade on the OTC market
ahead of the actual IPO which takes place by Dutch-style auction. An important feature of
these IPOs is the allocation of stock to management and employees as the joint stock entity
is created. This provides much of the liquidity that passes through the OTC market and
provides a potential source of stock for institutional investors to access. To date about
2,000 companies have been taken down this route (approximately only one third of the
total SOEs). Some of the most effective companies that were equitized have been
encouraged to be listed on the stock exchange since then.
The Vietnamese government has studied the China’s development model very carefully.
For example, foreign appetite for investment into areas such as banking is welcomed but
under the auspices of providing technical assistance to the domestic entities. Whilst willing
to sell some SOEs completely, the government has a long list of strategic industries where
it will remain a 51% controlling stake. Some of the most obvious areas include electricity
production, telecom infrastructure, mineral exploration and water supply.
Regarding foreign ownership, Vietnam has also studied the case of China, Thailand and
Taiwan in their early stages of developments. Private companies can be 100% foreign
owned but for listed companies, this falls to 49%. It was even lower at 30% during the
period from 08/2003 to 10/2005, and at 20% for the prior period to 08/2003. For banking
sector, the total shares that all foreign investors are permitted to hold are restricted at 30%,
irrespectively whether the banks are listed on the exchanges or not.
13
2.2.2 The Stock Exchanges
As mentioned above, the Ho Chi Minh Securities Trading Centre (HOSTC) was
established in July 2000, two years after the equitization process began. Five years later,
on the 14th July 2005, the Hanoi Securities Trading Centre (HASTC) was also opened in
Hanoi. While the HOSTC was established as the official stock exchange where equities
and bonds of the listed companies or institutions are traded, the HASTC was projected to
act as an OTC market. Listing companies on the HASTC are those which do not have
sufficient standard capabilities to be listed on the HOSTC. Besides, the HASTC also acts
as the place where IPOs are taken place. However, the functional difference between the
HOSTC and HASTC has been opaque except for the listing requirements.
Up to the current time, the role of the HASTC as an OTC market has not been fulfilled as
there exists another OTC (pre-listed) market. This OTC market currently comprises of
close to 2,000 companies that together trade between five and ten times the value seen on
the main board (VN-Index). However, transparency in this unregulated area is low.
Indicative prices are published in the press, and some of the local brokers will selectively
provide quotes. Transaction price is determined by an opaque agreement between specific
buyers and sellers, and this can take place anywhere, at anytime. To date, these OTC
companies have had little incentives to transition to the main board despite the preferential
treatments given by the government. In particular, companies listing on the main board
will be eligible for two years of tax-free earnings from the date that they list. For the two
years following this period, earnings will then only be taxed at half of the full 28% of
corporate tax rate. The reason lies in the requirements of informational disclosure applied
to listing companies. However, the Securities Law which is due to come into effect on the
1st January, 2007 may change this situation as OTC listed will become subject to the same
14
rules of disclosure and corporate governance as those currently imposed on the main
board.
For the purpose of this research, only information on the HOSTC will be provided and
discussed.
2.2.3 The Ho Chi Minh City Securities Trading Centre
2.2.3.1 Listing and Trading Profiles
To apply for listing at the HOSTC, the company is only required to have a registered
charter capital of VND10bn (approximately US$625,000), and to have registered a profit
for the past two years – for SOEs this requirement drops to a single year. The other major
requirement is that 20% of the shares are held by outside investors. For joint-stock
commercial banks, it is required that the listing of such banks should not only be approved
by the SSC but also by the State Bank of Vietnam (SBV) due to the critical and sensitive
role of the banking sector in the economy, especially in the condition of a newly
established stock market. The number of listed companies at the time of 30/06/2006 is
summarized in Figure 2.1.
15
Due to current regulation, the HOSTC is open for both domestic and foreign investors.
However, the shareholdings of foreign investors are restricted to the ratio of 49% of the
total share listed of listing companies and 30% of those of joint-stock commercial banks.
Currently, there is no tax impose on the trading of both domestic and foreign investors.
Trading on the HOSTC is automated and order-driven. There are only two types of orders
which are permitted on the Vietnam’s stock market. They are the limit orders and the ATO
(at the market price order). The orders are matched automatically by computer system
according to the principle of ‘price and time priority’.
Table 2.1 presents the trading features of the HOSTC
Figure 2.1: Number of listed company at 30/06/2006
5 10
20 2227
3338
0
10
20
30
40
2000 2001 2002 2003 2004 2005 2006timeNumber of listed
company
16
Table 2.1: Trading features promulgated on the HOSTC
TRADING
FEATURES
Trading hours Monday-Friday
- First session: 8.20-8.40 a.m.
- Second session: 9.10-9.30 a.m.
- Third session: 10.00-10.30 a.m.
- Dealing session: 10.30-11.00 a.m.
Settlement T+3 (Settlement, the exchange of both shares and money,
takes place on the third business trading day after the
transaction is carried out)
Prohibited activities Short sales and margin purchases
As indicated in the table above, the current daily trading section is divided into three
sessions for auction transaction and one session for dealing transaction. However, for the
period prior to 14/06/2006, there were only two trading sessions per day for auction
transactions. Similarly, prior to March 2002, there were also 3 trading days per week,
including Monday, Wednesday and Friday.
Besides these features, all equity stocks traded on the HOSTC are subject to daily price
limitations in an attempt to discourage speculative investments. The price limitations have
been changed from period to period due to the volatility of the markets. Details on price
limitations are provided in table 2.2.
17
Table 2.2: Price limitations on daily trading in the HOSTC
Period Price limitations07/2000 - 06/2001 2%06/2001 - 10/2001 7%10/2001 - 08/2002 2%08/2002 - 12/2002 3%12/2002 - to date 5%
As mentioned above, short sales and margin trading are strictly prohibited on the HOSTC.
Margin requirements are changed from time to time as a tool for regulating the demand for
stocks. Currently, it is set at the level of 70% of the purchase’s value.
2.2.3.2 The VN-Index
The VN-Index is the value-weighted stock price index with the base period of 100 points
accounted for the first trading day (28/07/2000). With a total market capitalization of just
over US$1.1bn, consisting of 45 listed stocks and one fund, the VN-Index is believed not
to be able to represent the health of the whole economy. Particularly, the ups and downs of
the VN-Index have little explanatory power on the bull and bust of the national economy.
This situation is expected to be improved when leading firms are privatized and listed in
the next few years.
Figure 2.2 provides a picture of the VN-Index movements through six-year operation.
18
Figure 2.2: The movements of the VN-Index and the trading volumes from 28/07/2000
to 30/06/2006.
The VN-Index data of the Vietnam’s stock market is not currently included in any of the
regional benchmarks constructed by either MSCI or FTSE. The combination of the limited
market capitalization as well as free float may well delay the likelihood of this for another
couple of years.
In the meantime, the main determinant will be the pace of the privatization that the
government manages to keep up. The supply of IPO’s such as Vietcombank, Bank for
Investment and Development of Vietnam, Vinaphone, Vietel and the downstream oil
companies will be critical in delivering the explanatory power to the index.
2.3.2.3 Market’s trading volume and investor base
For the six year period of operation, the average trading volumes of the listed market are
close to only US$1m, which was considerably low in compared with the total market
19
capitalization value of US$1.1bn in the first quarter of 2006. According to the SSC, retail
investors account for nearly 90% of the average daily trading volumes.
Indeed, the number of investors participating in the stock market is relatively tiny in
comparison with the population body of more than 80 million people. This is reflected by
the number of trading accounts opened from time to time since July 2000. Details are
provided in the following figure:
Figure 2.3: Number of trading accounts
800015000
25000
60000
0
10000
20000
30000
40000
50000
60000
70000
mid 2002 mid 2003 end 2005 mid 2006
Number of tradingaccounts
On the institutional side, the market now comprises of only 14 securities companies and
five major closed-end funds, ranging from pure venture capital to OTC and listed equities.
The presence of a small number of institutional investors as well as the market’s low
trading volume are characteristics commonly seen on newly emerged markets. We will see
in further part that these features have some important implications for testing the market
efficient hypothesis.
20
In conclusion, the Vietnamese stock market can be considered one of the youngest stock
market in the world. Like many emerging markets in their inception, the infant stock
market in Vietnam is characterized by high volatile, low liquidity, imperfect regulation
system, investor irrationality and state ownership problems. Although the stock market in
Vietnam is relatively immature, it is growing rapidly. It is still considerably small, but
promises huge potential. Therefore, studying the Vietnamese stock market is of both
practical and academic interests.
21
Much of the theory on the behavior of stock market prices over time can be traced to
French mathematician Louis Bachelier, who laid the theoretical groundwork for the
random walk theory and the Efficient Market Hypothesis (EMH). In his Ph.D. dissertation
titled “The Theory of Speculation” (1900), Bachelier recognized that “past, present and
even discounted future events are reflected in market price, but often show no apparent
relation to price changes”, thus concluding “The mathematical expectation of the
speculator is zero” 1. Unfortunately, his insights were so far ahead of the time that they
went largely unnoticed until half a century later. In 1953, Maurice Kendall published a
study in which he found to his great surprise that stock price movements followed no
discernible pattern. They were as likely to go up as they were to go down on any particular
day, irrespective of past movements2. At first blush, Kendall’s result appeared inconsistent
with the views of the majority of financial economists. Nevertheless, this empirical
observation came to be labeled the “random walk model” or the “random walk theory”.
The random walk theory asserts that price movements will not follow any patterns or
trends and that the past history of stock prices has no memory, thus cannot be used to
1 See Cootner, P.H., ed., 1964. The Random Character of Stock Market Prices. Cambridge: MIT Press, pp.17-78. 2 See Bodie, Z., Kane, A., and Marcus, A.J., 2005. Investments. 6th ed. New York: McGraw-Hill/ Irwin, pp. 369-405.
Chapter 3
BACKGROUND AND LITERATURE REVIEW
3.1 RANDOM WALK THEORY
22
make any meaningful predictions concerning the future price of the stock. Malkiel (2003)
explains the logic of the random walk idea by arguing that “if the flow of information is
unimpeded and information is immediately reflected in stock prices, then tomorrow’s price
changes will reflect only tomorrow’s news and will be independent of the price changes
today. But news is by definition unpredictable, and, thus, resulting price changes must be
unpredictable and random” (pp. 59).
Fama (1965) suggests that the theory of random walks in stock price actually involves two
separate hypotheses: (1) successive price changes are independent, and (2) the price
changes conform to some probability distribution. In more details, Campbell et al. (1997)
recognize three types of random walk hypotheses. The Random Walk 1 (RW1) assumes
independently and identically distributed (IID) increments. This hypothesis, however,
lacks flexibility to changes in the economic, social or institutional character because of the
identical distribution. Therefore, in the Random Walk 2 (RW2), the assumption of IID is
relaxed to independent but not identically distributed increments. As such, RW1 is a
special case of RW2. The assumption of independence in RW2 can be further relaxed to
obtain the Random Walk 3 (RW3), which is the most general one of the three hypotheses.
It assumes that the increments are independent and uncorrelated and contains both RW1
and RW2 as special cases.
In order to accept the random walk hypothesis, statistical evidence must be provided
indicating whether future path of stock price are independent of its historical movements.
In further parts, we will see that the random walk hypothesis is associated with the
Efficient Market Hypothesis. Moreover, most of the contemporary empirical tests for the
Efficient Market Hypothesis, especially for the weak form, are in fact based on the RW3.
23
The Efficient Market Hypothesis (EMH) is one of the most important paradigms in
modern finance. It is also the source of intense debate among academics and financial
professionals. The EMH states that at any given point in time, security prices already
reflect all available information.
The notion of the EMH evolved in the 1960s from the Ph.D. dissertation of Eugene Fama,
who persuasively made the argument that in an active market that includes many well-
informed and intelligent investors, securities will be appropriately priced and reflect all
available information. A rather comprehensive definition of the efficient market is given
by Fama in his article titled “Random Walks in Stock Market Prices” (1965). According to
Fama, “An ‘efficient’ market is defined as a market where there are large numbers of
rational, profit-maximizers actively competing, with each trying to predict future market
values of individual securities, and where important current information is almost freely
available to all participants. In an efficient market, competition among the many intelligent
participants leads to a situation where, at any point in time, actual prices of individual
securities already reflect the effects of information based on both events that have already
occurred and on events which, as of now, the market expects to take place in the future. In
other words, in an efficient market at any point in time the actual price of a security will be
a good estimate of its intrinsic value.” (pp. 35). As such, the implications of the EMH are
truly profound. Most investors buy and sell securities with the assumption that they
securities they are buying are worth more than the price that they are paying, while the
securities they are selling are worth less than the selling price. However, if markets are
efficient and current prices fully reflect all information, being a reliable estimate of the
3.2 EFFICIENT MARKET HYPOTHESIS
24
stock’s intrinsic value, then buying and selling securities in an attempt to outperform the
market will effectively be a game of chance rather than skill3.
The broad consensus in the literature about the taxonomy of the EMH follows Fama’s
(1965, 1970) classification that identifies the level of efficiency conditional to the relative
magnitude of different information set. The basis of this separation is what is meant by the
term “all available information”. There are three versions of the EMH4:
1. The weak-form hypothesis asserts that stock prices already reflect all information
that can be derived by examining market trading data such as the history of past
prices, trading volume, or short interest. If markets are efficient in the weak form,
then technical analysis which is the study of past stock prices is of no use. Prices
will follow a random walk.
2. The semi-strong form hypothesis states that all publicly available information
regarding the prospects of a firm must be reflected already in the stock price. Such
information includes, in addition to past prices, fundamental data on the firm’s
product line, quality of management, balance sheet composition, patents held,
earning forecasts, and accounting practices. Again, this version indicates that
fundamental analysis is fruitless.
3. The strong form hypothesis states that stock prices reflect all information, public
or private, which is relevant to the firm. This version implies that even insiders
who are privy to information before it becomes known to the rest of the market also
cannot earn any excess profits. In other words, insider information is of no use.
3 See http://www.investorhome.com/emh.htm 4 See Bodie, Kane and Marcus (2005, pp. 373).
25
Indeed, the strong form version of the EMH is so extreme that few would agree with it. In
fact, it is believed that markets are neither perfectly efficient nor completely inefficient.
The paradox of the EMH is that if every investor believed that markets were efficient, then
market would not be efficient because no one would analyze securities. Therefore, the
efficiency of a market depends on market participants who believe that market is
inefficient and trade securities in an attempt to outperform it.
3.3.1 Testing for Market Efficiency
3.3.1.1 Joint-testing Problem
Depending upon the sets of information mentioned in the earlier section, market might be
studied under three forms of efficiency. Over the years, a number of empirical tests for
market efficiency at different level have been performed. However, it should be noticed
that testing for market efficiency is made difficulty by the fact that there is no obvious
methodological approach. Fama (1970, pp.384) addresses this problem as being the effect
of the ambiguity in the definition of the term “fully reflect”: “The definitional statement
that in an efficient market prices ‘fully reflect’ available information is so general that it
has no empirically testable implications. To make the model testable, the process of price
formation must be specified in more detail. In essence we must define somewhat more
exactly what is meant by the term ‘fully reflect’.”. As such, Fama (1991) suggests that
market efficiency must be tested jointly with an equilibrium-pricing model. Nevertheless,
the joint testing of market efficiency and an asset-pricing model has always been a thorny
problem to researchers of the field, since there are so many controversies in the validity of
different models of expected returns. After examining so many return anomalies in the US
3.3 LITERATURE REVIEW
26
and abroad, Hawawini and Keim (1998) conclude that finance has no tests powerful
enough to distinguish market inefficiency from bad asset-pricing models. This problem,
however, does not make empirical research on efficiency less meaningful. Due to Fama
(1991), the empirical literature on efficiency has at least improved our understanding of
the behavior of security returns through time although we cannot expect precise inferences
about the degree of market efficiency.
3.3.1.2 Tests of Efficient Market Hypothesis
The literature of market efficiency includes three categories: weak form, semi-strong-form
and strong form tests. In Fama (1991), these categories are changed by a more general
identification. In particular, weak form tests which address the question of how well past
returns predict future returns are updated with a more general area covering tests for return
predictability. This is because contemporary research also incorporates into the forecasting
models with variables such as dividend yields, price/earning ratios and term-structure
variables. The empirical research on weak-form efficiency also covers investigations of
cross-sectional predictability of returns and anomalies such as January, holiday or seasonal
effects on returns.
Regarding the semi-strong-form and strong-form tests, Fama (1991) suggests changes in
the title, not the areas covered in the tests. Semi-strong-form tests which concern about the
issue of how quickly security prices reflect public information announcements are now
referred with a common title event studies. Event studies, or tests of the semi-strong form
efficiency hypothesis, are based on finding statistically significant causality relationships.
Strong-form tests examine the issue of whether any investors have any kind of private
information that is not fully reflected in market prices. The problems of this category are
27
self-evident as only indirect evidence can be tested. Therefore, Fama (1991) suggests that a
more correct title of strong-form tests is tests for private information. Tests for the strong
version of the hypothesis are conducted through indirect methods by examination of funds’
performances in comparison with other passive investments.
The empirical research, especially of the weak and semi-strong form EMH in the literature
is voluminous. Therefore, in the following part, I will provide an overview of the empirical
tests used for investigation of the weak-form EMH, as they are specifically relevant to my
research.
3.3.1.3 Tests for return predictability (Weak-form tests)
In most of the tests for the weak-form efficiency, the research question should be “How
well do past returns predict future returns?”. It should be reiterated that weak-form
efficiency of the market indicates that current price of a stock fully reflects information of
historical movements of stock price. If prices follow a non-random trends, stock price
changes are dependent; thus, past sequence of prices can be profitably exploited to
extrapolate potential patterns or trends in future price movements. Otherwise, if prices
follow a random walk, successive price changes are independent, thus cannot be used to
predict future trend of stock prices. Hence, the objective of empirical research on weak-
form market efficiency has been to test the hypothesis that successive price changes are
independent. Independence of price movements is the tenor of random walk theory.
Therefore, tests for random walk hypothesis have been widely used in empirical research
on weak form EMH. However, it should be noticed here that in some cases, evidence of
non-random characteristic of stock price movements could not be used to infer inefficiency
28
of market in the weak form. This problem will be mentioned in the later section of this
research.
In the following part, I will mention some of the most prevalent tests of the random walk
hypothesis. These tests are often used in the empirical research of the weak form EMH
also. Besides, according to Fama (1965, pp.98), “There are two different approaches to
testing for independence. First, one can carry out purely statistical tests. […] Second, one
can proceed by directly testing different mechanical trading rules to see whether or not
they provide profits greater than buy-and-hold”. Hence, a brief overview of technical
analysis method is also provided.
Random walk tests
One of the most direct and common tests of the random walk hypothesis is to check for
serial correlations of stock market returns. Serial correlation in the context of stock market
refers to the tendency for future stock returns to be related to past returns. In the presence
of serial correlation, positive correlation implies that positive (negative) returns tend to be
followed by positive (negative) returns. In contrast, negative correlation means that a
period of positive (negative) returns tends to be followed by a period of negative (positive)
returns5. In order to examine serial dependence in stock price changes, researchers have
employed different tests to measure their autocorrelations and test jointly that several
autocorrelations of stock returns are zero. Such a test is given by Box and Pierce (1970)
who propose the Portmanteau statistic, often referred to as Q statistic (Q test). Ljung and
Box (1978) modify the Q statistic to increase the power of the test in finite samples (LB
test).
5 See Bodie, Kane and Marcus (2005, pp.386).
29
The results of these tests may lead reveal the characteristic of a time series of stock returns,
depending on the significance of the tests’ statistics. If the autocorrelations are close to
zero, the price changes are said to be serially independent, or follow a random walk. On
the contrary, the presence of autocorrelation may imply a rejection of the random walk
hypothesis of stock prices changes.
Another test of stationarity (or nonstationarity6) that has also been widely used in empirical
research of random walk hypothesis is the unit root test. Unit root test was first proposed
in 1979 by Dickey and Fuller7, who later developed their DF test to a new version named
Augmented Dickey-Fuller (ADF) test to account for the case of correlated white noise
error term in time series returns. However, it has been cited (Gujarati, 2003, pp.819) that
most tests of the DF type have low power in the sense that they may find a unit root even
when none exists.
In 1988, Lo and MacKinlay developed the Variance Ratio test for autocorrelation
(LOMAC single variance ratio test), which later become very popular in empirical
research on market efficiency and random walk theory of stock market prices. The idea
behind the LOMAC test is that if a series follows a random walk process, the variance of
its q-differences would be q times the variance of its first difference. The LOMAC
variance ratio test is proven to be more reliable and powerful than the Dickey-Fuller and
Box-Pierce tests8. Besides, to test on long-horizon returns, the variance ratio test can also
6 Due to Gujarati (2003), “the terms nonstationarity, random walk, and unit root can be treated synonymous”. 7 For more details, see: Dickey, A.D., and Fuller, W.A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistic Association, vol.74, pp. 427-431. 8 For more details, see: Lo, A.W., and MacKinlay, A.C. (1989). The size and power of the Variance Ratio test in finite samples – A Monte Carlo Investigation. Journal of Econometrics, 40, 203-238.
30
be used to detect mean reversion (negative serial correlation)9. Faust (1992) in a study of
the optimality of the variance ratio test concludes: “a variance ratio test for mean reversion
is an optimal test for mean reversion and to illustrate the forms of mean reversion it is best
at detecting” (pp.1215).
Since the application by Lo and MacKinlay, variance ratio has been widely used by
researchers to test for the random walk hypothesis and EMH of different markets all over
the world. Five years later, Chow and Denning (1993) developed a multiple variance ratio
approach to test for autocorrelation. Chow and Denning’s approach, which is based on the
studentized maximum modulus, provides a multiple comparison of the variance ratios with
control of the test size. In 2000, Wright (2000) proposed modified variance ratio tests
using ranks and signs of returns10. He points out that tests based on ranks and signs may be
more powerful than alternative tests if the data are highly nonnormal. Studies in the past
few years have also shown interests in applying Wright’s modified variance ratio tests to
test for weak form market efficiency.
In the literature, there are many other tests which have been used to check for random walk
hypothesis of stock prices and for the weak form efficiency of stock markets. However,
only tests which have been performed in recent literature are mentioned because of their
potential applicability in this research.
Technical Analysis and Trading Rule Tests
Technical analysis, as opposed to the EMH, supposes that past trends in the market
movements can be used to forecast the future stock prices. Hence, technical analysis is the
9 Studies include, e.g., Kim et al (1988), Lo and MacKinlay (1988, 1989), Poterba and Summers (1988). 10 Wright, J.H. (2000). Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics, 18, 1-9.
31
process of analyzing past prices and other related statistics about stock trading in an
attempt to determine probable future prices. These techniques were originally proposed by
Charles Dow in the late 1800s, and are now commonly used by a majority of investors to
base their investment decision.
A key point to technical analysis is the assumptions that stock prices tend to move in
trends that persist for long period and that history repeats itself. Therefore, technical
analysts, also called chartists, believe that by looking at charting information such as the
current trend, historical high and low prices, support and resistance levels as well as the
volume associated with changes in prices, a reasonable forecast can be made.
According to Edwards and Magee (1966), technical analysis is based on a number of basic
assumptions11:
Market price of securities should be determined by the interaction of demand and
supply.
Demand for and supply of the securities is governed by both rational and irrational
factors.
Reversals of trends are caused by shifts in demand and supply.
Although there are minor fluctuations in the market, security prices tend to move in
trends that persist for long period of time.
Any change in demand for and supply of securities can be detected in charts.
For developing tools of technical analysis, the use of charts and the key indicator
series to project future market movements are essential.
The evidence concerning the effectiveness of technical analysis is crucial to the EMH. As
what have been mentioned, technical analysis and market efficiency hypothesis are
11 See Akhter and Misir (2005).
32
opposed to each other, thus the acceptance and validity of one imply the rejection of the
other. Therefore, there have been a number of research which test for market efficiency by
directly testing different trading rules provided in technical analysis. Trading rule tests are
used to determine whether technical analysis based on a certain trading rule can be used to
bit a naïve buy-and-hold strategy. If trading rules are explored to be able to help investors
earn abnormal profits in excess of those earned from a buy-and-hold investment, market
inefficiency might be inferred and vice versa.
3.3.2 Empirical Evidence
There are probably no other areas in finance that receive more concerns from both
academics and professionals than the hypothesis of market efficiency. Officially proposed
by Fama in the mid-1960s, the EMH was largely accepted to hold in the early years after
its advent. Jensen (1978) declared his belief that “there is no other proposition in
economics which has more solid empirical evidence supporting it”. Despite voluminous
supporting evidences in the early years, attacks have appeared more and more in the
literature over the time. As in many other debates in the area of financial theories, some
questions cannot be answered by an absolute ‘yes’ or ‘no’. Instead, rather than asking
whether specific markets are efficient, empirical research over the past decades has shown
that the question should be how efficient they are.
Indeed, there have been hundreds and thousands of empirical studies attempting to test
whether certain markets are efficient and if so to what degree. In the 1970s, weak form
efficiency was proved to hold, especially in long-standing developed markets such as the
US and UK by the larger part of the academic community, while this was never so clear
for semi strong or strong form efficiency. Given the available research in the literature up
33
to recent time, markets can be described as not conforming to the strong form EMH.
Furthermore, a number of anomalies inconsistent with market efficiency in the semi-strong
form and even the weak form have been confirmed.
Literature has seen great interest in and voluminous research on testing the EMH of well-
established and developed markets. Not so surprisingly, studies in the past two decades
have shown growing concerns and interests in investigating the efficiency level of the
stock markets in developing and emerging countries such as those in Asia, Latin America,
Eastern, and Southern Europe. Due to Malliaropulos and Priestley (1999), there are two
rationales for this. First, the potential diversification benefits that could be obtained by
incorporating emerging markets’ equities in investors’ portfolios have drawn increasing
attentions from investors. The second reason lies in the relatively unknown nature of those
markets. These factors have placed research in these countries at high priority.
The number of research in the literature is so huge that a full review would be impossible.
Hereafter, I will provide a brief review of the researches on the EMH in developing and
emerging countries in areas mentioned above, with special attention to Asian markets.
3.3.2.1 Evidence of the EMH and random walk hypothesis in developing and emerging stock markets
Recent empirical research of the EMH in the emerging markets was primarily concentrated
on Asian and Latin American markets. Like the cases of mature markets, there is hardly
any consensus on the efficiency of the developing and emerging stock markets.
One of the early works was that of Errunza and Losq (1985), who investigated the
behavior of stock prices during the period from December 1975 to April 1981 for 10 Less
Developed Country (LCD) markets, including Argentina, Brazil, Chile, Greece, India,
34
Jordan, Korea, Mexico, Thailand, and Zimbabwe. Based on the results of serial correlation
and runs tests, it was indicated that these LCD markets were inefficient.
By employing the single variance ratio approach proposed by Lo and MacKinlay (1988),
Huang (1995) tested the random walk hypothesis in nine Asian countries during the period
from January 1988 to June 1992. The test results showed that the markets of Malaysia,
Korea, Hong Kong, Singapore, Philippines, and Thailand manifest in themselves various
degrees of positive serial correlations. Thus, the random walk hypothesis is rejected for
these markets. Like Huang (1995), Malliaropulos and Priestly (1999) rejected the random
walk hypothesis for six stock markets in South East Asian countries. However, instead of
finding positive serial correlation, Malliaropulos and Priestley observed mean reversion
(negative correlation) in these markets.
Also examining the random walk model and market efficiency in these above markets but
employing the multiple variance ratio test instead of single variance ratio approach,
Karemera et al (1999) provided contradictory results. In particular, the random walk
hypothesis cannot be rejected for these markets. Moreover, their runs test suggests that
those markets are weak form efficient. Only Singapore appeared not to be efficient in the
weak form hypothesis. These results correspond to Lima and Tabak’s (2004) investigation
of the random walk theory of the three equity markets: China, Hong Kong, and Singapore.
Lima and Tabak also found that while Hong Kong equity markets are weak form efficient,
Singapore markets do not follow the random walk. For China’s case, while Class A shares
stock exchanges for Chinese nationals performs weak form efficiency, Class B shares
stock exchanges for foreign investors are not efficient in the weak form. The result of
Lima and Tabak was in line with Ma and Barnes’s (2001) on the differences in the
efficiency levels exhibited by the two main classes of shares of the Chinese stock market.
35
Besides the above two researches, there have been increasing concerns in the efficiency
level of the Chinese stock exchanges. Most of the tests were to investigate the weak form
efficiency of the market. However, evidence showed great controversies. Whilst a larger
part of the researches provided evidence to reject the market’s weak form efficiency (Su
and Fleischer, 1988; Darrat and Zhong, 2000; Ma and Barnes, 2001; Seddighi and Nian,
2004), there have been studies supporting the weak form EMH of the Chinese stock
market (Liu et al., 1997; Kai et al., 2000).
When testing for the individual case of the Kuala Lumpur Stock Exchange (KLSE), using
correlation coefficient test, runs test and spectra analysis, Barnes (1986) showed that the
KLSE exhibited a high degree of efficiency in the weak form despite of market thinness.
Looking back to the results of the various tests on Asian stock markets mentioned above,
the case of the Malaysian stock market is controversial among researchers in the literature.
The same situation is applied to the Philippine stock market. The most recent research on
the Philippine stock market given by Aquino (2006) not only supported the weak form
efficiency of the market, but also provided some evidences on the semi-strong form
efficiency. However, Aquino could not assert semi-strong form efficiency of the Philippine
stock market as support for semi-strong form efficiency of the market is mixed.
The random walk hypothesis has also been tested for the Indian stock market. Poshakwale
(2002) provided statistical evidence that rejected the random walk hypothesis for the
Indian market. By investigating daily returns of an equally weighted portfolio of 100
stocks and a sample of 38 most actively traded stocks in the Bombay Stock Exchange
(BSE) for the period from 01/01/1990 to 30/09/1998, Poshakwale found that daily returns
for most individual stocks and the customized portfolio exhibited significant non-linear
dependence. The behavior of stock returns on the BSE was also studied by Alimov et al
36
(2004), but for the period during July 2001 to October 2003. However, by employing the
Dickey-Fuller unit root test and the LOMAC variance ratio test the authors found that
stocks on the BSE followed a random walk.
Through out the literature on the EMH and random walk theory of Asian stock markets,
Hong Kong seems to be the case where there is wide consensus among researchers on its
weak form efficiency. Besides the results mentioned above, other researchers (Cheung and
Coutts, 2001; Otchere and Chan, 2003) also found evidence of weak form efficiency in the
Hong Kong stock markets. Especially, Otchere and Chan (2003) studied the short-run
overreaction phenomenon in Hong Kong market for the period consisting of the pre- and
post-Asian financial crisis. Although finding evidence of overreaction in the pre-crisis
period, Otchere and Chan concluded that such a phenomenon are economically
insignificant, thus could not reject the weak form efficiency of Hong Kong stock market.
Similar to the situation observed in EMH literature of Hong Kong stock market is the case
of South Korea. However, the results are less conclusive. Particularly, Ayadi and Pyun
(1994) applied the LOMAC variance ratio test to investigate the behavior of stock prices
on the Korean stock market for the four-year period between January 1984 and December
1988. Based on daily data, while the random walk hypothesis was rejected under the
assumption of homoscedasticity, it could not be rejected when heteroscedasticity was
assumed. When applying the test to longer horizons including weekly, 30-day, 60-day and
90-day data, the authors could not reject the random walk hypothesis. Besides, the authors
also found evidence suggesting the presence of spurious autocorrelation and
heteroscedasticity, probably due to official intervention and nonsynchronous trading.
Therefore, it was concluded that Korean stock market conformed to random walk
hypothesis. This conclusion was in line with Narayan and Smyth’s (2004) one-break and
37
two-break unit root tests’ results. Using the sample of 55 actively traded stocks on the
Korean stock exchange, Ryoo and Smith (2002) examined the effects of price limit regime
on the conformability of the market to the random walk hypothesis. The authors showed
that price limits did prevent stock prices from following a random walk process. However,
when price limits are increased through the time, the number of stocks whose prices
followed a random walk increased. When price limits were relaxed to ± 12%, the whole
market approached a random walk.
The random walk hypothesis and the weak-form EMH are also tested for other emerging
markets in European and Latin American countries. Smith and Ryoo (2003) used weekly
data from the third week of April 1991 to the last week of August 1998 to test the random
walk theory for five European markets including Greece, Hungary, Poland, Portugal, and
Turkey. Using the multiple variance ratio test of Chow and Denning (1993), the authors
rejected the random walk hypothesis for Greece, Hungary, Poland, and Portugal due to the
presence of autocorrelations of stock returns in these markets. For the case of Turkey, it
was found that the Istanbul Stock Exchange (ISE) followed the random walk hypothesis.
Especially, the authors suggested that liquidity could be the factor that affected the
conformation of a stock market to the random walk hypothesis. Indeed, liquidity was
found to be greater on the ISE than those of the other four markets. As a result, the
Istanbul market, characterized by higher levels of turnover relative to capitalization had a
more active price formation process. This factor was believed to have important
implications for weak-form efficiency. This result was in line with that of Buguk and
Brorsen (2003), who also tested the weak form market efficiency of the ISE using the
weekly data of the similar period from 1992 to 1999. Based on the empirical statistics from
38
the ADF unit root, GPH fractional integration12, and the LOMAC variance ratio tests, the
authors asserted that the ISE conformed to the random walk hypothesis. However, based
on the rank- and sign-based variance ratio tests, the random walk hypothesis was rejected
about one-third of the time (at k=2 for the composite and financial index, and at k=4 or the
industrial index13).
In line with Smith and Ryoo (2003), the weak form EMH and the random walk hypothesis
are rejected in most of the studies on the Athens Stock Exchange in Greece. Evidence can
be found in the direct studies on market efficiency and random walk hypothesis by
Kavussanos and Dockery (1996, 2001), Panagiotidis (2005); and indirect studies on market
anomalies and seasonalities by Siriopoulos et al (2000) and Coutts et al (2000).
For Latin American countries, Urrutia (1995) employed the variance ratio and the run
tests, using monthly data from December 1975 to March 1991, to investigate the random
walk hypothesis and market efficiency for the four markets of Argentina, Brazil, Chile,
and Mexico. Interestingly, the empirical statistics of the tests led to the rejection of the
random walk hypothesis but the acceptance of the weak-form market efficiency for the
four Latin American markets. The rejection of the random walk hypothesis in Latin
American emerging equity markets was later supported by Grieb and Reyes (1999), who
re-examined the presence of random walk in stock prices in Brazil and Mexico for the
period from 1988 to 1995. Tabak (2003) re-tested the random walk hypothesis for the
individual case of the Brazilian stock market and found supporting evidence in the sense
that he rejected the random walk hypothesis for the period between 1986 and 1994. After
1994, the random walk hypothesis cannot be rejected for the Sao Paulo Stock Exchange.
12 A semi-nonparametric test proposed by Geweke and Porter-Hudak (1983). 13 k is the sampling interval in the rank- and sign-based variance ratio test. In their studies, Buguk and Brorsen (2003) conducted the rank- and sign-based variance ratio test for the ISE’s indices at four intervals, k= 2,3,4 and 8 weeks.
39
The increase in the efficiency level of the Brazilian stock market was explained by the
increase of liquidity and the Government’s release of foreign capital control.
In conclusion, most of the empirical studies in the literature show that the equity markets
of the emerging countries in Asia, Latin America, and Europe are neither semi-strong form
nor strong form efficient. In most of the case, those markets are not even weak form
efficient and their stock prices do not follow the random walks. These evidence raise the
question of the potential applicability and validity of technical analysis or trading rules in
such markets. However, literature on this issue has been rather scarce, especially on
emerging markets.
3.3.2.2 Evidence of Technical Analysis in developing and emerging stock markets
Up to date, most of the empirical research on the applicability of technical trading rules
has been conducted for developed markets in the U.S. and U.K. Despite dominated
evidence on the inefficiency and deviation from the random walk hypothesis of developing
and emerging stock markets in Asia, Latin America and Eastern and Southern Europe,
there have been few studies examining whether these deviations reflect profit
opportunities.
Of the few studies on the profitability of technical trading rules in emerging stock markets
was that of Bessembinder and Chan (1995). On evaluating the three types of trading rules
including the Variable Length Moving Average (VMA) rules, Fixed Moving Average
(FMA) rules, and Trading Range Break (TRB) rules applied by Brock et al (1992), the
authors found that these rules had strong predictive power in the emerging markets of
40
Malaysia, Thailand, and Taiwan. The rules had less explanatory power in more developed
markets such as Japan, Hong Kong and South Korea. Tests on these rules were conducted
based on daily data of the stock prices indices in these markets during the period from
01/1975 to 12/1989. Hence, the authors’ observation of the profitability of technical
trading rules was consistent with the reasoning that these studied markets were during the
sample period, informationally inefficient.
Coutts and Cheung (2000) also examined the applicability of the Moving Average
Oscillator and the Trading Range Break rules in the Hang Seng Index of the Hong Kong
Stock Exchange for the period from 01/1985 to 06/1997. The authors found these rules to
be valid in the Hong Kong stock market and the TRB trading rule appeared to be the
strongest. However, the authors also suggested that both the two trading rules would fail to
provide positive abnormal returns, net of transaction costs and the associated opportunity
costs of investing. This suggestion is consistent with the notion of market efficiency, in
that no trading strategy exists which will consistently yield abnormal returns.
41
The objective of this dissertation is to examine the weak-form EMH of the Vietnamese
stock market. In order to test the weak-form efficiency, I will first examine the randomness
of the data as a key condition of the EMH. If stock price data exhibit properties of non-
randomness, I will then investigate the predictability of the data through tests of technical
trading rules. The choice of particular tests depends partly on the nature of the data.
Therefore, in the following sub-sections, characteristics of the data and their descriptive
statistics will be provided and explained.
4.1.1 Data
The data source for this research is the daily closing prices of the Ho Chi Minh Stock
Exchange market index (VN-Index), obtained from the databank of the State Securities
Commission of Vietnam (SSC). The VN-Index is the value-weighted, aggregate stock
price index, and is denominated in Vietnamese Dong (VND). From the raw data of daily
levels of the price index, daily and weekly returns will be computed. Tests will be
conducted based on these series of returns, where weekly returns are employed to test the
random walk hypothesis and daily returns are used to test the predictability of the stock
market prices.
Chapter 4
DATA AND METHODOLOGY
4.1 DATA AND DESCRIPTIVE STATISTICS
42
In order to test the random walk hypothesis of the Vietnamese stock market, the choice of
a weekly interval instead of daily or monthly is based on the following considerations.
First, as mentioned in chapter 2, the Ho Chi Minh Stock Exchange (HOSTC) was opened
in July 2000, but has only been traded daily since 01/03/200214. Hence, daily returns are
not available for the period prior to 01/03/2002. Besides, the choice of weekly instead of
daily sampling helps minimize the biases (i.e. spurious autocorrelations) caused by
problems such as non-trading, the bid-ask spread and asynchronous prices inherent in daily
data (Lo and MacKinlay, 1998). Second, monthly returns are not also used because of the
short time-span of the series since many tests for time series data require sufficient long
sample size15. According to Taylor (1986), a long-spanning data set improves error
variance and increases the power of random walk tests. Given the only six-year history of
the Vietnamese stock market, the use of weekly returns is the best possible choice.
The studied period spans from the inception of the market on July 28, 2000 to July 31,
2006 yielding 299-week observations after excluding holidays16. The weekly returns are
computed as the continuously compounded returns which are essentially the first
difference in natural logarithm of the price index:
rt = ln(Pt) – ln(Pt-1) = ∆ln(Pt)
Due to Campbell, Lo and MacKinlay (1997), the purpose of expressing stock prices in
natural logarithm is to stabilize the variance of the series over time and incorporate their
exponential growth behaviour. Following Lo and MacKinlay (1988), the weekly return
series are based on the closing value for Wednesday of each week. If Wednesday
14 Before 01/03/2002, stocks listed on the HOSTC were traded only 3 days a week, including Monday, Wednesday and Friday. 15 If monthly returns are employed, there will only be 72 observations. 16 Official holidays in Vietnam include New Year, the Lunar New Year, International Labour’s day and National day.
43
observation is missing, then the Tuesday’s closing price (or Thursday if Tuesday’s is also
missing) is used instead. In the event that Tuesday’s, Thursday’s and Wednesday’s closing
prices are missing, the return for that week is reported as ‘missing value’. The choice of
Wednesday’s values is guided by Huber (1997) in an attempt to avoid the popular day-of-
the-week effects often observed with Monday’s and Friday’s and to minimize the number
of holidays.
In order to examine the predictability of the data using technical trading rule tests, daily
returns are required. Due to the availability of the data, sample period spans from
01/03/2002 to 31/07/2006, totalling 1104 observations. The daily returns are also
calculated as the differences in natural logarithm of the index for two trading days. Besides
a series of non-overlapping 10-day returns will also be computed as a requirement of the
technical trading rule tests. In total, there are 111 observations of the non-overlapping 10-
day returns in the whole sample period. The 10-day returns (Rt) are the cumulative rates of
return calculated for the entire 10-day holding period and thus
Rt = ln(pt) – ln(pt-9)
It should be noted here that the weekly and daily returns computed in this study do not
include dividend yields. This is because dividend payments by listing companies on the
HOSTC are made semi-annually. Hence, according to Malliaropulos and Priestley (1999),
extrapolation of dividend yields to daily and weekly frequencies induces a smoothing term
into stock returns, hence, increasing their persistence and introducing a bias in variance
ratio tests towards non-rejection of the null hypothesis of a random walk. Besides, because
of the relatively small size of dividend yields compared to the capital appreciation term
and especially its variance, the exclusion of dividend payments is expected not to affect the
reliability of the tests’ results.
44
Besides, it can be seen that the weekly and daily samples are not homogenous due to the
unavailability of the daily data for the period prior to 01/03/2002. However, due to the
newness of the market as well as the short time-span of the data, it is expected that there is
no difference in the level of efficiency between the two periods prior to and after the date
01/03/2002. Therefore, I will not disaggregate the weekly sample into two sub periods.
4.1.2 Descriptive Statistics of the Data
4.1.2.1 Weekly data
The values of some main descriptive statistics, including mean, median, maximum,
minimum values, standard deviation, measures of skewness and kurtosis and the Jarque-
Bera statistic are provided by Eviews.
45
Table 4.1: Descriptive Statistics for the VN-Index’s weekly returns
(28/07/2000 – 31/07/2006)
Statistics Weekly return distribution
Observations 299
Mean 0.004275
Median 0.002534
Maximum 0.193483
Minimum -0.205518
Standard deviation 0.041997
Skewness -0.532614
Kurtosis 8.132389
Jarque-Bera statistic 342.3068
The fundamental statistics listed in the above table shed some light on the Vietnamese
stock market. Both the mean and median of weekly returns are positive, in line with the
expectation that stock prices increase over time. The parameters skewness and kurtosis are
the measures of asymmetry and peakedness of the probability distribution of weekly return
respectively. In accordance with the Jarque-Bera statistic, they are used to indicate whether
a data set is normally distributed or not. For normal distribution data, the value of
skewness and kurtosis are zero and three respectively. Given the value of skewness and
kurtosis of the data, it is indicated that the weekly return distribution is characterized as
negatively skewed and leptokurtic, having a more acute peak around the mean and a long
left tail.
Figure 4.1 graphs the frequency distribution of weekly returns of the data.
46
0
20
40
60
80
100
-0.2 -0.1 -0.0 0.1 0.2
Figure 4.1: Histogram of weekly return data
In addition to the measures of skewness and kurtosis, the value of the Jarque-Bera statistic
is of a sufficient magnitude to conclude that our time series data are not normally
distributed. The departure from normality for the weekly return data in the Vietnamese
stock market is understandable as this phenomenon is common across many other markets.
4.1.2.2 Daily data
Similar descriptive statistics are also computed for daily data. Table 4.2 contains summary
statistics for both one-day or daily returns and 10-day holding period returns for the
sample series.
47
Table 4.2: Descriptive statistics for the VN-Index’s daily returns
(01/03/2002 – 31/07/2006)
Statistics Daily Returns 10-day non-overlapping
holding period returns
Observations 1104 111
Mean 0.000718 0.006899
Median -0.000356 -0.003077
Maximum 0.047348 0.204129
Minimum -0.049714 -0.136377
Standard deviation 0.012006 0.047326
Skewness 0.445972 1.297067
Kurtosis 7.032827 7.106184
Jarque-Bera statistic 784.7258 109.1050
Similar to what have been observed in weekly sample, both the two series of one-day and
10-day return distributions do not conform to normality. These returns are leptokurtic and
exhibit signs of skewness.
It should be noticed here that the mean values reported in the above table are the
unconditional mean of each return distribution as a result of a buy-and-hold strategy over
the sample period. In other words, they are the rate of returns investors would have if they
hold a portfolio of all constituent stocks of the VN-Index from the time 01/03/2002 until
31/07/2006. This unconditional mean will be used to test the returns generated
conditionally on following technical trading rules’ signals (buy and sell).
In short, the violation from normal distribution examined from the time series of returns on
the Vietnamese stock market is similar with the situations in other emerging or developing
markets. This characteristic will be considered when deciding on the methodology of this
research.
48
The objective of this research is to examine the weak-form efficiency of the stock market
in Vietnam using trading data on the HOSTC. Hence, all the tests performed in this
dissertation are to address the question of whether the Vietnamese stock market is efficient
in the weak form or not.
Many of the previous researches on different stock markets associate tests of weak form
EMH with tests of the random walk hypothesis in the sense that if stock prices were found
not to follow random walk, then the market studied was said to be inefficient in the weak
form. Nevertheless, the rejection of the random walk model in a particular stock market
does not necessarily imply the inefficiency of that market (Lo and MacKinlay, 1988).
However, it is true that weak-form EMH is inextricably related to the random walk theory.
While weak-form efficiency asserts the unpredictability of stock prices, random walk
hypothesis insists on the randomness of price movements. If random walk hypothesis
holds, weak form EMH should also hold and technical analysis is of no use. Nonetheless,
if random walk hypothesis does not hold, this could be a consequence of either market
inefficiency or bad asset pricing models or both. In other words, it is possibly the case that
random walk hypothesis is rejected for a particular market while market efficiency still
holds. In the case that this really is the product of market inefficiency, then technical
analysis is expected to render certain degree of predictability. Therefore, weak-form EMH
should be understood in terms of unpredictability rather than randomness of stock prices
and returns.
In order to examine the weak-form market efficiency of the Vietnamese stock market, I
will first test for the randomness of stock prices. If stock market in Vietnam is found to
conform to the random walk hypothesis, it can be concluded to be weak-form efficient. On
4.2 METHODOLOGY
49
the contrary, if the behaviour of stock prices does not follow a random walk, then further
tests of the applicability and validity of technical analysis will be conducted to check for
the predictability of stock returns. If the series of stock returns examined exhibit predictive
ability, then we can conclude that market is not efficient in the weak form.
4.2.1 Tests of Randomness
Through out the literature of the random walk hypothesis, there are two main approaches
which have widely been employed to test for randomness: (1) tests for stationarity or
nonstationarity, and (2) tests for serial correlation of the time series.
Regarding the first approach, a time series can be characterized by a stationary or
nonstationary process. In particular, a stochastic process is said to be stationary if its mean,
variance and autocovariances at various lags are constant over time; that is they are time
invariant. On the contrary, if a time series is not stationary, it is called a nonstationary time
series which have a time-varying mean or time-varying variance or both (Gujarati, 2003).
The distinction between stationary and nonstationary has critical implication on whether
the trend of a time series is deterministic or stochastic. Particularly, a trend in a time series
is said to be deterministic if it is predictable and not variable; in this case we have a
stationary time series. Otherwise, if the trend is unpredictable, it is considered stochastic
and the time series is nonstationary. Therefore, if a series of stock price changes is random,
it should be characterized by a nonstationary process.
Another common aspect of randomness is related to the independence structure of a time
series. A stock return series is said to be random if subsequent price changes are
independent or uncorrelated of past changes. If there exists any relationship or correlation
50
in successive stock price changes, the return series is said to be dependent or serially
correlated. Hence, serial correlation refers to the tendency for stock returns to be related to
past returns.
It should be noted that tests for stationarity or nonstationarity and serial correlation address
different aspects of randomness. While tests for stationarity/nonstationarity is to detect
patterns of the trend in a time series, tests for serial correlation is to detect the dependence
or correlating relationship among different observations in a series. The presence of
stationarity in a series does not imply correlations, but the deterministic pattern of the
trend.
Given the limited time span of the data in this study, different aspects of randomness
should be tested in order to draw a reliable and comprehensive conclusion about the
conformity of the Vietnamese stock market to the random walk hypothesis. Hence, I will
employ the following tests of randomness on the series of weekly returns to explore the
behaviour of stock prices in Vietnam’s stock market: (1) Portmanteau tests; (2) Unit Root
tests and (3) Lo and MacKinlay’s single variance ratio test.
4.2.1.1 Explanations of the tests chosen
According to Campbell, Lo and MacKinlay (1997), one of the most direct and intuitive test
of the random walk hypothesis for an individual time series is to check for serial
correlation which is the correlation between two observations of the same series at
different dates. Therefore, portmanteau, also called autocorrelation tests, will be performed
first. Autocorrelation is the most common measure of serial dependence in time series
analysis. A time series will be said to be random if all autocorrelations are zero. Therefore,
in order to examine the dependence structure of the researched data, a simple test statistic
51
which was proposed by Ljung and Box (1978) to detect departures from zero
autocorrelations will be used. However, it should be noticed that the Ljung-Box test
statistic is robust to linear dependence only. It cannot be used to detect non-linear
dependence. Therefore, if the data are characterized by non-linear dependence instead of
linear dependence, other tests which are robust to non-linear dependence (i.e. the BDS
tests proposed by Brock, Dechert and Sheinkman,1987) should be used in addition. In
other words, if the Ljung-Box test rejects the hypothesis of no autocorrelations in the data,
indicating the presence of linear dependence, then the BDS test will not be conducted.
Otherwise, if Ljung-Box fails to reject the null hypothesis, BDS test will be conducted in
order to check for non-linear dependence. In this respect, methodology behind the BDS
test will also be presented in the later part of this chapter.
After checking for serial dependence in the data, unit root tests will be performed.
Statistical tests for the presence of unit root are of additional interest as they can help to
evaluate whether the return series is nonstationarity or stationarity. Particularly, such tests
are designed to reveal whether the trend is stochastic (nonstationary), through the presence
of a unit root, or deterministic (stationary), through the presence of a polynomial time
trend (Phillips and Perron, 1988). Unit root tests are related to the previous portmanteau
tests in the sense that stationarity may possibly be an explanation for the dependence
structure of the time series. Therefore, unit root hypothesis will be tested using the
Augmented Dickey-Fuller (ADF) test (Said and Dickey, 1984) and the Phillips-Perron test
(Phillips and Perron, 1988). The reason for using both the above test statistics lies in the
robustness of the two tests in different conditions. In particular, the Phillips-Perron test is
more powerful for models with positive moving average errors, while the ADF test is
better for models with moving average errors and negative serial correlation.
52
Variance ratio test, which was proposed by Lo and MacKinlay (1988), have been widely
used and proved to be particularly useful and robust for examining the behaviour of stock
price indices in which returns are frequently not normally distributed. Given the fact that
the series of weekly return in this study does not conform to normality, the use of Lo and
MacKinlay’s variance ratio test (LOMAC test) is appropriate. Besides, it has been well
documented in the literature that most of stock returns are conditionally heteroskedastic
with regard to time. In this respect, the LOMAC test is superior to many other tests such as
autocorrelations and unit root tests since its test statistics are robust under both
homoskedasticity and heteroskedasticity. Thus the use of the LOMAC single variance ratio
test, together with the other two tests, is expected to yield a more comprehensive and
reliable conclusion about the degree of randomness of stock returns in the Vietnam’s
market.
4.2.1.2 Portmanteau tests
Ljung-Box Test
One of the approaches to testing the random walk hypothesis is to test jointly that all
autocorrelations are zero. In particular, stock prices will be said to follow a random walk if
their returns are uncorrelated at all leads and lags. A measure of the degree of dependence
(correlation), or independence (uncorrelation) of different observations in a time series is
given by:
]var[
),cov(]var[]var[
),cov()(t
ktt
ktt
ktt
rrr
rrrrk +
+
+ ==ρ
53
where )(kρ is the kth order autocorrelation coefficients; rt is the return at time t; and k is
the time lag. In my sample, I calculate rt as the natural logarithm of weekly returns of the
VN-Index. In order to test the hypothesis that all autocorrelation coefficients are
simultaneously equal to zero, Ljung-Box Q test statistic is applied. Based on the
Portmanteau Q*(m) statistic developed by Box and Pierce (1970), Ljung-Box modified
and proposed the Q(m) statistic
∑= −
+≡m
k kTkTTmQ
1
2 )()2()( ρ ,
where m is the number of lags being tested and T is the sample size. The Ljung-Box Q(m)
statistic is used as a test statistic for the null hypothesis
H0: )1(ρ = )2(ρ = … = )(mρ = 0,
against the alternative hypothesis
H1: )(kρ ≠ 0 for some },...,1{ mk∈
The decision rule is to reject H0 if 2)( αχ>mQ , where 2αχ refers to the th)1(100 α−
percentile of a chi-squared distribution with m degree of freedom. In practice, it is warned
that the selection of m may affect the performance of the Q(m) statistic. In particular, if too
few are used, the presence of higher-order autocorrelation may be missed; if too many are
used, the test may not have much power due to significant higher-order autocorrelations
(Campbell et al., 1997). To resolve this problem, Tsay (2005) suggest that the choice of
)ln(Tm ≈ will provide better power performance. In my research, I will follow these rules.
54
BDS Test 17
The BDS test statistic, as suggested by Brock, Dechart and Scheinkman (1987) provides a
powerful test of long run non-linear dependence within a time series based on correlation
dimension. It is another kind of portmanteau tests for time based dependence in a series.
The BDS test is based on a null hypothesis of independent and identical distribution (IID)
and is widely used as a means of examining non-linear dependence.
Let {xt} be a time series of length T whose observations may be embedded in an m-
dimensional space by forming vector ),...,,( 11 −++= mtttmt xxxx named m-history. The BDS
statistic is derived from the correlation integral )ε(mC :
),())(1(
2)(1
1
1
1
1
0, jtjs
mT
s
mT
st
m
jTm XXI
mTmTC ++
+−
=
+−
+=
−
=∑ ∑ ∏−+−
= εε
where (T – m + 1) is the maximum number of overlapping vectors which can be formed
with a time series of length T, and εI is an indicator function that equals 1 if ε<ms
mt xx ,
and that otherwise equals 0, ms
mt xx , being the sub norm.
In the Eviews, the BDS test statistic is given by
mmTTmTm CCb )()()( 1,1,, εεε +−−=
Under the assumption of independence, this statistic is expected to be close to zero.
17 See Blasco et al. (1997) and Eviews User Guide.
55
4.2.1.3 Unit Root Tests
Augmented Dickey-Fuller (ADF) test
To begin, the standard ADF test is applied to examine the nature of stationarity or
nonstationarity of the researched data. The ADF unit root test, proposed by Said and
Dickey (1984), is a version of the Dickey-Fuller (1979) test (DF test) for a larger and more
complicated set of time series models. In particular, under the former DF test, the error
term is assumed to be uncorrelated. However, in fact it is possibly the case that the error
term is correlated. Therefore, the ADF test was designed to account for correlated error
term by adding the lagged values of the dependent variable ∆Yt, - the first difference in
values between the two variables in a time series.
The ADF test is based on the following auxiliary regression:
t
k
iittt yyy εψβα +∆++=∆ ∑
=−−
11
18, (1)
and
t
k
iittt yyTy εψβδα +∆+++=∆ ∑
=−−
11 19, (2)
where ∆ denotes the first differences; yi is the natural logarithm of the price index; tε is a
pure white noise error term; and T denotes the deterministic time trend. The lag length k is
selected using the Akaike Information Criterion (AIC). The first equation tests for the null
hypothesis of a unit root against a mean stationary alternative in yi. The second equation
tests the null hypothesis of a unit root against a trend stationary alternative. The null
hypothesis (H0) is that β equals 0, indicating that the series is nonstationary or that unit
18 With constant, no trend 19 With constant, with trend
56
roots exist in the time series. The alternative hypothesis (H1) is that β <0. The null
hypothesis of nonstationarity will be rejected if the ADF test statistic, known as the τ-
statistic, is larger in absolute value than the critical value. This τ-statistic is a negative
number. The more negative it is, the stronger the rejection of the hypothesis that there is a
unit root at some level of confidence. Failing to reject H0 implies that the time series of has
the property of random walk.
Phillips-Perron Test 20
Also testing for unit roots, Phillips and Perron (1988) proposed an alternative non-
parametric statistical method to take care of the serial correlation in the error terms without
adding lagged difference terms. The approach of Phillips and Perron is to first calculate the
above unit root tests from regression equations with k=0. The test statistics are then
transformed to remove the effects of serial correlation on the asymptotic distribution of the
test statistic. Under this approach, Phillips and Perron provide new test statistics. They
prove that these test statistics not only have the same asymptotic power as the conventional
ADF test, but also allow for a more general class of error processes.
Under the Phillips-Perron’s test (PP test), the null, the alternative hypotheses and the
critical values are the same as those used in the ADF test. The PP test statistic can be
obtained using Eviews 5.0. The null hypothesis of nonstationarity will be rejected if the PP
test statistic is larger in absolute value than the critical values.
20 See Blasco et al., (1997); Phillips and Perron (1988).
57
4.2.1.4 Variance Ratio test
The variance ratio test, proposed by Lo and MacKinlay (1988), is based on the premise
that under the random walk hypothesis, the increments in asset price series are serially
uncorrelated and that the variance of random walk increments in a finite sample is linear in
the sampling interval. In other words, if the natural logarithm of a time series is a pure
random walk, the variance of its q-differences grows proportionally with the difference q.
For example, with weekly data, if random walk is the true process generating the stock
price series, the variance of the weekly series should be five times the variance of the daily
series. Therefore, if we have a series of (nq + 1) observations including X0, X1, X2,..., Xnq
at equally spaced intervals, the variance of (Xt – Xt-q) should be q times the variance of (Xt
– Xt-1) under the random walk hypothesis. Lo and MacKinlay (1988) provides a single test
for this hypothesis using the single variance ratio, denoted by VR(q). The variance ratio of
q observations, VR(q) is defined simply as
)1()()( 2
2
σσ qqVR =
where )(2 qσ is 1/q times the variance of q-differences and )1(2σ is the variance of the
first differences. The LOMAC variance ratio tests the null hypothesis that the variance
ratio, VR(q), equals 1. Alternatively, values for VR(q) that are greater than 1 imply
positive serial correlations while values that are less than 1 imply negative serial
correlations or mean reversion. These hypotheses are tested under both the homoskedastic
and heteroskedastic specifications of the variances.
The values of )1(2σ and )(2 qσ are given by Lo and MacKinlay as follows:
58
∑=
− −−−
=nq
ttt YY
nq 1
21
2 )ˆ()1(
1)1( µσ
22 )ˆ(1)( µσ −−= −=∑ qtt
nq
qtYY
mq
where
⎟⎟⎠
⎞⎜⎜⎝
⎛−+−=
nqqqnqqm 1)1(
and
)(1ˆ 0YYnq nq −=µ
Under the assumption of homoskedasticity, the standard test statistic Z(q) is derived:
)1,0(~)]([
1)()( 2/1 NqqVRqZ
φ−
=
where
)(3)1)(12(2)(
nqqqqq −−
=φ
Lo and MacKinlay (1988) also refined the Z* test statistic which is robust to
heteroskedasticity:
)1,0(~)]([
1)()( 2/1** N
qqVRqZ
φ−
=
with
59
)(ˆ)(2)(21
1
* jq
jqqq
jδφ ∑
−
=⎥⎦
⎤⎢⎣
⎡ −=
and
∑
∑
=−
+=−−−−
−−
−−−−= nq
ttt
nq
jtjtjttt
YY
YYYYj
1
221
1
21
21
])ˆ[(
)ˆ()ˆ()(ˆ
µ
µµδ
In a further research, with the use of Monte-Carlo simulations Lo and MacKinlay (1989)
have shown that the asymptotic distribution of Z*(q) performs well in finite sample, and
the variance ratio test performs better than both the Ljung-Box test of serial correlation and
the ADF test of unit roots.
To empirically examine the random walk hypothesis in the Vietnamese stock market, I
will apply the above battery of tests on the weekly series of the VN-Index’s returns.
4.2.2 Tests of Technical Analysis
There are many technical trading rules which can be tested in this study. Many of the
previous academic researches on the effectiveness of technical analysis mainly implement
filters, moving averages, momentum, support and resistance rules. In this study I replicate
the analysis of Brock et al. (1992) by testing one of the simplest and most popular
mechanical trading rules – the moving average oscillator.
The moving average trading rule is one of the most common techniques of technical
analysis and is also perceived to be one of the most consistent mechanical trading rules.
Although there are many variations of the moving average rule, I will use two of the
60
simplest versions which have been employed by Brock et al. (1992): the Variable Length
Moving Average (VMA) and the Fixed Length Moving Average (FMA).
Moving averages are defined as the recursively updated averages of past prices. The idea
behind moving averages is that they yield insight in the underlying trend of a price series
and smooth out its volatility. The moving average rule considers two moving averages of
the level of the index price Pt, including the short-run and the long-run moving averages
(MA). Based on the comparison of the short-run with long-run MAs, ‘buy’ and ‘sell’
signals are generated. In particular, the MA decision rule is that buy (sell) signals are
generated when the short run average is above (below) the long run average. According to
Brock et al. (1992), this rule is designed to replicate returns from a trading strategy where
traders buys when the short MA penetrates the long MA from below and stays in the
market until the short MA crosses the long MA again from above. Employing the notation
of Mills (1998), the short run moving average of order n is given by
∑−
=−=
1
0
1)(n
iitt P
nnS
and the long run moving average of order m is given by
∑−
=−=
1
0
1)(m
iitt P
mmL
where clearly n<m. The key variable is therefore the length of time associated with the
short run and long run averages. The larger the variable n and m, the slower the MA adapts
and the more volatility is smoothed out. The most popular average rule is 1-200, where the
short average is one day and the long average is 200 days. Besides this rule, Brock et al.
(1992) also suggested and tested other common rules including: 1-50, 1-150, 5-150 and 2-
61
200. The rule can also be further modified by adding a band around the MAs. Brock et al.
(1992) used a one percent band and suggested that the introduction of a band would reduce
the number of buy (sell) signals by eliminating ‘whiplash’ signals when the short and long
period moving averages are close (pp. 1735). With the inclusion of a certain percentage
band, buy (sell) signals are now only initiated if the short run MA (St(n)) exceeds (is less
than) the long run MA (Lt(m)), by the pre-specified percentage band. Particularly, with a
one percent band, a buy signal is emitted if
01.1)()( ∗> mLnS tt
and a sell signal is emitted if
99.0)()( ∗< mLnS tt
When the short moving average is inside and band, no signal is indicated on that day.
Whereas, a band of zero (without band) will classify all days into either buys or sells.
Given the rules and the band defined above, once a signal, either ‘buy’ or ‘sell’, is
generated, the Variable Moving Average rule (VMA) call for the position to be maintained
until the short and long moving averages cross again, irrespectively of the time period
between the two opposite signals. Whereas, the Fixed Moving Average rule (FMA) rule
requires holding the position for a fixed number of days and any other signals emitted
during that period will be ignored. This is the only difference between the VMA and the
FMA.
In order to test for the hypothesis of equality between the unconditional return of a buy-
and-hold strategy and the returns generated from employing the moving average rules, the
two test statistics specified by Brock et al. (1992) will also be employed. In particular,
62
mean returns will be computed separately for the buy and sell periods. The significance of
these mean values will then be examined using the t-statistics. For buy (sell) period, the
statistic is given by
2/122 )//( r
r
NN σσµµ
+−
where rµ and rN are the mean return and number of signals for the buys and
sells;µ , N and 2σ are the unconditional mean, number of observations and estimated
variance for the whole sample respectively. For the difference in the mean returns of the
buy-sell periods, the test statistic is given by
2/122 )//( SB
SB
NN σσµµ
+−
where Bµ and BN are the mean return and number of signals for the buys; Sµ and SN are
the mean return and number of signals for the sells. Given certain degree of freedom, if the
absolute values of these above statistics are greater than the critical values, then the null
hypothesis of equality will be rejected.
In this research, I will test both the VMA and the FMA using five pairs of short and long
term lengths suggested by Brock et al. (1992), with and without a 1% band. For the FMA
rule, I utilize a common fixed holding period of ten days. Thus, for each type of moving
average, there will be 10 tests totally, including: (1,50,0), (1,50,0.01), (1,150,0),
(1,150,0.01), (5,150,0), (5,150,0.01), (1,200,0), (1,200,0.01), (2,200,0) and (2,200,0.01).
All of these tests will be conducted at the 5% level of significance.
63
To summarize, the weak-form efficiency of the Vietnamese stock market will be
investigated using two groups of tests. The first group of tests will check for the
randomness of the data to see if the stock market in Vietnam follows the random walk
hypothesis. The second group of tests will examine the predictability/unpredictability of
the market by testing one of the most widely used technical trading rule – the moving
average rule.
64
In this section, the empirical results of the tests chosen will be performed. To recall, the
following three kinds of test will be employed to examine the random walk hypothesis in
the Vietnamese stock market: (1) Portmanteau tests; (2) Unit root tests; and (3) Variance
ratio test. The above approaches will be tested on the series of weekly returns of the VN-
Index.
5.1.1 Portmanteau test In testing for autocorrelations in my data, the Ljung-Box test is conducted first. Under the
portmanteau test, the null and alternative hypotheses are formed as follows
H0: no autocorrelations exist in the data
H1: autocorrelations exist in the data
Following Tsay (2005), the Ljung-Box test statistics Q(5) and Q(6)21 are applied to the
weekly return distributions. Besides, the Ljung-Box statistics for lags 1 and 10 are also
presented to yield a more comprehensive view. Table 5.1 reports the test statistic result.
21 The number of autocorrelations (m) is chosen as m ≈ ln(T). While there are 299 observations totally, m takes the value of 5.7 approximately.
Chapter 5
EMPIRICAL RESULTS AND ANALYSIS
5.1 TESTS OF RANDOM WALK HYPOTHESIS
65
Table 5.1: Ljung-Box test statistics22
VN-Index Weekly Return Q(1) Q(5) Q(6) Q(10)
Test statistics 22.285* 63.967* 68.095* 72.606*
p-value 0 0 0 0
Note: * Significant at the 5% level.
The Ljung-Box statistics reported for the four lags are all significant at the 5% level.
Hence, the null hypothesis of no autocorrelations in the data is rejected, suggesting that
autocorrelations exist in weekly return series. As what have been mentioned, the Ljung-
Box test statistic is very robust to detect linear dependence in time series data. Given the
above results, it can be said that the data of weekly return on the Vietnamese stock market
are characterized by linear dependence. As such, there is no need to further conduct the
BDS test of non-linear dependence.
5.1.2 Unit Root tests In order to test for unit roots in the weekly series data, both the ADF and the Phillips-
Perron tests are performed. Under both the two tests, the null and alternative hypotheses
are as follows:
H0: unit root exists in the data (nonstationarity)
H1: no unit root exists in the data (stationarity)
22 The Ljung-Box test is performed using Eviews.
66
The results are given in Table 5.2. Panel A presents the result of the ADF test for weekly
returns of the VN-Index, or the first difference of the weekly price index. Panel B shows
the Phillips-Perron test statistic.
Table 5.2: Unit Root Tests23
Test with No Trend With Trend A. ADF test Test statistical value -5.65201 -5.643231 Critical values - 1% level -3.452366 -3.989472 - 5% level -2.871128 -3.425132 -10% level -2.57195 -3.135675 B. Phillips-Perron test Test statistical value -14.07982 -14.07212 Critical values - 1% level -3.452141 -3.989153 - 5% level -2.871029 -3.424977 -10% level -2.571897 -3.135584
As can be seen from Table 5.2, both the ADF and the Phillips-Perron tests, with and
without trend, strongly reject the null hypothesis of a unit root at all three levels of
significance. They indicate that the weekly return series is stationary and exhibit linear
trend. This is in line with the Ljung-Box test’s results. Besides, stationarity may possibly
be an explanation for the serial correlation of the data. Up to this stage, we can reject the
random walk hypothesis for the Vietnamese stock market. Nevertheless, further test,
particularly the LOMAC variance ratio test, should be conducted and reported to finally
confirm the non-randomness of the data.
5.1.3 Variance Ratio Test 23 Unit root tests are performed using Stata.
67
Using a base interval of one week, I conduct the single variance ratio tests proposed by Lo
and MacKinlay (1988) for lags of two weeks to four months. Under the LOMAC test, the
null and the alternative hypotheses are as follows:
H0: VR(q) = 1 The return series follow a random walk
H1: VR(q) ≠1 The return series does not follow a random walk
The variance ratios VR(q) and both the z-statistics under homoskedasticity and
heteroskedasticity conditions are reported in Table 5.3.
Table 5.3: Variance-ratio estimates VR(q) and variance-ratio test statistics z(q) and
z*(q) for a one-week base observation period24
Number of q of base observations aggregated to form variance ratio Time period
Number of nq of base observations
2 4 8 16 1.2728 1.6956 2.4306 2.8502
(4.7173)* (6.429)* (8.363)* (7.2683)*28/07/2000 - 31/07/2006
299
[2.9182]* [4.1281]* [5.2348]* [4.5582]*Note: Variance ratio test of the random walk hypothesis for the value-weighted market index for the sample period from 28/07/2000 to 31/07/2006. The variance ratios VR(q) are reported in the main row with the homoskedasticity z(q) given in parentheses immediately below the main row, and the heteroskedasticity z*(q) given in brackets below the z(q) statistics. Under the random walk hypothesis, the value of VR(q) is one. The critical value at the 5% significance level for both the z(q) and z*(q) is 2.49 * Significant at 5% level.
The idea of the variance ratio test is to check whether the variance of the increments can be
described as a linear function of the time interval. From the tests’ results performed in
Table 5.3, the null hypothesis of a random walk can be rejected at the 5% significance
level for the studied sample. Besides, the estimates of variance ratio are larger than 1 for
all cases. Lo and MacKinlay (1988) suggest that the variance ratios approximately equal to
24 Test is performed using MATLAB
68
one plus the first-order autocorrelation coefficient estimator of weekly returns. Hence, that
all the variance ratios of the test are larger than one indicates positive autocorrelation for
weekly holding period returns. Moreover, both the z(q) and z*(q) statistics increase with
time interval implying that the significance of rejections becomes stronger as coarser
sample variances are compared to weekly variances.
Given the results of the three tests performed above, it can be seen that the random walk
hypothesis can be strongly rejected for the Vietnamese stock market. In order to conclude
whether the rejection of the random walk hypothesis is a consequence of market
inefficiency or not, further tests for predictive ability in the stock price changes should be
conducted.
69
To recall, in order to examine the predictability of stock price index in the Vietnamese
market, I employ the tests of mechanical trading rules proposed by Brock et al. (1992). In
this section, results from trading strategies based on moving average rules will be
presented. Before reporting more details, it should be noted here that in their research
Brock et al. (1992) used bootstrap techniques to explore the stochastic properties of stock
returns, accounting for some missing pieces that standard statistical tests cannot cover.
Given that the purpose of this research is to explore whether there is any degree of
predictability once the series of stock returns has been confirmed not to follow a random
walk, standard statistical tests are assumed to be sufficient.
5.2.1 VMA Results
Results from the VMA trading rules for the full sample are presented in Table 5.4. These
rules differ by the length of the short and long period and by the size of the band. For
example (1, 50, 0) indicates that the short period is one day, the long period is 50 days and
the band is zero percent. Below I present results for the 10 rules which have been chosen.
If technical analysis does not have any power to predict price changes, then it should be
observed that returns from technical trading strategies do not differ from unconditional
returns from buy-and-hold strategy. Besides, we should also observe that returns on days
when the rules emit buy signals do not differ significantly from returns on days when the
rules emit sell signals.
5.2 TESTS OF TECHNICAL ANALYSIS
70
Table 5.4: Standard test results for the VMA rules25
(01/03/2002 – 31/07/2006)
Test N(Buy) N(Sell) Buy Sell Buy > 0
Sell > 0
Buy - Sell
543 561 0.003029 -0.001518 0.5856 0.3583 0.004546(1,50,0) (3.6716)* (-3.5912)* (6.2897)*
220 122 0.006446 -0.003878 0.6318 0.3115 0.010323(1,50,0.01) (5.5201)* (-3.6297)* (7.6173)*
568 536 0.002123 -0.000770 0.5317 0.4049 0.002894(1,150,0) (2.2669)* (-2.3547)* (4.0024)*
384 442 0.003008 -0.000825 0.5469 0.4027 0.003832(1,150,0.01) (3.0875)* (-2.1802)* (4.5754)*
572 532 0.001832 -0.000479 0.5262 0.4098 0.002311(5,150,0) 1.8014 -1.8891 (3.1960)*
380 437 0.002735 -0.000358 0.5395 0.4233 0.003092(5,150,0.01) (2.7052)* -1.5117 (3.6720)*
570 534 0.001948 -0.000594 0.5316 0.4045 0.002543(1,200,0) (1.9869)* (-2.0737)* (3.5165)*
409 424 0.002490 -0.000789 0.5330 0.3797 0.003279(1,200,0.01) (2.4442)* (-2.1041)* (3.9404)*
569 535 0.001783 -0.000413 0.5272 0.4037 0.002196(2,200,0) 1.7181 -1.7886 (3.0369)*
408 427 0.002253 -0.000643 0.5270 0.3841 0.002896(2,200,0.01) (2.1169)* (-1.9056)* (3.4846)*
Mean 0.002765 -0.001027 0.003791
Note: * Significant at the 5% level. Returns are reported in daily. Rules are defined as (short, long, band). “N(Buy)” and “N(Sell)” are the number of buy and sell signals reported during the sample. Column 4 and 5 report the mean returns during buy and sell periods with corresponding t-statistics in parentheses, testing equality with the unconditional mean. Column 8 indicates the differences between the mean buy and sell returns with corresponding t-statistics testing the buy-sell difference from zero.
Results of the test of VMA rules shed some light on the potential predictability on the
Vietnamese stock market. The buy returns are all positive with an average one-day return
of 0.276 percent, which is remarkably high as compared with a mean daily return of 0.072
percent from holding the index unconditionally throughout the sample period. Similar to 25 Tests are performed by Excel Spreadsheet.
71
what have been explored in other stock markets26, all sell returns are negative with an
average daily returns for 10 tests of -0.103 percent which is about -23 percent at an annual
rate. The differences between mean buy and sell returns and unconditional returns are
extremely significant, confirmed by the test statistics given in Table 5.4. Fifteen out of the
twenty tests reject the null hypothesis that the returns conditional on the VMA trading
rules equal those from buy-and-hold strategy at the 5 percent significance level using the
two-tailed test. The other test statistics are marginally significant.
The “Buy > 0” and “Sell > 0” columns present the fraction of buy and sell returns that are
greater than zero. The buy fraction is consistently greater than fifty percent, while that for
sells is considerably less, being between 31% and 42% approximately. Under the null
hypothesis that technical trading rules do not produce useful signals, these fractions should
be the same (Brock et al., 1992, pp. 1740). By performing a binomial test whose results are
presented in the last column, it is shown that all these differences are highly significant
with test statistics being between 3 and 7.6. Thus, the null hypothesis of equality can be
rejected.
In short, returns from the VMA rules are appreciably different from a buy-and-hold
strategy and hence offer degrees of predictive ability. Moreover, different from previous
researches which found the number of significant negative returns were as twice as those
of positive returns, this research explores the opposite results. In particular, the number of
significant positive returns from the buy signals is greater than that of significant negative
returns from the sell signals. This eliminates the doubt in the sustainability of this technical
trading rule as a profitable investment strategy which can be applied for the previous
period in the Vietnamese stock market.
26 See Bessembinder and Chan (1995), Brock et al. (1992), Coutts and Cheung (2000), Hudson et al. (1996) and Mills (1998)
72
5.2.2 FMA Results
Table 5.5: Standard test results for the FMA rules
(01/03/2002 – 31/07/2006)
Test N(Buy) N(Sell) Buy Sell Buy > 0
Sell > 0
Buy - Sell
56 55 0.026738 -0.013302 0.6250 0.2727 0.0400 (1,50,0) (2.5576)* (-2.5886)* (4.4567)*
22 13 0.061009 -0.039873 0.8182 0.1538 0.1009 (1,50,0.01)
(4.8992)* (-3.3714)* (6.0935)*56 55 0.022080 -0.008559 0.5714 0.3273 0.0306
(1,150,0) (1.9571)* (-1.9808)* (3.4103)*
39 46 0.032317 -0.008653 0.6667 0.2826 0.0410 (1,150,0.01)
(2.8853)* -1.8740 (3.9771)*56 55 0.022080 -0.008559 0.5714 0.3273 0.0306
(5,150,0) 1.9571 (-1.9808)* (3.4103)*
38 44 0.031696 -0.006826 0.6579 0.2955 0.0385 (5,150,0.01)
(2.7877)* -1.6279 (3.6755)*58 53 0.018753 -0.006074 0.5862 0.3019 0.0248
(1,200,0) 1.5459 -1.6418 (2.7606)*
41 43 0.029606 -0.007910 0.6585 0.3023 0.0375 (1,200,0.01)
(2.6254)* -1.7421 (3.6317)*57 54 0.019027 -0.005903 0.5789 0.3148 0.0249
(2,200,0) 1.5726 -1.6304 (2.7739)*
40 44 0.028948 -0.006785 0.7000 0.3182 0.0357 (2,200,0.01)
(2.5264)* -1.6231 (3.4561)*
Mean 0.029225 -0.011244 0.0405
Note:*Significant at the 5% level.
Under the FMA rules, buy and sell signals are triggered by the crossing of short and long
run moving averages and returns are computed as cumulative over a ten-day post signal
holding period. Results are presented in Table 5.5.
Again, these results support the same conclusions as those obtained from the VMA rules.
The mean return over the ten-day holding periods following a buy signal is 2.922 percent
73
and the mean return over the ten days following a sell signal is -1.124 percent. These
returns are significantly different from the unconditional cumulative rate of return over the
ten-day holding period of 0.689 percent. For twenty tests of significance across the buy
and sell decisions, eleven are significant at the 5 percent confidence level. Again, the
number of significant positive returns from buy signals is greater than that of significant
negative returns from sell signals. For all the tests, the fraction of buys greater than zero
exceeds the fraction of sells greater than zero. The differences between mean buy returns
and mean sell returns are highly significant with all the test statistics greater than 2.7. With
all of the above results, we again reject the null hypothesis of equality.
The results of the tests on the VMA and FMA trading rules can be summarized as follows:
i. Mean returns on both the VMA and FMA rules are significantly different from
the unconditional returns generated from buy-and-hold strategy. Particularly,
the mean return of (buy-sell) from the VMA trading rules is 5.3 times greater
than the unconditional mean return, while that from the FMA rules is 5.8 times
greater than the unconditional mean return.
ii. The buy signals are more significant than the sell signals for both moving
average rules with the number of significant positive buy signals exceeding the
number of significant negative sell signals.
iii. Both rules appear (exclusive of transaction costs) to offer profitable investment
tools which could be applied in the Vietnamese stock market over the studied
period.
74
Up to this point it can be concluded that the application of the two moving average trading
rules examined above can render predictive ability. In other words, stock price changes in
the Vietnamese market could be forecasted with the use of technical analysis. However, in
light of recent debates on efficient market hypothesis, one last crucial factor to deduce
market efficiency or inefficiency is whether these trading rules can be translated into
abnormal returns in a costly trading environment. It would likely be the case that the
inclusion of transaction costs would eradicate any abnormal returns derived from
implementing these trading rules. However, in the case of the Vietnamese stock market,
there are reasons to believe that these above trading rules could be profitably exploitable
net of transaction costs.
The profits that can be derived from these trading rules depend on the magnitude of
transaction costs and the number of transactions triggered as a result of buy and sell
signals. Particularly, whenever the short moving average penetrates the long moving
average, a signal is generated, either buy or sell or hold as for the case of no signal, and
thus a transaction is recorded until the two moving averages cross again. Transaction costs
only incur when a transaction is carried out. Basically, transaction costs include trading
cost (brokerage fees) and income tax on capital appreciation. In the case of the Vietnamese
stock market, both of these costs are relatively small. In particular, there is currently no
taxation imposed on the trading of securities in Vietnam. Besides, brokerage fees are
strictly regulated by the exchange authority, varying within a range set by the Exchange in
order to encourage the participations in the stock market. For the studied period, brokerage
fees are set within a range from 0.25% to 0.4% per transaction at the maximum. Average
across the ten VMA rules, 5.3 buy signals and 6 sell signals are issued during the studied
period of 4 years and 4 months, or 4.333 years approximately. These numbers are even
smaller for FMA rules. A trader relying on FMA rules with a ten-day holding period
75
would have entered positions based on buy signals 3.4 times, and would have entered
positions based on sell signals 3.8 times over the sample period. Combining these two
factors, trading costs per annual are relatively small as compared to the average annual
return (buy or sell) of approximately 60.5527 percent and 65.7628 percent conditionally on
following the VMA rules and FMA rules respectively.
Given the factors discussed above, it can be safely concluded that the two trading rules
examined had predictive ability and could be used as profitably investment tools over the
period from 01/03/2002 to 31/07/2006 in the Vietnamese stock market.
Given the results of both the tests of randomness and tests of predictability performed
above, it can be concluded that the Vietnamese stock market does not conform to the weak
form of market efficiency. In this section, I provide some explanations for the inefficiency
of the Vietnamese stock market. For this purpose, a brief summary of the tests’ results are
given first.
5.3.1 Summary of the tests’ results In order to examine the weak-form market efficiency of the Vietnamese stock market, two
groups of tests which test for the randomness and predictability of the data have been
chosen. Randomness tests are applied on weekly stock returns over a full sample spanning
27,28 Annual return = (1 + average one-day return)250 – 1. Average one-day return is computed using the mean (buy-sell) returns, given in last column of Table 5.4 and Table 5.5, divided by two. Number of trading days per annual is assumed to be 250 days.
5.3 ANALYSIS OF THE TESTS’ RESULTS
76
from the inception of the market on 28/07/2000 to 31/07/2006, while predictability tests
are applied on daily data over a sub-period from 01/03/2002 to 31/07/2006.
Tests which have been chosen for testing the randomness of the weekly series of returns
include the portmanteau test for autocorrelations, the ADF and Phillips-Perron unit root
tests for stationarity and the LOMAC variance ratio test. Under the three tests chosen, the
null hypothesis is rejected, confirming that the Vietnamese stock market does not follow a
random walk. Particularly, the Ljung-Box statistics from the portmanteau test are highly
significant at the four lags 1, 5, 6 and 10, suggesting that the data of weekly returns on the
VN-Index is linear dependent. Second, both the ADF and Phillips-Perron unit root test
indicates that the null hypothesis of unit root can be rejected, proposing that stationarity
might be an explanation for linear dependence of the time series data examined by the first
test. Finally, results from the LOMAC variance ratio test once again confirm the non-
random characteristics of the weekly return series. All the variance ratios are greater than
one, suggesting positive serial correlation in returns. Therefore, the random walk
hypothesis can be straightforwardly rejected for the Vietnamese stock market. The first
condition of weak-form market efficiency has been declined.
Empirical results from the moving average are in favour of a conclusion that the
Vietnamese stock market is inefficient, even in the lowest form. Particularly, both the
VMA and FMA rules suggest that the stock price changes in the Vietnamese stock market
are predictable, and that investment opportunities can be profitably exploited net of
transaction costs.
As a result, based on the empirical findings, it can be concluded that the Vietnamese stock
market is inefficient during the studied period. My result is in line with many previous
77
researches in the literature of efficient market hypothesis in developing and emerging
countries.
5.3.2 Possible explanations of the market inefficiency in Vietnam
The empirical results from the two groups of tests consistently indicate that the
Vietnamese stock market is not efficient in the weak-form. Obviously, finding evidence
against weak-form market efficiency is not unique with my study as many other
researchers have reached similar conclusions (for example, see Buguk and Brorsen, 2003,
for the Turkey stock market; Karemera, Ojah and Cole, 1999, for fourteen markets in Latin
American and Asian countries, etc.). As the case of many other developing and emerging
countries, the rejection of market efficiency in Vietnam is understandable and explanations
for this are not difficult to find. The newness of the stock market in Vietnam suggests
typical problems commonly seen in a newly emerged market such as market thinness, low
liquidity, investors’ irrational behaviours, unsatisfactory corporate governance system,
dubious accounting practice, market manipulation and insider trading.
First, market thinness characterized by non-synchronous or infrequent stock trading can
provide some justifications for positive serial dependence in stock prices. First, this
phenomenon can be accounted for by the explanations by Lo and MacKinlay (1988) who
posit that small-capitalized firms trade less frequently than large-capitalized firms.
Therefore, information is impounded first into large-capitalized firms’ prices first, and then
small-capitalized firms’ with a lag and this lag may induce positive serial correlation in the
index series. A second reason lies in the case where new information that arrives late in the
day after the last transaction. Hence, this information will only be reflected in next day’s or
78
even next week’s prices. This may create some degrees of predictability in market returns
as new information is not instantly embedded in stocks’ prices, thus offering profitably
exploitable opportunities. Another possible cause for this is the imposition of price limits
on daily trading prices by the State Securities Commission of Vietnam (SSC). Price limits
in Vietnam’s stock market may create artificial positive correlations in stock price returns
as stocks may need certain time to reach their equilibrium price. One last reason lies in the
shareholding structure of the listing companies in the Vietnamese stock markets. As what
have been mentioned in Chapter 2, most of the listing companies on the HOSTC are
privatized entities which were originally state-owned enterprises. After privatization, the
government of Vietnam still holds major shares in some companies. These shares cannot
be traded on the exchange. In such cases, both domestic and foreign institutional investors
are not interested in investing in those companies where they cannot have influential roles.
Therefore, it is understandable that a market which is characterized by a considerable
number of such kinds of shares will provide low liquidity.
Another possible explanation for the inefficiency of the Vietnamese stock market is the
unsatisfactory corporate governance system which in turns leads to the problem of
informational asymmetries, dubious accounting system, creating an exploitable
environment for insider trading and market manipulation. Although the SSC does
promulgate regulations on informational disclosure, it does not have the mechanism strong
enough to supervise the compliance with such requirements. Therefore, informational
disclosures by listing companies in Vietnam’s market have been inadequate, less up-to-
date and tend to be delayed, enabling a minority group of people to engage in insider
trading.
79
As having been stressed by Fama (1965), the degree of efficiency of a market depends
largely on market players. Market is efficient if there are a sufficient number of
‘sophisticated traders’, or experts and professional investors, whose activities can
effectively erase dependencies of price changes. However, in a newly emerged stock
market like Vietnam, the majority of investors are characterized as irrational with ‘herding
behaviour’, especially during boom and bust periods. Investors’ herding behaviour would
render ‘non-random’ patterns in stock price changes as a result of investors’ mimicking the
behaviour of the majority of other investors irrationally. As such, the high level of
predictability reported in this research could be partly explained by investor’s irrational
behaviour.
Overall, market imperfections which are common in developing and emerging markets
like Vietnam due to the ineffective legal structures, lack of transparency that prevent the
smooth transfer of information, and investors’ irrational behaviours are all contributing
factors to the inefficiency of the Vietnamese stock market. It has been postured that the
presence of persistent autocorrelations in a market may be the outcome of an unusual rapid
growing economy rather than evidence against the EMH. Even though it is true that the
growth rate of the Vietnamese economy has been relatively high over the past ten years,
approximately 7.3% annually, and is the second highest growing economy after China in
the Asia Pacific region, I believe that this is not the true reason of market inefficiency in
the Vietnamese stock market. Given all the relevant factors discussed above, the finding of
market inefficiency in the Vietnam’s market is not surprising.
80
This study tests the weak-form efficiency of the Vietnamese stock market, a developing
market, to fill the gap in the literature of the Vietnam’s market. This was done by
investigating the randomness of the data through tests of the random walk hypothesis and
examining the applicability and validity of technical analysis through tests of technical
trading rules.
From the results of the Ljung-Box portmanteau test, the ADF and Phillips-Perron unit root
tests and the robust LOMAC’s single variance ratio test, the random walk hypothesis was
rejected for the Vietnamese stock market. However, the rejection of random walk does not
necessarily imply market inefficiency. Hence, further tests of technical trading rules
including the Variable Moving Average and the Fixed Moving Average rules were
conducted. It was revealed that the Vietnamese stock price index had predictive ability
which helped generate quite significant returns net of trading costs. Evidence from both of
the tests was significant enough to reject the weak-form efficient market hypothesis for the
Vietnamese market. The evidence of market inefficiency is understandable for a newly
emerged stock market like Vietnam, where market imperfections and investor irrationality
are the two main sources of price predictability.
Chapter 6 CONCLUSION
6.1 CONCLUDING REMARKS
81
The findings achieved from this study are quite decisive and conclusive in the sense that
all of the tests taken are extremely significant in rejecting the weak-form efficient market
hypothesis of the Vietnamese stock market. However, the conclusions of this research
should be treated with casualty as there exist some limitations in both the data and the
methodology.
First, as what have been mentioned for several times throughout this study, the data period
of six year’s time is quite limited as compared to that of other empirical researches on
various markets. Since statistical tests of randomness require sufficiently lengthy data, the
reliability of the tests’ results may be biased. Hence, as time passes and more stable data
becomes available, a further study on the same issue should give out a more reliable and
encouraging result.
Second, tests of the applicability of the two technical trading rules could not address the
cost of information and the associated opportunity costs of switching funds among
securities and other assets when considering the net returns generated by conditionally
following these rules. No information is costless, and in the real world the costs of
obtaining and analyzing information as well as the benefits of possessing particular
information is quite variant across different investors. Therefore, returns from following
technical trading rules would possibly be reduced considerably if these costs of
information could be quantified, especially in an infant stock market like Vietnam’s.
However, given the significance of all of the tests which have been performed, it can be
said that these tests’ results are capable of revealing the true level of efficiency of the
6.2 LIMITATIONS AND SUGGESTIONS FOR FURTHER
RESEARCH
82
Vietnamese stock market. The inefficiency of the Vietnam’s stock market as well as many
other developing and emerging markets may be explained by Grossman and Stiglitz
(1980), who posit that perfectly informationally efficient markets are an absolute
impossibility, as if markets are perfectly efficient, the return to gathering information is
zero, in which case there would be little reason to trade and markets would eventually
collapse. Rather, the degree of market inefficiency determines the effort investors are
willing to expand to gather and trade information. As such, non-degenerate market
equilibrium will arise only when there are sufficient profit opportunities, i.e. inefficiencies
to compensate investor for the cost of trading and information gathering. Such profits may
be viewed as the excess profit that accrues for the willingness to engage in such activities.
83
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