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DISSERTATION - M99EFA

DISSERTATION TITLE:

January effect in Vietnamese stock market: Empirical

evidence from the period of 2000 - 2015

Supervisor: Mr. Uchenna Tony - Okeke

Student: Tat Dat Nguyen

Student ID: 5896176

Coventry, August 10th 2015

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Abstract

The main objectives of this study is to examine the presence of January effect in

Vietnamese stock market since it was established in July 2000 until the end of April

2015, as well as investigating the impact of 2007 – 2008 financial crisis and

significant increases in monthly mean trading volume of VN – Index on the behaviour

of January effect. The sample will be split up by the financial crisis which is defined

by BB turning-point detection method; and break points in trading volume which is

spotted by a structural breaks test called “global L breaks versus none”. Then, OLS

regression and TARCH model will be run on the entire period as well as sub –

periods. Results from these models provide supporting evidence for the presence of

January effect and suggest that abnormal returns in January tend to be lower during

the crisis than in non – crisis period. Finally, it is believed that January effect tends to

be weakened when the trading volume increases.

Keywords: VN – Index, January effect, financial crisis, trading volume, OLS,

TARCH.

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Acknowledgement

Firstly, I would like to express my gratitude to my supervisor Mr. Uchenna Tony –

Okeke for his encouragement and help since the very first steps of my project.

Next, I wish to thank first and foremost my parents. Without their unconditional

support and motivation, it would be imposibble for me to finish my project.

Last but not least, I would like to take this opportunity to give a thank to my friends

for their care during my master course and helpful comments on my final project.

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Table of Contents

CHAPTER1:INTRODUCTION........................................................................................................7

I. Background of the study.......................................................................................................7II. Motivation and Contribution of the study..................................................................7

1. Motivation........................................................................................................................72. Contribution.....................................................................................................................8

III. Aims and Objectives of the study.................................................................................91. Research question...........................................................................................................92. Objectives.........................................................................................................................9

CHAPTER2:LITERATUREREVIEW.........................................................................................10

I. A brief overview on Efficient Market Hypothesis and Random Walks.............101. Three forms of EMH...................................................................................................102. Random walks...............................................................................................................113. Calendar effects............................................................................................................12

II. Overview on Calendar anomalies...............................................................................121. The day-of-the-week effect.......................................................................................122. The turn-of-the-month effect....................................................................................133. The Halloween effect..................................................................................................144. The holiday effect........................................................................................................15

III. The January effect............................................................................................................161. The definition and characteristics of January effect.........................................162. International evidence of January effect...............................................................173. January effect in Vietnam stock market...............................................................18

IV. Some brief explanations for January effect..............................................................181. The tax-loss selling hypothesis................................................................................192. The Gamesmanship and the window-dressing hypothesis.............................19

CHAPTER3:METHODOLOGY......................................................................................................21

I. Overview of Vietnamese stock market..........................................................................21II. Data.......................................................................................................................................22III. Methodology......................................................................................................................22

1. Methodology of defining the financial crisis......................................................232. Methodology of examining significant changes in monthly mean trading volume..........................................................................................................................................243. OLS regression and TARCH model......................................................................25

CHAPTER4:DATAANALYSISANDDISCUSSION...............................................................29

I. Data description.....................................................................................................................29

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II. Results of defining the financial crisis and significant changes in trading volume...............................................................................................................................................30

1. The financial crisis.......................................................................................................302. Significant changes in trading volume..................................................................32

III. Results from running OLS regression and TARCH model................................361. The whole-period test.................................................................................................362. The behaviour of the January effect before, during and after the financial crisis 373.ThebehaviouroftheJanuaryeffectandsignificantincreasesintradingvolume.........................................................................................................................................40

IV. Discussion...........................................................................................................................43CHAPTER5:CONCLUSIONS.........................................................................................................45

LIST OF REFERENCES.................................................................................................................47

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List of Figures

Figure1: The distribution of monthly mean returns of VN - Index.................................29

Figure2:Results from the Ng – Perron unit root test on monthly mean trading

volume....................................................................................................................................................32

Figure3:Estimation output of “global L breaks versus none” test’s underlying model

...................................................................................................................................................................33

Figure4:Results from structural breaks analysis...................................................................34

Figure5:Results from OLS and TARCH on the whole period 2000-2015...................37

Figure6:Results from OLS regression before, during and after the financial crisis..38

Figure7:Results from TARCH model before, during and after the financial crisis..39

Figure8:Results from the OLS regression with changes in trading volume................41

Figure9:Results from the TARCH model with changes in trading volume.................42

List of Graphs

Graph1:Monthly mean returns of VN - Index........................................................................30

Graph2:Historical prices of VN-Index.....................................................................................31

Graph3:Monthly mean trading volume of VN – Index from July 2000 to April 2015

...................................................................................................................................................................33

Graph4:Changes in trading volume of VN – Index.............................................................35

List of Tables

Table1:Bull and Bear phases in Vietnamese Stock Market 2000-2015........................31

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CHAPTER 1: INTRODUCTION

I. Background of the study

In recent years, the rapid development of stock markets around the world has

provided both individual and institutional investors with opportunities of gaining

positive returns. However, stock markets are not developing at a same pace or level.

There are markets which are more developed and efficient than others. In general, in

those markets, investors have less chance to gain abnormal returns than in less

developed ones. Therefore, this phenomenon still garners much researchers’ attention.

Market anomalies or investors’ abnormal return in stock markets has been frequently

debated in financial literature throughout the last decades. This phenomenon appears

to be related to calendar so it is called Calendar effect. Perhaps, January effect is the

strongest and most well known Calendar effect, which has been studied widely and

continuously in all over the world. When it exists, investors are able to obtain higher

return from stock markets in the first trading days of the year, or earn higher average

return in January, compared with those in another 11 months of that year. Investors

could gain this abnormal return when they buy a stock that underperforms or has

falling prices at the end of the current year and then sells it in January of the following

year when its price rebounds.

This phenomenon, however, contradicts Efficient Market Hypothesis (hereafter

EMH), which was introduced by Fama (1970). This is a basic seminal theory in

financial literature. Based on EMH, a number of different financial theories and

models have been built upon. Hence, they are significantly affected by the validity of

EMH. According to EMH, at any given point in time, it is impossible for investors to

consistently outperform the market.

II. Motivation and Contribution of the study 1. Motivation

This study will aim to fill the gap in financial literatures about the phenomenon of

seasonal anomalies in developing markets. The market chosen to be investigated is

Vietnamese stock market and the effect to be examined is January effect. This

selection is motivated by two main reasons.

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First of all, several empirical researches reported that instead of following a random

walk, stock returns has seasonal patterns, within which, January effect is considered

as the strongest and most well known phenomenon. This result is a serious challenge

to EMH, and subsequently, to a number financial theories that are based on EMH. I

decided to research into this issue with the motivation of providing further practical

results and clarification either for or against January effect.

In addition, I would choose Vietnamese stock market for conducting this research

because the stock market in Vietnam is still immature and has dramatically grown

since it was established in 2000. It is witnessed a high level of volatilities in the stock

return of this market through the time. Finding out whether those volatilities are just

random walk movements or are resulted from certain pattern is a valuable and

applicable topic. It could provide supporting information to answer the question that

whether investors can earn abnormal returns by applying buy – sell strategies that are

built upon seasonal patterns of stock returns.

2. Contribution

Concerning the potential contribution of this study, until now, there have been a few

researches that investigated January effect in Vietnamese stock market. Recently, a

study carried out by Friday and Hoang (2015) in this market reported supporting

evidence for the presence of January effect in VN – Index during the period of 2000-

2010. However, to deliver this result, they employ a basic OLS model, which has

some limitations and is considered as insufficient for analysing and modeling a time

series financial data series. In addition, the last year of their research period is the year

2010, when the global economy was still struggling to overcome the great recession

in 2008. This research will provide more recent information by updating the data up

to the year 2015, when the global economy is now in the recovery.

Furthermore, a different method, named as Threshold Autoregressive Conditional

Heteroskedasticity (TARCH), will be employed in this study. This method has been

proven more efficient and accurate in analysing and modeling financial data by a

number of financial literatures. Therefore, I would expected that this study will

provide more recent, comprehensive and efficient results, which can contribute to the

process of clarifying this issue.

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III. Aims and Objectives of the study 1. Research question

January effect in Vietnam Stock Market: Empirical evidence for the period 2000-

2015.

2. Objectives

a. Critically evaluate the presence of January effect in VN-Index.

b. Assess the relationship between significant changes in monthly mean

trading volume of VN - Index and January effect.

c. Assess the impact of the global financial crisis 2007-2008 on the

behaviour of January effect.

To achieve three above objectives, this study will, firstly, define the period of

financial crisis in Vietnamese stock market by employing BB turning-point detection

method developed by Bry and Boschan (1971) and modified by Pagan and Sossounov

(2003) and Canova (1994, 1998, 1999). Besides, significant changes in monthly mean

trading volume of VN – Index will be examined by structural breaks of Bai (1997),

Bai and Perron (1998) and Bai and Perron (2004). The beginning and the end of the

crisis, as well as the breaks points in the series of trading volume will be used to split

up the entire period into sub – periods. Then, OLS (Ordinary Least Squared)

regression and TARCH (Threshold Autoregressive Conditional Heteroskedasticity)

model will be run on all of the periods including the entire one.

The remainder of this paper is structured as follow. Chapter 2 provides detailed

review of previous literatures. Methodologies of testing models are presented in

Chapter 3. Chapter 4 contains data description, results and discussion. Finally,

Chapter 5 concludes the study.

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CHAPTER 2: LITERATURE REVIEW

I. A brief overview on Efficient Market Hypothesis and

Random Walks

In modern financial literature, Efficient Market Hypothesis (hereafter EMH) that was

introduced by Fama (1970) has been being considered as a cornerstone. This theory

states that it is impossible for investors to consistently beat or outperform the stock

market because its efficiency causes stock prices to always reflect universally

available information. The two key determinants of the efficiency of the market

would be the set and nature of information and the time that the market needs to

adjust all these information to share prices.

1. Three forms of EMH

According to EMH, there are three different levels or forms of efficiency, which are

weak form, semi – strong form and strong form. In weak form of market efficiency,

all information contained in past price movements is reflected in share prices. The

result is that any effort to technically analysis and examine historical price

movements could be useless in helping investors predict future prices and outperform

the market. There is a big body of financial literature that researching and testing for

weak – form market efficiency in all over the world. Khan, Ikram and Mehtab (2011)

reject the presence of weak – form efficiency in Indian capital market. Hamid et al.

(2010) conclude that all of 14 Asian equity markets in their research are not weak

form efficient and investors may benefit from arbitrage opportunities due to the

inefficiency of these markets. Similarly, Lim (2009) confirms the absence of weak –

form efficiency in five equity markets in Middle East and Africa. Worthington and

Higgs (2004) although conclude that 14 out of 20 European stock markets are not

weak – form efficient, still point out that the remaining 6 markets (the United

Kingdom, Germany, Hungary, Ireland, Portugal and Sweden) comply with the

strictest criteria of a weak – form efficient market.

In semi-strong form of market efficiency, along with past price movements, all

relevant publicly available information is fully reflected in share prices. This causes

fundamental analysis to be of no use. Fundamental analysis utilizes firms’ financial

and non – financial information that is published through periodical and non –

periodical reports and announcements to predict price movements. However, because

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share prices reflect all publicly available information, fundamental analysis provides

no further sign that can help investors to beat the market. Obviously, any market that

is not weak – form efficient cannot be efficient in neither semi – strong nor strong

form.

Finally, in strong form of market efficiency, as well as past price movements and

publicly available information, anything that is privately held is also reflected in share

prices. This could prevent inside traders from taking advantages of their access into

private information in order to outperform the market. Therefore, in short, there is no

way for investors in any form of EMH to consistently earn abnormal returns or

outperform the market.

2. Random walks

The theory of Random walks is closely related to weak – form market efficiency. As

previously mentioned, in a market that is weak – form efficient, it is impossible to

predict future movements using historical patterns of stock prices. This is also what

random walks theory refers to. Fama (1995) defines a random walk market as where

changes in prices of individual stocks are independent and a series of historical

changes in prices cannot be use to predict future movements. In other words, a series

of future prices of a stock is just similar to a series of random numbers. Although an

efficient stock market does not necessarily fully follow a random walk, the amount of

prices changes that are dependent could be too small so that it could be considered as

unimportant.

The random walks theory and market efficiency have been frequently tested and been

either accepted or rejected in different markets in all around the world. Worthington

and Higgs (2004) test for random walks and weak – form market efficiency in twenty

European equity markets including sixteen developed and four emerging markets.

Using three different methods, which are runs tests, unit root tests and multiple

variance ratio tests, they report that of the emerging markets, only Hungary follow

random walks and thus, is weak – form efficient. Whereas, of the developed markets,

only Sweden, Ireland, Portugal, Germany and the United Kingdom observe the most

strict criteria of random walks. Researching into the same topic, Hoque, Kim and

Pyun (2007) find that stock prices in eight Asian markets (Singapore, Phillippines,

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Malaysia, Taiwan, Korea, Thailand, Indonesia and Hongkong) do not follow random

walks.

3. Calendar effects

A number of academic researches have provided a large body of evidence in support

of EMH. However, a considerable amount of opposition also exists, within which,

one that has garnered much attention are Calendar anomalies. This term refers to any

market anomaly that appears to have a relationship with particular time period and

cannot be explained by any accepted financial theory including EMH (Zafar et al.

2012). Calendar anomalies include the Day-of-the-week effect, the Turn-of-the-month

effect, Halloween effect, Holiday effect and January effect or the Turn-of-the-year

effect in some articles. Researchers in their recent studies have verified the existence

of these effects.

II. Overview on Calendar anomalies 1. The day-of-the-week effect

The Day-of-the-week effect refers to a phenomenon that in term of returns, the market

has the tendency to experience significantly positive returns on Friday, but

significantly decline on Monday (French 1980). Therefore, it is often called

“Weekend” or “Monday” effect (Jacobs and Levy 1988). However, in some particular

markets such as Turkey, Japan and Australia, instead of being found in Monday,

decline or negative returns of the stock market exhibit on Tuesday and are

documented in financial literature as “Tuesday” effect.

There are several reasonable explanations for this phenomenon. First of all,

Lakonishok and Maberly (1990) indicate that because of having more time for

decision-making process over weekends, individual investors become more active in

the market on Mondays. However, the opposite is true of institutional investors when

they are less active in the market on Mondays due to the fact that it is, in common, the

day of strategic planning. This results in the decline of total trading volume and

returns of the market on Mondays. Along with that, Lakonishok and Maberly (1990)

also find that sell transactions increase relatively to buy transactions on Mondays.

This could also lead to negative returns in Mondays.

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Another explanation is the fact that settlement dates and trade dates are not

necessarily the same. Commonly, market regulations allow transactions to be settled

after three business days. Therefore, investors who buy on Mondays or Tuesdays

must pay within the same week, but those who buy on Wednesday, Thursday and

Friday can delay their payment until the following week. Thanks to that, they could

benefit from extra 3 days of interest-free from sellers or brokers. The obvious

consequence is that share prices on Mondays must be lower than those on Friday. In

addition, firms tend to delay releasing their bad news until the weekend, according to

the information release hypothesis. Hence, the market starts a new trading week with

bad news leading to lower demand for securities on Mondays (Lakonishok and Levi

1982).

Recently, more evidence of the day-of-the-week effect has been documented. Bayar

and Kan (2012) investigate the presence of daily patterns in returns of 19 stock

markets during the period from 1993 to 1998 and find 14 markets that exhibit a daily

pattern in local currencies returns and 12 with dollar returns. Kenourgios, Samitas and

Papathanasiou (2005) find significant day-of-the-week effect in Athens Stock

Exchange over the period 1995-2000, but the effect appears to loose its significance

over the period of 2001-2004, which may be due to the enter of Greek into EU and

the improvement of the market. Similarly, Nath and Dalvi (2004) report the day-of-

the-week in Indian equity market over the period of 1999-2003. On the other side,

Hui (2005) rejects the presence of this anomaly in some Asian-Pacific markets such

as Hong Kong, Korea, Taiwan, and two developed markets: the US and Japan; with

the only exception of Singapore.

2. The turn-of-the-month effect

The Turn-of-the-month, meanwhile, refers to the anomalous returns at the turn of

each month. In the study carried out by Lakonishok and Smidt (1988) using average

returns for each trading day of Dow Jones Industrial Average (hereafter DJIA) during

the period of 1897-1986, researchers find that daily average returns in the last day of

the previous month and in the first three days of the current month are significantly

higher than those in the rest of the two months. They also state that even when this

anomaly has weakened, it has consistently existed throughout the period.

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Researching into this area, some believe that probably, month – end portfolio

rebalancing could explain this effect. According to this theory, accumulated cash

dividends are often reinvested at the turn of each month leading to higher trading

volume in stock markets (Jacobs and Levy 1988). Other practitioners suggest that

investors, at the end of each month, have higher cash inflows that may come from

salaries or interest received when treasury bills mature. This could cause the demand

for securities at month – end to increase leading to higher returns. Another possible

explanation could be the timing of earning announcements. While good news about

earnings is often disclosed quickly and voluntarily, firms try to delay disclosing their

bad news until the next mandatory quarterly report. Substantially high returns in the

first days of each month reflect the converging of good news about positive earnings

(Jacobs and Levy 1988).

Recently, further evidence was found in Finland over the period of 1991-1997 when

Booth, Kallunki and Martikainen (2001) point out that the turn-of-the-month does

exist. Reschenhofer (2010) reports a significant day-of-the-month effect in S&P500

from 1952 to 2010. Similarly, McGuinness and Harris (2011) verify the presence of

this effect in Shanghai, Shenzhen and Hong Kong stock market over the period of

1995-2010.

3. The Halloween effect

The Halloween effect or “Sell in May” effect has been recently revealed with

supporting evidence as seen in U.S market sectors from 1926 to 2006, where this

effect was statistically significant (Jacobsen and Visaltanachoti 2009). When this

effect exists, returns in stock markets in winter months (November – April) tend to be

considerably higher than in summer months (May – October). In a study about market

anomaly, Bouman and Jacobsen (2002) find that during the summer in many

countries, investors should put their money in saving accounts instead of investing in

stock markets. They also suggest that Halloween effect seems to be unrelated to other

market anomalies. More recent supporting evidence for this effect is provided by

Lean (2011) with Halloween effect being found in Singapore, Japan, Hong Kong,

China, Malaysia and India over the period 1991-2008. Similarly, Abu Zarour (2007)

cannot reject the presence of this effect in seven Middle East countries, which are

Abu Dhabi, Bahrain, Dubai, Egypt, Kuwait, Oman and Palestine from 1991 to 2004.

However, Siriopoulos and Giannapoulos (2006) find no evidence of an exploitable

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Halloween effect in Greek stock market over the period of 1986-2004. This

contradicts with the finding of Bouman and Jacobsen (2002), which reports

significant Halloween effect in this market.

Several studies have tried to explain this phenomenon but they still remain

controversial. One main argument is Halloween effect relates to investors’ behaviour.

For instance, from the research of Bouman and Jacobsen (2002), the change in

investors’ risk aversion due to vacations is considered as a potential explanation for

Halloween effect. According to Kamstra, Kramer and Levi (2003), changes in

investor’s risk aversion are down to Seasonal Affective Disorder (SAD), which

indicates the link between weather condition with the behaviour of people or with the

risk-taking behaviour of investors, to be specific. On the other hand, Jacobsen and

Macquering (2009) provide some other potential explanations that relate to

seasonality in liquidity, production and consumption. However, these seasonal effects

do not widely affect the whole market. Instead, they tend to have impacts on

particular sectors or have different impacts on different sectors.

4. The holiday effect

The Holiday effect refers to the fact that equities tend to experience abnormal returns

just prior to holidays (Brockman and Michayluk 1998). According to Lakonishok and

Smidt (1988), this effect has existed for at least ninety years and is responsible for

about 50% of returns on DJIA. However, abnormal returns prior to holidays does not

heighten the level of risk when the standard deviation of pre-holiday returns is even

lower than those of non-holidays (Jacobs and Levy 1988). Researchers have also

found that Holiday effect does interact with other market anomalies. For example,

Rogalski (1984) suggests that it has a relationship with Size effect with small firms’

stocks experiencing higher pre-holidays returns. It also affects the day-of-the-week

effect which has significantly negative returns on Mondays. Lakonishok and Smidt

(1988) find that on average, returns on Mondays which precedes a Tuesday holiday

are positive. As a part of the search for possible explanation for the holiday effect,

Kim and Park (1994) conclude that holiday effect is not rooted in the institutional

arrangements of different stock markets or different countries. Hence, institutional

factors are hardly internationally accepted as plausible explanations for holiday effect

because these factors are different between countries. In addition, Kim and Park

(1994) also suggest that the relationship between holiday effect and firm size effect

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cannot be the basement for any explanation. In brief, no theory that can fully and

satisfactorily explains holiday effect has been discovered yet. In this case, psychology

still seems to be the most promising explanation (Jacobs and Levy 1988).

III. January effect 1. The definition and characteristics of January effect

January effect or the turn-of-the-year effect refers to a phenomenon that the average

stock market return in January is significantly higher than average monthly returns

during the remaining 11 months of the year. The idea about January effect was first

introduced in a research into seasonal movements of stock prices carried out by

Wachtel (1942). However, it only garnered attention since being re-introduced by

Rozeff and Kinney (1976). In their seminal study using an equal-weighted index of

New York Stock Exchange (hereafter NYSE) price over the period from 1904 to

1977, they found that stock prices did not follow a random walks, but had seasonal

patterns. To be specific, the average return in January was 3.48%, whereas the

average monthly return during the rest 11 months of each year was only 0.42%. This

signifies the presence of January effect in this market.

Nevertheless, equal weight index NYSE that was used by Rozeff and Kinney (1976),

was just the simple average of prices of all listed companies regardless their relative

market capitalisation. Hence, that method gives small companies greater weight than

what they could be based on its market values. Ultimately, the influence of small

firms on the result of the study was exaggerated and predominated the impact of large

ones. Lakonishok and Smidt (1988) use DJIA during the period from 1897 to 1986 to

examine the presence of different seasonal anomalies on US stock market. DJIA is a

reasonable proxy for large capitalisation industrial companies. It comprised 19 stocks

during the period of 1896-1916. After that, the list expanded to 20 stocks and finally,

since 1928, DJIA comprised 30 stocks, which represent approximately 25 percent of

the market value of all stocks in the NYSE. In sort, DJIA is totally the index of large

firms. They found that January returns were not statistically higher than the average

returns of each year. Therefore, January effect is primarily a small firm effect.

The finding of Lakonishok and Smidt (1988) provided support for previous studies

such as the study of Reinganum (1983), when he stated that January effect is a small-

capitalisation phenomenon. Roll (1983) also confirmed the same. Researching into

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small firm effect, Banz (1981) concludes that small firms gain higher risk adjusted

returns than larger ones. Sharing the same notion, Keim (1983) even found that this

effect is concentrated in term of time while half of small companies’ excess returns

arrived in January and half of total return in January received in the first five trading

days. Reinganum (1983) provided further clarification for this phenomenon by

pointing out that small firms with declining stock prices in the previous year earn

higher returns in January. Whereas, at the same time, small winners – firms with

increasing stock prices in the previous year did not exhibit excess returns in the first

five trading days of January.

2. International evidence of January effect

While those studies above all focused on US market, there were several researches

into international markets and also provided support for January effect in different

areas in all over the world. Gultekin and Gultekin (1983) researched into seasonal

patterns in 16 countries in different areas of the world using ordinary least squared

regression with dummy variables. They reported significant January effect in 15 of

them, with the exception of Australia that did not have the effect and the UK that had

excess returns in April. They also pointed out that average return in January in

Belgium, the Netherlands and Italy exceeds the average return for the entire year. A

number of other researches employ the same procedure. Athanassakos (2002)

discovered abnormal January return in both small and large companies in Canada

throughout the period of 1980-1998. This might indicate that size is not the dominant

reason of January effect. Meanwhile, this effect was observed only in small

companies in Japanese stock market (Reyes 2001).

January effect is also present in less developed countries. Felix Ayadi et al. (1998)

found excess January return in Ghana. While according to Robinson (2005), January

effect was present in Jamaica during the period of 1992-2001. In a research into Asia

Pacific stock markets, Yakob et al., (2005) instead of using OLS regression, use a

new and more efficient model called Generalised Autoregressive Conditional

Heteroscedasticicy (GARCH) to test for the presence of this effect. With the

exception of Japan and Singapore, they find that January effect exhibited in Taiwan,

Malaysia, Australia, South Korea, India, Hong Kong, China and Indonesia during that

period. Interestingly, their finding that Australia did exhibit January effect contradicts

with the suggestion of Gultekin and Gultekin (1983), which is January effect was not

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present in that market. It could be the two different methods that drive their different

results.

Nevertheless, there is supporting evidence for the consideration that January effect is

not a universal phenomenon. This effect is not present in Amsterdam, Colombo,

Greek, Zimbabwean, Nigerian, Kuwait and Ukrainian stock markets (Van Der Sar

2003, Yatiwella and Silva 2011, Floros 2008, Ayadi et al. 1998, Moosa 2010).

Despite appearing in certain areas and not being found in the others, January effect

does consist over the last decades. There is no evidence that this phenomenon has

disappeared from NYSE, even when this market is considered as one of the most

efficient stock markets in the world (Haugen and Jorion 1996).

3. January effect in Vietnamese stock market

There is recently a study carried out by Friday and Hoang (2015), which researched

into seasonal anomalies in Vietnamese stock market index (VN – Index) over the

period of 2000-2010. The study conducts monthly returns of VN – Index throughout

this entire period and then divided them into two sub-periods: August 2000, when the

market was established, to December 2005 and from January 2006 to June 2010. They

justify splitting up the entire period by pointing out that the trading volume in

Vietnamese stock market had significantly increased after 2005. The study just simply

calculates the mean return of each month throughout the whole period, and then

compares them to see which month has the highest mean return. Although its

methodology is quite simple, the study could still provide supporting evidence for

January effect when it found excess return in January throughout the entire period.

The study also provides evidence against tax-loss selling hypothesis by reporting

significant positive correlation between the return of the prior year and return in

January.

IV. Some brief explanations for January effect

There are three main explanations for January effect: tax-loss selling hypothesis,

gamesmanship hypothesis and window dressing hypothesis. However, gamesmanship

and window dressing hypothesis could be combined and considered as one main

explanation as they have a close linkage.

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1. The tax-loss selling hypothesis

Branch (1977) and Dyl (1977) are considered to be among the first researchers who

use tax-loss selling hypothesis to explain January effect. According to this hypothesis,

at the year-end, individual investors tend to sell stocks that have falling prices, or the

‘losers’ within their portfolio in order to make capital losses. By dint of this, they can

avoid or reduce tax to be set on their capital gain. Hence, prices of those stocks which

have falling price during the year will be under a downward pressure. Subsequently,

in January of the following year, as the selling pressure disappears, the downward

pressure on prices of those stocks is also diminished letting stock prices bounce back

to its real prices in the market. Therefore, this effect provides investors with a chance

of exploiting abnormal returns at the turn of each tax year (Fountas and Segredakis

2002).

According to Roll (1983), tax-loss selling hypothesis is more likely to affect small-

sized firms rather than large-sized ones, or in other words, it might be considered as a

small-firm phenomenon. Similar findings are reported in a study carried out by

Reinganum (1983) which pays attention to US capital market. Sharing the same

notion, Brown et al. (1983) claim that stocks of small-sized firms are likely affected

by tax-loss selling because of its higher price volatility and subsequently a higher

probability of huge fall in prices.

Nevertheless, tax-loss selling hypothesis is not always the reasonable explanation for

January effect. Berges et al. (1982) report the presence of January effect in Toronto

stock exchange during the period of before 1972 when there was no taxes on capital

gains in Canada. Ho (1990) points out that most of Asia Pacific markets do not

exhibit significantly abnormal return in the first month of a tax year. Haug and

Hirschey (2006) found that January effect has been unaffected by Tax Reform Act of

1986 in the United State. The effect was continuously presence after this event,

providing evidence that could weaken the argument of tax-loss selling.

2. The Gamesmanship and window-dressing hypothesis

Haugen and Lakonishok (1987) provide another explanation for January effect, which

is called gamesmanship hypothesis. They argue that at the beginning of the year,

P a g e 20|52

institutional investors tend to less concern about well-know stocks and buy small and

risky ones for the sake of seeking for higher returns and outperforming the market.

Subsequently, prices of those stocks are pushed higher. However, over the year,

portfolios are rebalanced and are locked in at the end of the year. This is when

institutional investors have the motivation of selling small, risky and poorly

performing stocks (or losers), and buy winners and well-known ones in order to make

their portfolios look better, in other words, window-dressing their portfolios.

Consequently, there is a downward pressure on prices of those small, risky and poorly

performing stocks at year-end time. Ritter (1988) provides additional evidence of

buying pressure at the beginning and selling pressure at the end of the year.

P a g e 21|52

CHAPTER 3: METHODOLOGY

I. Overview of Vietnamese stock market The emergence of Vietnamese Stock Market was partly rooted in the financial and

economic reform, which was started by the government in 1980s and so-called “Doi

Moi”. The reform consisted of liberalizing financial system including banks and

credit institutions, introducing new financial components and launching the first stock

market in July 2000, called Ho Chi Minh City Securities Trading Center (HSTC). In

March 2005, the second stock market in Viet Nam had been launched in Ha Noi and

named as Ha Noi Securities Trading Center (HaSTC). These two stock markets had

been reformed and renamed Ho Chi Minh Stock Exchange (HoSE) and Ha Noi Stock

Exchange (HNX) in August 2007 and Jun 2009 respectively. VN-index – the

capitalisation weighted index of all listed companies on HoSE is commonly

considered as the index of the whole Vietnamese stock market because of its majority

in both trading volume and market capitalisation (Farber and Vuong 2006, Narayan

and Narayan 2010). Therefore, this study will use VN-index to research into the

whole market.

Since established, Vietnamese stock market has rapidly grown, in term of the number

of listed companies, trading volume and market capitalisation. From only 2

companies listed in HoSE when it was launched, the number rose to 32 in 2006, 171

in 2008, 279 in 2011 and 308 in April 2014. Friday and Hoang (2015) report a

significant increase in trading volume after the year 2005. Whereas, according to

Narayan and Narayan (2010), the market capitalisation of all listed companies rose

from only 1 percent of the GDP in 2004 to 28.5 percent of the GDP in 2007, while

price of the index dramatically went up by 281 percent from the end of 2005 to the

end of February 2007. They also report that growth rate of Vietnamese stock market

has been higher than those of other emerging markets, such as China, India and South

Africa.

There are some important changes which were made during the operation of the

market. First, from July 2000 to February 2002, HSTC market was open for trading

only three days a week: Monday, Wednesday and Friday, except for holidays. Since

March 2002, full-week trading rule has been applied to allow shares to be traded five

days a week, also except for holidays. Another change was in the size of a round lot.

Before May 2003, a round lot was a set of 100 shares of one stock. After that, it was a

P a g e 22|52

set of 10 shares with the aim to increase the liquidity of individual stocks. Besides, at-

the-open order (ATO) was introduced in May 2003, which allow investors to just

have to set an ATO and wait for the system to match their orders, instead of having to

preset their price for an order. These changes have remarkably contributed to the

rapid growth of the market.

II. Data

This research will use a data set, which comprises monthly average return of VN-

Index over the period from July 2000 to April 2015. The VN-Index is the market

index of Ho Chi Minh Stock Exchange, which comprises all listed companies in the

market. Starting with 100 points since HoSE was established on 28th July 2000, VN-

Index reaches the value of 562.4 on 27th April 2015. In 2012, HoSE has introduced

new indices, of which include VNAllshare. This index is considered as more efficient

and do not have calculating-cause limitation as VN-Index. However, due to the short

period since it was applied, VNAllshare is not sufficient for this research. Therefore, I

decided to keep using VN-Index.

In order to deliver monthly return of the index, the closing price of the last day of

each month during the period will be collected from financial data provider Thomson

Reuters Datastream. Monthly return of VN-Index will be then calculated using the

following formula:

!" = ln('"'"()

)

where:

!": monthly return of the month t

'": current month last day closing price

'"(): previous month last day closing price

III. Methodology

In this research, two different methods will be use in order to deeply and

comprehensively examine the presence of January effect: Ordinary Least Squared

(OLS) with dummy variables and Threshold autoregressive conditional

heteroskedasticity (TARCH). In addition, with the purpose of analysing the effect of

significant changes in monthly mean trading volume of VN - Index on the behaviour

P a g e 23|52

of January effect, a structural breaks test will be employed to define break points of

trading volume, by which the entire period will be split up into sub - periods.

Similarly, to assess the impact of the global financial crisis 2007-2008, three sub-

periods will be formed: before, during and after the crisis. The precise time frame for

each sub-period will be determined in later parts of this project. Then, both OLS and

TARCH model will be undertaken on all of the sub-periods as well as the entire

period.

Microsoft Excel and Eviews will be used to analysis the data and run testing models.

1. Methodology of defining the financial crisis

As reported by Chauvet and Potter (2000), “recessions are always associated with a

bear market” and a bull or bear market refers to a period of general increase or

decrease of market price respectively. Therefore, it could be reasonable when this

project will first, identify a bear phase of the stock market focusing on the period of

2007-2008, and then use it as an indicator of the financial crisis during this period.

In order to define that bear phase, this research will employ the method that was used

by Gonzalez et al. (2005) in their research into bull and bear market cycles. This

method is called BB turning-point detection method, because it is closely similar to a

algorithm developed by Bry and Boschan (1971). The algorithm was then modified

by Pagan and Sossounov (2003) and Canova (1994, 1998, 1999). According to this

method, firstly, points where the index price is higher or lower than those of 6 months

on either side are spotted. Those are peaks and troughs. Turning points are then

identified by selecting the highest of those multiple peaks or troughs. 15 months is the

minimum length for a complete cycle (peak to peak or trough to trough), and a single

phase must last at least 5 months (peak to trough or trough to peak). In a month when

the index price increases or decreases more than 20 percent, the requirement of

minimum phase length is no longer applied. This allows an event where very large

movements of the price occur for a short period of time to be captured, such as the

market crash in October 1987, in which there were only 3 months between the peak

and the trough. To avoid counting a bull or bear phase twice, any bull (bear) phase

occur in an ongoing bull (bear) phase will be considered as a part of that phase.

To examine the effect of stock market crash on the behaviour of January effect, this

research will follow the procedure that was used by Kok and Wong (2004) in a study

P a g e 24|52

about seasonal anomalies in ASEAN equity markets. They divided the whole data set

into 3 sub-periods, which approximately correspond to the pre-crisis, crisis and post-

crisis period. For each period, OLS regression model and GARCH(p,q)-M model are

used to test the presence of seasonal anomalies in those markets. Thus, this study will

identify bull and bear phases in Vietnamese stock market, but focus on the period of

2007 – 2008 when the global financial crisis occurred. A significant bear phase during

this period will signify the crisis. Subsequently, two estimating models: OLS

regression and TARCH will be run on three sub – periods of before, during and after

the crisis to see whether there is any change in the behaviour of the January effect.

2. Methodology of examining significant changes in monthly mean trading

volume

Friday and Hoang (2015), in their research into the presence of January effect in

Vietnamese stock market, divide the whole data set into two sub-periods: August

2000 to December 2005 and January 2006 to June 2010 and show a significantly

difference between the monthly mean trading volume of those two sub-periods. They

also point out that the behaviour of January effect tends to differ from before and after

the significant change in trading volume. Hence, this study will also divide the entire

period into sub-periods based on significant changes in monthly trading volumes of

VN-Index, then run OLS regression and TARCH model on all of those periods.

Results from those two models could contribute to the investigation into the

relationship between trading volume and January effect in particular, the seasonality

of stock returns in general.

However, due to the continuously increase of the trading volume over the time,

average trading volumes of VN – Index of prior years are always smaller than that of

later years. Therefore, the method of Friday and Hoang (2015) could be too simple to

be able to correctly and completely capture significant changes in the trading volume.

In order to deal with this issue, my study will employ a procedure that was used by

Bai (1997), Bai and Perron (1998) and Bai and Perron (2003) to determine any

structural breaks in the trading volume. Technically, structural breaks are points

where the data exhibit noticeable changes. In this case, they are points where the

trading volume significantly increases, which will be then used to split up the entire

sample.

P a g e 25|52

According the Bai (1997), Bai and Perron (1998) and Bai and Perron (2004), the

process of determining structural breaks starts with a unit root test, followed by the

“global L breaks versus none” test. The unit root test is used to test whether a time

series data is stationary or not. As researchers have suggested that if the testing data is

non – stationary, results from running tests and models on this data set will not be

considered as efficient and reliable. In this study, the unit root test will follow the

method of Ng and Perron (2001). The “global L breaks versus none” test is a multiple

structural breaks test used by Bai and Perron (2003), which is able to spot any

structural breaks in the testing data. It consists of building an underlying model, of

which the monthly mean return trading volume is the dependent variable, whereas on

the right hand side, a constant and a trending are explaining variables.

+ = , + . + /"

where:

M: monthly mean trading volume of VN – Index

c: the constant

t: the trending variable

/": the error term

The above model will be estimated by OLS regression. Subsequently, statistical

description of results from the estimation will provide signs of structural breaks.

3. OLS regression and TARCH model

a. OLS regression OLS regression with dummy variables used to be the most common method, which is

used to test for calendar effects. In this study, with monthly returns of VN-index

being available, following the procedure that had been used by Haugen and Jorion

(1996), the regression for testing January effect could be set as below:

0" = 1 + 2 ∗ 4" + /"

where:

0": monthly rate of return in month t (. = 1, 2, 3, … 12)

P a g e 26|52

4": dummy variable, which takes the value of 1 if t equals to 1 or it is in January,

otherwise, 4" = 0

/": the residual or unexplained component of the return in month t (here, assume that

residuals are normally distributed)

The coefficient 2 of the variable J measures the difference between the average return

of VN-Index in January with average returns in other months of the year. In the case

that 2 is statically significant, return in January are significantly higher than that of

other months. In other words, supporting evidence for January effect is found.

The advantage of OLS regression is its simplicity and ease in building up models and

interpreting results. One of assumptions for an efficient OLS estimation is that

residuals have constant variance. However, according to a number of studies focus on

financial market, residuals of financial time series data commonly have time-varying

and clustered variances. This phenomenon, so called heteroskedasticity, accompanies

with clustered returns, which also go through periods of high and low variances. French, Schwert, and Stambaugh (1987) find conditional heteroskedasticity in daily

returns of S&P500 Index. Researching into the same market, Engle and Mustafa

(1992) report the similar characteristic in returns of individual stocks, while Connolly

(1989) discards constant variance model. Ignoring this issue could result in inefficient

or even fail estimations. Therefore, OLS regression could be considered as inefficient

in analysing financial data.

b. TARCH model

In order to deal with the issue above, Engel (1982) introduced autoregressive

conditional heteroskedasticity (ARCH) model, where the variance is ‘conditioned’ on

prior error terms. Therefore, this model allows the variance varies over the time.

Bollerslev (1986) had specified Engel’s initial model and introduced generalized

ARCH (GARCH), which is considered as particularly suited for analysing and

modelling financial time series data. The conditional variance of this model could be

written as below:

;"< = 2= + 2)/"(><

?

>@)

+ 2<;"(><

A

B@)

where:

P a g e 27|52

;"<: variance of residuals at time t

2=: the mean

/"(>< : lagged squared residuals, which indicate the news about volatility from previous

period at time t-i (the ARCH term)

q: the order of ARCH terms

;"(>< : forecast variances of prior periods (the GARCH term)

p: the order of GARCH terms

Typically, the standard GARCH(1,1) model is sufficient for this type of data. In this

case, conditional variance could be written as:

;"< = 2= + 2)/"()< + 2<;"()<

GARCH model imposes a symmetric conditional variance structure, where positive

and negative news have the same level of impact on volatilities of the market.

However, researches report higher volatilities follow downward fluctuations in the

financial market, while upward movements of the same level lead to lower

volatilities. Therefore, GARCH model may not be appropriate for analysing and

modeling movements of stock returns. In order to deal with this issue, Rabemananjara

and Zakoian (1993) and Zakoian (1994) propose Threshold ARCH (TARCH) model,

which can model the asymmetric structure of variances.

TARCH(1, 1) model could be formed as below:

!" = C + /"

;"< = D + 1/"()< + E/"()< F"() + 2;"()<

where !" is the return at the time t, and expressed as a random walk process C plus

residual or error term at the time t. The error term has zero mean and a variance of ;"<

that is modeled by the mean volatilities D, the news about volatilities from the prior

period /"()< (the ARCH term), the asymmetric item /"()< F"() and forecast variance of

previous period.

Within the asymmetric component /"()< F"(), the dummy variable F"() takes the value

of 1 when /"() < 0, otherwise, it takes the value of 0. Positive error terms refer to

‘good news’, while negative error terms represent ‘bad news’. In this specification,

P a g e 28|52

the impact of ‘good news’ is 1, whereas the affect of ‘bad news’ is 1 + E. Hence, E >

0 refers to asymmetric impact of the news.

To test for January effect in VN-Index, this research will employ the mean equation

that had been used by Haugen and Jorion (1996) for testing this effect in NYSE for

the period of 1926-1993, along with the asymmetric variances structure model

proposed by Zakoian (1994) as below:

!" = 1 + E ∗ 4" + /"

;"< = 2= + 2)/"()< + 2</"()< F"() + 2I;"()<

In the mean equation, the dummy variable 4" equals to zero if it is January, otherwise,

it takes the value of 1. Under the null hypothesis: there is no difference between

return in January and that of other months or there is no January effect, E = 0

statistically.

P a g e 29|52

CHAPTER 4: DATA ANALYSIS AND DISCUSSION

I. Data description

In order to statistically describe characteristics of monthly VN – Index returns, a

number of descriptive statistic value are calculated and presented in Figure 1 below.

Figure1: The distribution of monthly mean returns of VN - Index

As can be seen, the average monthly return of VN – Index in the entire period from

July 2000 to April 2015 is 0.00967 or 0.967%. However, the median is 0.000462,

along with the negative skewness show that among the series of monthly mean

returns, a major of observations take values of below the mean. Besides, monthly

mean returns range from the minimum of -0.420634 to the maximum of 0.325824

with the standard deviation taking the value of 0.107623, which signifies high

volatilities. In other words, VN – Index could be considered as carrying high risk.

P a g e 30|52

Graph1:Monthly mean returns of VN - Index

It is noteworthy to point out that the series exhibits a kurtosis of 4.492029, which far

exceeds the excess kurtosis that takes the value of 3. This means monthly mean

returns follow a distribution that features leptokurtosis. Researchers have suggested

that leptokurtosis is rooted in a pattern of volatilities in the market. To put it simply, it

is where periods of relative stability precede periods of high volatilities.

Additionally, from Figure 1, the test statistic of Jacque – Bera test is 17.33737, and

the probability value is 0.000172 that is less than any usual significance level (such as

0.10, 0.05 or 0.01). This means the null hypothesis of the test is rejected. Under the

null, the data is normally distributed. Therefore, the series of monthly mean returns of

VN – Index do not follow a normal distribution.

II. Results of defining the financial crisis and significant

changes in trading volume 1. The financial crisis

Historical prices of VN-index are shown in the following graph.

P a g e 31|52

Graph2:Historical prices of VN-Index

Applying BB turning-point detection method, bull and bear phases in Vietnam stock

market are identified as below:

Peak to Trough (Bear phase) Trough to Peak (Bull phase)

Dates Duration

(months) Dates

Duration

(months)

2/7/2001 – 31/10/2003 28 3/11/2003 – 31/3/2004 5

1/4/2004 – 30/11/2004 8 1/12/2004 – 28/4/2006 17

1/11/2006 – 28/2/2007 (*) 4

1/3/2007 – 27/2/2009 24 2/3/2009 – 30/10/2009 8

2/11/2009 – 30/12/2011 26

2/5/2012 – 30/11/2012 7 3/12/2012 – 31/5/2013 6

3/9/2013 – 29/8/2014 12

Table1:Bull and Bear phases in Vietnamese Stock Market 2000-2015

(*) The price of the index in 30/11/2006 rose 21.31 percent compared with the

previous month, which is more than the threshold level of 20%. Hence, the minimum

5-month length requirement is ignored.

0

200

400

600

800

1000

1200

P a g e 32|52

A bull market starts at the beginning of the month that follows the trough, while a

bear market starts at the beginning of the month that follows the peak. Therefore,

there are 5 bear phases and 6 bull phases identified.

As can be seen, the most severe downward trend or bear phase is the period of March

2007 – February 2009, which corresponds to the global financial crisis at that time.

Therefore, the beginning of this financial crisis in Vietnamese stock market is

01/03/2007 and its end is 27/02/2009. As a result, the entire period of 2000-2005 will

be then divided in to three sub-periods:

• Pre-crisis period: 28/07/2000 – 28/02/2007

• Crisis period: 01/03/2007 – 27/02/2009

• Post-crisis period: 01/03/2009 – 27/04/2015

2. Significant changes in trading volume

a. Unit root test

As aforementioned, the test for unit root in this project follows the method that is

applied by Ng and Perron (2001). Monthly mean trading volumes of VN-Index are

examined for unit root over the entire period from July 2000 to April 2015, which

contains 178 observations. In addition, the unit root test in my project will allow a

constant and a trend in the test equation. Results from the test are presented in the

following figure.

MZa MZt MSB MPT

Ng-Perron test statistics -25.6232 -3.57105 0.13937 3.60651

Asymptotic critical values*: 1% -23.8000 -3.42000 0.14300 4.03000

5% -17.3000 -2.91000 0.16800 5.48000

10% -14.2000 -2.62000 0.18500 6.67000

Figure2:Results from the Ng – Perron unit root test on monthly mean trading volumes

From above figure, the absolute value of the test statistic MZa is 25.6232, which is

greater than the absolute asymptotic critical value at all of 1%, 5% and 10%

significance level (23.8000, 17.3000 and 14.2000 respectively). Therefore, the null

hypothesis of the test could be rejected. In other words, the series of monthly mean

trading volumes of VN – Index is stationary during the period from July 2000 to April

2015.

P a g e 33|52

Technically, the above results imply that monthly mean returns of VN – Index hold

important characteristics: constant mean, constant variance and a constant

autocorrelation structure. Results also signify that there is no specific trend in the

trading volume series over the entire period. As suggested by a number of statistical

literatures, when the data is stationary, statistical methods, tests or models could be

efficiently employed without the need of any further modification.

b. Structural breaks in monthly mean trading volume of VN- Index

The graph below plots monthly mean trading volumes of VN – Index over the entire

testing period.

Graph3:Monthly mean trading volumes of VN – Index from July 2000 to April 2015

The “global L break versus none” test in this project is set with the monthly mean

return of VN – Index as a dependent variable, while the constant is the explaining

variable. Results from the test are shown as below. Variable Coefficient Std. Error t-Statistic Prob. C 26206524 15708610 1.668290 0.0970 Figure3:Estimation output of “global L breaks versus none” test’s underlying model

As the model above has only the constant as an explaining variable, this variable will

be used in the structural breaks analysis. In addition, in order to weaken the impact of

outliner bias in the testing data, 15% is set on the trimming percentage. As can be

seen from Graph 2, there are potentially three significant breaks in monthly mean

0.00

20,000,000.00

40,000,000.00

60,000,000.00

80,000,000.00

100,000,000.00

120,000,000.00

140,000,000.00

160,000,000.00

180,000,000.00

P a g e 34|52

trading volumes. Therefore, the test is also set with maximum of 3 breaks. A 5%

significance level is also set as default in this project. Results from the test are

presented in the following figure.

Scaled Weighted Critical

Breaks F-statistic F-statistic F-statistic Value

1 * 15.22213 15.22213 15.22213 8.58

2 * 22.18067 22.18067 26.35875 7.22

3 * 15.27289 15.27289 21.98681 5.96

* Significant at the 0.05 level.

** Bai-Perron (Econometric Journal, 2003) critical values.

Estimated break dates:

1: 2009M04

2: 2009M04, 2013M01

3: 2006M11, 2009M04, 2013M01

Figure4:Results from structural breaks analysis

As can be seen from Figure 2, in all three cases, critical values at 5% significance

level are all less than corresponding F-statistics, which means that we can reject all

the null hypotheses. In other words, it could be all reasonable to report that there is

only one, there are two or there are three structural breaks in the series of monthly

mean returns of VN – Index. However, the F-statistic of the test in the case of two

breaks is 22.18067, which is higher than those in two other cases. This suggests that

two breaks in the series is probably the most significant result.

In an attempt to examine the most significant breaks in the series, the following graph

which is extracted from results of the test, shows the series of monthly trading

volumes and mean trading volumes for each period between two breaks.

P a g e 35|52

Graph4:Changes in trading volume of VN – Index

From the graph, it is clear to realise that there are two significant jumps in the average

trading volume. The average trading volume during the period from January 2013 to

April 2015 is noticeably higher than that during the period from April 2009 to

December 2012, which is, however, still much higher than the average trading volume

of the period from July 2000 to March 2009. This result, along with results from

Figure 2, suggest that there are two significant structural breaks in monthly mean

trading volumes of VN – Index over the entire period, which are April 2009 and

January 2013.

Concerning the first break which is April 2009, a reasonable explanation for it could

be the recovery of Vietnamese stock market after the financial crisis. From the

previous section, financial crisis in Vietnam is defined as beginning in March 2007

and ending in February 2009. When the crisis ends and the stock market shows signs

of the recovery with increasing stock prices, it is expected that investors will re-enter

the market leading to a surge in the total trading volume. Therefore results from above

tests well captured this break.

Regarding the second break, the huge growth in trading volumes since January 2013

could be due to effects of a number of decrees, circulars and solutions issued by

Vietnamese government, the Ministry of Finance and the State Securities Commission

of Vietnam. These moves are taken in an attempt to raise the demand and improve the

P a g e 36|52

liquidity of the stock market after a long period of gradually downward trend in the

market since the end of the year 2009. In 2012, the new decree 58/2012/NĐ-CP was

issued, allowing foreign investors to have the right to hold 100 percent of domestic

securities companies’ shares. Besides, the Ministry of Finance, in that year, issued the

circular 213/2012/TT-BTC that focus on simplifying and reducing administrative

procedures required for foreign organisations investing in Vietnamese stock market.

Additionally, a number of actions had been taken in 2012 and the beginning of 2013

such as lengthening daily trading time, fastening the process of payment from T+4 to

T+3 and increasing the price amplitude to 7 percent in HoSE and 10 percent in HNX.

Thanks to these actions, the year 2013 is considered as one of the most successful

years of Vietnamese stock market with the upward trend in both stocks prices and

trading volumes. This could justify the soar in the monthly trading volumes since

January 2013 where the second structural break is discovered.

In brief, according to results from structural break analysis, the entire period could be

divided into three sub-periods by two break points:

• The first period: 28/07/2000 – 30/03/2009

• The second period: 01/04/2009 – 28/12/2012

• The third period: 01/01/2013 – 27/04/2015

III. Results from running OLS regression and TARCH model 1. The whole-period test

In this part, OLS regression and TARCH model will be employed to test for the

present of January effect in Vietnamese stock market during the whole long period of

2000-2015. This period contains 177 observations after adjustments.

The results of running those models on the whole sample are presented in the figure

below.

Panel A: OLS model

Variable Coefficient Std. Error t-Statistic Prob.

C 0.005345 0.008405 0.635958 0.5256

JAN 0.051036 0.028872 1.767650 0.0789 Panel B: TARCH model

Variable Coefficient Std. Error z-Statistic Prob.

P a g e 37|52

C 0.002311 0.006776 0.341073 0.7330

JAN 0.056400 0.016516 3.414860 0.0006 Note: JAN represents the dummy variable, which is equal to 1 if it is January; otherwise, it

takes the value of 0.

Figure5:Results from OLS and TARCH on the whole period 2000-2015

The results from OLS regression imply that, at 5% significance level, there is no

January effect in Vietnamese stock market throughout the whole period from 2000 to

2015. However, if we use 10% significance level as the criterion, results then still

provide support for the presence of January effect in the market. At 10% significance

level, the mean return of January during this period is 5.1% higher than those of the

remaining months of each year. In comparison with findings of Friday and Hoang

(2015), they find 4.98% return in January was the highest monthly mean return for the

entire period of 2000-2010. Although their method is simple when they only calculate

monthly mean returns in order to know whether the mean return in January is higher

than those of other months, their results can still provide support for January effect. In

short, at 5% significance level, results from OLS regression reject the presence of

January effect in Vietnamese stock market; but at 10% significance level, they are in

line with findings of Friday and Hoang (2015) and provide supporting evidence for

January effect in this market.

On the other hand, results from TARCH model suggest that the mean return in

January is 5.64% higher than those of other months of the year. The coefficient of the

variable JAN is statistically significant even at 1% significance level as its probability

is 0.006 or 0.6%, which is less than 1%. Similar to results from OLS regression at

10% significance level, this strongly supports the presence of January effect in

Vietnamese stock market during the entire period.

2. The behaviour of the January effect before, during and after the financial

crisis

As aforementioned in previous sections, the entire period will be divided into three

sub-periods: July 2000 to February 2007, March 2007 to February 2009 and March

2009 to April 2015, which represent three periods of before, during and after the

financial crisis. The first period contains 79 observations, the second period includes

24 observations and the last one involves 74 observations after adjustments.

P a g e 38|52

Results of applying OLS regression and TARCH model on these periods are

presented in the following tables.

a. Results from OLS regression

Panel A: Pre-crisis

Variable Coefficient Std. Error t-Statistic Prob.

C 0.025854 0.014151 1.827021 0.0716

JAN 0.053396 0.047538 1.123226 0.2648

Panel B: Crisis

Variable Coefficient Std. Error t-Statistic Prob.

C -0.063576 0.028395 -2.238998 0.0356

JAN -0.003327 0.098363 -0.033820 0.9733

Panel C: Post-crisis

Variable Coefficient Std. Error t-Statistic Prob.

C 0.005929 0.008239 0.719631 0.4741

JAN 0.064869 0.028933 2.242023 0.0280

Note: JAN represents the dummy variable, which is equal to 1 if it is January; otherwise, it

takes the value of 0. Figure6:Results from OLS regression before, during and after the financial crisis

As can be seen, in the pre-crisis period, results from OLS regression show

insignificant excess return in January or the mean return of January is not

significantly higher than those of other months of the year. In other words, it implies

the absence of January effect in Vietnamese stock market before the financial crisis.

Similarly, results given in panel B also suggest that January effect did not exist during

the financial crisis. Moreover, the mean return of January tended to be just equal to or

even lower than those of the rest of the year. In this case, the probability of variable

JAN’s coefficient during the crisis is much higher than those during the pre-crisis and

post – crisis period, which means during the crisis, the coefficient of the dummy

variable JAN is closer to zero than those in two other periods. In other words, the

abnormal return in January tends to be diminished during the crisis in comparison

with non – crisis time.

P a g e 39|52

On the opposite side, post-crisis period exhibits a significant January effect at 5%

significance level. During this period, the mean return of January is 6.49% higher

than those of remaining months of the year.

b. Results from TARCH model

Panel A: Pre-crisis

Variable Coefficient Std. Error z-Statistic Prob.

C 0.011545 0.011051 1.044660 0.2962

JAN -0.042154 0.024061 -1.751978 0.0798

Panel B: Crisis

Variable Coefficient Std. Error z-Statistic Prob.

C -0.080556 0.025360 -3.176489 0.0015

JAN 0.021406 0.571616 0.037448 0.9701

Panel C: Post-crisis

Variable Coefficient Std. Error z-Statistic Prob.

C -0.002266 0.006802 -0.333194 0.7390

JAN 0.078018 0.030189 2.584357 0.0098

Note: JAN represents the dummy variable, which is equal to 1 if it is January; otherwise, it

takes the value of 0.

Figure7:Results from TARCH model before, during and after the financial crisis

Results given in Panel A show that there is no January effect during the pre-crisis

period at 5% significance level and this is similar to the finding from OLS model.

But, if we use 10% significance level, January effect is present and the mean return of

January is now interestingly 4.22% lower than those of the rest of the year, which is

out of line with what is found using OLS model. However, in either case, the

probability values of JAN’s coefficient from TARCH model are much lower than that

from OLS regression, which demonstrates that OLS rejects the presence of January

effect at a much stronger extent than TARCH model. This could arise from the fact

that the variances in TARCH model are conditioned and allowed to vary over the

time, which makes this model more efficient than the basis OLS regression in

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modelling financial time series data. Therefore, TARCH model could have superior

ability to capture financial events, here in this research, January effect.

Results in panel B and C imply that January effect did not exist during the crisis

period but is significantly present during the post-crisis period at 5% and even at 1%

significance level, with the mean return of January being 7.80% higher than those of

the remaining months. These results are similar to those from OLS model.

Moreover, as can be seen, the probability of the variable JAN’s coefficient during the

crisis is also much higher than that of the pre-crisis and post – crisis period. Thus,

results from either TARCH model or OLS regression suggest that the abnormal return

in January tends to be diminished during the crisis, compared with the non – crisis

periods. This is in line with what Dash, Sabharwal and Dutta (2011) report. In their

study into seasonality (particularly, the month-of-the-year effect) and market crashes

in Indian stock markets, they state that seasonal effects are reduced by the incident of

market crashes. Differences in the behaviour of calendar effects before, during and

after a financial crisis are also documented in the study of Holden, Thompson and

Ruangrit (2005). They point out that the behaviour of stock returns in Thai stock

markets differs from before, during and after the ‘Asian crisis’. Therefore, results

from TARCH model and OLS regression above could be considered as reasonable.

In brief, at 5% significance level, both OLS regression and TARCH model report

similar findings that there is no January effect during the pre-crisis and the crisis

period, but this effect does exist during the post-crisis period. The only different and

interesting point is if 10% significance level is employed, the pre-crisis period shows

a negative January effect, when the mean return of January is lower than those of the

rest of the year.

3. The behaviour of the January effect and significant increases in trading

volume

As aforementioned in previous sections, the entire period will be divided into three

sub-periods: from 28/07/2000 to 30/03/2009, from 01/04/2009 to 28/12/2012 and

from 01/04/2009 to 28/12/2012, which represent three different level of average

trading volume. The first period contains 104 observations, the second period includes

45 observations and the last one involves 29 observations after adjustments.

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Results of applying OLS regression and TARCH model on these periods are

presented in the following figures.

a. Results from OLS regression

Panel A: 28/07/2000 – 30/03/2009

Variable Coefficient Std. Error t-Statistic Prob.

C 0.006270 0.013185 0.475566 0.6354

JAN 0.040501 0.044821 0.903619 0.3683

Panel B: 01/04/2009 – 28/12/2012

Variable Coefficient Std. Error t-Statistic Prob.

C 0.006275 0.012006 0.522645 0.6039

JAN 0.035219 0.046500 0.757403 0.4529

Panel C: 01/01/2013 – 27/04/2015

Variable Coefficient Std. Error t-Statistic Prob.

C 0.000268 0.008762 0.030584 0.9758

JAN 0.099832 0.026767 3.729697 0.0009

Note: JAN represents the dummy variable, which is equal to 1 if it is January; otherwise, it

takes the value of 0.

Figure8:Results from the OLS regression with changes in trading volume

As can be seen, during the first two periods, mean returns in January are not

significantly higher than those of the remaining months of the year as the probability

value of the dummy variable JAN’s coefficient are greater than the default 5%

significance level of this study, and even greater than 10% significance level. In other

words, January effect is not present during these two periods. Additionally, the

probability value of the first period is lower than that of the second one, which means,

in the first period, the null hypothesis is accepted at a higher extent. Along with that,

the variable JAN’s coefficient of the first period is less than that of the second one;

thus abnormal return in January tend to be lowered when the trading volume

increases.

P a g e 42|52

However, in the period from January 2013 to April 2015, it is clear that the null

hypothesis is rejected as the probability value is 0.0009 which is much lower than the

default significance level of 5%. The null is even rejected at 1% significance level. As

can be seen in Panel C, the mean return of January is 9.98% higher than that of the

rest of the year. Thus, there is noticeable January effect during this period. This shows

an opposite trend to what is found by comparing the first two periods. Here, the

January effect comes into being when the trading volume increases, whilst according

Panel A and B, it tend to be weakened.

Briefly, OLS regression suggests that there is no January effect in the first two

periods, but the effect is present in the last one. Besides, it shows no specific

relationship between increases in the trading volume and the behaviour of January

effect.

b. Results from TARCH model

Panel A: 28/07/2000 – 30/03/2009

Variable Coefficient Std. Error z-Statistic Prob.

C 0.007261 0.013816 0.525571 0.5992

JAN 0.024573 0.027434 0.895721 0.3704

Panel B: 01/04/2009 – 28/12/2012

Variable Coefficient Std. Error z-Statistic Prob.

C -0.004980 0.008239 -0.604401 0.5456

JAN 0.047699 0.027225 1.752017 0.0798

Panel C: 01/01/2013 – 27/04/2015

Variable Coefficient Std. Error z-Statistic Prob.

C 0.009184 0.009551 0.961565 0.3363

JAN 0.091507 0.028487 3.212268 0.0013

Note: JAN represents the dummy variable, which is equal to 1 if it is January; otherwise, it

takes the value of 0.

Figure9:Results from the TARCH model with changes in trading volume

As can be seen in Panel A, it is clear that the null hypothesis cannot be rejected in the

first period as the probability value of JAN is 0.3704 which is greater than 5%

P a g e 43|52

significance level. Therefore, January effect is absence during this period. This result

is similar to the result from OLS regression.

In the second period, the null hypothesis still cannot be rejected at 5% significance

level as the probability value is 0.0798 which is still higher than 5% or 0.05.

However, this probability value is much lower than that of the first period implying

that in the second period, the null hypothesis is accepted at much lower extent.

Beside, the variable JAN’s coefficient in this period is 0.047699, which is greater than

that of the previous period. Therefore, results from TARCH model suggest that

abnormal returns in January tend to be heightened when the trading volume increases.

This contradicts the output of OLS regression. However, as mentioned in previous

sections of the study, TARCH model is considered as much more efficient than the

basic OLS model in modelling and analysing financial time series data. Therefore,

outputs of TARCH model should be used to make a conclusion in this part.

Concerning the last period, similar to OLS regression, the result from TARCH model

cannot reject the null hypothesis, but confirms the presence of January effect, as the

probability value is smaller than the significance level of 5% or 0.05. From Panel C,

variable JAN’s coefficient is 0.091507, which implies that January’s mean return in

this period is 9.15% higher than those of the rest of the year. This is a great abnormal

return corresponding to a strength January effect.

In brief, according to outputs of TARCH model, there is no January effect in the first

two periods, but a pronounced one is present in the last period. Moreover, January

effect shows a tendency of getting stronger when the trading volume increases.

IV. Discussion

Applying BB turning-point detection method, there are a number of bull and bear

phases over the entire testing period. However, the most severe bear phase is from

March 2007 to February 2009, which corresponds to the global financial crisis at that

time. This result is in line with what is expected when I set the second objective for

this study, which is properly define the financial crisis in Vietnamese stock market in

order to examine the behaviour of January effect before, during and after this crisis.

Concerning the structural breaks test, its outputs are not similar to findings from the

research carried out by Friday and Hoang (2015). From the test, there are two breaks

where the monthly mean trading volume of VN – Index surges. They are April 2009

P a g e 44|52

and January 2013, whereas according to Friday and Hoang (2015), the first break is

January 2006. Although the second break from the test is as significant as the first

one, it cannot be compared as Friday and Hoang (2015) only get the data until the

year 2010. However, as mentioned in previous sections, I believed that the structural

breaks test is more robust than the method used by them.

Moving on to results from testing for January effect on the whole period, OLS

regression reject the presence of this effect at 5% significance level. But at 10%

significance level, it cannot be rejected. At the same time, TARCH model highly

suggests that January effect does exist during this period. Friday and Hoang (2015)

report evidence that supports the presence of January effect in the period from 2000 to

2010. Therefore, these results suggest that TARCH model could capture this effect

better than OLS regression.

Regarding impacts of the financial crisis on the behaviour of January effect, at the

default 5% significance level of this study, both OLS and TARCH model lead to the

same findings. Both models report the absence of January effect before and during the

financial crisis, but support its existence after the crisis. Results from both models

also show that the abnormal return in January during the crisis tends to be lower than

that of the pre – crisis and post – crisis period. This is in line with findings of Dash,

Sabharwal and Dutta (2011) when they suggest that seasonal effects tend to be

weakened during market crashes.

Finally, OLS regression and TARCH model lead to conflicting findings in examining

the relationship between January effect and significant increases in trading volume.

However, as TARCH model is considered as more efficient, my discussion is mainly

based on its outputs. These outputs signify that January effect has the tendency of

getting stronger when trading volume increases. Friday and Hoang (2015) do not

report any specific trend of January effect like that as their method is simply compare

monthly returns in each period. Therefore, they find evidence that supports January

effect but are not able to detect the trend.

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CHAPTER 5: CONCLUSIONS

This study investigates the presence and behaviour of January effect in Vietnamese

stock market, which is classified as one of the emerging markets in the world.

Employing OLS regression and TARCH model, three objectives set from the

beginning are testing for the presence of January effect in the market during the entire

period, examining the relationship of January effect with the 2007 – 2008 financial

crisis as well as with significant changes in trading volume.

Regarding the first objectives, results from TARCH model highly support the

presence of January effect in Vietnamese stock market over the entire period. OLS

model cannot capture this effect at the default significance level of this study, which

is 5%, but at 10% significance level, it can produce similar results as TARCH model.

Based on these results, I would suggest that TARCH model is more powerful in

capturing January effect.

Concerning the second objectives, the 2007 – 2008 financial crisis is defined as

beginning in March 2007 and ending in February 2009 particularly for Vietnamese

stock market. This crisis corresponds to the most severe bear phase in monthly VN –

Index, which is examined by BB turning-point detection method. In this case, both

OLS regression and TARCH model deliver the same results with the presence of

January effect being rejected before and during the financial crisis. But in the last

period from March 2009 to April 2015, strong January effect does exist. Results from

both models also suggest that abnormal returns in January tend to be lowered in the

period of financial crisis, compared with the pre – crisis and post – crisis period.

The third objectives experiences conflicting results from OLS regression and TARCH

model. OLS regression does not report any specific trend in the behaviour of the

January, whereas TARCH model supports the tendency of which January effect gets

stronger when the trading volume increases. However, as researchers have suggested

that TARCH model is more efficient in modelling financial time series data, there is

probably that tendency in the behaviour of January effect.

Basically, results from this study could be useful for those who would like to research

further into seasonal effects and the efficiency of Vietnamese stock market, which is

still not studied as much as other developed markets. Besides, investors who are

trading in this market or have intention to invest in this market could also benefit from

P a g e 46|52

findings of this study. At the present, January effect does exist. Therefore, it is

possible for investors to plant proper trading policies in order to exploit abnormal

returns from this market inefficiency.

On the other hand, this study still has some limitations. First of all, because of the lack

of supporting literatures, I use structural breaks in monthly mean trading volumes as a

basement to split up the entire period, and then run testing models on sub – periods to

examine the relationship between trading volume and the January effect. This might

not be the most robust method. Further researches could develop more efficient

procedures to investigate this issue. For example, new variables could be generated

and added in to testing models, such as trading volume, or liquidity and other ratios

which are related to trading volume. This will allow researchers to model the impact

of trading volume on January effect.

Secondly, this study only focuses on January effect, which could be seen as a part of

the month – of – the year effect since consistent abnormal returns in other months of

the year are found in other markets. It could be the case that in specific periods, due to

changes in the economy or government’s policies, January effect does not exist, but

another month has excess return. However, due to difficulties in getting access to

needed data, this study is not able to investigate those issues. Therefore, if further

researches can access those sets of data, they will significant contribute to the

literature of this researching area.

Finally, because Vietnamese stock market has just been operating for 15 years, the

testing sample in this study cannot be as sufficient as those from developed markets.

This results in the small number of observations in each sub – period, which can

ultimately reduce the efficiency of tests and models employed. Besides, as mentioned,

VNAllshare is a new index that was introduced in 2012 and considered as more

efficient than VN – Index. Thus, in the future, researchers will be able to conducts

studies with larger sample of a better index and deliver more robust results.

P a g e 47|52

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