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GB30006 INDUSTRIAL TRAINING List of Abbreviations Abbreviat ions Meaning ASE Amman Stock Exchange CEE Central and Eastern Europe CIS Commonwealth of Independent States KLSE Kuala Lumpur Stock Exchange KLSEB Kuala Lumpur Stock Exchange Berhad MSE Malta Stock Exchange SCCS Securities Clearing and Computer Services Pte Ltd SES Singapore Stock Exchange List of Figures Figu re Title Pages 1 Monthly Return Based on Regression 18 List of Tables Tabl e Title Pages 1 Descriptive Statistics for daily returns stratified monthly for Kuala Lumpur Stock Exchange (KLSE) 11 2 Descriptive Statistics for daily 12 1

Monthly Effect in Malaysia & Singapore stock market

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Page 1: Monthly Effect in Malaysia & Singapore stock market

GB30006 INDUSTRIAL TRAINING

List of Abbreviations

Abbreviations Meaning

ASE Amman Stock Exchange

CEE Central and Eastern Europe

CIS Commonwealth of Independent States

KLSE Kuala Lumpur Stock Exchange

KLSEB Kuala Lumpur Stock Exchange Berhad

MSE Malta Stock Exchange

SCCS Securities Clearing and Computer Services Pte Ltd

SES Singapore Stock Exchange

List of Figures

Figure Title Pages

1 Monthly Return Based on Regression 18

List of Tables

Table Title Pages

1 Descriptive Statistics for daily returns stratified monthly for

Kuala Lumpur Stock Exchange (KLSE)

11

2 Descriptive Statistics for daily returns stratified monthly for

Singapore Exchange (SGX)

12

3 Regression Results for Monthly effect 13

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Table of ContentsList of Abbreviations....................................................................................................................................... 1

List of Figures................................................................................................................................................. 1

List of Tables.................................................................................................................................................. 1

Abstract.......................................................................................................................................................... 3

Introduction..................................................................................................................................................... 3

Literature Review............................................................................................................................................6

Data and Methodology..................................................................................................................................10

Empirical Results of the Analysis..................................................................................................................10

Descriptive Statistics.................................................................................................................................10

Regression............................................................................................................................................... 13

Conclusion.................................................................................................................................................... 14

References................................................................................................................................................... 14

Appendix....................................................................................................................................................... 18

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Abstract

The objectives of this paper it to investigate the existence of monthly effect in Malaysia and Singapore

stock markets. We use daily returns of Kula Lumpur Stock Exchange (KLSE) for a period of 3 rd December

1993 to 31st December 2013 and from 11th October 2001 to 31st December 2013 for (SGX). Unlike previous

research reported evidence supporting the calendar effect such as monthly effect in these two markets, the

empirical results show that there do not exists monthly effect in KLSE and SGX. These results show that

this study fails to detect any other persistent monthly effect.

Keywords: Monthly effect, Kuala Lumpur Stock Exchange, Singapore Exchange.

Introduction

There are several studies have investigated on the calendar or seasonality anomalies in stock returns all

over the world. Calendar anomalies are the pattern of stock returns in the market which are related to

specific calendar events. Researchers have examined on several calendar anomalies in the stock markets

such as Day-of-the week effect, Turn-of-the month effect, January effect, and holiday effect. One of the

most important and interesting calendar anomalies is the monthly effect. The monthly effect is a

phenomenon where the mean stock market returns of a specific month is different from the others month.

The monthly effect is also known as the January effect. This effect explains there is a high stock market

returns in January than in any other months of the year (Gultekin and Gultekin, 1983; Keim, 1983; Floros,

2008).

Most researchers found evidence of a January effect for the stock returns of the markets. The

results of their study show that it is better to invest in January compared to the other months of the year.

The most common reason is the year-end tax-loss selling phenomena. This phenomenon occurs when

most of the people start to think about their tax ability when come to the end of the year. The stocks are

expected to have low yield or losers towards the end of year are sold off in order to claim a capital loss for

tax purposes. Then the investors buy them back once the tax calendar rolls over a new year in January and

cause the stock prices to rise (Branch, 1977; Gao and Kling, 2005). For a recent study, Nawaz and Mirza

also explained that January effect is mainly affect by the size, window dressing and tax-loss selling

phenomena. They also suggest that January anomaly will lose its effect over time because as more and

more investors aware of this abnormal tax-selling phenomena and investors will utilize this opportunity.

Besides that, Ritter (1988) suggests that small size stocks also tend to generate higher returns in January

compared to large stocks. Institutional investors also window-dress their year-end returns by selling losers

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and holding back winners. On the other hand, Chen and Singal (2004) argue that window dressing does

not cause the January effect because they stated that a similar pattern should exist during the other

quarters too.

However, there is several literatures show that January is not always having the highest return

among the other months. Bhabra, Dhilon and Ramirez (1999), and Chia and Liew (2012) finds the

existence of November effect in Nikkei 225 index of Tokyo Stock Exchange. While Gao and Kling (2005)

find that Chinese stock markets such as Shanghai and Shenzhen stock exchanges have the highest

average returns in March and April. An April effect in Ghana Stock Exchange is found by Alagidedel and

Panagiotidis (2006). The existence of the monthly effect is an important implication for the markets and

investors. Investors might be not able to take advantage of relatively regularly patterns in the market by

designing trading strategies if monthly effect existed in the stock returns.

Stock market is an institution where corporations able to raise money. Stock market is a place

where people buy and sells pieces of paper called stock. Corporations issue shares of stock to raise money

in order to expand their corporation such as hire more employees, build more factories or offices and

upgrade their equipment. Stock market plays an important role in the economic strength and development

of a country (Kok and Goh, 1995).

Bursa Malaysia is the only stock market in Malaysia. It plays a significant role in assisting the

development of the Malaysian capital market and enhancing global competitiveness. Bursa Malaysia is

committed to maintaining an efficient, secure and active trading market for local and global investors. The

importance of Bursa Malaysia has been acknowledged by the development of the securities industry in

Malaysia (Kok and Goh, 1995). Bursa Malaysia Berhad is an exchange holiday company which listed on

the Main Board of Bursa Malaysia Securities. It operates a fully integrated exchange; offer a complete

range of exchange-related services, including trading, clearing, settlement and depository services. Bursa

Malaysia provides information services related to the Malaysian securities market too.

Today, Bursa Malaysia has over 1000 listed companies offering a wide range of investment

choices to the world. The companies are either listed on the Bursa Malaysia Securities Main Board or the

Second Main Board for larger capitalized companies while the Second Board which acts as a complements

of the Main Board enables smaller companies that have a strong growth potential to look for a listing on the

Exchange, the Second Board was established on 11 November 1988 (Bursa Malaysia, 2014).

The first formal securities business organization in Malaysia was the Singapore Stock brokers’

Association which was established on 23rd June 1930. In 1937, it was re-registered as the Malaysian

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Stockbrokers’ Association. The Malayan Stock Exchange was established in 1960 and the public trading of

shares started in Malaya. In 1961, the board system consists of trading rooms in Singapore and Kuala

Lumpur was linked by direct telephone lines.

The Stock Exchange of Malaysia was formed in 1964. Singapore was separated from Malaysia in

1965 and the Stock Exchange of Malaysia became known as the Stock Exchange of Malaysia and

Singapore. The currency interchangeability between Malaysia and Singapore was terminated in 1973, and

the Stock Exchange of Malaysia and Singapore was separated into Kuala Lumpur Stock Exchange Berhad

(KLSEB) and Singapore Stock Exchange (SES). The Kuala Lumpur Stock Exchange Berhad was

incorporated on 14th of December 1976 as a company limited by guarantee and it took over the operations

of the KLSEB during the same year. KLSE provide a central market place for buyers and sellers to transact

business in shares, bonds, and various other securities of Malaysian listed companies. On 1 January 1990,

all Singapore incorporated companies were delisted from the KLSE and vice-versa for Malaysian

companies listed on the SES.

KLSE were demutualized according to the Demutualization Act and converted into a public

company limited by shares on 5th of January 2004. KLSE were then known as Kuala Lumpur Stock

Exchange Berhad. Upon the conversion, KLSE were vested and the securities exchange businesses were

transferred to a new wholly-owned subsidiary, Bursa Securities. On 14 th of April 2004, Bursa Securities

became an exchange holding company and were renamed as Bursa Malaysia Berhad. Bursa Malaysia was

listed on the Main Board of Bursa Malaysia Securities Berhad on the 18 th March 2005 (Bursa Malaysia,

2014).

Singapore Exchange (SGX) as a holding company was formed in 1st December 1999. The share

capital of some former exchange companies such as Stock Exchange of Singapore (SES), Singapore

International Monetary Exchange (Simex) and Securities Clearing and Computer Services Pte Ltd (SCCS)

was cancelled. The new shares that issued in these companies were fully paid up by SGX. All assets that

previously owned by these three companies were transferred to SGX. The shareholders that previously

holding shares in SES, Simex and SCCS received newly issued SGX shares (Singapore Exchange, 2013).

Singapore Exchange (SGX) has become the second exchange in Asia-Pacific that listed via public

offer and a private placement on 23rd November 2000. The SGX stock is a component of benchmark

indices such as the MSCI Singapore Free Index and the Straits Time Index.

Singapore Exchange (SGX) is the Asia’s most internationalized exchange. It connects investors in

search of Asian growth to corporate issuers in search of global capital. SGX represents the premier access

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point for managing Asia capital and investment exposure with more than 40% of companies listed on SGX

originating from overseas. SGX also offers its clients the world’s biggest offshore market for Asian equity

index derivatives which centered on Asia’s three largest economies – China, India and Japan. In addition,

SGX also offer a fully integrated value chain of services ranging from trading and clearing to settlement and

depository services. SGX are a full-service exchange that operates Asia’s pioneering central clearing

house. It is also headquartered in Asia’s most globalized city and centered within the AAA strength and

stability of Singapore’s island nation. SGX is a peerless Asian counter party for the clearing of financial and

commodity products.

The rest of this paper is arranged as follows. Section 2 offers the literature review on monthly

effects in global stock market. Section 3 describes on the data and methodology used in this study and

Section 4 shows the empirical results of the analysis. The final part, Section 5 will be the conclusion.

Literature Review

The monthly effect on stock market returns have been well documented in several researches. Rozeff and

Kinney (1976) are the first who reported that there is a January effect in the USA market, and then Guletkin

and Guletkin (1983) find out there is a significant higher stock returns in January in most of the 17

developed countries. Keim (1983) also studied on the size effects in stock returns and he found that small

firms significantly have a higher return than large firms in January month. This effect attributed to the

finding of tax-loss selling and information hypothesis. Others than that, Reinganum (1983) and Roll (1983)

also confirm that January effect can be assigned to the first trading days during January. Choudhry (2001)

found significant January effect in the UK stock market. Lucey and Whelan (2004) which studied on the

Irish stock market for the period of 1934 to 2000 conclude that there is a presence of January effect.

Besides that, Anderson, Gerlach and DiTraglia (2007) affirmed on the January effect and found that returns

in January were higher compare with other months. January effect was found in Athens Stock Market with

high positive returns in that month by Giovanis (2008). Alagigege (2013) found positive and significant

returns in January for Egypt, Nigeria and Zimbabwe, while a higher returns in Kenya, Morocco and South

Africa, and there is no monthly effect in Tunisia. He also agreed that the liquidity constraints and risk factors

are the main explanation for January effect. Guler (2013) has studied existence of January effect in

Argentina, China, and Turkey daily returns but there is no evidence for Brazil and India market. Kuria and

Riro (2013) have studied on the three types of anomalies and the analysis shows the presence of the

seasonal effect such as day of the week effect, weekend effect and monthly effect in Nairobi Securities

Exchange (NSE) in Kenya stock markets. So, this paper proved that Kenya is still in seasonal anomalies in

spite of the increasing in the usage of information technology and developments on regulatory. Hanna

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Alrabadi and Ahmad (2012) found that there is a highly significant January effect exists in Amman Stock

Exchange which is useful to Jordanian investors to formulate their investment strategies accordingly.

Meanwhile, some researchers had rejected the January effect in their study. Boudreaux (1995) has

been studied on the seven countries’ stock market, which has not been study thoroughly by the others

researchers. He reported that there is a significance positive monthly effect on Australia and Canada

markets, but there is a negative monthly effect on Japan’s market. But there is no January effect found

although the result shows significant. So he concludes that the existence of monthly effect could not be

explained by the January effect. Pandey (2002) examined the existence of January effect in India stock

market returns and the capital market was not efficient enough. He also mentioned that tax-loss selling

hypothesis is related with the January effect. In Floros (2008) paper it stated that there is no January effect

in Greece stock market. Although there is a high return on other months than January but the estimated

coefficients are not significant. He argues that Greece has a small capital market which the tax-loss selling

at the end of the year did not lead to a lower returns in December and higher returns in January. Doran et

al. (2008) and Rezyanian et al. (2008) found no significant January effect in the Chinese stock market.

There is no strong evidence on the month of the year effect in Estonia stock market although there is high

daily return in December and January (Makela, 2008). Resvanian (2008) concluded that there no existence

of January effect in Chinese equity markets and there is low significant returns for the months of the year

correlation analysis results. Giovanis (2009) has investigate on fifty-five stock market indices from fifty-one

countries and the January effect is rejected as it is appeared only in seven stock markets, while December

effect is present in twelve markets with higher returns on that month. Silva (2010) also found no January

effect in her research on calendar anomalies in Portuguese stock market. In the paper of Patel (2012)

stated that there is no longer an existence of January effect for many developed and emerging markets.

Besides January effect, high return also found in other month in the stock markets. Choudhry

(1998) examines on the month of the year and January effect in the mean stock returns of Germany, the

US and the UK during pre- WWI period. The outcome shows there is an evidence of the month of the year

effect and January effect on the US and the UK stock market returns. While, there is only the month of the

year effect on German returns. Cao (2006) has studied on the calendar effect on A-Share Index return in

Chinese stock market. There is a monthly effect for high returns in March and January, while there are a

negative returns in September and December in Shenzhen stock market, and highest returns in January

and March. For Shanghai stock market, the highest return and lowest return are remarked in March and

September accordingly. Andriy (2008) found the existence of the month of the year effect for the half of

countries of CIS and CEE countries. He also mentioned that the quality of the results on the month of the

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year effect should not be overestimated due to the small number of observations. Camilleri (2008)

concluded that there is an existence of monthly seasonality in Malta Stock Exchange. January effect is

found exist on MSE, where the volatility has a tendency to be higher during this month compared to the

other months of the year.

Raj and Thurston (1994) examined that there is no statistically significant result of January and

April effect in the New Zealand stock market. In the research of Marrette and Worthington (2001) it show

significant higher returns in April, July and December with the evidence of a small cap effect together with

systematically higher returns in January, August and December in Australian stock market. Gao and Kling

(2005) rejected the January effect with the explanation that the year ends in February for China, so there is

only March and April effect found. Alagidede and Panagiotidis (2006) examined the month of the year effect

by using daily closing prices of major share index on Ghana Stock Exchange for the period of 1994 to

2004, and they found an April effect in this market. While Chia and Liew (2012) found significant November

effect in the Nikkei 225 index of the Tokyo Stock Exchange (TES) for the period of January 2000 to June

2009.

However, Brown, Keleidon and Marsh (1983) found that there is evidence of December-January

and July-August effects in Australian stock market returns with the latter due to a June-July tax year.

Balaban and Bulu (1996) find that there is no existence of the monthly (semi-month) effect in Turkey stock

market. In the investigation of Maghayereh (2003) shows no evidence of monthly effect and January effect

in Amman Stock Exchange. However, ASE is not implicated as a weak form although there is no significant

difference in monthly returns. Furthermore, in the observation of KC and Joshi (2005) in Nepal Stock

Exchange shows no evidence of month of the year effect, although the returns are high and positive in

October (not significant) and January (significant). They concluded that this anomaly happens due to the

presence of Great festivals of Hindu and information hypothesis. Ali and Akbar (2009) found that although

the market is not efficient in the short run and there is an existence of daily effects on the Pakistani stock

market, but there is no monthly effect in the equity market. Other than that, Borges (2009) also concluded

that there is no strong evidence on the month of the year effect in seventeen European stock market

indexes for the period of 1994 to 2007, even though stock returns are lower in the month of August and

September. Tangjitprom (2011) examined on Thailand stock market and the result shows that there is high

return in December and January which is not significant. This irregular result is known as turn of the month

effect.

Nevertheless, there is too less research on Malaysia and Singapore stock markets in the

international literature. Aggarwal and Rivoli (1989) studied on four emerging markets, includes Hong Kong,

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Singapore, Malaysia and the Philippine on January effect, and they found that there is existence of January

effect for the three countries except Philippines. Boudreaux (1995) also stated that a negative monthly

effect found in a Pacific basin market of Singapore/Malaysia. Yakob and Delpachitra (2005) proved that

there is monthly effect in the six countries stock market but not in China, Hong Kong, Japan and Malaysia.

Wong, Agarwal and Wong (2006) stated that the January effect is being disappeared in the Singapore

stock market during the recent years due to investors are being aware and taking advantage of this effect.

This appearance has important implication for the efficient market hypothesis and the trading behavior of

investors. Padmakanthi (2006) also explained that there is no significant monthly effect in Singapore stock

market due to the awareness of investors on the tax-loss selling anomalies. Wong, Ho and Dollery (2007)

failed to provide strong evidence for the existence of January effect or monthly effect in the KLCI returns for

the thirteen year periods.

Therefore, the purpose of this paper is to investigate the monthly effect in Malaysia and Singapore

stock markets by using Kuala Lumpur Stock Exchange (KLSE) and Singapore Exchange (SGX). The

monthly effects will give opportunity to individual investors or investment firms to invest in these markets.

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Data and Methodology

In order to test whether the monthly effect exists in Malaysia and Singapore stock markets, the daily

adjusted close prices from 3rd December 1993 to 31st December 2013 and 11th October 2001 to 31st

December 2013 for both Kuala Lumpur Stock Exchange (KLSE) and Singapore Exchange (SGX)

respectively will used and analyzed. All the data of the stock markets were downloaded from Yahoo

Finance website.

First, daily returns for the Malaysia and Singapore stock markets are calculated as follows:

rt=pt−pt−1pt−1

(1)

Where rt is daily stock market return on day t; pt represent adjusted price index on day t; pt−1 is the

adjusted price index on day t-1.

Then, we conducted multiple regression models to test for monthly effect in stock market returns.

We carried out the test with monthly dummy variable (Pandey, 2002; Maghayereh, 2003; KC and Joshi,

2005)

Rt=β0+β1DFeb+ β2DMar+ β3DApr+β4DMay+β5D Jun+β6DJul+β7DAug+β8DSep+β9DOct+β10DNov+β11DDec+ε t(2)

Where, Rt refer to the return of stock index on day t.DFeb, DMar,DApr, DMa y, DJun, DJul, DAug, DSep, DOct,

DNov and DDec are dummy variables for February, March, April, May, June, July, August, September,

October, November and December which takes a value of 1, for example if the day t is February otherwise

it takes the value of zero. β0 will represent the mean return for January and the coefficients β1 through β11

measure the difference between mean return for January and other months of the year. ε t is the error term.

Empirical Results of the Analysis

Descriptive Statistics

Table 1 below shows the entire period and each month data for Kuala Lumpur Stock Exchange from 3 rd

December 1993 to 31st December 2013. There were 5238 observations for the whole period of the study.

Returns for the months of February and December are higher than returns of other months. The results

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shows that May had the lowest mean which is -6E-05 and the highest mean was on December (0.0019).

This shows that the return on every May of the year is lower compared to other months of the year. Returns

in the months of March, May, June and September are negative. On the other hand, September had the

highest standard deviation of 0.0258 compared to other months of the year. The lowest standard deviation

was on June and July (0.0101). Stock returns show negative skewness for four months and positive for

seven months. The kurtosis for every months of the year were not normal since the difference from 3 was

extremely high from the normal kurtosis (3). Kurtosis is one measure of how different a distribution is from

the normal distribution. Table 1 also shows that September had the lowest index among the minimum index

in all months of the year. February had the highest index among the maximum index in all months of the

year. The mean return for the entire period is 0.0002, which is positive.

Table 1: Descriptive Statistics for daily returns stratified monthly for Kuala Lumpur Stock Exchange

(KLSE) [rt=p t−p t−1pt−1

¿

KLSE Mean Std. Dev. Kurtosis Skewness Minimum Maximum Observation

All days 0.0002 0.0148 60.0337 1.7058 -0.2146 0.2314 5238

January 0.0004 0.0202 46.3026 1.7594 -0.1751 0.2197 437

February 0.0013 0.0198 67.2765 4.5933 -0.1442 0.2314 398

March -0.0006 0.0114 11.9415 -1.4756 -0.0950 0.0359 442

April 0.0005 0.0115 6.0615 -0.2716 -0.0615 0.0529 428

May -6E-05 0.0108 4.628 0.2499 -0.0489 0.0517 444

June -0.0002 0.0101 3.1277 -0.1542 -0.0426 0.0464 428

July 0.0002 0.0101 4.0430 -0.3923 -0.0473 0.0356 442

August -0.0011 0.0132 10.3988 0.0775 -0.0769 0.0881 445

September -0.0001 0.0258 34.2521 1.3076 -0.2146 0.2246 427

October 0.0005 0.0110 8.4637 0.0733 -0.0664 0.0674 443

November 1.27E-05 0.0126 19.7862 -1.1287 -0.1108 0.0783 429

December 0.0019 0.0125 19.6674 1.0944 -0.0741 0.1137 460

Table 2 reported the result of Singapore Exchange from 11th October 2001 to 31st December 2013.

The mean return for the entire study period on SGX was 0.0003 and the standard deviation of the return

was 0.123 with a skewness of 0.1690. The finding indicates that November has a mean return of 0.0003,

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while May had a mean return of -6.4E-06. This shows that the return on November is higher compare to

other months of the year for SGX. Meanwhile, the mean return is not higher enough to indicate the

existence of monthly effect. September had the highest index among the maximum index in all months of

the year. Meanwhile, October had the lowest index among the minimum index in all months of the year.

The result also show leptokurtic (kurtosis>3) distribution for the nine months of the year. That means they

have flatter tails than the normal distribution.

Table 2: Descriptive Statistics for daily returns stratified monthly for Singapore Exchange (SGX)

[rt=p t−p t−1pt−1

¿

SGX Mean Std. Dev. Kurtosis Skewness Minimum Maximum Observation

All days 0.0003 0.0123 11.0019 0.1690 -0.0954 0.1283 3189

January -0.0003 0.0107 2.5139 -0.3496 -0.0447 0.0396 258

February 0.0002 0.0101 4.2832 -1.0915 -0.0422 0.0352 217

March 0.0008 0.0120 5.6797 0.8095 -0.0440 0.0645 270

April 0.0008 0.0094 1.1873 0.0954 -0.0335 0.0306 260

May -6.4E-06 0.0103 3.3046 0.4935 -0.0374 0.0469 271

June -0.0006 0.0109 0.7171 -0.1453 -0.0338 0.0305 262

July 0.0004 0.0114 4.5391 0.5619 -0.0383 0.0591 272

August -3.4E-0.6 0.0126 4.2344 -0.3997 -0.0603 0.0447 274

September -4.8E-0.5 0.0129 7.5699 -1.3640 -0.0824 0.0450 253

October 0.0006 0.0181 15.4280 1.0285 -0.0954 0.1283 286

November 0.0009 0.0146 6.4932 0.1383 -0.0634 0.0725 285

December 0.0004 0.0102 15.7662 -1.2222 -0.0819 0.0452 295

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Regression

The regression results for each month returns from the equation (2) are summarized in Table 3 and Figure

1 is plotted. Excel data provide us the regression summary output which includes the coefficients, t-stat,

and p-value. It is clear that monthly effect cannot be identified in the results, however, a December effect

was found but was not statistically significant. We can observe the result on table 3 below that there is a

higher coefficients in December (0.0015) and lower coefficients in August (0.0015) for KLSE. However,

results indicate that there is no significant p-value at the required level for the highest coefficients.

In Singapore, the highest coefficients shows in March, April and November (0.0012), which none of

them shows significant level. January and June carried the lowest return, which is -0.0003.

Therefore, it is clear that there is no evidence of monthly effect in KLSE and SGX because all

coefficients are statistically insignificant in n=0.05, n=0.05, and n=0.10. Furthermore, it is clear that the

coefficients return for January are no higher than others months of the year. In fact, the returns for

December are higher than January returns although it is not statistically significant for both KLSE and SGX.

So we can conclude that there is no month or January effect in Malaysia and Singapore stock markets.

Table 3: Regression Results for Monthly effect

Parameter January February March April May June July August September October November December

KLSE 0.0004

(0.5816)

0.0009

(0.3573)

-0.0010

(0.3230)

0.0002

(0.8800)

-0.0004

(0.6559)

-0.0006

(0.5385)

-0.0002

(0.8364)

-0.0015

(0.1238)

-0.0005

(0.6031)

0.0001

(0.9111)

-0.0004

(0.7084)

0.0015

(0.1272)

SGX -0.0003

(0.6657)

0.0002

(0.8308)

0.0012

(0.2738)

0.0012

(0.2761)

0.0003

(0.7646)

-0.0003

(0.7552)

0.0008

(0.4812)

0.0003

(0.7624)

0.0003

(0.7956)

0.0010

(0.3490)

0.0012

(0.2568)

0.0008

(0.4663)

Notes: *, ** and *** denote significant at 1, 5 and 10% level respectively. Numbers in the bracket depict p-value; the regression formula is

Rt=β0+β1DFeb+ β2DMar+ β3DApr+β4DMay+β5D Jun+β6DJul+β7DAug+β8DSep+β9DOct+β10DNov+β11DDec+ε t

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Conclusion

The monthly effect in Malaysia and Singapore stock market are tested by applying simple regression

method. The dummy variables on daily returns of the stock markets are used. We concluded that the

results differ from the finding obtained from other literatures in the world.

Generally, the results are mixed, but we conclude that monthly effect or January effect does not

exist in Malaysia and Singapore stock markets. Therefore, KLSE and SGX as a regulator of Malaysia and

Singapore stock markets need to take steps in order to increase the informational efficiency of securities

market operation, this will enable investors to obtain fully advantages of investing at KLSE and SGX. The

absence of monthly effect in these indices possibly shows that the Malaysia and Singapore stock markets

are not completely random enough.

Finally, there are many limitations of this study which need to be improved in further study. First of

all, the estimation model is not very good for the investigation of monthly effect. Econometric models such

as GARCH (1,1) model or other higher order GARCH models should be used to improve the efficiency of

estimation. Secondly, other seasonality effects such as day-of-the week effect, turn-of-the-month effect and

holiday effect are worthwhile to examine the interactions between the seasonality. Perhaps other forms of

calendar effects will be unique to the Malaysia and Singapore stock markets. Thirdly, determination on

reasons for the existence of monthly effect would be done in further research to explain whether it is

decreasing or disappearing as some researchers claim in their literatures.

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Appendix

Figure 1: Monthly Return Based on Regression

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January

February

March Ap

rilMay

June

July

August

September

October

November

December

-0.0020

-0.0015

-0.0010

-0.0005

0.0000

0.0005

0.0010

0.0015

0.0020

0.0004

0.0009

-0.0010

0.0002

-0.0004-0.0006

-0.0002

-0.0015

-0.0005

0.0001

-0.0004

0.0015

-0.0003

0.0002

0.0012 0.0012

0.0003

-0.0003

0.0008

0.0003 0.0003

0.00100.0012

0.0008

KLSE SGX

18