Post on 20-Jan-2016
description
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.
References
Aggarwal, R., & Rivoli, P. (1989). Seasonal and Day-of-the-Week Effects in Four Emerging Stock Markets.
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Aggarwal, R., R. Rao and T. Hiraki. (1990). Regularities in Tokyo Stock Exchange Security Returns: P/E,
Size and Seasonal Influences. Journal of Financial Research, 13, 49-263.
Alagidede, P., & Panagiotidis, T. (2006). Calendar anomalies in the Ghana stock exchange. Journal of
Emerging Market Finance, 8(1), 1-23.
Alagidede, P. (2013). Month of the year and pre-holiday effects in African Stock Markets. South African
Journal of Economic and Management Sciences, 16(1), 64-74.
Ali, S., & Akbar, M. (2009). Calendar Effects in Pakistani Stock Market. International Review of Business
Research Papers, 5(1), 389-404.
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Appendix
Figure 1: Monthly Return Based on Regression
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GB30006 INDUSTRIAL TRAINING
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