International Journal of Information Technology and Business Management 29
th July 2012. Vol.3 No. 1
© 2012 JITBM & ARF. All rights reserved
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[64]
CALENDAR EFFECTS IN THE PHILIPPINE STOCK
MARKET
Catherine Kalayaan S. Almonte
Email: [email protected]
ABSTRACT
The returns of the Philippine stock market’s Composite Index (PSEi) were tested for conformity
with Fama’s (1970) weak form of market efficiency (Almonte, 2004) using daily values from 2001 to 2010.
Analyses were made annually and cumulatively. The results revealed the existence of a day-of-the-week
effect, the month-of-the-year effect was evidently absent, and the quarter-of-the-year effect was also absent
with the exception of the phenomenon occurring in 2002. Thus, generally, traders are advised to buy
equities on a Tuesday and sell them on a Thursday or Friday.
Keywords: weak form of market efficiency, day-of-the-week effect, month-of-the-year effect,
quarter-of-the-year effect, stock market
1. INTRODUCTION
One engages in trading equities for the
purpose of earning returns that are substantially
higher than the risk-free rate. It would be
beneficial for a trader to know if a pattern exists
in a certain stock market to time one’s buying
and selling activities in order to maximize
returns (Almonte, 2004). Financial theory
suggests that timing the market is a waste of time
(Fama, 1970). However, as discussed by
Almonte (2004), various empirical research
indicate otherwise (e.g. Poshakwale, 1996; Chia,
Liew, & Wafa, 2008; and Wyème & Olfa, 2011).
Hence, this paper aims to determine if a trend is
present in the Philippine stock market’s
Composite Index (PSEi) by studying the day-of-
the-week (Almonte, 2004), month-of-the-year,
and quarter-of-the-year effects using daily
returns. The presence of any calendar effect may
be used by traders in conjunction with technical
analysis.
1.1. Theoretical Framework
The efficient market hypothesis (EMH)
states that prices of securities reflect all available
information (Fama, 1970). It assumes a “. . .
“perfect” market in which (1) securities are
typically in equilibrium, (2) security prices fully
reflect all public information available and react
swiftly to new information, and, (3) because
stocks are fully and fairly priced, investors need
not waste time looking for mispriced securities”
(Gitman, 2009, p. 344).
There are three forms of market
efficiency: (1) weak, (2) semi-strong, and (3)
strong (Fama, 1970). The weak form asserts that
past prices is already factored into securities’
prices; the semi-strong form suggests that public
information is already incorporated into
securities’ prices; and the strong form says that
asymmetric information is already reflected into
securities’ prices (Fama, 1970).
Similar to a research done by Almonte
(2004), this paper focuses on the weak form of
market efficiency (Fama, 1970). The weak form
is related to the random walk behavior of stock
prices (Fama, 1970). In essence, if securities
prices follow a random walk, then trends (such
as calendar effects) should not be present (Fama,
1970; Aly, Mehdian, & Perry, 2004; Almonte,
2004).
1.2. Day-of-the-Week Effect
The weekend effect (also called the
Monday Effect or the day-of-the-week effect)
refers to the trend that stock returns are higher on
Fridays compared to Mondays (Weekend Effect,
n.d.; Hirt & Block, 2012). Consistent with
Almonte (2004), a day-of-the-week effect is
defined as at least one trading day in a week
wherein returns for that day are statistically
International Journal of Information Technology and Business Management 29
th July 2012. Vol.3 No. 1
© 2012 JITBM & ARF. All rights reserved
ISSN 2304-0777 www.jitbm.com
[65]
significantly different from at least one other
day.
1.3. Month-of-the-Year Effect
The January Effect refers to the
phenomenon that stock prices are generally
higher on January because investors sell their
stocks on December to record losses for tax
purposes and then buy back the shares on
January (Mishkin & Eakins, 2006; Fama &
French, 1980 (in Hirt & Block, 2012)). For
purposes of this paper, consistent with
Selvakumar (2011), a month-of-the-year effect is
defined as at least one month in a calendar year
wherein returns for that month are statistically
significantly different from at least one other
month.
1.4. Quarter-of-the-Year Effect
The quarter-of-the-year effect is the
occurrence where securities prices for at least
one quarter are statistically significantly different
from at least one other quarter (Davidsson,
2006).
1.5. The Association of Southeast Asian
Nations (ASEAN) Exchanges
The ASEAN Exchanges is comprised of
seven exchanges (located in six countries) that
work together to promote the ASEAN capital
market (ASEAN Exchanges website,
http://www.aseanexchanges.org/, 2011).
Selected data about the member exchanges are
given in Table 1 (ASEAN Exchanges website,
http://www.aseanexchanges.org/, 2011).
Table 1.
SELECTED DATA: ASEAN EXCHANGES MEMBERS
As of March 31, 2011 (for BM, HNX, HOSE, IDX, PSE, SET); As of December 31, 2010 (for SGX)
Country Malaysia Vietnam Vietnam Indonesia Philippines Thailand Singapore
Stock
Exchange
Bursa
Malaysia
(BM)
The
Hanoi
Stock
Exchange
(HNX)
The
Hochi-
minh
Stock
Exchange
(HOSE)
Indonesia
Stock
Exchange
(IDX)
The
Philippine
Stock
Exchange
(PSE)
The Stock
Exchange
of
Thailand
(SET)
Singapore
Exchange
(SGX)
Listed
Compa-
nies
955 379 283 422 249 541 782
Domestic
Capitali-
zation (in
USD
millions)
426,000 5,359 28,286 376,599 159,402 277,732 728,760
Turnover
Velocity
42% No
available
data
57.1% 32.21% 17.16% 83.5% 43%
Mainboard,
32% Catalist
The BM has the most number of
companies that are publicly listed, representing
26% (955 out of 3,611) of the total. The SGX
leads the group when it comes to domestic
capitalization, representing 36% (728,760
divided by 2,002,138) of the total while the SET
leads the pack in terms of turnover velocity.
The focus of the paper is on the
Philippine market. The number of firms listed in
the Philippines represents only 7% (249 out of
3,611) of the total. In terms of domestic
capitalization, it is ranked fifth (behind the
Singapore, Malaysia, Indonesia, and Thailand
exchanges). Moreover, the Philippines’
domestic capitalization is only 8% (159,402
divided by 2,002,138) of the group. With
regards to turnover velocity, save for the HNX
wherein no data was available, the PSE has the
lowest number at 17%. Clearly, the Philippine
equities market has room for improvement.
1.6. An Overview of the Philippine Stock
Market
The Philippine Stock Exchange (PSE) is
the organized securities exchange in the
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th July 2012. Vol.3 No. 1
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[66]
Philippines. As one industry veteran better
describes the exchange, it “. . . is the organized
securities exchange for equities and equity-
linked derivatives in the
Philippines. . . ” (J.J.F. Lago, personal
communication, January 30, 2012).
Its main index, the Philippine
Composite Index (PSEi), is comprised of 30
firms (PSE Academy website,
http://www.pseacademy.com.ph/LM/glossary/Gl
ossary.html#P, 2011) belonging to different
sectors such as: financials, holding companies,
industrials, property, services, and mining and
oil (The Philippine Stock Exchange, Inc.,
2011a).
The PSE uses a five-day trading week
(from Monday to Friday) except for holidays and
when the Clearing Office of the Bangko Sentral
ng Pilipinas (BSP) is closed (The Philippine
Stock Exchange, Inc. website,
http://www.pse.com.ph/, 2007). Up until
September 2011, trading hours start from 9:30
AM and ends at 12:10 PM (The Philippine Stock
Exchange, Inc. website, http://www.pse.com.ph/,
2007). From October 2011 until December
2011, trading hours were extended until 1:00 PM
(The Philippine Stock Exchange, Inc., 2011b).
Beginning the year 2012, trading hours were
changed to last for the whole day, from 9:30 AM
to 3:30 PM (The Philippine Stock Exchange,
Inc., 2011c).
2. LITERATURE
2.1. Day-of-the-Week Effect
A paper about the Egyptian stock
market did not support the existence of the day-
of-the-week effect (Aly et al., 2004); thus, said
research supports the weak form of market
efficiency (Fama, 1970).
On the other hand, other studies
contradict said phenomenon (Almonte, 2004).
Some examples are: a study of the Bombay
Stock Exchange National Index (BSENI)
wherein it was concluded that Monday returns
were at their lowest while Friday returns were at
their highest (Poshakwale, 1996), another paper
on the Indian stock market showed that Friday
returns were highest compared to the other
trading days of the week (Selvakumar, 2011), a
research on the Philippine stock market’s
Composite Index that showed Monday and
Tuesday returns were significantly lower
compared to Friday returns (Almonte, 2004), a
paper on the “. . . Taiwan, Singapore, Hong
Kong and South Korea stock markets” (Chia,
Liew, & Wafa, 2008, p. 1) that determined
Monday returns were negative while Friday
returns were positive for all markets except
South Korea’s (Chia et al., 2008), and a study on
the Pakistani stock market that concluded
positive returns for Tuesdays (Hussain, Hamid,
Akash, & Khan, 2011).
Thus, as with Almonte (2004), various
empirical researches provide inconclusive
evidence regarding the presence of the day-of-
the-week effect in different equities markets.
2.2. Month-of-the-Year Effect
A study on the returns of the Bahrain
All Share Index revealed that a month-of-the-
year effect was non-existent, even when the time
period of the study was divided into two periods:
(1) pre-global financial crisis and (2) during the
crisis (Al-Jafari, 2011). Selvakumar’s (2011)
paper also did not support a month-of-the-year
effect.
Contrary to the results of the Al-Jafari
(2011) and Selvakumar (2011) researches, other
studies supported the existence of a month-of-
the-year effect. Examples include: a paper that
examined the returns of the Karachi Stock
Exchange where May returns were negative
compared to January returns (Zafar, Urooj, &
Farooq, 2010), a research on the returns of the
Australian stock market where “. . . April, July
and December. . . ” (Marrett & Worthington,
2011, p. 3) returns were higher compared to that
of other months (Marrett & Worthington, 2011),
and a study on the returns of the Tunis Stock
Exchange (TSE) where April returns were higher
compared to that of other months (Wyème &
Olfa, 2011).
Therefore, similar with the studies
about day-of-the-week effects (Almonte, 2004),
different studies show inconclusive evidence
regarding the existence of the month-of-the-year
effect in various stock markets.
2.3. Quarter-of-the-Year Effect
To date, very few empirical studies
have been found with regards to the quarter-of-
the-year effect. Davidsson (2006) found such an
effect in his study of the S&P 500 index using
data from 1970-2005 (the fourth quarter was the
International Journal of Information Technology and Business Management 29
th July 2012. Vol.3 No. 1
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[67]
best period). The CXO Advisory Group, LLC
(http://www.cxoadvisory.com/4080/calendar-
effects/end-of-quarter-effect/, June 14, 2012)
also studied the existence of the quarter-of-the
year effect in the S&P 500 index using data from
1950-2012 and, consistent with Davidsson
(2006), determined that the fourth quarter was
the best.
The lack of literature on this particular
calendar anomaly is primarily the reason why it
is being studied in this paper.
3. HYPOTHESES
H1 The day-of-the-week effect
exists in the Philippine stock
market (Almonte, 2004).
H2 The month-of-the-year effect
exists in the Philippine stock
market.
H3 The quarter-of-the-year effect
exists in the Philippine stock
market.
4. METHODOLOGY
The PSEi was used as the sample to
study the presence of calendar effects in the
Philippine stock market.
Since the trading hours of the Philippine
market were changed on October 2011 (The
Philippine Stock Exchange, Inc., 2011b), the
time frame of the study was set from the year
2001 to 2010 (the last ten years wherein the last
year reflected the end of a calendar year).
The data of the PSEi was obtained
through Technistock (used by professionals to
access data regarding securities).
Using daily returns as the main data,
analyses were conducted annually (e.g. 2001,
2002, and so on) and cumulatively (e.g. from
2001 to 2002, 2001 to 2003, and so on) to test if
any of the calendar effects exists and/or persists.
Returns were computed using the
formula (Keown, Martin, Petty, & Scott, Jr.,
2005):
11t at time Value
tat time Value Return
Similar to Almonte (2004), numeric
codes were used to signify the day in the week,
the month in the year, and the quarter in the year:
a day referring to Monday was coded as 1,
Tuesday was coded as 2, and so on; a month
referring to January was coded as 1, February
was coded as 2, and so on; a quarter referring to
the first quarter of the calendar year was coded
as 1, a quarter referring to the second quarter of
the calendar year was coded as 2, and so on.
All statistical calculations were done
using the software XLSTAT 2011.
The returns were tested for normality.
The results indicate that the returns of the PSEi
are not normally distributed (Table 2A and Table
2B). Hence, consistent with Almonte (2004), the
Kruskal-Wallis Test was used to test the
presence of calendar effects in the market.
Any existence of a calendar effect was
further explored by determining which day,
month, or quarter was statistically significantly
different from a different day, month, or quarter
by using the Steel-Dwass-Critchlow-Fligner
Procedure embedded in the software.
Summary statistics were analyzed
somewhat similar to what was done by Agathee
(2008).
This research is an updated and more
detailed version of what was done by Almonte
(2004).
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th July 2012. Vol.3 No. 1
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[68]
Table 2. A.
TEST FOR NORMALITY
Jarque-Bera Test at = 0.05
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
JB
(Observed
Value)
20,119
.316
31.008 27.793 11.26
8
6.728 41.803 397.41
4
315.81
2
18.479 10.509
JB (Critical
Value)
5.991 5.991 5.991 5.991 5.991 5.991 5.991 5.991 5.991 5.991
DF 2 2 2 2 2 2 2 2 2 2
p-value <
0.0001
<
0.0001
<
0.0001
0.004 0.035 <
0.0001
<
0.0001
<
0.0001
<
0.0001
0.005
Table 2. B.
TEST FOR NORMALITY
Jarque-Bera Test at = 0.05
2001
to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
JB
(Observed Value)
39,362
.009
43,163
.114
46,151
.256
45,956
.672
39,790
.870
29,210
.826
21,293
.392
20,243
.562
22,052
.724
JB (Critical Value) 5.991 5.991 5.991 5.991 5.991 5.991 5.991 5.991 5.991
DF 2 2 2 2 2 2 2 2 2
p-value <
0.0001
<
0.0001
<
0.0001
<
0.0001
<
0.0001
<
0.0001
<
0.0001
<
0.0001
<
0.0001
5. RESULTS AND ANALYSIS
Based on the data presented in Table
3A, Tuesdays had the most number of lowest
mean returns (five out of ten years, i.e. 2001,
2002, 2005, 2006, and 2009) followed by
Mondays (four out of ten years, i.e. 2003, 2004,
2005, and 2007). On the other hand, Thursdays
had the most number of highest mean returns
(six out of ten years, i.e. 2003, 2005, 2006, 2007,
2009, and 2010). In terms of trading days,
Mondays had the least number in eight out of ten
years (i.e. 2001, 2002, 2004, 2005, 2006, 2007,
2008, and 2010) while Tuesdays had the most
number in seven out of ten years (i.e. 2002,
2003, 2004, 2005, 2008, 2009, and 2010)
followed by Wednesdays (in six out of ten years,
i.e. 2001, 2005, 2006, 2007, 2008, and 2010).
According to the results presented in
Table 3B, Tuesdays had the most number of
lowest mean returns (nine out of nine times, i.e.
2001 to 2002, 2001 to 2003 until 2001 to 2010).
Fridays had the most number of highest mean
returns (seven out of nine times, i.e. 2001 to
2002, 2001 to 2003 until 2001 to 2008) followed
by Thursdays (five out of nine times, i.e. 2001 to
2006, 2001 to 2007 until 2001 to 2010). With
regards to the number of trading days, Mondays
had the least number of trading days in eight out
of nine times (i.e. all cumulative periods with the
exception of the years 2001 to 2003) while
Tuesdays had the most number in six out of nine
times (i.e. 2001 to 2003, 2001 to 2004, 2001 to
2005, 2001 to 2006, 2001 to 2009, and 2001 to
2010) followed by Wednesdays (in four out of
nine times, i.e. 2001 to 2002, 2001 to 2003, 2001
to 2007, and 2001 to 2008).
International Journal of Information Technology and Business Management 29
th July 2012. Vol.3 No. 1
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[69]
Table 3. A.
SUMMARY STATISTICS
Day-of-the-Week Effect
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Return
1
Obs. 47 48 51 47 45 47 44 45 46 44
Mean 0.001 -0.001 0.001 -0.002 -0.001 0.002 -0.001 -0.004 0.002 0.001
Return
2
Obs. 49 50 51 52 51 50 50 51 52 51
Mean -0.005 -0.003 0.002 0.001 -0.001 -0.002 0.004 -0.003 0.000 0.000
Return
3
Obs. 52 49 49 50 51 51 52 51 50 51
Mean -0.003 -0.001 0.001 0.003 0.002 -0.002 -0.001 0.003 0.002 0.001
Return
4
Obs. 50 50 49 50 51 50 50 49 49 51
Mean 0.000 0.000 0.002 0.001 0.002 0.006 0.004 -0.005 0.004 0.004
Return
5
Obs. 49 49 47 48 48 49 48 50 45 47
Mean 0.003 0.001 0.002 0.001 0.001 0.004 -0.001 -0.003 0.003 -
0.001
Table 3. B.
SUMMARY STATISTICS
Day-of-the-Week Effect
2001
to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
Return 1
Obs. 95 146 193 238 285 329 374 420 464
Mean 0.000 0.000 0.000 0.000 0.000 0.000 -0.001 0.000 0.000
Return 2
Obs. 99 150 202 253 303 353 404 456 507
Mean -0.004 -0.002 -0.001 -0.001 -0.001 0.000 -0.001 -0.001 -0.001
Return 3
Obs. 101 150 200 251 302 354 405 455 506
Mean -0.002 -0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Return 4
Obs. 100 149 199 250 300 350 399 448 499
Mean 0.000 0.001 0.001 0.001 0.002 0.002 0.001 0.002 0.002
Return 5
Obs. 98 145 193 241 290 338 388 433 480
Mean 0.002 0.002 0.002 0.002 0.002 0.002 0.001 0.001 0.001
The Kruskal-Wallis Test shows that the
day-of-the-week effect is only evident during the
years 2001 and 2006 (Table 4A). However,
when the years were accumulated, the
phenomenon is present at all times (Table 4B).
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th July 2012. Vol.3 No. 1
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Table 4. A.
TEST FOR DAY-OF-THE-WEEK EFFECT
Kruskal-Wallis Test at = 0.05
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
K
(Observed
Value)
12.893 6.718 0.878 6.636 2.432 19.555 2.243 6.419 3.190 6.668
K (Critical
Value)
9.488 9.488 9.488 9.488 9.488 9.488 9.488 9.488 9.488 9.488
DF 4 4 4 4 4 4 4 4 4 4
Asymptotic
p-value
(two-tailed)
0.012 0.152 0.928 0.156 0.657 0.001 0.691 0.170 0.527 0.155
Table 4. B.
TEST FOR DAY-OF-THE-WEEK EFFECT
Kruskal-Wallis Test at = 0.05
2001 to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
K (Observed
Value)
18.867 12.347 11.073 12.818 19.167 17.063 12.129 14.516 15.347
K (Critical Value) 9.488 9.488 9.488 9.488 9.488 9.488 9.488 9.488 9.488
DF 4 4 4 4 4 4 4 4 4
Asymptotic p-
value (two-tailed)
0.001 0.015 0.026 0.012 0.001 0.002 0.016 0.006 0.004
For the year 2001 (Table 5A), there was
a significant difference between Tuesday and
Friday returns. The result suggests that one buy
on a Tuesday (since it has the lowest mean rank
of 99.306) and sell on a Friday (since it has the
highest mean rank of 149.918)
Table 5. A.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of
Ranks
Mean of
Ranks
Return 1 47 5,612.0000 119.404
Return 2 49 4,866.0000 99.306
Return 3 52 6,314.0000 121.423
Return 4 50 6,490.0000 129.800
Return 5 49 7,346.0000 149.918
Significant
p-value Return 2 and Return 5 at 0.003
For the year 2006 (Table 5B), there
were several significant differences among the
trading days. Specifically, Tuesday returns were
different from Thursday returns while
Wednesday returns were different from Thursday
returns. The results indicate that one buy on
either a Tuesday or a Wednesday (although
Wednesday is preferred given that it has the
lowest mean rank of 100.843 among the five
trading days) and sell on a Thursday (since it has
the highest mean rank of 157.860).
Table 5. B.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2006
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of
Ranks
Mean of
Ranks
Return 1 47 5,740.0000 122.128
Return 2 50 5,415.0000 108.300
Return 3 51 5,143.0000 100.843
Return 4 50 7,893.0000 157.860
Return 5 49 6,437.0000 131.367
Significant
p-values Return 2 and Return 4 at 0.011,
Return 3 and Return 4 at 0.000
For the years 2001 to 2002 (Table 5C),
2001 to 2003 (Table 5D), and 2001 to 2004
(Table 5E), there were consistently significant
differences between Tuesday and Friday returns.
The results suggest that one buy on a Tuesday
(the day with the lowest mean rank) and sell on a
Friday (the day with the highest mean rank).
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th July 2012. Vol.3 No. 1
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[71]
Table 5. C.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2002
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of
Ranks
Mean of
Ranks
Return 1 95 22,725.000 239.211
Return 2 99 20,040.000 202.424
Return 3 101 24,918.000 246.713
Return 4 100 25,828.000 258.280
Return 5 98 28,260.000 288.367
Significant
p-value Return 2 and Return 5 at 0.000
Table 5. D.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2003
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of
Ranks
Mean of
Ranks
Return 1 146 52,830.000 361.849
Return 2 150 49,688.000 331.253
Return 3 150 54,495.000 363.300
Return 4 149 56,890.000 381.812
Return 5 145 60,267.000 415.634
Significant
p-value Return 2 and Return 5 at 0.007
Table 5. E.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2004
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 193 89,588.000 464.187
Return 2 202 92,434.000 457.594
Return 3 200 100,392.000 501.960
Return 4 199 100,876.000 506.915
Return 5 193 104,288.000 540.352
Significant
p-value Return 2 and Return 5 at 0.037
For the years 2001 to 2005 (Table 5F),
there were significant differences between
Monday and Friday returns as well as Tuesday
and Friday returns. The result suggests that one
buy on either a Monday or a Tuesday (although
Tuesday is preferred given its lowest mean rank
value of 573.664) and sell on a Friday (since it
has the highest mean rank of 668.535).
Table 5. F.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2005
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 238 137,610.000 578.193
Return 2 253 145,137.000 573.664
Return 3 251 156,800.000 624.701
Return 4 250 160,097.000 640.388
Return 5 241 161,117.000 668.535
Significant
p-values Return 1 and Return 5 at 0.039,
Return 2 and Return 5 at 0.031
For the years 2001 to 2006 (Table 5G),
there were several significant differences among
the different trading days. Specifically, Monday
returns were different from Thursday returns,
Monday returns were different from Friday
returns, Tuesday returns were different from
Thursday returns, and Tuesday returns were
different from Friday returns. The result
indicates that one buy either on a Monday or
Tuesday (although Tuesday is preferred given
that it has the lowest mean rank of 681.261) and
sells either on a Thursday or a Friday (although
Friday is preferred given that it has the highest
mean rank of 798.831).
Table 5. G.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2006
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 285 199,747.000 700.867
Return 2 303 206,422.000 681.261
Return 3 302 219,186.000 725.781
Return 4 300 238,924.000 796.413
Return 5 290 231,661.000 798.831
Significant
p-values Return 1 and Return 4 at 0.046,
Return 1 and Return 5 at 0.041,
Return 2 and Return 4 at 0.013,
Return 2 and Return 5 at 0.008
For the years 2001 to 2007 (Table 5H),
there were again several significant differences
among the five trading days. Specifically,
Monday returns were different from Thursday
returns, Tuesday returns were different from
Thursday returns, and Tuesday returns were
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different from Friday returns. The result
suggests that one buy either on a Monday or a
Tuesday (although Tuesday is preferred given
that has the lowest mean rank of 810.082) and
sell either on a Thursday or a Friday (although
Thursday is preferred given that it has the
highest mean rank of 927.666).
Table 5. H.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2007
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 329 269,036.000 817.739
Return 2 353 285,959.000 810.082
Return 3 354 297,798.000 841.237
Return 4 350 324,683.000 927.666
Return 5 338 309,474.000 915.604
Significant
p-values Return 1 and Return 4 at 0.030,
Return 2 and Return 4 at 0.020,
Return 2 and Return 5 at 0.045
For the years 2001 to 2008 (Table 5I),
2001 to 2009 (Table 5J), and 2001 to 2010
(Table 5K), there were consistently significant
differences between Tuesday and Thursday
returns. The results suggest one buy on a
Tuesday (the day with the lowest mean rank) and
sell on a Thursday (the day with the highest
mean rank).
Summarizing the combinations with
significant differences, Tuesday differing from
Friday returns occurs seven times, Tuesday
differing from Thursday returns occurs six times,
Monday differing from Thursday returns occurs
two times, Monday differing from Friday returns
occurs two times, and Wednesday differing from
Thursday returns occurs one time. The
popularity of the Tuesday/Friday and the
Tuesday/Thursday combinations may be due to
holidays and/or non-trading days that fall on a
Monday and/or a Friday.
Table 5. I.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2008
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 374 353,760.000 945.882
Return 2 404 373,821.000 925.300
Return 3 405 400,089.000 987.874
Return 4 399 414,511.000 1,038.875
Return 5 388 399,254.000 1,029.005
Significant
p-value Return 2 and Return 4 at 0.045
Table 5. J.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2009
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 420 446,348.000 1,062.733
Return 2 456 471,602.000 1,034.215
Return 3 455 506,864.000 1,113.987
Return 4 448 524,788.000 1,171.402
Return 5 433 497,976.000 1,150.060
Significant
p-value Return 2 and Return 4 at 0.014
Table 5. K.
SIGNIFICANCE OF THE DAY-OF-THE-WEEK
EFFECT FOR 2001 to 2010
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of Ranks Mean of
Ranks
Return 1 464 551,144.000 1,187.810
Return 2 507 582,224.000 1,148.371
Return 3 506 626,972.000 1,239.075
Return 4 499 653,156.000 1,308.930
Return 5 480 603,700.000 1,257.708
Significant
p-value Return 2 and Return 4 at 0.004
Based on the data on Table 6A, March,
June, September, and November had the most
number of lowest mean returns (each at two out
of ten years). For March, the years are 2004 and
2005. For June, the years are 2002 and 2006.
For September, the years are 2001 and 2009.
For November, the years are 2003 and 2010. On
the other hand, January, July, and September had
the most number of highest mean returns (each at
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three out of ten years). For January, the years
are 2002, 2005, and 2007. For July, the years are
2006, 2008, and 2009. For September, the years
are 2004, 2006, and 2010. In terms of trading
days, December had the least number in seven
out of ten years (i.e. 2001 to 2004, 2006 to 2008)
while March and July had the most number (each
at five out of ten years). For March, the years
are 2004, 2006, 2007, 2009, and 2010. For July,
the years are 2002, 2003, 2007, 2008, and 2009.
As per the results shown in Table 6B,
March, August, and October had the most
number of lowest mean returns (each at five out
of nine times). For March, the following periods
were covered: 2001 to 2004, 2001 to 2005 until
2001 to 2008. For August, the following periods
were covered: 2001 to 2003, 2001 to 2006 until
2001 to 2009. For October, the following
periods were covered: 2001 to 2002, 2001 to
2003, 2001 to 2008, 2001 to 2009, and 2001 to
2010. January consistently had the highest mean
returns (i.e. from 2001 to 2002, 2001 to 2003
until 2001 to 2010). With regards to the number
of trading days, December consistently had the
least number of trading days (i.e. from 2001 to
2002, 2001 to 2003 until 2001 to 2010) while
July had the most number in five out of nine
times (i.e. 2001 to 2004, 2001 to 2005, 2001 to
2008, 2001 to 2009, and 2001 to 2010), followed
by August and October (both at four out of nine
times). For August, the following periods were
covered: 2001 to 2002, 2001 to 2005, 2001 to
2006, and 2001 to 2007. For October, the
following periods were covered: 2001 to 2002,
2001 to 2003, 2001 to 2004, and 2001 to 2005.
Table 6. A.
SUMMARY STATISTICS
Month-of-the-Year Effect
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Return
1
Obs. 22 22 22 20 21 22 22 22 20 20
Mean 0.006 0.007 0.002 0.002 0.005 0.001 0.004 -0.004 -
0.001
-
0.002
Return
2
Obs. 20 19 19 19 19 20 20 20 20 20
Mean -
0.002
0.002 -0.002 -0.001 0.002 0.000 -0.003 -0.002 0.001 0.002
Return
3
Obs. 22 19 21 23 20 23 22 19 22 23
Mean -
0.005
0.000 0.001 -0.002 -0.003 0.001 0.002 -0.002 0.003 0.002
Return
4
Obs. 18 21 19 19 21 18 18 21 19 19
Mean -
0.003
-0.002 0.002 0.005 -0.002 0.002 0.001 -0.004 0.003 0.002
Return
5
Obs. 21 22 20 20 21 22 21 21 20 19
Mean 0.001 -0.001 0.000 -0.001 0.002 0.001 0.003 0.001 0.006 0.000
Return
6
Obs. 20 19 20 22 21 21 20 20 21 20
Mean 0.000 -0.007 0.007 0.002 0.000 -0.002 0.003 -0.007 0.001 0.002
Return
7
Obs. 22 22 23 22 20 20 22 23 22 22
Mean -
0.002
-0.001 0.001 0.000 0.002 0.005 -0.002 0.002 0.006 0.001
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Return
8
Obs. 23 22 20 22 22 22 21 19 18 21
Mean -
0.003
-0.001 -0.002 0.000 -0.001 -0.001 -0.001 0.002 0.002 0.002
Return
9
Obs. 20 21 22 22 22 19 20 22 20 21
Mean -
0.006
0.001 0.004 0.005 0.000 0.005 0.003 -0.002 -
0.001
0.007
Return
10
Obs. 23 22 23 21 20 21 21 22 22 20
Mean -
0.005
-0.003 0.003 0.002 0.000 0.003 0.003 -0.011 0.002 0.002
Return
11
Obs. 19 20 19 19 19 21 19 20 19 19
Mean 0.007 0.000 -0.003 0.000 0.004 0.001 -0.002 0.001 0.002 -
0.004
Return
12
Obs. 17 17 19 18 20 18 18 17 19 20
Mean 0.002 -0.002 0.005 0.000 0.000 0.004 0.001 -0.003 0.000 0.003
Table 6. B.
SUMMARY STATISTICS
Month-of-the-Year Effect
2001
to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
Return 1
Obs. 44 66 86 107 129 151 173 193 213
Mean 0.007 0.005 0.004 0.005 0.004 0.004 0.003 0.002 0.002
Return 2
Obs. 39 58 77 96 116 136 156 176 196
Mean 0.000 -0.001 -0.001 0.000 0.000 -0.001 -0.001 -0.001 0.000
Return 3
Obs. 41 62 85 105 128 150 169 191 214
Mean -0.003 -0.001 -0.002 -0.002 -0.001 -0.001 -0.001 0.000 0.000
Return 4
Obs. 39 58 77 98 116 134 155 174 193
Mean -0.002 -0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Return 5
Obs. 43 63 83 104 126 147 168 188 207
Mean 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.001
Return 6
Obs. 39 59 81 102 123 143 163 184 204
Mean -0.003 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000
Return 7
Obs. 44 67 89 109 129 151 174 196 218
Mean -0.001 -0.001 0.000 0.000 0.001 0.000 0.001 0.001 0.001
Return 8
Obs. 45 65 87 109 131 152 171 189 210
Mean -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 0.000
Return 9
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Obs. 41 63 85 107 126 146 168 188 209
Mean -0.002 0.000 0.001 0.001 0.002 0.002 0.001 0.001 0.002
Return 10
Obs. 45 68 89 109 130 151 173 195 215
Mean -0.004 -0.002 -0.001 -0.001 0.000 0.000 -0.001 -0.001 -
0.001
Return 11
Obs. 39 58 77 96 117 136 156 175 194
Mean 0.003 0.001 0.001 0.002 0.001 0.001 0.001 0.001 0.001
Return 12
Obs. 34 53 71 91 109 127 144 163 183
Mean 0.000 0.002 0.001 0.001 0.002 0.001 0.001 0.001 0.001
The Kruskal-Wallis Test (Table 7A and
Table 7B) shows that the month-of-the-year
effect is non-existent in the Philippine stock
market.
It is quite surprising that the month-of-
the-year effect is non-existent in the local
equities market given that the Philippines is part
of the world’s emerging economies. Perhaps,
the result has something to do with how equity
sales are locally taxed. In the Philippines, there
is a flat tax rate based on the value of the
transaction (number of shares multiplied by the
selling price per share), regardless of the sale
being a gain or a loss (The Philippine Stock
Exchange, Inc. old website,
http://www2.pse.com.ph/, 2001). Other
countries have different tax laws such that one
may strategically sell on December (to report a
loss) and buy back the shares on January
(Mishkin & Eakins, 2006; Fama & French, 1980
(in Hirt & Block, 2012)); thus, creating a month-
of-the-year pattern.
Table 7. A.
TEST FOR MONTH-OF-THE-YEAR EFFECT
Kruskal-Wallis Test at = 0.05
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
K (Ob-
served
Value)
16.811 14.616 11.016 8.486 9.322 9.474 6.490 9.474 11.331 12.907
K
(Critical
Value)
19.675 19.675 19.675 19.675 19.675 19.675 19.675 19.675 19.675 19.675
DF 11 11 11 11 11 11 11 11 11 11
Asymp-
totic
p-value
(two-
tailed)
0.114 0.201 0.442 0.669 0.592 0.578 0.839 0.578 0.416 0.299
Table 7. B.
TEST FOR MONTH-OF-THE-YEAR EFFECT
Kruskal-Wallis Test at = 0.05
2001
to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
K
(Observed Value)
16.153 10.361 9.582 11.943 13.687 15.484 14.095 11.173 9.618
K (Critical Value) 19.675 19.675 19.675 19.675 19.675 19.675 19.675 19.675 19.675
DF 11 11 11 11 11 11 11 11 11
Asymptotic
p-value
(two-tailed)
0.136 0.498 0.568 0.368 0.251 0.161 0.228 0.429 0.565
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Based on the results presented in Table
8A, quarter 4 had the most number of lowest
mean returns (four out of ten years). These were
in 2007 until 2010. The rest of the quarters had
lowest mean returns each at three out of ten
years. For quarter 1, these were in the years
2003, 2004, and 2009. For quarter 2, there were
in 2002, 2005, and 2006. For quarter 3, these
were in 2001, 2005, and 2007. On the other
hand, quarters 2 and 3 generated the most
number of highest mean returns (each at four out
of ten years). For quarter 2, these were in years
2003, 2004, 2007, and 2009. For quarter 3, these
were in years 2004, 2006, 2008, and 2010.
Quarter 4 is not far behind, incurring highest
mean returns in three out of ten years. These
were in 2001, 2005, and 2006. In terms of
trading days, quarter 4 had the least number in
eight out of ten years (i.e. 2001, 2002, 2004 until
2009) while quarter 3 had the most number in
seven out of ten years (i.e. 2001 until 2005,
2008, and 2010).
As presented in Table 8B, quarter 2 had
the most number of lowest mean returns (seven
out of nine times). These were in 2001 to 2002,
2001 to 2004 until 2001 to 2007, 2001 to 2009,
and 2001 to 2010. Quarter 3 was next (incurring
lowest mean returns in six out of nine times;
2001 to 2002 until 2001 to 2007). On the other
hand, quarter 1 had the most number of highest
mean returns (seven out of nine times). These
were in 2001 to 2002 until 2001 to 2007, and
2001 to 2010. No lowest and no highest mean
returns were assigned to any quarter for 2001 to
2008 because all periods generated equal values.
In terms of trading days, quarter 4 and quarter 3
were consistent in having the least and most
number of trading days (for all periods),
respectively.
Table 8. A.
SUMMARY STATISTICS
Quarter-of-the-Year Effect
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Return
1
Obs. 64 60 62 62 60 65 64 61 62 63
Mean 0.000 0.003 0.000 0.000 0.001 0.001 0.001 -0.003 0.001 0.001
Return
2
Obs. 59 62 59 61 63 61 59 62 60 58
Mean 0.000 -0.003 0.003 0.002 0.000 0.000 0.002 -0.003 0.004 0.001
Return
3
Obs. 65 65 65 66 64 61 63 64 60 64
Mean -0.003 0.000 0.001 0.002 0.000 0.003 0.000 0.001 0.002 0.003
Return
4
Obs. 59 59 61 58 59 60 58 59 60 59
Mean 0.001 -0.002 0.002 0.001 0.001 0.003 0.000 -0.005 0.001 0.000
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Table 8. B.
SUMMARY STATISTICS
Quarter-of-the-Year Effect
2001
to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
Return 1
Obs. 124 186 248 308 373 437 498 560 623
Mean 0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000 0.001
Return 2
Obs. 121 180 241 304 365 424 486 546 604
Mean -0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Return 3
Obs. 130 195 261 325 386 449 513 573 637
Mean -0.002 -0.001 0.000 0.000 0.000 0.000 0.000 0.001 0.001
Return 4
Obs. 118 179 237 296 356 414 473 533 592
Mean 0.000 0.000 0.000 0.001 0.001 0.001 0.000 0.000 0.000
The Kruskal-Wallis Test shows that
there is no quarter-of-the-year effect (Table 9A
and Table 9B) except for 2002.
The non-existence of both the month-
of-the-year and quarter-of-the-year effects are
quite interesting given the common belief of
window dressing activities towards the end of
the calendar year.
Table 9. A.
TEST FOR QUARTER-OF-THE-YEAR EFFECT
Kruskal-Wallis Test at = 0.05
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
K (Observed
Value)
3.582 9.342 0.744 2.180 0.487 2.248 2.018 2.482 2.234 2.827
K (Critical
Value)
7.815 7.815 7.815 7.815 7.815 7.815 7.815 7.815 7.815 7.815
DF 3 3 3 3 3 3 3 3 3 3
Asymptotic
p-value
(two-tailed)
0.310 0.025 0.863 0.536 0.922 0.523 0.569 0.479 0.525 0.419
Table 9. B.
TEST FOR QUARTER-OF-THE-YEAR EFFECT
Kruskal-Wallis Test at = 0.05
2001 to
2002
2001
to
2003
2001
to
2004
2001
to
2005
2001
to
2006
2001
to
2007
2001
to
2008
2001
to
2009
2001
to
2010
K
(Observed Value)
3.069 1.819 0.207 0.479 0.945 0.853 0.961 0.201 0.447
K (Critical Value) 7.815 7.815 7.815 7.815 7.815 7.815 7.815 7.815 7.815
DF 3 3 3 3 3 3 3 3 3
Asymptotic
p-value
(two-tailed)
0.381 0.611 0.976 0.924 0.815 0.837 0.811 0.977 0.930
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[78]
For the year 2002 (Table 10A), there
was a significant difference between the return of
quarter 1 and the return of quarter 2. The results
indicate that one buy on quarter 2 (lowest mean
rank of 106.984) and sell on quarter 1 (highest
mean rank of 144.500).
Table 10.A.
SIGNIFICANCE OF THE QUARTER-OF-THE-
YEAR EFFECT FOR 2002
Steel-Dwass-Critchlow-Fligner Procedure;
Bonferroni Correction
Obs. Sum of
Ranks
Mean of
Ranks
Return 1 60 8,670.000 144.500
Return 2 62 6,633.000 106.984
Return 3 65 8,232.000 126.646
Return 4 59 6,846.000 116.034
Significant
p-value Return 1 and Return 2 at 0.021
6. CONCLUSION
Although the annual analysis of the
existence of the day-of-the-week effect in the
PSEi indicate that only two of ten years (i.e.
2001 and 2006) are statistically significant
(Tables 4A, 5A, and 5B), the summary statistics
(Table 3A) indicate a frequency pattern of lower
mean returns on Mondays and Tuesdays higher
mean returns during Thursdays. Based on the
cumulative analysis of the existence of the day-
of-the-week effect, the summary statistics (Table
3B) are consistent with the results of the
Kruskal-Wallis Test (Tables 4B, 5C to 5K).
Since the phenomenon is present in all periods
when cumulative analysis was utilized, this
suggests a holding period of at least two years.
The top two combinations with significant
differences are (1) Tuesday versus Friday returns
and (2) Tuesday versus Thursday returns.
Generally, the results imply that one buy on a
Tuesday and sell either on a Thursday or a
Friday. The results are fundamentally in line
with the research done by Almonte (2004).
Consistent with the findings of Al-Jafari
(2011) and Selvakumar (2011), the month-of-
the-year effect is apparently non-existent in the
market (Tables 7A and 7B). For the annual
analysis, the results of the Kruskal-Wallis Test
are supported by the summary statistics in that
several months accounted for the most number of
lowest and highest mean returns (Table 6A). As
for the cumulative analysis, the summary
statistics (Table 6B) indicate that there were
several months that accounted for the most
number of lowest mean returns and that January
consistently had the highest mean returns
(although statistically insignificant). A possible
explanation for January’s highest mean returns
could be traders and/or investors trying to make-
up for their December trading (since December
had the lowest number of trading days in the
annual and cumulative analyses).
As per the annual analysis, with the
exception of 2002, there is no quarter-of-the-year
effect in the market (Table 9A). The results of
the summary statistics support the statistical test
in that no quarter was dominant in generating the
lowest or highest mean returns (Table 8A). The
significant result that occurred in 2002 may just
be due to chance. As for the cumulative
analysis, the summary statistics (Table 8B)
indicate that the lowest mean returns mostly
occur during quarter 2 while the highest mean
returns mostly occur on quarter 1 (although
results of the Kruskal-Wallis Test reveal that the
returns of one quarter are statistically the same as
the returns of any other quarter). The results of
the quarter-of-the-year effect disagree with those
of Davidsson (2006) and the CXO Advisory
Group, LLC
(http://www.cxoadvisory.com/4080/calendar-
effects/end-of-quarter-effect/, June 14, 2012).
Therefore, only the first research
hypothesis is supported by the results of this
study.
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