Does the January Effect Still Exists?

Abstract

The issue of the January Effect has attracted a lot of interest by both practitioners and researchers. The idea that stock returns in January are statistically bigger than in other months was first presented several decades ago. This study analyzes the issue of the January effect in a systematic and global way of studying the performance of 106 indexes in 86 countries and jurisdictions. It was observed that while this effect can still be appreciated in some markets it would appear that it is decreasing globally over time. It was also found that there appears to be an Inverted January Effect in several markets with the returns in January being lower than the returns in some other months. This analysis was performed with nonparametric tests. The hypothesis that the returns of the indexes do not follow in general a normal distribution was also confirmed with several tests.

Full text

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 50 ISSN 1923-4023 E-ISSN 1923-4031
Does the January Effect Still Exists?
Gerardo “Gerry” Alfonso Perez1
1 University of Cambridge, UK
Correspondence: Gerardo “Gerry” Alfonso Perez, University of Cambridge, UK.
Received: August 24, 2017 Accepted: October 2, 2017 Online Published: December 3, 2017
doi:10.5430/ijfr.v9n1p50 URL: https://doi.org/10.5430/ijfr.v9n1p50
Abstract
The issue of the January Effect has attracted a lot of interest by both practitioners and researchers. The idea that stock
returns in January are statistically bigger than in other months was first presented several decades ago. This study
analyzes the issue of the January effect in a systematic and global way of studying the performance of 106 indexes in
86 countries and jurisdictions. It was observed that while this effect can still be appreciated in some markets it would
appear that it is decreasing globally over time. It was also found that there appears to be an Inverted January Effect in
several markets with the returns in January being lower than the returns in some other months. This analysis was
performed with nonparametric tests. The hypothesis that the returns of the indexes do not follow in general a normal
distribution was also confirmed with several tests.
Keywords: January effect, market anomalies, stock returns
1. Introduction
There are a large number of market abnormalities identified both in the academic literature as well as by practitioners.
One of these abnormalities is the January effect. The January effect refers to the observation that returns in January
appear to be higher than returns in other months. One of the firsts, if not the first, academic article describing the
January Effect was (Watchel, 1942). Since then several authors, such as (Haugen, 1988), (Thaler, 1987), (Jones,
1989), (Moller, 2008), (He, 2011), have analyzedthis issue.The existence of a January effect, as many other market
abnormalities, has been used as an argument supporting the idea that markets are not completely efficient. The idea
behind this approach is that if such market abnormality exist and can be exploited for trading purposes then, at least
in principle, it would be possible to outperform the market in a consistent way, which would contradict the market
efficiency hypothesis. The scope of this article it is not to study the link between abnormal returns and market
efficiencies but to analyze, in a global basis, what markets present this phenomenon.
The January effect has been observed in several countries for some specific time periods such as for instance the U.S.
for some decades after World War I (Charles, 1989). The absence of a January effect before World War I was
detected in other countries such as Germany (Taufiq, 2016). Interestingly, the results of (Taufiq, 2016) for the U.K.
and the U.S. suggest that there was a January Effect in the pre-war period which seems to contradict several articles
such as (Schultz, 1985). The majority of the literature available seems to support the hypothesis that there was no
such effect for the period before the World War I, particularly in the U.S. Other countries where the January Effect
has been detected are Japan (Li, 2015), Jordan, Morocco from 1988 to 2014 (Gharaibeh, 2017), Turkey (Guler, 2013),
India (Kaur, 2017) and several countries in Western Europe (Asteriou, 2006) from 1991 to 2003. In some other
markets, like Pakistan, an abnormal return in January has been identified (Hashmi, 2014) but the authors mentioned
that the effect is small and no profitable strategy can be built after accounting for transaction costs. It is also
interesting that the January effect seems to be changing over time with several articles, such as (Gu, 2013), (Mehdian,
2002), (Patel, 2016) pointing to a declining January effect in the U.S. market. In these articles the authors observed
a decline in the effect in the U.S. market starting in the late eighties. The performance in January has been even
treated as a precursor of the performance for the rest of the year (Cooper, 2006). There are also abundant articles
defending the idea that there is no January effect in some markets such as New Zealand (Li, 2010), India (Mehta,
2009), (Pandeu, 2016) or Indonesia (Simbolon, 2015).The January Effect has been studied not only in equities but
also in fixed income investments. For instance, (Starks, 2006) detected the presence of a January Effect on closed
end municipal bond funds. Interestingly the authors detected the presence of a January effect on the funds but not on
the bonds constituting these funds. The authors attribute these results to tax harvesting.

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The idea that stock returns in one month could be higher than in another month could be supported by cyclical
factors and human behavior. In principle there is no clear reason supporting the idea that stock performance should
be stronger in January that in other months. Some of the frequently mentioned explanations are:
1.1 Tax rationale
One of the frequently mentioned potential explanations for this event in the U.S. is the federal income tax effect
(Jones, 1989) with the existing academic literature supporting that there was no January effect before the
introduction of federal income tax. It should be noted that there is less of an agreement of the existence of the
January effect for some of the years just after the introduction of federal income taxes. Related to tax issues, other
authors have found that there has been no obvious impact in the January Effect by some large tax reforms such as for
instance the Reform Act in 1986 (Haug, 2006). Tax reasons (Honghui, 2004) are among the most frequently cited
explanations for the January effect.The idea behind a tax argument is rather simple. Investors in order to minimize
their annual tax bill sell some of the losing positions before year end to increase their losses. This is commonly
known as tax harvesting. A byproduct of this tax harvesting is a larger cash pool in January that it is then reinvested
pushing prices up. It should also be noted thecaveat mentioned by (Taufiq, 2016) that the German case cannot be
used for analyzing the impact of taxes in the January effect as there were no applicable capital gain taxes in Germany
during that period (Taufiq, 2016). The tax argument is however not universally accepted with articles such as (Gu,
2005) providing some empirical evidence against it in the Chinese market.
1.2 Psychological Rationale
Psychological factors are also frequently used in an attempt to explain the reasons behind the January effect
(Anderson, 2007). Some authors, such as (Ciccone, 2011), have mentioned that the new year is a period of renewed
optimism and that such optimism likely spreads also to the stock market. This is clearly an explanation not related to
market fundamentals but to human behavior. (Ciccone, 2011) does provide some quantitative data. For instance, the
authors mentioned that the University of Michigan Consumer Confidence Index tends to peak in January, which is
used in this article as a proxy for investors optimism.
1.3 Window Dressing
Window dressing is one of the most popular explanations behind this effect (Haugen, 1988), (Klock, 2014). The idea
is that fund managers will try to make their portfolios look as good as possible by the end of the year. In order to do
that they will sell losing stocks during the end of the year, keeping those that have performed strongly. The funds that
were obtained from those stock are then reinvested in January in more speculative stocks in an attempt to obtain
strong performances. This inflow of funds in January will push prices up, causing the returns in the month of January
to increase.
1.4 Gifts
Another explanation of the January effect, postulated by (Gamble, 1993), suggests that the January effect is caused
by gifts, typically to younger investors, during the end of year and Christmas festivities. The idea is that some of the
cash gifts are invested in the stock market. More specifically, the author suggests that older investors tend to own
stable, well capitalized companies and sell a fraction of those holdings at the end of the year to free up funds for gifts.
Always according to the author, these stable, large companies are only moderately impacted by this selling. Younger
investors tend to more speculative investors, favoring smaller companies. Purchases of those names are likely to
push the prices up given their comparatively poor liquidity, driving the market up.
Regardless of what is the real reason behind this effect it seems reasonable to try to determine for which markets
there is empirical evidence of such effect actually happening that is the main objective of this article. In an attempt to
answer that question a very large data base of stock returns across many countries and jurisdictions was analyzed.
The steps followed for this analysis are presented in the next section.
2. Methodology
2.1 Hypothesis
The null hypothesis in this article is that the returns in the month of January are not statistically different from the
returns on any other month of the year. This analysis is performed on a global basis including a large amount of
countries and jurisdictions. It was not assumed that the returns of the indexes follow a normal distribution. There is
ample literature supporting the argument that stocks returns are not normally distributed. Nevertheless, several tests
were carried out to confirm such assumption.

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2.2 Data
The data are composed of monthly closing values of 106 indexes covering 86 countries and jurisdictions. It includes
indexes representing supranational entities such as Europe or the GCC as well as special administrative areas such as
Hong Kong in China. According to data from Bloomberg the combined market capitalization of those 84 countries
and jurisdictions accounted for approximately 92.3% of the global market capitalization as of July 2017. There is no
double counting, with the estimate excluding the market capitalization of supranational indexes such as those
covering Europe or the GCC.
The length of the time series varies from country to country and from index to index. The Dow Jones index for
instance has a much longer time series than some of the emerging markets indexes. The analysis was performed
using the entire data set for each index as well as using, from comparability purposes, only the last 15 years of values
as of end of June 2017. For consistency in all the cases the same numbers of data points per month were used. All the
data were obtained from Bloomberg. Monthly returns were obtained using monthly closing prices and formula [1].
The data was then grouped by month (from January to December).
  

  [1]
2.3 Procedure
In a preliminary test the hypothesis that the index returns are normally distributed were checked with a Lillie test and
an Anderson Darling test for each index for every month. As expected, for most cases the hypothesis that the index
returns are normally distributed was rejected at a 5% confidence level. The results of the Anderson-Darling and the
Lillie tests for every month for every index can be found in Appendix 2 and Appendix 3. The null hypothesis of the
Anderson Darling test is that the data come follow a normal distribution. For the vast majority of the indexes the
hypothesis that the monthly returns follow a normal distribution (for all the months of the year) cannot be accepted.
According to the Anderson Darling test there were only 4 indexes, out of the 106 analyzed, in which the assumption
that the returns follow a normal distribution for all the 12 months of the year cannot be rejected. Those four indexes
are the PSI All Share (Portugal), Nigerian Stock Exchange Index (Nigeria), Tunisian Stock Exchange Index (Tunisia)
and the S&P NZX All Index (New Zealand). Using the Lillie test similar results were obtained with no rejection of
the null hypothesis of a normal distribution only in 10 out of 106 indexes analyzed at a 5% significance level. The
null hypothesis in the Lillie test is that the underlying data follows a normal distribution. The 10 indexes for which
the hypothesis that their returns follow a normal distribution are the S&P 1500 (U.S.), Colombia Colcap Index
(Colombia), Ibex 35 (Spain), PSI All Share All Share Index (Portugal), Oslo All Share Index (Norway), Vienna Stock
Exchange Index (Austria), Tunisia Stock Exchange Index (Tunisia), Nigeria Stock Exchange Index (Nigeria),
Tadawull All Share Index (Saudi Arabia) and Bloomberg GCC 200 (GCC).Given that for the vast majority of the
indexes the monthly returns do not appear to follow normal distribution hence nonparametric tests, such as the
Wilcoxon Rank Sum and the Kruskal Wallis tests, were used to compare the returns. These tests do not assume that
the data follows a normal distribution. The Wilcoxon test compares the medians of two data sample to determine if
they are statistically equal at a certain confidence level. The purpose of the Kruskal Wallis test is determining if two,
or more, samples of data come from the same distribution or not at a determined confidence level.
The returns in January were compared with the returns for all the other eleven months of the year using the Wilcoxon
test. The results for the Wilcoxon tests for the entire available data set for the data provider can be found in Table 2.
The results obtained using the Kruskal Wallis test can be found in Table 4. Given that the entire data series available
for each index are not of the same size it seemed reasonable, for comparability purposes, to do some further analysis
using the same data time period for all the indexes. The time period used was 15 years (ending in June 2017). It
should be noted that not all the indexes have a time series of monthly returns for 15 years. In fact, of the 106 indexes
analyzed 16 did not have data available for the required period. The list of indexes that did not fulfill this
requirement can be seen in Table 1.
Table 1. Excluded indexes
FTSE Italia All Share (Italy)
Tanzania All Sh. Ind (Tanzania)
Dubai Fin. Market Ind. (Dubai)
Ljubljana St.Ind (Slovenia)
Nairobi Sec. Exc. All (Kenya)
Chile 65 (Chile)
St. Ex. Rep.Srpska (Serbia)
Ghana Composite Ind. (Ghana)
Bloomberg GCC (GCC)
MBI 10 (Macedonia)
Kuwait St. Exc. Ind. (Kuwait)
Laos Comp. Ind. (Laos)
Cyprus Gen. Exc.Ind (Cyprus)
Bahrain Bourse All (Bahrain)
FTSE JSE Nam. Ind (Namibia)
QE All Share (Qatar)

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The same process as before was repeated with these shorter times with a Wilcoxon and a Kruskal Wallis test
performed in all of indexes. The results of the Wilcoxon test for this reduced data series can be found on Table 3
while the results of the Kruskal Wallis tests can be found on Table 4.
Table 2. Wilcoxon test comparing January returns with the rest of month (p-values) for the entire time series
Index
Location
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
S&P 500
U.S.
0.773
0.139
0.977
0.192
0.347
0.369
0.049
0.198
0.625
0.401
0.316
Dow Jones
U.S.
0.480
0.042
0.682
0.171
0.548
0.146
0.006
0.912
0.916
0.219
0.122
Nasdaq Com.
U.S.
0.393
0.461
0.615
0.186
0.984
0.577
0.885
0.48
0.671
0.794
0.848
Nasdaq 100
U.S.
0.189
0.58
0.208
0.196
0.962
0.978
0.895
0.857
0.558
0.54
0.778
N.Y.S.E.I
U.S.
0.825
0.723
0.925
0.43
0.306
0.105
0.048
0.763
1
0.143
0.117
S&P 100
U.S.
0.773
0.139
0.977
0.192
0.347
0.369
0.049
0.198
0.625
0.401
0.316
S&P 1500
U.S.
0.534
0.805
0.379
0.991
0.193
0.103
0.46
0.614
0.879
0.474
0.405
Russell 1000
U.S.
0.269
0.523
0.543
0.116
0.651
0.264
0.659
0.823
0.622
0.996
0.831
Russell 2000
U.S.
0.483
0.234
0.682
0.123
0.557
0.306
0.347
0.921
0.938
0.897
0.791
Russell 3000
U.S.
0.182
0.55
0.477
0.116
0.682
0.255
0.636
0.848
0.682
0.913
0.946
S&P Toronto
Canada
0.048
0.193
0.8
0.196
0.293
0.378
0.132
0.234
0.76
0.75
0.851
S&P BMV
Mexico
0.948
0.056
0.496
0.983
0.416
0.758
0.861
0.775
0.482
0.282
0.141
Bol.al .Pan.
Panama
0.048
0.193
0.8
0.196
0.293
0.378
0.132
0.234
0.76
0.75
0.851
B.A. .In.
Argentina
0.901
0.852
0.78
0.144
0.721
0.576
0.106
0.256
0.828
0.208
0.27
Ibovespa
Brazil
0.438
0.295
0.587
0.393
0.426
0.181
0.14
0.801
0.13
0.194
0.174
Chile 65
Chile
0.977
0.977
0.84
0.751
0.237
0.175
0.997
0.126
0.26
0.126
0.089
Caracas In.
Venezuela
0.843
0.75
0.598
0.598
0.725
0.792
0.328
0.219
0.66
0.701
0.895
S&P BVL
Peru
0.533
0.836
0.822
0.378
0.769
0.64
0.822
0.876
0.333
0.742
0.556
Colombia Colcap
Colombia
0.507
0.047
0.082
0.32
0.125
0.868
0.023
0.59
0.213
0.34
0.068
BCT
Costa Rica
0.637
0.223
0.792
0.404
0.792
0.792
0.056
0.334
0.323
0.598
0.302
Bermuda In.
Bermuda
0.699
0.177
0.236
0.236
0.597
0.751
0.307
0.519
0.474
0.716
0.319
B. Eur. 500
Europe
0.409
0.337
0.839
0.797
0.039
0.133
0.044
0.441
0.19
0.172
0.133
MSCI Europe
Europe
0.275
0.2
0.788
0.788
0.035
0.235
0.11
0.862
0.335
0.367
0.11
S&P Europe 350
Europe
0.261
0.248
0.862
0.716
0.038
0.261
0.125
0.987
0.42
0.42
0.11
DAX
Germany
0.687
0.132
0.865
0.23
0.264
0.104
0.234
0.127
0.431
0.113
0.344
CAC 40
France
0.297
0.371
0.652
0.404
0.093
0.52
0.099
0.61
0.404
0.186
0.036
Ibex 35
Spain
0.284
0.6
0.464
0.311
0.297
0.796
0.54
0.83
0.935
0.62
0.483
FTSE 100
U.K.
0.259
0.317
0.798
0.419
0.088
0.481
0.02
0.929
0.33
0.788
0.121
FTSE All
U.K.
0.816
0.171
0.051
0.788
0.031
0.092
0.004
0.03
0.109
0.079
0.001
Swiss Mar. In.
Switzerland
0.128
0.988
0.219
0.131
0.744
0.816
0.641
0.465
0.913
0.988
0.988
FTSE MIB
Italy
0.704
0.255
0.365
0.815
0.007
0.08
0.051
0.255
0.267
0.122
0.038
FTSE Italia All
Italy
0.63
0.124
0.346
0.323
0.07
0.535
0.077
0.241
0.37
0.135
0.057
PSI All Share
Portugal
0.191
0.697
0.792
0.586
0.465
0.732
0.153
0.25
0.197
0.244
0.375
Irish Overall
Ireland
0.336
0.681
0.83
0.481
0.736
0.473
0.052
0.094
0.227
0.394
0.361
Iceland St. Exc.
Iceland
0.813
0.19
0.024
0.829
0.307
0.392
0.115
0.051
0.085
0.198
0.212
Amsterdam In.
Netherlands
0.946
0.171
0.646
0.528
0.252
0.227
0.003
0.387
0.488
0.094
0.164
Belgium 20
Belgium
0.615
0.084
0.905
0.791
0.203
0.405
0.01
0.276
0.284
0.667
0.098
Brussels St. Exc.
Belgium
0.052
0.928
0.182
0.241
0.641
0.688
0.176
0.399
0.737
0.456
0.851
Luxemburg In.
Luxemburg
0.912
0.537
0.496
0.58
0.384
0.304
0.056
0.764
0.125
0.937
0.987
OMX Cop.
Denmark
0.063
0.633
0.327
0.2
0.352
0.651
0.782
0.314
0.98
0.421
0.669

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OMX Helsinki
Finland
0.141
0.446
0.994
0.807
0.13
0.201
0.923
0.093
0.134
0.473
0.223
Oslo All
Norway
0.314
0.96
0.209
0.227
0.615
0.706
0.669
0.92
0.9
1
0.529
OMX All
Sweden
0.325
0.222
0.09
0.195
0.418
0.28
0.341
0.214
0.13
0.991
0.596
Vienna St. Ex.
Austria
0.574
0.67
0.509
0.449
0.561
0.756
0.112
0.352
0.214
0.801
0.229
Athens St. Exc.
Greece
0.186
0.501
0.728
0.923
0.59
0.412
0.853
0.492
0.631
0.53
0.492
Warsaw St. Exc.
Poland
0.876
0.179
0.405
0.905
0.365
0.833
0.087
0.268
0.245
0.602
0.151
Prague St. Exc.
Czech Rep.
0.244
0.022
0.18
0.629
0.392
0.758
0.03
0.153
0.227
0.203
0.195
MICEX
Russia
0.502
0.907
0.62
0.93
0.243
0.267
0.35
0.08
0.22
0.243
0.231
Budapest In.
Hungary
0.848
0.26
0.978
0.481
0.978
0.589
0.284
0.268
0.641
0.934
0.136
Ukraine PFTS
Ukraine
0.62
0.838
0.293
0.307
0.815
0.704
0.17
0.414
0.108
0.884
0.661
Kazakhstan In.
Kazakhstan
0.605
0.47
0.81
0.389
0.654
0.605
0.191
0.491
0.27
0.81
1
Slovak Share
Slovakia
0.66
0.119
0.02
0.758
0.693
1
0.062
0.965
0.272
0.312
0.983
Zagreb St. Exc.
Croatia
0.74
0.244
0.431
0.431
0.868
0.648
0.561
0.229
0.967
0.772
0.74
Ljubljana In.
Slovenia
0.945
0.206
0.597
0.395
0.28
0.872
0.63
0.124
0.801
0.663
0.124
Rep. Srpska In.
Serbia
0.473
0.918
0.027
0.065
0.442
0.918
0.356
0.682
0.2
0.124
0.282
OMX Tallinn
Estonia
0.706
0.019
0.021
1
0.352
0.05
0.022
0.003
0.119
0.008
0.102
MBI 10
Macedonia
0.194
0.977
0.507
0.403
0.624
0.112
0.471
0.312
0.665
0.089
0.061
OMX Riga
Latvia
0.068
0.021
0.302
0.318
1
0.408
0.535
0.085
0.705
0.757
0.039
OMX Vilnius
Lithuania
0.973
0.203
0.605
0.318
0.558
0.945
0.973
0.036
0.945
0.286
0.654
Bulgaria Ind
Bulgaria
0.611
0.235
0.777
0.559
0.692
0.51
0.51
0.418
0.985
0.44
0.836
Borsa. Ist. 100
Turkey
0.058
0.06
0.756
0.096
0.043
0.256
0.009
0.01
0.244
0.166
0.017
Cyprus General
Cyprus
0.624
0.931
0.751
0.665
0.312
0.1
0.708
0.583
0.544
0.795
0.624
Malta St. Exc.
Malta
0.2
0.119
0.841
0.481
0.102
0.268
0.014
0.066
0.58
0.563
0.669
FTSE JSE All
S. Africa
0.33
0.597
0.446
0.991
0.218
0.824
0.307
0.55
0.699
0.716
0.275
EGX 30
Egypt
0.137
0.484
0.838
0.255
0.54
0.559
0.051
0.129
0.64
0.748
0.414
MADEX
Morocco
0.561
0.481
0.062
1
0.384
0.384
0.199
0.038
0.025
0.804
0.068
Tunisia St. Exc.
Tunes
0.912
0.716
0.289
0.788
0.693
0.319
0.58
0.189
0.669
0.537
0.275
FTSE JSE Nam.
Namibia
1
0.124
0.798
0.608
0.112
0.538
0.505
0.72
0.2
0.151
0.282
Botswana Gaborone
Botswana
0.818
0.695
0.31
0.655
0.218
0.441
0.076
0.86
0.968
0.543
0.598
Nigerian Ind
Nigeria
0.953
0.953
0.579
0.599
0.54
0.431
0.54
0.414
0.448
0.414
0.559
Tanzania All
Tanzania
0.021
0.91
0.97
0.031
0.308
0.91
0.273
0.791
0.017
0.385
0.734
Nairobi Sec. All
Kenya
0.931
1
0.063
1
0.796
0.796
1
0.489
0.667
0.34
0.667
Ghana Com.
Ghana
0.818
0.818
0.31
0.31
0.818
0.31
0.818
0.485
0.24
0.937
0.394
Kuwait St. Exc.
Kuwait
0.791
0.308
0.678
0.162
0.91
0.623
0.791
0.427
0.273
0.623
0.045
Tel Aviv St. Exc.
Israel
0.015
0.712
0.112
0.816
0.816
0.712
0.67
0.222
0.522
0.861
0.574
Blom Ind
Lebanon
0.407
0.087
0.024
0.529
0.314
0.466
0.9
0.138
0.092
0.651
0.209
Bahrain All
Bahrain
0.918
0.918
0.918
0.608
0.918
0.608
0.505
0.356
0.305
1
0.72
Tadawull All
S. Arabia
0.645
0.282
0.13
0.775
0.93
1
0.16
0.826
0.312
0.282
0.087
Amman St. Exc.
Jordan
0.705
0.371
0.158
0.513
0.945
0.973
0.836
0.256
0.203
0.783
0.513
Muscat MSM 30
Oman
0.877
0.861
0.548
0.771
0.362
0.277
0.985
0.561
0.727
0.438
0.426
Blg. GCC 200
GCCC
0.948
0.264
0.793
0.555
0.896
0.646
0.511
0.694
0.264
0.646
0.115
QE All Share
Qatar
0.473
0.427
0.85
0.678
0.91
0.308
0.241
0.623
0.308
0.385
0.212
Dubai F. M. G.
UAE
0.798
0.2
0.412
0.238
0.682
0.608
0.838
0.72
0.124
0.878
0.101
Abu Dhabi G.
UAE
0.59
0.229
0.804
0.431
0.709
0.481
0.431
0.934
0.068
0.74
0.281
M. SEMDEX
Mauritius
0.096
0.762
0.641
0.193
0.967
0.915
0.103
0.031
0.863
0.98
0.915
Tokyo St. Ind.
Japan
0.346
0.723
0.874
0.268
0.388
0.691
0.12
0.043
0.713
0.493
0.234

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 55 ISSN 1923-4023 E-ISSN 1923-4031
Nikkei 225
Japan
0.922
0.645
0.297
0.209
0.645
0.293
0.176
0.345
0.785
0.235
0.26
NSE Nifty 50
India
0.756
0.592
0.604
0.299
0.426
0.836
0.201
0.945
0.511
0.917
0.863
S&P Sensex
India
0.51
0.415
0.971
0.358
0.374
0.728
0.12
0.938
0.47
0.744
0.84
HIS Index
HK – China
0.463
0.454
0.076
0.307
0.223
0.356
0.607
0.722
0.892
0.064
0.454
CSI 300
M. China
0.199
0.678
0.431
0.901
0.868
0.709
0.534
0.836
0.184
1
0.709
Shanghai Comp.
M. China
0.375
0.615
0.708
0.876
0.934
0.301
0.978
0.949
0.268
0.481
0.355
Shenzhen Comp.
M. China
0.194
0.509
0.655
0.522
0.509
0.985
0.244
0.742
0.146
0.742
0.954
Kospi
South Korea
0.627
0.239
0.17
0.604
0.779
0.331
0.364
0.905
0.837
0.285
0.517
Bangkok SET
Thailand
0.446
0.674
0.947
0.888
0.819
1
0.277
0.277
0.52
0.888
0.404
Straits Time
Singapore
0.318
0.039
0.558
0.783
0.113
0.513
0.085
0.428
0.449
0.046
0.068
FTSE KLCI
Malaysia
0.567
0.718
0.093
0.156
0.51
0.15
0.081
0.683
0.634
0.358
0.607
Jakarta Ind.
Indonesia
0.568
0.436
0.042
0.115
0.415
0.272
0.272
0.946
0.84
0.995
0.736
Philippine Ind
Philippines
0.6
0.674
0.004
0.158
0.923
0.234
0.284
0.501
0.429
0.631
0.959
Karachi KSE100
Pakistan
0.6
0.674
0.004
0.158
0.923
0.234
0.284
0.501
0.429
0.631
0.959
Sri Lanka Ind.
Sri Lanka
0.692
0.311
0.814
0.317
0.888
0.103
0.372
0.825
0.634
0.424
0.087
MSE top 20
Mongolia
0.022
0.42
0.962
0.912
0.812
0.764
0.693
0.693
0.812
0.764
0.261
Laos Index
Laos
0.589
1
0.009
0.132
0.093
0.31
0.31
0.041
0.485
0.818
0.041
Ho Chi Minh In.
Vietnam
0.491
0.371
0.513
0.215
0.449
0.605
0.428
0.335
0.679
0.535
0.918
Australian All
Australia
0.687
0.037
0.063
0.758
0.165
0.544
0.71
0.129
0.507
0.888
0.238
S&P/NZX All
New Zealand
0.063
0.362
0.461
0.023
0.6
0.383
0.277
0.614
0.535
0.342
0.497
Data source: Bloomberg
Table 3. Wilcoxon test comparing January returns with the rest of month (p-values) for the last 15 years
Index
Jurisdiction
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
S&P 500 Index
U.S.
0.089
0.868
0.481
0.534
0.320
0.678
0.804
0.362
0.901
0.901
0.836
Dow Jones Industrial
U.S.
0.384
0.431
0.709
0.868
0.320
0.320
0.507
0.340
0.507
0.481
0.125
Nasdaq Composite
U.S.
0.097
1.000
0.263
0.455
0.263
0.804
0.246
0.263
0.590
0.619
0.648
Nasdaq 100
U.S.
0.031
0.619
0.300
0.481
0.281
0.709
0.125
0.125
0.481
0.455
0.619
N.Y. Stock Ex Comp.
U.S.
0.678
0.561
0.836
0.934
0.229
0.481
0.281
0.678
0.709
0.590
0.147
S&P 100
U.S.
0.213
0.300
0.507
0.740
0.281
0.561
0.934
0.320
0.804
0.678
0.340
S&P 1500
U.S.
0.106
0.709
0.431
0.561
0.340
0.648
0.804
0.431
0.967
0.934
0.868
Russell 1000
U.S.
0.106
0.772
0.455
0.590
0.340
0.678
0.934
0.431
0.967
0.868
0.901
Russell 2000
U.S.
0.590
0.836
0.619
0.590
0.590
0.431
0.868
0.281
0.836
0.534
0.868
Russell 3000
U.S.
0.097
0.678
0.455
0.590
0.340
0.678
0.868
0.407
1.000
0.967
0.901
S&P Toronto Com.
Canada
0.281
0.740
0.967
0.455
0.836
0.868
0.709
0.709
0.507
0.836
0.868
S&P BMV Mexico
Mexico
0.868
0.047
0.678
0.507
0.431
0.455
0.590
0.431
0.089
0.213
0.106
Bolsa de Valores Pan.
Panama
0.362
0.772
0.431
0.590
0.967
0.115
0.125
0.320
0.362
0.016
0.062
Buenos Aires Stock Ex.
Argentina
0.804
0.384
0.836
0.159
0.147
0.619
0.407
0.184
0.507
0.320
0.384
Ibovespa
Brazil
0.590
0.135
0.171
0.481
0.062
0.199
0.125
0.934
0.115
0.106
0.159
Caracas Stock Exc.
Venezuela
0.320
0.281
0.590
0.481
0.340
0.300
0.184
0.804
0.263
0.648
0.619
S&P BVL Peru
Peru
0.934
0.534
0.619
0.534
0.507
0.619
0.648
0.455
0.340
0.507
0.431
Colombia Colcap
Colombia
0.507
0.047
0.082
0.320
0.125
0.868
0.023
0.590
0.213
0.340
0.068
BCT Costa Rica
Costa Rica
0.663
0.787
0.362
0.772
0.678
0.246
0.431
0.804
0.934
0.619
0.934
Bermuda Stock Ex.
Bermuda
0.804
0.047
0.125
0.125
0.934
0.619
0.340
0.362
0.263
0.740
0.362
Bloomberg Europe 500
Europe
0.281
0.199
0.740
1.000
0.281
0.740
0.340
0.772
0.384
0.868
0.407
MSCI Europe Index
Europe
0.362
0.171
0.709
1.000
0.263
0.590
0.300
0.868
0.481
0.868
0.263

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 56 ISSN 1923-4023 E-ISSN 1923-4031
S&P Europe 350
Europe
0.340
0.263
0.678
0.967
0.281
0.561
0.340
0.836
0.534
0.901
0.281
DeustcheBorse DAX
Germany
0.340
0.407
0.229
0.740
0.300
0.407
0.481
0.619
0.868
0.934
0.507
CAC 40
France
0.362
0.135
0.836
0.901
0.125
0.648
0.407
0.804
0.648
0.619
0.159
Ibex 35
Spain
0.934
0.213
0.836
0.481
0.042
0.678
0.213
0.868
0.590
0.534
0.246
FTSE 100
U.K.
0.362
0.263
0.772
0.868
0.213
0.740
0.213
0.229
0.384
0.507
0.340
FTSE All Share
U.K.
0.320
0.320
0.836
0.772
0.199
0.709
0.263
0.362
0.384
0.590
0.455
Swiss Market Index
Switzerland
0.199
0.171
0.648
0.901
0.407
0.804
0.455
0.678
0.561
0.934
0.246
FTSE MIB Borsa
Italy
0.481
0.159
0.534
0.561
0.047
0.481
0.171
0.534
0.507
0.320
0.068
PSI All Share
Portugal
0.246
0.967
0.619
0.740
0.481
0.901
0.362
0.901
0.868
0.320
0.281
Irish Stock Ex. Overall
Ireland
0.281
0.678
0.678
0.384
0.678
0.481
0.184
0.836
0.507
0.836
0.534
Iceland Stock Exc.
Iceland
0.772
0.320
0.062
0.772
0.263
0.590
0.229
0.089
0.184
0.619
0.106
Amsterdam Stock Ex.
Netherlands
0.648
0.125
0.836
1.000
0.362
0.967
0.229
1.000
0.967
0.772
0.836
Belgium 20
Belgium
0.407
0.115
0.320
0.561
0.229
0.836
0.068
0.648
0.229
0.281
0.229
Brussels St. Exc.
Belgium
0.097
0.619
0.901
0.934
0.772
0.678
0.199
0.340
0.678
1.000
0.481
Luxemburg Sto. Exc.
Luxemburg
0.901
0.481
0.561
0.934
0.507
0.590
0.171
0.678
0.147
0.740
0.934
OMX Copenhagen
Denmark
0.171
0.561
0.561
0.340
0.362
0.678
0.836
0.362
0.709
0.384
0.868
OMX Helsinki
Finland
0.213
0.804
0.678
0.709
0.263
0.407
1.000
0.507
0.407
1.000
0.740
Oslo All Share
Norway
0.481
0.901
0.159
0.362
0.678
0.561
1.000
0.590
0.772
0.481
0.740
OMX Stockholm All
Sweden
0.648
0.184
0.648
0.619
0.340
0.184
0.125
0.678
0.028
0.901
0.320
Vienna Stock Exchange
Austria
0.590
0.407
0.868
0.967
0.213
0.772
0.199
0.320
0.481
0.648
0.229
Athens St. Exc. Gen.
Greece
0.455
0.481
0.901
0.967
0.320
0.934
0.868
0.678
1.000
0.648
0.263
Warsaw St. Exc.
Poland
0.836
0.106
0.534
0.934
0.125
0.804
0.507
0.740
0.431
0.125
0.213
Prague St. Exc.
Czech Rep.
0.619
0.062
0.075
0.740
0.135
0.534
0.082
0.199
0.115
0.125
0.082
MICEX
Russia
0.263
0.455
0.246
0.455
0.300
0.229
0.246
0.125
0.171
0.507
0.199
Budapest St. Exc.
Hungary
0.709
0.300
0.772
0.740
0.836
0.772
0.740
0.481
0.561
0.901
0.213
Ukraine PFTS
Ukraine
0.804
0.534
0.619
0.868
0.648
0.709
0.159
0.384
0.038
0.868
0.619
Kazakhstan St. Exc.
Kazakhstan
0.934
0.229
0.934
0.619
0.561
0.340
0.199
0.407
0.184
0.678
0.868
Slovak Share
Slovakia
0.246
0.089
0.047
0.246
0.340
0.229
0.125
0.678
0.147
0.051
0.740
Zagreb St. Exc.
Croatia
0.740
0.147
0.431
0.431
0.868
0.648
0.561
0.229
0.967
0.772
0.740
OMX Tallinn
Estonia
0.362
0.034
0.082
0.407
0.300
0.147
0.115
0.020
0.281
0.047
0.135
OMX Riga
Latvia
0.097
0.031
0.678
0.281
0.619
0.804
0.678
0.047
0.772
0.534
0.062
OMX Vilnius
Lithuania
0.868
0.300
0.740
0.407
0.246
0.481
0.740
0.125
0.740
0.455
0.901
Bulgaria St. Exc.
Bulgaria
0.678
0.263
0.407
0.384
1.000
0.455
0.481
0.300
0.836
0.534
0.934
The BorsaIstambul 100
Turkey
0.455
0.023
0.590
0.068
0.051
0.678
0.184
0.159
0.199
0.281
0.056
Malta St. Exc.
Malta
0.868
0.038
0.648
0.384
0.213
0.507
0.042
0.023
0.967
0.934
0.836
FTSE JSE Africa All
S. Africa
0.089
0.648
0.967
0.619
0.709
0.804
1.000
0.561
0.709
0.772
1.000
Egyptian Exc. EGX 30
Egypt
0.246
0.229
0.772
0.320
0.507
0.384
0.082
0.229
0.901
0.967
0.407
MADEX Casablanca
Morocco
0.561
0.481
0.062
1.000
0.384
0.384
0.199
0.038
0.025
0.804
0.068
Tunisia St. Exc.
Tunes
0.740
0.678
0.384
0.967
0.836
0.534
0.678
0.125
0.740
0.320
0.115
Botswana Gaborone
Botswana
0.678
0.740
1.000
0.709
0.648
0.709
0.115
0.836
0.772
1.000
0.740
Nigerian Sto. Exc.
Nigeria
0.031
0.023
0.003
0.025
0.042
0.003
0.340
0.320
0.147
0.018
0.534
Tel Aviv St. Exc.
Israel
0.042
0.868
0.068
0.590
0.709
0.678
0.678
0.534
1.000
0.648
0.648
Blom Stock Index
Lebanon
0.836
0.590
0.023
0.934
0.455
0.648
0.455
1.000
0.740
0.229
0.619
Tadawull All Share
Saudi Arabia
0.619
0.340
0.590
0.868
1.000
0.709
0.213
0.561
0.089
0.171
0.106
Amman St. Exc.
Jordan
0.678
0.619
0.246
0.590
0.934
0.967
0.590
0.184
0.362
0.967
1.000
Muscat MSM 30
Oman
0.804
0.507
0.481
0.648
0.431
0.125
0.836
1.000
0.740
0.159
0.184

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 57 ISSN 1923-4023 E-ISSN 1923-4031
Abu Dhabi General
UAE
0.590
0.229
0.804
0.431
0.709
0.481
0.431
0.934
0.068
0.740
0.281
Mauritius SEMDEX
Mauritius
0.281
0.868
0.709
0.082
0.934
0.934
0.340
0.097
0.709
0.772
0.804
Tokyo St. Exc. Ind.
Japan
0.836
0.619
0.507
0.709
0.590
0.481
0.171
0.561
0.868
0.804
1.000
Nikkei 225
Japan
0.934
0.836
0.362
0.648
0.648
0.340
0.097
0.300
0.967
0.590
0.804
NSE Nifty 50
India
0.934
0.455
0.740
0.281
0.934
0.648
0.590
0.481
0.362
0.678
1.000
S&P BSE Sensex
India
0.770
0.540
0.815
0.559
0.770
0.579
0.280
0.953
1.000
0.661
0.930
HIS Index
HK – China
0.772
0.018
0.836
0.678
0.171
0.678
0.901
1.000
0.534
0.590
0.097
CSI 300
M. China
0.199
0.678
0.431
0.901
0.868
0.709
0.534
0.836
0.184
1.000
0.709
Shanghai Comp.
M. China
0.184
0.481
0.619
0.836
0.619
0.507
0.384
0.804
0.246
0.678
0.534
Shenzhen Comp.
M. China
0.068
0.868
0.561
0.648
0.590
0.967
0.678
0.772
0.229
0.868
0.740
Korea St. Exc.Kospi
South Korea
0.709
0.147
0.772
0.868
1.000
0.967
0.590
0.836
0.709
0.561
0.229
Bangkok SET
Thailand
0.362
0.229
0.868
0.619
0.455
0.678
0.384
0.934
0.125
0.804
0.135
Straits Time
Singapore
0.407
0.034
0.229
0.561
0.263
0.868
0.213
0.901
0.481
0.062
0.082
FTSE Bursa KLCI
Malaysia
0.362
0.097
0.431
0.901
0.031
0.648
0.082
0.648
0.340
0.300
0.340
Jakarta St. Exc. Ind.
Indonesia
0.901
0.281
0.147
0.481
0.740
0.934
0.075
0.967
0.619
0.320
0.199
Philippine St. Exc.
Philippines
0.619
0.199
0.028
0.772
0.320
0.199
0.901
0.507
0.709
0.362
0.362
Karachi KSE100
Pakistan
0.431
0.229
0.804
0.431
0.263
0.384
0.135
0.147
0.619
0.159
0.068
Sri Lanka Ind.
Sri Lanka
0.934
0.868
0.804
0.199
0.619
0.023
0.384
0.455
0.678
0.199
0.147
MSE top 20
Mongolia
0.082
0.340
0.772
0.709
0.967
0.772
0.590
0.407
0.590
0.507
0.340
Ho Chi Minh St. In.
Vietnam
0.534
0.407
0.678
0.648
0.934
0.901
0.320
0.199
0.901
0.407
0.740
Australian All Or. In.
Australia
0.901
0.125
0.051
0.507
0.507
0.455
0.561
0.097
0.772
0.171
0.740
S&P/NZX All index
New Zealand
0.147
0.229
0.135
0.362
0.340
1.000
1.000
0.246
0.934
0.619
0.340
Data source: Bloomberg
Table 4. Kruskal-Wallis probability tables
Name
Ticker
Full
series
Last 15
years
Name
Ticker
Full
series
Last 15
years
S&P 500
SPX
0.0835
0.4622
Ljubljana Exc. Ind.
SBITOP
0.2967
*
Dow Jones Industrial
INDU
0.0020
0.1999
Rep. SrpskaInd.
BIRS
0.1192
*
Nasdaq Composite
CCMP
0.8294
0.5898
OMX Tallinn Ind.
TALSE
0.0070
0.4359
Nasdaq 100
NDX
0.7929
0.176
MBI 10 Ind.
MBI
0.1151
*
N.Y. Stock Ex Comp.
NYA
0.2297
0.4391
OMX Riga Ind.
RIGSE
0.1218
0.0829
S&P 100
OEX
0.2793
0.2742
OMX Vilnius Ind.
VILSE
0.4460
0.4415
S&P 1500
SPR
0.2764
0.4772
Bulgaria St. Exc.
SOFIX
0.7399
0.6701
Russell 1000
RIY
0.4163
0.4464
BorsaIstambul 100
XU100
0.2077
0.2056
Russell 2000
RTY
0.3885
0.765
Cyprus Gen Exc.
CYSMMAPA
0.3863
*
Russell 3000
RAY
0.429
0.5147
Malta St. Exc. Ind.
MALTEX
0.2197
0.0400
S&P Toronto Com. Ind.
SPTSX
0.0037
0.6951
FTSEJSE Africa All
JALSH
0.5511
0.3757
S&P BMV Mexico Ind.
MEXBOL
0.3131
0.393
Egyptian EGX 30
EGX30
0.0607
0.0624
Bolsa de Valores Pan.
BVPSBVP
0.3441
0.3528
MADEX
Casablanca
MOSEMDX
0.1005
0.1005
Buenos Aires Ind.
MERVAL
0.284
0.6235
Tunisia St. Exc. Ind.
TUSISE
0.5602
0.5449
Ibovespa Ind.
IBOV
0.9127
0.4844
FTSE JSE Namibia
FTN098
0.3473
*
Chile 65 Ind.
CHILE65
0.1564
*
Botswana Gaborone
BGSMDC
0.1261
0.6382
Caracas St. Exc. Ind.
IBVC
0.4893
0.9816
Nigerian Sto. Exc.
NGSEINDX
0.0269
0.0255
S&P BVL Peru Ind.
SPBLPGPT
0.9048
0.9929
Tanzania All Share
DARSDSEI
0.0275
*
Colombia Colcap Ind.
COLCAP
0.3908
0.3908
Nairobi Exc. All
NSEASI
0.4341
*
BCT Costa Rica Ind.
CRSMBCT
0.8017
0.9302
Ghana Composite
GGSECI
0.5366
*
Bermuda Stock Ex.
BSX
0.2968
0.4588
Kuwait St. Exc. In
SECTMIND
0.4231
*
Bloomberg Europe 500
BE500
0.0904
0.4593
TelAviv St. Exc.
TA-125
0.1009
0.0918

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MSCI Europe Ind.
MXEU
0.0953
0.4528
Blom Stock Index
BLOM
0.1714
0.2673
S&P Europe 350 Ind.
SPEURO
0.0956
0.5236
Bahrain Bourse All
BHSEASI
0.7358
*
DeustcheBorse DAX
DAX
0.0612
0.2732
Tadawull All Share
SASEID
0.7182
0.7326
CAC 40 Ind.
CAC
0.0993
0.0001
Amman St. Exc.
JOSMGNFF
0.3991
0.5056
Ibex 35 Ind.
IBEX
0.6770
0.4845
Muscat MSM 30
MSM30
0.5979
0.1575
FTSE 100 Ind.
UKX
0.0358
0.0545
Bberg GCC 200
BGCC200
0.1357
*
FTSE All Share Ind.
ASX
0.0103
0.0903
QE All Share Ind.
QEAS
0.0915
*
Swiss Market Ind.
SMI
0.4326
0.434
Dubai Fin. Mar. Ind.
DFMGI
0.1759
*
FTSE MIB BorsaItaliana
FTSEMIB
0.1169
0.4294
Abu Dhabi General
ADSMI
0.0674
0.0674
FTSE Italia All Share
ITLMS
0.3817
*
Mauritius SEMDEX
SEMDEX
0.1966
0.5281
PSI All Share Ind.
BVLX
0.2533
0.6323
Tokyo St. Exc. Ind.
TPX
0.0618
0.6331
Irish Stock Ex. Overall
ISEQ
0.0548
0.5013
Nikkei 225 Ind.
NKY
0.1395
0.4443
Iceland Stock Exc. Ind.
ICEXI
0.1467
0.3665
NSE Nifty 50 Ind.
NIFTY
0.6363
0.6352
Amsterdam Stock Ex.
AEX
0.0825
0.7233
S&P BSE Sensex
SENSEX
0.3559
0.9332
Belgium 20 Ind.
BEL20
0.1872
0.4421
HIS Index Ind.
HSI
0.0069
0.0558
Brussels St. Exc. Belgian
BELSTK
0.3395
0.5831
CSI 300 Ind.
SHSZ300
0.5993
0.5993
Luxemburg Sto. Exc.
LUXXX
0.3395
0.7235
Shanghai Comp.
SHCOMP
0.5402
0.5488
OMX Copenhagen
KAX
0.3074
0.4715
Shenzhen Comp.
SZCOMP
0.3397
0.4412
OMX Helsinki Ind.
HEX
0.1610
0.6601
Kospi
KOSPI
0.216
0.7547
Oslo All Share Ind.
OSEAX
0.4304
0.6236
Bangkok SET Ind.
SET
0.9354
0.8611
OMX Stockholm All
SAX
0.0147
0.2498
Straits Time Ind.
STI
0.0678
0.0202
Vienna Stock Exchange
WBI
0.1059
0.5944
FTSE Bursa KLCI
FBMKLCI
0.0177
0.0579
Athens St. Exc. General
ASE
0.4789
0.7365
Jakarta Stock Exc.
JCI
0.0778
0.0514
Warsaw St. Exc. Ind.
WIG
0.6257
0.7541
Philippine St. Exc.
PCOMP
0.0443
0.1797
Prague St. Exc. Ind.
PX
0.0112
0.2971
Karachi KSE100
KSE100
0.3742
0.7760
MICEX Ind.
INDEXCF
0.7925
0.8423
Sri Lanka Colombo
CSEALL
0.1497
0.0019
Budapest St. Exc. Ind.
BUX
0.3000
0.6054
MSE top 20 Ind.
MSETOP
0.0997
0.0987
Ukraine PFTS Ind.
PFTS
0.1164
0.1496
Laos Composite
Ind.
LSXC
0.0230
*
Kazakhstan St. Exc.
KZKAK
0.6398
0.6789
Ho Chi Minh St.
VNINDEX
0.8557
0.7380
Slovak Share Ind.
SKSM
0.1011
0.3488
Australian All Ind.
AS30
0.0382
0.0760
Zagreb St. Exc. Ind.
CRO
0.7734
0.6876
S&P/NZX All Ind.
NZSE
0.0125
0.0334
*No data available for the period requested
Data source: Bloomberg
3. Results
The average returns per month for all the indexes analyzed as well as their standard deviations can be found in
Appendix 1. These results, for an easier visualization, are presented grouped according to geographical
characteristics and divided into five regions: Americas, Western Europe, Eastern Europe, Middle East & Africa and
Asia & Oceania.
3.1 Wilcoxon (Entire Time Series)
Using the entire time series available in Bloomberg for the previously mentioned 106 indexes and after making the
number of data points for each month equal a total of 66 indexes, representing 62.3% of the total, were found to have
no statically different returns in the month of January compared to all the other 11 months of the year, 27 indexes
had one month with returns statistically different from January, 8 with 2 months, 2 with 3 months, 2 with 4 months
and 1 with 6 months. In most of the cases then there was no statistically significant difference. These results can be
found in Table 2. The index with 6 months of statistically different returns compared to the returns of January was
the OMX Tallin, an index representing the Estonian stock market. The median return, point estimate, for the OMX
Tallin index is negative in January and statistically significantly lower than in March, April, July, August, September
and November. The point estimates for the median in all these months were positive. These results would suggest
that the returns in January for the OMX Index have been lower than the results in several other months. The January
Effect is typically understood as the opposite effect with January having bigger returns than other months. The two
indexes with 4 months statistically different from the results in January were the FTSE All Share Index (U.K.) and

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 59 ISSN 1923-4023 E-ISSN 1923-4031
the BorsaIstambul 100 Index (Turkey). In both cases there was no significant difference in returns when comparing
January to the rest of months in the first quarter. In the case of the U.K. index the point estimate for the median value
of the return for the month of January was negative but small. The index had positive returns in all the 4 months with
statistically significantly different returns (June, August, September and December). In the case of the Borsa
Istambul Index the point estimates of the median followed a similar pattern than in the FTSE All Share index, with
the same distribution of months (including the negative value for January and the positive value for all the other
months), but with larger differences in the point estimated for the returns. The two indexes with 3 months with
statistically different returns when compared to January are the Tanzanian All Share Index (Tanzania) and the Laos
Composite Index (Laos). There is more disparity of results with these two cases. On one hand there is the case of the
index for Tanzania. The results for this index were similar to the previous cases with the point estimate for the
median coming negative but small in January followed by a few months of positive results (February, May and
October). On the other hand there is the case of Laos, with rather strong results in January and three months of
statistically significantly returns (April, September and December). It should be noted that the data series for the
index is Laos is relatively short with only 6 years of returns available in Bloomberg (always fulfilling the
requirement of having the same number of data points per month).
The eight indexes that have two months with returns statistically different from January can be seen in table 5. For
none of the eight indexes there was a statistically significant difference between January and February. For a majority
of indexes, 5 out of 8, there was a statistically significant difference between January and March. The results for the
tests for these eight indexes (over the entire data series) seem to support that the results were statistically similar for
most of the month. For the other two months the results in January seem to be weaker than in those two months for
all these eight indexes.
Table 5. Indexes with 2 months
Dow Jones Industrial (U.S.)
Prague St. Exc. Index(Czech Rep.)
Colombia Colcap (Colombia)
OMX Riga (Latvia)
Bloomberg Europe 500 (Europe)
MADEX Casablanca (Morocco)
FTSE MIB Borsa(Italy)
Straits Time Index (Singapore)
Of the 106 indexes there were 27 that had one month with statistically different results when compared to January.
The list of these indexes can be found in Table 6. Of the 27 cases the point estimate for the median was higher than
the month with different returns, according to the Wilcoxon test, in 11 cases. Those 11 indexes are the S&P Toronto
Composite, Bolsa de Valores de Panama Index, MSCI Europe Index, Tel Aviv Stock Exchange Index, Blom Stock
Exchange Index, Jakarta Stock Exchange Index, Philippines Stock Exchange Index, Karachi Stock Exchange Index,
MSE Top 20 and the Australian All Index. Only in four of these cases (S&P Toronto Composite, Bolsa de Valores de
Panama Index, Tel Aviv Stock Exchange Index and MSE Top 20 Index) the results in February seem to be lower than
the returns in January. Another four indexes had lower returns in April with the rest of months with lower returns
distributed across the first half of the year. There were no months with significantly lower returns than January in the
second half of the year i.e., after June.
Table 6. Indexes with one month statistically different from January
S&P 500
OMX Vilnius
N.Y. Stock Ex Comp.
Malta St. Exc.
S&P 100
Kuwait St. Exc.
S&P Toronto Com.
Tel Aviv St. Exc.
Bolsa de Valores de Panama Index
Blom Stock Index
MSCI Europe Index
Mauritius SEMDEX
S&P Europe 350
Tokyo St. Exc. Ind.
CAC 40
Jakarta Stock Exchange
FTSE 100
Philippine St. Exc.
Iceland Stock Exc.
Karachi KSE100
Amsterdam Stock Ex.
MSE top 20

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Published by Sciedu Press 60 ISSN 1923-4023 E-ISSN 1923-4031
Belgium 20
Australian All Or. In.
Slovak Share
S&PNZX All index
St. Exc. Ind. Rep.Srpska
3.2 Wilcoxon (Last 15 Years)
The first thing to notice is that, as previously mentioned, when performing the analysis on the data series including
the last 15 years of returns there is a shorter number of indexes. This is due to the fact that not all the indexes have 15
years of returns available. For a list of the indexes excluded from this analysis please see Table 1. Of the 90 indexes
examined 67, representing 74.4% of the total, presented no statistically significant different returns when using the
Wilcoxon test. This represents a higher proportion that when analyzing the entire dataset available i.e., all the years,
but excluding the indexes with less than 15 years of track record (the indexes in those two approaches would be the
same but the time length of the data series would be different). When using the entire data series for the 90 indexes
there is a total of 54 cases, representing approximately 60% of the cases, in which there is no statistically significant
difference between the performance in January and the performance in any of the other months of the year. This
seems to support the idea that the January-Effect might be dissipating over time.
When analyzing the last 15 years of data 17 indexes had returns statistically different in the month of January
compared to another month in the same year. Three indexes had two months statistically different, two indexes three
months and one index 7 months. The index that had 7 months with returns different from the returns on January was
the Nigeria Stock Exchange Index. It would appear that in the case of Nigeria, during the last 15 years, that there are
indications of a sizeable January Effect. The point estimate of the returns of this index is positive and statistically
different from those on February, March, April, May, June, July and November with the majority of the point
estimate for the returns in those months actually being negative.
The indexes that had three months of statisticallydifferent results were the OMX Tallim (Estonia) and the Malta
Stock Exchange Index (Malta). Both of these indexes had negative median returns in the month of January. Similarly
to the case when the entire data set is analyzed the Estonian index is one of the indexes that has the largest amount of
months with statistically different returns when compared to the month of January. The returns in Estonian index
seem to be negative for the month of January. It should be noted that the results in both cases, with the entire data set
or only the last 15 years, the median of the returns is negative but the magnitude of the point estimate appears to
decrease. This perhaps points to an inverse, but decreasing, January-Effect in the Estonian case. In the case of Malta
the point estimate of the returns in January is also negative, with the months of March, August and September having
statistically different results and positive point estimates for the median returns.
The indexes that have two months of statistically different results, compared to January, are the OMX Riga (Latvia),
the Madex Casablanca (Morocco) and the Colcap Index (Colombia). In all these cases the point estimate of the
median return for the month of January was negative or very close to zero while the point estimates for the months
with statistically different returns were all positive. These results seem to indicate that there is no January-Effect in
the traditional sense.
The 17 indexes that had one month of statistically different returns can be seen in table 7. Of all these 17 indexes the
returns were higher in January, compared to the other statistical different month, for five indexes. These indexes are
the Nasdaq 100 Index (U.S.), the Tel Aviv Stock Exchange Index (Israel) and the Blom Index (Lebanon), Philippines
Stock Exchange Index (Philippines) and Sri Lanka Colombo Index (Sri Lanka). In most of these cases the months of
lower returns followed closely after January. In the cases of theNasdaq 100 Index and the Tal Aviv Stock Exchange
Index it occurred in February and in the cases of the Blom Index and the Philippines Stock Exchange Index in April.
The exception of this trend was the Sri Lanka Colombo Index with the significantly different month happening in
July. The point estimates of the median returns of all these three month were negative. There are no indications of
higher returns in January for the other 14 indexes. Most of the statistically different performances happened in the
first half of the year with only four cases happening after June. The four cases with different performances after June
were the Bolsa de Valores de Panama Index (November), the OMX Stockholm All Index (October), Ukraine PFTS
Index (October) and the Sri Lanka Colombo Index (July).
Table 7. Indexes with one month statistically different from January (time series of 15 years)
Nasdaq 100 (U.S.)
Bora Istambul 100 (Turkey)
S&P BMV (Mexico)
Tel Aviv Stock Exchange Index (Israel)

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Bolsa de Valores Index (Panama)
Blom Stock Index (Lebanon)
Bermuda Stock Exchange Index (Bermuda)
HSI Index (Hong Kong - China)
Ibex 35 (Spain)
Straits Time Index (Singapore)
FTSE MIB Borsa (Italy)
FTSE Bursa KLCI (Malaysia)
OMX Stockholm All (Sweden)
Philippine Stoc. Exc. Ind. (Philippines)
Ukraine PFTS (Ukraine)
Sri Lanka Colombo Index (Sri Lanka)
Slovak Share Index (Slovakia)
3.3 Kruskal-Wallis
The results for the Kruskal-Wallis test, for both the entire time series as well as the last 15 years, can be found in
Table 1. This test tries to determine if the returns for all the 12 months come from the same distribution. Therefore
one p value is obtained for all the 12 months rather than having 11 p values for the comparison of the months from
February to December (comparing them to the returns in January).
At a 5% confidence level an according to the results from the Kruskal-Wallis test 85.8% of the indexes , using the
entire data set available, the returns for all the 12 months came from the same distribution. When the entire length of
the time series is used but those indexes with less than 15 years of data are excluded then in 85.6% of the cases the
data come from the same distribution. When only the last 15 years of data are used, and indexes with less than those
15 years of returns are included in the analysis, then 93.3% of the cases appear to come from the same distribution
supporting the idea that the January-Effect is apparently dissipating over time. It should be noted that the test does
not specifically compared the performance in January with the performance in the rest of months. The test analyzes
the returns in all those months in its entirety. Using the entire time series the 15 indexes that do not appear to have
the returns for all the months coming from the same distribution can be seen in Table 8. The 13 and 6 indexes for the
entire time series length (excluding those indexes with less than 15 years of returns) as the case only including the
last 15 years of returns respectively can be seen in Tables 9 and 10.
Table 8. Entire time series, indexes with returns not coming from the same distribution
Dow Jones Industrial Ind.
Tanzania All Share Ind.
S&P Toronto Com. Ind.
HSI Index Ind.
FTSE 100 Ind.
FTSE Bursa KLCI Ind.
FTSE All Share Ind
Philippine St. Exc. Ind.
OMX Stockholm All Ind.
Laos Securities Composite Ind.
Prague St. Exc. Ind.
Australian All Ordinaries Ind.
OMX Tallinn Ind.
S&P/NZX All Ind.
Nigerian Sto. Exc. Ind.
Table 9. Entire time series (excluding indexes with less than 15 years of returns), indexes with returns not coming
from the same distribution
Dow Jones Industrial Ind.
Nigerian Sto. Exc. Ind.
S&P Toronto Com. Ind.
HSI Index Ind.
FTSE 100 Ind.
FTSE Bursa KLCI Ind.
FTSE All Share Ind.
Philippine St. Exc. Ind.
OMX Stockholm All Ind.
Australian All Ordinaries Ind.
Prague St. Exc. Ind.
S&P/NZX All Ind.
OMX Tallinn Ind.
Table 10. 15 last years, indexes with returns not coming from the same distribution
CAC 40 Ind.
Straits Time Ind.
Malta St. Exc. Ind.
Sri Lanka Colombo Ind.
Nigerian Sto. Exc. Ind.
S&P/NZX All Ind.
4. Conclusions

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Published by Sciedu Press 62 ISSN 1923-4023 E-ISSN 1923-4031
While there are markets, such as for instance Nigeria, in which there continues to appear to exist the traditional
January Effect there seems to be a global trend for the January Effect to gradually dissipate. This was been tested
across the vast majority of countries with functioning equity markets. This conclusion is supported for both the
results from the Wilcoxon and the Kruskal Wallis test. It was also observed that it is equally, if not more, prevalent an
Inverse January Effect, with returns lower in January than in other months, than the traditional January Effect. The
Estonian case is a good example of this trend of having lower returns in January than in several other months during
the year. When there are statistically significant differences between the returns in January and of the months they
tend to happen for month in the first half of the year. It should be noted that there are exceptions to this general trend.
The assumption that the monthly return does not, in general, follow a normal distribution was tested with an
Anderson Darling and a Lillie tests. In both cases the results point toward not accepting that assumption, which is in
agreement with the existing literature. This non normality of returns is the reason why non parametric test were used
to compare performance.
References
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Asteriou Dimitrios, & Kavetsos Georgios. (2006). Testing for the existence of the January Effect in transition
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Choudry Taufiq. (2011). Month of the year and January effect in pre-WWI stock returns: evidence from a non-linear
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Appendix 1. Median returns and standard deviation by geography
Figure 1. Average return per month – Americas
Data source: Bloomberg
Figure 2. Standard deviation per month – Americas
Data source: Bloomberg
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
SP500 Dow Jones NYA OEX SPR
RIY RTY RAY NDX IBOV
CCMP SPTSX MEXBOL BVPSBVP MERVAL
Chile65 IBVC SPBLPGT COLCAP CRSMBCT
BSX
0
0.1
0.2
0.3
0.4
0.5
0.6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
SP500 Dow Jones NYA OEX SPR
RIY RTY RAY NDX IBOV
CCMP SPTSX BVPSBVP MERVAL
Chile65 IBVC SPBLPGT COLCAP CRSMBCT
BSX

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Figure 3. Average return per month – Western Europe
Data source: Bloomberg
Figure 4. Standard deviation per month – Western Europe
Data source: Bloomberg
-0.04
-0.02
0.00
0.02
0.04
0.06
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IBEX DAX CAC BE500 MXEU
SPEURO BGCC200 UKX ASX SMI
FTSEMIB ITLMS BVLX ISEQ ICEXI
AEX BEL20 BELSTK LUXXX KAX
HEX OSEAX SAX WBI ASE
0.00
0.05
0.10
0.15
0.20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IBEX DAX CAC BE500 MXEU
SPEURO BGCC200 UKX ASX SMI
FTSEMIB ITLMS BVLX ISEQ ICEXI
AEX BEL20 BELSTK LUXXX KAX
HEX OSEAX SAX WBI ASE

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Published by Sciedu Press 66 ISSN 1923-4023 E-ISSN 1923-4031
Figure 5. Average return per month – Eastern Europe
Data source: Bloomberg
Figure 6. Standard deviation per month – Eastern Europe
Data source: Bloomberg
-0.15
-0.10
-0.05
0.00
0.05
0.10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
WIG PX INDEXCF BUX PFT
KZKAK SKSM CRO SBITOP BIRS
TALSE BIRS RIGSE VILSE SOFIX
XU100 CYMMAPA MALTEX
0.00
0.05
0.10
0.15
0.20
0.25
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
WIG PX INDEXCF BUX PFT
kzkak SKSM CRO SBITOP BIRS
TALSE BIRS RIGSE VILSE SOFIX
XU100 CYMMAPA MALTEX

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Published by Sciedu Press 67 ISSN 1923-4023 E-ISSN 1923-4031
Figure 7. Average return per month – Middle East and Africa
Data source: Bloomberg
Figure 8. Standard deviation per month – Middle East and Africa
Data source: Bloomberg
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
JALSH EGX30 MOSEMDX TUSISE FTN098
BGSMDC NGSEINDX DARDESEI NSEASI GGSECI
SECTMIND TA125 BLOM BHSEASI SASEIDX
JOSMGCFF MSM30 BGCC200 QEAS DFMGI
ADSMI SEMDEX
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
JALSH EGX30 MOSEMDX TUSISE FTN098
BGSMDC NGSEINDX DARDESEI NSEASI GGSECI
SECTMIND TA125 BLOM BHSEASI SASEIDX
JOSMGCFF MSM30 BGCC200 QEAS DFMGI
ADSMI SEMDEX

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Published by Sciedu Press 68 ISSN 1923-4023 E-ISSN 1923-4031
Figure 9. Average return per month – Asia and Oceania
Data source: Bloomberg
Figure 10. Standard deviation per month – Asia and Oceania
Data source: Bloomberg
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TPX HIS SHSZ300 SHCOMP SZCOMP KOSPI
SENSEX SET STI FBMKLCI JCI PCOMP
KSE100 CSEALL MSETOP LSXC VNINDEX Nifty
NKY AS30 NZSE
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TPX HIS SHSZ300 SHCOMP SZCOMP KOSPI
SENSEX SET STI FBMKLCI JCI PCOMP
KSE100 CSEALL MSETOP LSXC VNINDEX NIFTY
NKY AS30 NZSE

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Published by Sciedu Press 69 ISSN 1923-4023 E-ISSN 1923-4031
Appendix 2. AD Test
Index
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
S&P 500 Ind.
0.001
0.001
0.002
0.001
0.001
0.001
0.112
0.007
0.271
0.039
0.001
0.001
Dow Jones Industrial
0.001
0.001
0.238
0.001
0.001
0.001
0.014
0.001
0.144
0.010
0.001
0.001
Nasdaq Composite Ind.
0.091
0.784
0.498
0.045
0.026
0.071
0.056
0.002
0.548
0.155
0.019
0.390
Nasdaq 100 Ind.
0.103
0.064
0.197
0.052
0.080
0.025
0.238
0.001
0.291
0.115
0.075
0.472
N.Y. Stock Ex Comp.
0.343
0.327
0.346
0.161
0.063
0.001
0.033
0.072
0.672
0.944
0.372
0.702
S&P 100 Ind.
0.603
0.297
0.323
0.105
0.270
0.007
0.211
0.073
0.468
0.507
0.565
0.114
S&P 1500 Ind.
0.361
0.212
0.689
0.048
0.330
0.086
0.027
0.189
0.187
0.239
0.786
0.542
Russell 1000 Ind.
0.486
0.323
0.201
0.201
0.529
0.001
0.081
0.025
0.480
0.246
0.416
0.383
Russell 2000 Ind.
0.197
0.429
0.588
0.030
0.170
0.004
0.104
0.545
0.869
0.975
0.028
0.516
Russell 3000 Ind.
0.501
0.326
0.182
0.232
0.451
0.001
0.051
0.022
0.553
0.265
0.326
0.394
S&P Toronto Com. Ind.
0.001
0.010
0.497
0.001
0.001
0.002
0.096
0.014
0.507
0.188
0.001
0.011
S&P BMV Mexico Ind.
0.878
0.060
0.587
0.356
0.640
0.172
0.848
0.001
0.321
0.047
0.792
0.389
Bolsa de Valores Pan.
0.001
0.010
0.497
0.001
0.001
0.002
0.096
0.014
0.507
0.188
0.001
0.011
Buenos Aires St. Ind.
0.001
0.001
0.347
0.001
0.001
0.814
0.302
0.001
0.001
0.001
0.001
0.003
Ibovespa Ind.
0.001
0.001
0.817
0.004
0.002
0.198
0.001
0.001
0.001
0.001
0.001
0.701
Chile 65 Ind.
0.002
0.248
0.923
0.028
0.361
0.886
0.021
0.064
0.322
0.929
0.220
0.856
Caracas Stock Exc. Ind.
0.001
0.001
0.143
0.076
0.049
0.006
0.001
0.032
0.161
0.049
0.003
0.009
S&P BVL Peru Ind.
0.001
0.111
0.259
0.001
0.001
0.002
0.046
0.001
0.977
0.001
0.002
0.568
Colombia Colcap Ind.
0.382
0.124
0.121
0.302
0.044
0.034
0.095
0.989
0.098
0.066
0.780
0.153
BCT Costa Rica Ind.
0.008
0.006
0.013
0.928
0.061
0.663
0.012
0.001
0.055
0.084
0.001
0.001
Bermuda Stock Ex. Ind.
0.003
0.062
0.642
0.495
0.495
0.028
0.004
0.002
0.058
0.651
0.001
0.291
Bloomberg Europe 500
0.209
0.400
0.308
0.002
0.166
0.316
0.306
0.517
0.264
0.371
0.552
0.057
MSCI Europe Ind.
0.307
0.836
0.229
0.004
0.060
0.228
0.167
0.705
0.429
0.439
0.561
0.040
S&P Europe 350 Ind.
0.330
0.807
0.178
0.006
0.067
0.273
0.270
0.653
0.408
0.333
0.562
0.041
DAX Ind.
0.782
0.274
0.271
0.001
0.009
0.530
0.022
0.057
0.475
0.487
0.723
0.063
CAC 40 Ind.
0.955
0.931
0.677
0.031
0.461
0.028
0.364
0.616
0.054
0.173
0.680
0.435
Ibex 35 Ind.
0.038
0.332
0.638
0.130
0.326
0.016
0.397
0.962
0.247
0.145
0.423
0.359
FTSE 100 Ind.
0.630
0.312
0.883
0.601
0.951
0.001
0.641
0.940
0.386
0.437
0.990
0.199
FTSE All Share Ind.
0.414
0.601
0.663
0.099
0.058
0.001
0.006
0.063
0.001
0.023
0.003
0.009
Swiss Market Ind.
0.265
0.887
0.816
0.001
0.369
0.028
0.415
0.291
0.941
0.354
0.852
0.810
FTSE MIB BorsaItaliana
0.890
0.279
0.682
0.077
0.035
0.021
0.953
0.534
0.554
0.244
0.425
0.380
FTSE Italia All
0.522
0.393
0.202
0.192
0.179
0.047
0.257
0.438
0.146
0.624
0.620
0.359
PSI All Share Ind.
0.361
0.684
0.432
0.232
0.105
0.124
0.951
0.785
0.881
0.442
0.535
0.722
Irish Stock Ex. Overall
0.689
0.155
0.761
0.053
0.067
0.001
0.051
0.007
0.524
0.542
0.592
0.068
Iceland Stock Exc. Ind.
0.085
0.599
0.001
0.260
0.017
0.001
0.070
0.001
0.192
0.031
0.028
0.081
Amsterdam St. Ex. In.
0.830
0.708
0.061
0.024
0.007
0.001
0.152
0.176
0.033
0.709
0.763
0.111
Belgium 20 Ind.
0.628
0.574
0.032
0.050
0.038
0.001
0.097
0.855
0.203
0.027
0.605
0.166
Brussels St. Exc. Ind.
0.306
0.334
0.016
0.014
0.022
0.017
0.037
0.666
0.543
0.122
0.702
0.054
Luxemburg Sto. Exc. Ind.
0.321
0.923
0.928
0.496
0.009
0.001
0.663
0.334
0.528
0.032
0.070
0.026
OMX Copenhagen Ind.
0.330
0.958
0.685
0.773
0.012
0.003
0.131
0.648
0.417
0.925
0.693
0.032
OMX Helsinki Ind.
0.419
0.128
0.393
0.016
0.782
0.584
0.082
0.007
0.932
0.010
0.594
0.109
Oslo All Share Ind.
0.557
0.591
0.666
0.021
0.103
0.081
0.396
0.393
0.215
0.425
0.243
0.452
OMX Stockholm All
0.121
0.575
0.194
0.107
0.207
0.028
0.510
0.311
0.577
0.067
0.290
0.258

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Vienna St. Exc. Ind.
0.247
0.700
0.532
0.083
0.019
0.003
0.217
0.827
0.168
0.256
0.228
0.471
Athens St. Exc. Gen. Ind.
0.240
0.001
0.025
0.015
0.036
0.107
0.198
0.854
0.543
0.013
0.002
0.001
Warsaw St. Exc. Ind.
0.001
0.061
0.011
0.001
0.053
0.088
0.333
0.001
0.002
0.411
0.007
0.001
Prague St. Exc. Ind.
0.040
0.652
0.238
0.018
0.097
0.045
0.327
0.074
0.017
0.685
0.559
0.166
MICEX Ind.
0.017
0.164
0.047
0.011
0.706
0.048
0.002
0.375
0.379
0.009
0.002
0.214
Budapest St. Exc. Ind.
0.377
0.595
0.867
0.047
0.091
0.026
0.776
0.024
0.001
0.705
0.081
0.800
Ukraine PFTS Ind.
0.091
0.178
0.673
0.039
0.293
0.486
0.190
0.003
0.095
0.532
0.019
0.003
Kazakhstan St.Ind.
0.154
0.784
0.320
0.374
0.064
0.004
0.186
0.174
0.013
0.001
0.001
0.003
Slovak Share Ind.
0.867
0.402
0.040
0.835
0.058
0.204
0.021
0.065
0.189
0.001
0.562
0.099
Zagreb St. Exc. Ind.
0.001
0.605
0.941
0.848
0.752
0.001
0.025
0.001
0.833
0.070
0.316
0.116
Ljubljana St. Exc. Ind.
0.640
0.401
0.632
0.196
0.105
0.005
0.544
0.244
0.388
0.425
0.306
0.312
St. Exc. Ind. Rep. Srpska
0.324
0.077
0.803
0.004
0.027
0.015
0.883
0.072
0.003
0.087
0.090
0.416
OMX Tallinn Ind.
0.007
0.167
0.001
0.001
0.051
0.002
0.013
0.576
0.006
0.696
0.094
0.193
MBI 10 Ind.
0.001
0.203
0.097
0.058
0.036
0.050
0.474
0.166
0.990
0.048
0.001
0.001
OMX Riga Ind.
0.307
0.625
0.001
0.015
0.008
0.371
0.181
0.150
0.104
0.134
0.869
0.284
OMX Vilnius Ind.
0.179
0.020
0.060
0.001
0.073
0.006
0.111
0.016
0.171
0.678
0.894
0.029
Bulgaria St. Exc. Ind.
0.535
0.560
0.370
0.024
0.181
0.013
0.044
0.009
0.137
0.003
0.107
0.002
The BorsaIstambul 100
0.204
0.022
0.738
0.193
0.017
0.844
0.039
0.001
0.001
0.052
0.546
0.244
Cyprus General Exc. Ind.
0.031
0.492
0.236
0.001
0.157
0.350
0.804
0.130
0.713
0.134
0.342
0.214
Malta St. Exc. Ind.
0.008
0.124
0.682
0.091
0.851
0.277
0.001
0.001
0.038
0.420
0.013
0.763
FTSE JSE Africa All Ind.
0.271
0.223
0.173
0.001
0.439
0.554
0.123
0.620
0.532
0.984
0.206
0.313
EGX 30 Ind.
0.964
0.688
0.861
0.531
0.813
0.003
0.023
0.397
0.934
0.401
0.044
0.813
MADEX Casablanca Ind.
0.111
0.009
0.961
0.282
0.559
0.042
0.990
0.044
0.213
0.814
0.706
0.737
Tunisia St. Exc. Ind.
0.957
0.518
0.200
0.334
0.610
0.456
0.642
0.886
0.287
0.371
0.111
0.427
FTSE JSE Namibia Ind.
0.158
0.875
0.585
0.338
0.147
0.033
0.609
0.701
0.700
0.023
0.082
0.022
Botswana Gaborone Ind.
0.044
0.246
0.421
0.001
0.009
0.001
0.587
0.001
0.824
0.219
0.060
0.086
Nigerian Sto. Exc. Ind.
0.575
0.628
0.458
0.569
0.847
0.701
0.539
0.488
0.162
0.076
0.183
0.206
Tanzania All Share Ind.
0.055
0.023
0.097
0.001
0.036
0.004
0.019
0.177
0.106
0.099
0.609
0.613
Nairobi Sec. Exc. All Ind.
0.599
0.034
0.024
0.596
0.023
0.013
0.280
0.461
0.371
0.001
0.815
0.156
Ghana Composite Ind.
0.285
0.042
0.268
0.935
0.210
0.859
0.077
0.297
0.213
0.398
0.042
0.209
Kuwait St. Exc. Ind.
0.093
0.694
0.573
0.990
0.053
0.002
0.465
0.012
0.806
0.174
0.749
0.113
Tel Aviv St. Exc. Ind.
0.213
0.107
0.690
0.495
0.057
0.005
0.478
0.977
0.220
0.008
0.721
0.820
Blom Stock Index Ind.
0.001
0.001
0.149
0.621
0.024
0.022
0.128
0.001
0.001
0.001
0.943
0.089
Bahrain Bourse All Share
0.253
0.347
0.506
0.012
0.001
0.168
0.011
0.492
0.613
0.953
0.378
0.554
Tadawull All Share Ind.
0.235
0.035
0.111
0.456
0.071
0.081
0.442
0.025
0.018
0.635
0.946
0.122
Amman St. Exc. Ind.
0.927
0.228
0.005
0.111
0.886
0.001
0.251
0.280
0.189
0.316
0.511
0.030
Muscat MSM 30 Ind.
0.809
0.219
0.010
0.043
0.343
0.001
0.090
0.063
0.492
0.078
0.003
0.381
Bloomberg GCC 200 Ind.
0.570
0.091
0.713
0.336
0.088
0.027
0.546
0.299
0.547
0.076
0.731
0.800
QE All Share Ind.
0.306
0.134
0.979
0.321
0.544
0.002
0.328
0.693
0.134
0.080
0.901
0.212
DFM G. Ind.
0.147
0.004
0.139
0.051
0.242
0.036
0.049
0.756
0.675
0.736
0.214
0.266
Abu Dhabi Gen. Ind.
0.234
0.038
0.191
0.558
0.683
0.006
0.022
0.382
0.847
0.215
0.001
0.069
Mauritius SEMDEX Ind.
0.001
0.180
0.164
0.003
0.255
0.003
0.001
0.897
0.451
0.004
0.007
0.128
Tokyo St. Exc. Ind. Ind.
0.136
0.078
0.450
0.561
0.252
0.005
0.535
0.145
0.240
0.245
0.351
0.013
Nikkei 225 Ind.
0.072
0.141
0.500
0.294
0.229
0.006
0.169
0.894
0.216
0.334
0.800
0.031
NSE Nifty 50 Ind.
0.118
0.120
0.054
0.781
0.288
0.380
0.800
0.254
0.651
0.012
0.038
0.876
S&P BSE Sensex Ind.
0.075
0.080
0.023
0.116
0.244
0.027
0.806
0.044
0.818
0.003
0.033
0.359

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 71 ISSN 1923-4023 E-ISSN 1923-4031
HIS Index Ind.
0.410
0.020
0.302
0.215
0.272
0.001
0.001
0.001
0.485
0.001
0.191
0.001
CSI 300 Ind.
0.741
0.677
0.077
0.285
0.044
0.080
0.941
0.103
0.805
0.833
0.487
0.083
Shanghai Comp. Ind.
0.001
0.035
0.299
0.001
0.777
0.102
0.018
0.133
0.004
0.246
0.131
0.043
Shenzhen Comp. Ind.
0.717
0.206
0.119
0.001
0.824
0.023
0.896
0.490
0.880
0.525
0.968
0.005
Korea St. Exc. Kospi Ind.
0.551
0.011
0.162
0.493
0.783
0.008
0.186
0.193
0.001
0.728
0.088
0.740
Bangkok SET Ind.
0.232
0.384
0.141
0.039
0.726
0.001
0.253
0.165
0.256
0.257
0.610
0.001
Straits Time Ind.
0.023
0.173
0.690
0.616
0.127
0.001
0.808
0.473
0.682
0.034
0.003
0.026
FTSE Bursa KLCI Ind.
0.307
0.045
0.054
0.012
0.194
0.002
0.007
0.001
0.510
0.004
0.722
0.157
Jakarta Stock Exc. Ind.
0.745
0.519
0.404
0.001
0.251
0.015
0.015
0.001
0.217
0.024
0.445
0.323
Philippine St. Exc. Ind.
0.154
0.028
0.248
0.035
0.069
0.075
0.831
0.001
0.744
0.002
0.926
0.297
Karachi KSE100 Ind.
0.154
0.028
0.248
0.035
0.069
0.075
0.831
0.001
0.744
0.002
0.926
0.297
Sri Lanka Colombo Ind.
0.048
0.178
0.065
0.408
0.075
0.119
0.016
0.003
0.199
0.140
0.068
0.015
MSE top 20 Ind.
0.081
0.793
0.024
0.424
0.001
0.122
0.015
0.785
0.377
0.577
0.001
0.341
Laos Composite
0.027
0.088
0.420
0.905
0.570
0.562
0.614
0.283
0.990
0.068
0.140
0.804
Ho Chi Minh St. Ind.
0.055
0.641
0.150
0.018
0.224
0.039
0.634
0.001
0.024
0.099
0.228
0.240
Australian All Ord. Ind.
0.097
0.061
0.001
0.051
0.261
0.022
0.021
0.001
0.164
0.458
0.013
0.001
S&P NZX All Ind.
0.056
0.703
0.586
0.088
0.639
0.122
0.115
0.653
0.084
0.331
0.329
0.058
Data source: Bloomberg
Appendix 3. Lillie Test
Index
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
S&P 500 Ind.
0.010
0.017
0.065
0.001
0.006
0.001
0.045
0.014
0.429
0.044
0.001
0.001
Dow Jones Industrial Ind.
0.001
0.019
0.500
0.002
0.001
0.001
0.011
0.001
0.061
0.066
0.027
0.001
Nasdaq Composite Ind.
0.012
0.500
0.500
0.153
0.017
0.140
0.102
0.023
0.500
0.441
0.016
0.381
Nasdaq 100 Ind.
0.028
0.121
0.500
0.297
0.170
0.073
0.176
0.001
0.216
0.227
0.165
0.336
N.Y. Stock Ex Comp. Ind.
0.092
0.500
0.164
0.335
0.159
0.006
0.190
0.126
0.500
0.500
0.338
0.500
S&P 100 Ind.
0.500
0.179
0.232
0.108
0.196
0.018
0.223
0.041
0.500
0.500
0.193
0.143
S&P 1500 Ind.
0.149
0.194
0.423
0.082
0.500
0.174
0.146
0.233
0.073
0.353
0.406
0.224
Russell 1000 Ind.
0.252
0.500
0.136
0.214
0.500
0.007
0.316
0.037
0.263
0.294
0.500
0.302
Russell 2000 Ind.
0.194
0.498
0.500
0.008
0.141
0.091
0.060
0.452
0.500
0.500
0.063
0.243
Russell 3000 Ind.
0.148
0.449
0.090
0.196
0.494
0.005
0.207
0.030
0.298
0.428
0.500
0.242
S&P Toronto Com. Ind.
0.003
0.012
0.500
0.016
0.019
0.017
0.186
0.027
0.309
0.075
0.014
0.017
S&P BMV Mexico Ind.
0.500
0.296
0.324
0.142
0.500
0.317
0.500
0.003
0.449
0.258
0.500
0.500
Bolsa de Valores Pan. Ind.
0.003
0.012
0.500
0.016
0.019
0.017
0.186
0.027
0.309
0.075
0.014
0.017
Buenos Aires St. Ex. Ind.
0.001
0.001
0.077
0.001
0.008
0.500
0.500
0.002
0.078
0.001
0.001
0.018
IbovespaInd.
0.003
0.001
0.500
0.048
0.010
0.246
0.061
0.003
0.003
0.001
0.012
0.500
Chile 65 Ind.
0.015
0.391
0.500
0.082
0.292
0.500
0.007
0.181
0.112
0.500
0.203
0.500
Caracas Stock Exc. Ind.
0.007
0.001
0.065
0.193
0.054
0.032
0.001
0.048
0.307
0.075
0.032
0.035
S&P BVL Peru Ind.
0.001
0.027
0.254
0.001
0.001
0.007
0.145
0.001
0.500
0.002
0.030
0.500
Colombia ColcapInd.
0.286
0.103
0.220
0.098
0.102
0.271
0.401
0.500
0.055
0.096
0.500
0.330
BCT Costa Rica Ind.
0.044
0.003
0.004
0.500
0.094
0.298
0.023
0.010
0.112
0.305
0.004
0.002
Bermuda Stock Ex. Ind.
0.013
0.263
0.500
0.500
0.500
0.056
0.013
0.007
0.020
0.372
0.020
0.372
Bloomberg Europe 500
0.464
0.235
0.280
0.002
0.179
0.392
0.146
0.458
0.089
0.500
0.500
0.036
MSCI Europe Ind.
0.253
0.500
0.434
0.005
0.094
0.500
0.072
0.500
0.358
0.500
0.500
0.011
S&P Europe 350 Ind.
0.365
0.500
0.360
0.006
0.099
0.500
0.065
0.500
0.202
0.471
0.500
0.015

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 72 ISSN 1923-4023 E-ISSN 1923-4031
DeustcheBorse DAX Ind.
0.437
0.233
0.475
0.006
0.189
0.500
0.159
0.304
0.500
0.500
0.391
0.036
CAC 40 Ind.
0.500
0.500
0.500
0.019
0.117
0.015
0.430
0.500
0.183
0.500
0.500
0.500
Ibex 35 Ind.
0.200
0.378
0.422
0.174
0.227
0.059
0.500
0.500
0.420
0.095
0.405
0.491
FTSE 100 Ind.
0.500
0.363
0.500
0.500
0.500
0.001
0.500
0.500
0.500
0.464
0.500
0.171
FTSE All Share Ind.
0.500
0.500
0.500
0.035
0.111
0.007
0.029
0.232
0.001
0.184
0.012
0.028
Swiss Market Ind.
0.500
0.500
0.500
0.013
0.380
0.017
0.500
0.373
0.500
0.325
0.500
0.500
FTSE MIB BorsaItaliana
0.500
0.295
0.500
0.034
0.249
0.012
0.500
0.500
0.410
0.223
0.303
0.500
FTSE Italia All Share Ind.
0.317
0.365
0.120
0.115
0.500
0.028
0.143
0.500
0.031
0.411
0.500
0.500
PSI All Share Ind.
0.333
0.500
0.171
0.212
0.076
0.213
0.500
0.500
0.500
0.500
0.368
0.500
Irish Stock Ex. Overall Ind.
0.500
0.144
0.500
0.083
0.151
0.004
0.161
0.014
0.417
0.500
0.500
0.481
Iceland Stock Exc. Ind.
0.344
0.500
0.001
0.223
0.158
0.001
0.334
0.001
0.340
0.122
0.085
0.201
Amsterdam Stock Ex. Ind.
0.500
0.500
0.082
0.199
0.035
0.007
0.465
0.182
0.172
0.500
0.500
0.023
Belgium 20 Ind.
0.379
0.500
0.039
0.042
0.104
0.033
0.474
0.500
0.385
0.422
0.334
0.466
Brussels St. Exc. Ind.
0.244
0.291
0.093
0.004
0.164
0.079
0.076
0.500
0.500
0.221
0.500
0.163
Luxemburg Sto. Exc. Ind.
0.278
0.500
0.500
0.486
0.031
0.025
0.500
0.429
0.500
0.159
0.262
0.017
OMX Copenhagen Ind.
0.500
0.500
0.500
0.431
0.034
0.074
0.160
0.500
0.428
0.500
0.305
0.038
OMX Helsinki Ind.
0.236
0.073
0.249
0.032
0.500
0.500
0.036
0.101
0.343
0.088
0.500
0.083
Oslo All Share Ind.
0.500
0.494
0.500
0.072
0.328
0.071
0.296
0.146
0.315
0.500
0.198
0.500
OMX Stockholm All Ind.
0.090
0.500
0.116
0.062
0.263
0.016
0.500
0.500
0.500
0.092
0.325
0.235
Vienna St. Exc. Ind,
0.265
0.500
0.500
0.108
0.195
0.119
0.211
0.500
0.115
0.500
0.240
0.500
Athens St. Exc. Gen. Ind.
0.252
0.011
0.026
0.084
0.084
0.063
0.393
0.500
0.500
0.044
0.066
0.001
Warsaw St. Exc. Ind.
0.001
0.408
0.109
0.001
0.180
0.042
0.125
0.011
0.036
0.423
0.003
0.001
Prague St. Exc. Ind.
0.105
0.500
0.128
0.060
0.195
0.100
0.384
0.130
0.018
0.493
0.261
0.243
MICEX Ind.
0.003
0.229
0.018
0.034
0.500
0.070
0.041
0.500
0.385
0.049
0.030
0.410
Budapest St. Exc. Ind.
0.500
0.441
0.500
0.152
0.191
0.219
0.500
0.041
0.001
0.483
0.044
0.500
Ukraine PFTS Ind.
0.389
0.067
0.500
0.057
0.413
0.473
0.048
0.031
0.142
0.500
0.115
0.012
Kazakhstan St. Exc. Ind.
0.163
0.500
0.497
0.208
0.339
0.011
0.311
0.401
0.006
0.001
0.022
0.006
Slovak Share Ind.
0.500
0.500
0.018
0.313
0.069
0.080
0.014
0.157
0.156
0.003
0.246
0.463
Zagreb St. Exc. Ind.
0.006
0.239
0.500
0.500
0.467
0.001
0.095
0.001
0.500
0.320
0.297
0.244
Ljubljana St. Exc. Ind.
0.500
0.346
0.500
0.241
0.110
0.024
0.500
0.384
0.500
0.392
0.500
0.434
St. Exc. Ind. Rep. Srpska
0.500
0.036
0.500
0.019
0.068
0.008
0.500
0.056
0.013
0.124
0.045
0.500
OMX Tallinn Ind.
0.012
0.113
0.001
0.001
0.059
0.016
0.008
0.427
0.053
0.500
0.216
0.045
MBI 10 Ind.
0.001
0.254
0.095
0.183
0.039
0.040
0.439
0.264
0.500
0.146
0.003
0.001
OMX Riga Ind.
0.467
0.500
0.020
0.005
0.010
0.471
0.500
0.267
0.057
0.087
0.500
0.189
OMX Vilnius Ind.
0.125
0.036
0.047
0.001
0.029
0.035
0.059
0.033
0.500
0.500
0.500
0.042
Bulgaria St. Exc. Ind.
0.326
0.500
0.500
0.026
0.265
0.068
0.025
0.006
0.389
0.001
0.068
0.003
The BorsaIstambul 100
0.134
0.090
0.500
0.319
0.008
0.500
0.051
0.001
0.001
0.012
0.500
0.078
Cyprus General Exc. Ind.
0.074
0.342
0.098
0.005
0.132
0.255
0.500
0.216
0.500
0.044
0.327
0.387
Malta St. Exc. Ind.
0.059
0.206
0.500
0.018
0.500
0.182
0.014
0.017
0.073
0.210
0.001
0.500
FTSE JSE Africa All Ind.
0.124
0.324
0.031
0.002
0.335
0.500
0.058
0.500
0.404
0.500
0.329
0.110
Egyptian Exc. EGX 30 Ind.
0.500
0.500
0.500
0.446
0.500
0.015
0.044
0.301
0.500
0.204
0.086
0.500
MADEX CasablancaInd.
0.266
0.015
0.500
0.479
0.500
0.101
0.500
0.061
0.302
0.500
0.500
0.500
Tunisia St. Exc. Ind.
0.500
0.078
0.500
0.052
0.500
0.217
0.406
0.500
0.242
0.500
0.285
0.500
FTSE JSE Namibia Ind.
0.189
0.500
0.500
0.133
0.446
0.168
0.381
0.500
0.500
0.050
0.016
0.039
Botswana Gaborone Ind.
0.037
0.309
0.278
0.001
0.032
0.006
0.500
0.012
0.500
0.500
0.085
0.131
Nigerian Sto. Exc. Ind.
0.330
0.500
0.370
0.500
0.500
0.500
0.500
0.500
0.341
0.053
0.056
0.052

http://ijfr.sciedupress.com International Journal of Financial Research Vol. 9, No. 1; 2018
Published by Sciedu Press 73 ISSN 1923-4023 E-ISSN 1923-4031
Tanzania All Share Ind.
0.022
0.041
0.085
0.001
0.017
0.022
0.022
0.063
0.165
0.058
0.500
0.500
Nairobi Sec. Exc. All Ind.
0.273
0.007
0.008
0.500
0.075
0.161
0.500
0.500
0.500
0.006
0.500
0.266
Ghana Composite Ind.
0.500
0.189
0.366
0.500
0.156
0.500
0.140
0.123
0.170
0.500
0.037
0.169
Kuwait St. Exc. Ind.
0.154
0.431
0.447
0.500
0.019
0.007
0.416
0.027
0.500
0.204
0.500
0.053
Tel Aviv St. Exc. Ind.
0.240
0.166
0.373
0.500
0.037
0.079
0.345
0.500
0.500
0.019
0.500
0.500
Blom Stock Index Ind.
0.001
0.001
0.178
0.500
0.102
0.021
0.289
0.001
0.001
0.003
0.500
0.289
Bahrain Bourse All Share
0.345
0.398
0.190
0.055
0.001
0.171
0.006
0.500
0.500
0.500
0.389
0.356
Tadawull All Share Ind.
0.339
0.104
0.320
0.500
0.274
0.360
0.437
0.114
0.175
0.500
0.500
0.259
Amman St. Exc. Ind.
0.500
0.229
0.009
0.260
0.500
0.006
0.358
0.403
0.273
0.355
0.500
0.114
Muscat MSM 30 Ind.
0.500
0.254
0.015
0.121
0.500
0.062
0.373
0.315
0.248
0.165
0.001
0.247
Bloomberg GCC 200 Ind.
0.318
0.212
0.500
0.500
0.052
0.091
0.241
0.182
0.500
0.101
0.500
0.500
QE All Share Ind.
0.257
0.277
0.500
0.380
0.239
0.001
0.500
0.500
0.383
0.236
0.500
0.292
Dubai Fin. Mar. Gen. Ind.
0.369
0.032
0.100
0.062
0.300
0.049
0.095
0.453
0.500
0.500
0.100
0.255
Abu Dhabi General Ind.
0.180
0.039
0.500
0.500
0.482
0.045
0.044
0.500
0.500
0.187
0.001
0.240
Mauritius SEMDEX Ind.
0.001
0.443
0.286
0.024
0.301
0.006
0.005
0.500
0.291
0.034
0.120
0.045
Tokyo St. Exc. Ind. Ind.
0.161
0.311
0.235
0.466
0.500
0.037
0.500
0.323
0.286
0.445
0.500
0.172
Nikkei 225 Ind.
0.006
0.148
0.500
0.500
0.277
0.034
0.310
0.500
0.203
0.496
0.500
0.038
NSE Nifty 50 Ind.
0.365
0.200
0.267
0.500
0.284
0.451
0.500
0.387
0.500
0.014
0.109
0.500
S&P BSE Sensex Ind.
0.239
0.230
0.088
0.093
0.279
0.002
0.500
0.077
0.500
0.019
0.141
0.255
HIS Index Ind.
0.500
0.109
0.042
0.139
0.500
0.001
0.002
0.001
0.448
0.001
0.144
0.003
CSI 300 Ind.
0.500
0.500
0.007
0.334
0.021
0.088
0.500
0.247
0.500
0.500
0.305
0.212
Shanghai Comp. Ind.
0.001
0.005
0.149
0.001
0.500
0.050
0.033
0.081
0.036
0.234
0.046
0.162
Shenzhen Comp. Ind.
0.500
0.249
0.162
0.012
0.500
0.014
0.500
0.500
0.500
0.500
0.500
0.038
Korea St. Exc. KospiInd.
0.500
0.100
0.077
0.500
0.500
0.010
0.017
0.248
0.005
0.500
0.086
0.500
Bangkok SET Ind.
0.024
0.490
0.492
0.039
0.500
0.006
0.038
0.228
0.386
0.500
0.500
0.001
Straits Time Ind.
0.053
0.500
0.500
0.500
0.089
0.011
0.500
0.269
0.500
0.058
0.029
0.276
FTSE Bursa KLCI Ind.
0.253
0.078
0.045
0.178
0.351
0.018
0.027
0.005
0.463