SEASONALITY IN THE THAI STOCK INDEX
H.SWINT FRIDAY, Ph.D.
TEXAS A&M UNIVERSITY -CORPUS CHRISTI, USA
NHIEU A. BO
TEXAS A&M UNIVERSITY -CORPUS CHRISTI, USA
World Finance and Banking Symposium Conference
Singapore- December 12-13, 2014
INTRODUCTION/ABSTRACT
The paper examines seasonality in returns for the Stock Exchange of Thailand (SET).We use
historical returns on SET composite and SET50 since the stock market was established to
December 2013 to examine whether the weather has generated abnormal returns and
seasonal effects on the two indices. In our previous study, we found that “Halloween effect” or
“Go away in May come back Halloween Day” in theVietnam stock index (VN-index) were
statistically attached to the rainy season during the observed period from 2000-2010
inclusively.We find that Sell in May or Halloween effect presents in both SET composite and
SET 50 indices even though the results are not statistically significant. Also, we find
significant returns for December and January so-called turn-of-the-month effects.We conclude
that Halloween effect is actually December and January effect in disguise.
LITERATURE REVIEW
I.
• The Stock Exchange of Thailand
II.
• Study of Seasonality in Stock
Index
III.
• Data and Methodology
Thailand Insight and SET Index
Figure: GDP Growth (Annual %) of Thailand and East Asia Pacific
Source: World Bank Data
Thai Stock Market Performance
Lehman Brother
Crisis
Floods
Figure 1: Thai stock index (SET) performance from 1987-2014
Source: Trading Economic
SET Index Series
SET Index ( Composite):
• Capitalization-weighted price index
• Calculated from the prices of all common stocks
(with certain exceptions)
• Adjustment: in line with changing of the values of
stocks and number of stocks
• Base value: 100 points
• Base date: April 30, 1975
SET 50 Index (Large-cap Index)
• Capitalization-weighted price index
• Calculated from the prices of 50 selected SET stocks
• Adjustment: in line with changing of the values of
stocks and number of stocks
• Base value: 100 points
• Base date: April 30, 1995
Source: The Stock Exchange of Thailand
Figure 1: Stock selection for SET 50
Study of Seasonality in Stock Index
Literature Reviews:
•
Bouman and Jacobsen (2002) found evidences for Halloween effects across 36 stock markets in the total of 37
observed countries includingThailand.
•
Maberly and Pierce (2003) documented Halloween effect in Japanese equity market over prior years of the mid1980s.This effect was strongly evident over bull market observed in the data set.
•
Gultekin M. and Gultekin B. (1983) documented that significantly large mean returns were found at the turn of tax
year in stock markets observed in 18 countries. Remarkably, January was the month with significantly high return.
•
Fountas and Segredakis (2002) tested eighteen emerging stock markets for the period 1987-1995 including Thailand and
found that January effect and tax-loss selling hypothesis were not statistically supported in stock markets being observed.
In the other words, the result supported the existence of EMH those stock markets.
•
In previous study, we found “Halloween effect” in VN-index during the observed period from July 2000 to
December 2010. The effect primarily occurred between 2000 and 2007. In addition, January has highest average
return over the period 2000-2010, which supports for the January effect. (“Seasonality in the Vietnam Stock
Index”).
Is Thai stock market efficient??
Research Data and Methodology
• Examine the SET Composite and SET 50 since the stock exchanges was established in May 1975
to December 31st, 2013 inclusively.
• SET monthly returns are calculated from daily returns using the following equation:
Methodology
In this paper, we use Brauer and Chang’s (1990) model:
Where:
• 𝑅jt is the return on j index in month tth
• Dit is a dummy variable which takes value of 1 if the month is tth and zero otherwise
• αt: represents the coefficient for the month tth
The second model we use in our examination to test Halloween effect comes from
Lucey and Zhao’s (2008) model:
𝐑jt = α + β Wt + µt
Where:
• Rjt is the return on index j in the month tth
• Wt is dummies for Halloween indicator, which takes value 1 if the
month falls from November to April and zero otherwise
• β: represents the coefficient for Halloween indicator
TABLE I
Mean Monthly Returns for SET and SET 50
Index (%)
Aver.
Monthly*
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
May-Oct
HPR
Nov-Apr
HPR
SET50
(1996-2013)
2.54
1.85
-0.70
0.13
-0.65
-0.13
-0.46
-2.19
2.07
-1.11
-0.08
4.20
SET
(1996-2013)
2.47
0.58
-0.41
1.47
0.27
1.25
1.23
0.16
0.34
1.34
-0.38
3.02
0.98
1.04
1.08
1.07
*Average monthly returns across the years being observed
Source: Quandl Dataset
Initial Evidences:
• Higher mean returns for Jan and
Dec.
• “Halloween” effect might be present in
this market.
TABLE II
Mean and Standard Deviation for SET
More volatile
and SET50 Index
Index
Full Sample
(12 month HPR)
November-April
HPR**
Mean Std. Dev Mean
% Positive Std. Dev Mean
% Positive Std. Dev
SET (1975-2013)
15.36%
6.86%*
63.16%
17.19% 5.84%*
50.00%
29.30%
SET 50 (1995-2013)
8.53% 42.70% 7.41%*
66.67%
18.36% (3.16%)*
38.89%
25.58%
8.90%
87.50%
10.80%
50%
30.78%
SET100 (2006-2013)
44.5
May-October
HPR
3.81%
** November-April Holding Period Return (HPR) is calculated the months within the calendar year for tax purposes.
• 63.16% of positive returns for Nov-Apr as compared to 50% positive returns for MayOct for SET Index.
• Similarly, 66.67% of positive returns as compared to only 38.89% for SET50 Index.
• Significantly, 87.5% and 50% respectively for SET100 Index.
TABLE III
The Test of Seasonal Effects for SET
Indices
SET Index
(1975-2013)
SET50 Index
(1995-2013)
Coefficients a,b
Unstandardized
Coefficients
Std.
B
Error
Standardized
Coefficients
Beta
Unstandardized
Coefficients
Sig.
.066
Standardized
Coefficients
B
Std. Error
Beta
D1
4.748
2.410
.134
1.970 0.05*
D1
2.473
1.344
.085
t
1.840
D2
.578
1.344
.020
.430
.667
D2
-.207
2.410
-.006
-.086
.932
D3
-.408
1.344
-.014
-.304
.761
D3
-1.412
2.410
-.040
-.586
.559
D4
1.469
1.344
.051
1.093
.275
D4
2.715
2.410
.077
1.126
.261
D5
.002
1.327
.000
.002
.999
D5
-2.231
2.410
-.063
-.926
.356
D6
1.263
1.327
.044
.952
.342
D6
-.462
2.410
-.013
-.192
.848
D7
1.372
1.327
.048
1.034
.302
D7
-.084
2.410
-.002
-.035
.972
D8
.170
1.327
.006
.128
.898
D8
-1.295
2.410
-.037
-.537
.592
D9
.170
1.327
.006
.128
.898
D9
.370
2.346
.011
.158
.875
D10
1.277
1.327
.045
.962
.336
D10
-.602
2.346
-.017
-.256
.798
D11
-.424
1.327
-.015
-.320
.749
D11
.926
2.346
.027
.395
.693
D12
2.801
1.327
.098
2.111 0.035*
D12
2.772
2.346
.080
1.182
.239
Model
1
Coefficients a,b
Model
1
a. Dependent Variable: Returns
a. Dependent Variable: Returns
b. Linear Regression through the Origin
b. Linear Regression through the Origin
t
Sig.
The results support for December effect (SET Index) and January effect (SET50 Index).
TABLE IV
Summary Statistics for Halloween Effect Adjusted
with December Effect for SET Composite Index
(1975-2013)
Rt= α + β1 Wt-adj +β2Dect+ + µt
(4)
Coefficients a,b
Model
4 Nov-Apr
Adjusted
Dec
Unstandardized Standardized
Coefficients
Coefficients
Std.
B
Error
Beta
.682
.590
.053
t
1.156
Sig.
.248
2.801
1.322
.098
2.119 0.035*
a. Dependent Variable: Returns
b. Linear Regression through the Origin
This suggest that Halloween effect is statistically explained by December effect
TABLE V:
Summary Statistics for Halloween Effect Adjusted
with January Effect for SET 50Index (1995-2013)
Rt= α + β1 Wt-adj +β2Jant + µt
(5)
Coefficients a,b
Unstandardized Standardized
Coefficients
Coefficients
Std.
Model
5 Nov-Apr
Adjusted
Jan
B
Error
Beta
.978
1.050
.062
4.748
2.375
.134
t
.931
Sig.
.353
1.999 0.047*
a. Dependent Variable: Returns
b. Linear Regression through the Origin
This result is consistent with findings of Bouman and Jacobsen (2001) when they found that in many
countries including Thailand, Halloween effect is the January effect in disguise.
January effect and Tax loss selling
hypothesis
To further explore the tax loss selling hypothesis associated with SET 50 Index, the
following regression model is estimated:
January return = f (prior years return, prior years standard deviation of returns).
Tax loss selling in theory:
• The coefficient of January returns and prior year returns should be negative.
• Standard deviation of returns for prior years should be positive as the market is more
volatile to generate more losses.
TABLE VI
Regression Analysis for January
Returns SET 50 Index
Explanatory
Variables
Coefficients
a
Unstandardized
Coefficients
Model
Regression
Intercept
Analy sis
Prior Year
B
S td. Error
.004
.068
.616
S td. Dev.
Prior year
-.167
HPRs
a. Dep endent Variable: January returns
S tandardized
Coefficients
Beta
t
.066
S ig.
.948
.673
.203
.915
.376
.071
-.520
-2.348
0.034*
*Significant level of 5%
R-Squa re :
0.63
F-Sta ti s ti c
4.151*
D.F.
Dependent
Variable
16
The results support the tax-loss selling hypothesis when one would expect the negative
coefficient for the previous year’s mean returns and a positive coefficient on the previous year’s
standard deviation of returns.
Conclusions
 Halloween effect on both SET Composite and SET50 are not strongly
supported during the observed periods respectively.
• December effect statistically explains for Halloween effect for SET
Composite index.
• Halloween effect for SET 50 is actually January effect in disguise.
 Results statistically support hypothesis of tax-loss selling for SET50
index as January effect exists in the SET market.
 Is rainfall negatively correlated with monthly returns?
• Follow-up Research: “The Market Pricing of AnomalousWeather:
Evidence from Thailand.”
THANK YOU !
Questions ???