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“The affect of weather conditions on KSE-100
index”
Ahsan ul Faizan 1, Syed Zohaib hussain 2,
1 MBA- Iqra University, Karachi- Pakistan
2 MBA- Iqra University, Karachi- Pakistan
ABSTRACT
Pleasant weather is linked to concentrated mood, good memory and
cognitive style. One possible explanation for the rise and fall of weather
effect over time is the entry of small investors into the market during periods
in which equity investments attracts popular attention. These non-
professionals’ misattribution of good mood and sunny days extends to their
investment decision making process more so than professional investor
sallow for such a psychological bias.
In this research it is modulated to find the effect of different weather
determinants like temperature, wind speed, visibility, precipitation amount,
and humidity on the market capitalization of KSE-100 index. Through this
one can understand the relation of weather condition and the mood or
behavior of a person (an investor) in a stock exchange. The sample is taken
of about ten years, in order to conclude.
Recent research in behavioral economics, for instance Loewestein (2000, p.
246), argues that emotions 'propel behavior in directions that are different
from that dictated by a weighing of the long-term costs and benefits of
disparate actions.' . Behavioral finance researchers have recently begun to
Page 0
investigate whether investors' emotions influence their decision making and
if such an impact on behavior has significant economic outcomes. The
results if this research indicates that the variables of weather conditions
have positive and negative effect as well on the market capitalization of KSE-
100 index Therefore it is been found that weather does have an impact on
the mood and behavior of a person about investment activity
Page 1
1. INTRODUCTION
Humidity, temperature and sunshine have the greatest of impact on the
mood of a person. High level of humidity and high level of temperature
lowered the concentration of a person. Pleasant weather is linked to
concentrated mood, good memory and cognitive style. One possible
explanation for the rise and fall of weather effect over time is the entry of
small investors into the market during periods in which equity investments
attracts popular attention. These non-professionals’ misattribution of good
mood and sunny days extends to their investment decision making process
more so than professional investor sallow for such a psychological bias.
Hotter weather during summer lowered mood levels and the effect of
pleasant weather far less noticeable in other seasons. The impact of weather
on mood and cognitive has been difficult to demonstrate because people in
industrialized countries on an average, spend 93% of their time indoors
making them largely disconnected from the impact of change in weather*.
Therefore it is not easy to predict mood or behavior of a person about the
investment as per the weather conditions.
In this research it is modulated to find the effect of different weather
determinants like temperature, wind speed, visibility, precipitation amount,
and humidity on the market capitalization of KSE-100 index. Through this
one can understand the relation of weather condition and the mood or
behavior of a person (an investor) in a stock exchange. The sample is taken
of about ten years, in order to conclude some results closes to the population
parameters.
It is concluded in the end that occurrence of rain and drizzle and
precipitation amount does not fit in this model therefore they are removed,
the model than analyzes and find that weather variables have significant but
a very low impact on the market capitalization of KSE-100 index and the
mend wind speed is negatively related with the market capitalization.
Therefore it is concluded that weather have impact on the behavior about
the investment of a person.
Page 2
2. RESEARCH PROBLEM
Investment is either done by public or private sector it is always related to
human as they act in all the stages of it. The environment, atmosphere and
weather affects the mood of a person and the mood eventually describes the
behavior. It is clear from above statement that if any investment is done
human will be involved and also the human behavior in the act of doing the
investment. Therefore in this research it is analyzed how human behavior is
affected by different weather conditions on the act of investment in the KSE-
100 index.
3. SPECIFIC OBJECTIVE
In this research a comprehensive empirical study is conceded out in line with
the following objectives,
Affect of weather conditions on human behavior.
How human behavior affect his/her investment.
How different weather conditions determinants impacts by individual and
unite basis on investment done in market capitalization in KSE-100 index.
4. SCOPE AND JUSTIFICATION OF THE RESEARCH
The findings of this research will show the affect of different variables of
weather conditions on the market capitalization in the KSE-100 index.
Findings of this research will also show whether the weather conditions have
an impact on human behavior about investment or not, If human behavior is
related with the different weather conditions then how much they are
related, either one could predict the investment measures by measuring the
weather conditions or not.
Page 3
5. LITERATURE REVIEW
Ecological psychology has tried to explain how ambiance affect human.
Weather is one of the main factors that influence a person's mood and the
way one feels. Experiments of Bell et al. (2003) have shown that cold makes
people be more predisposed to sadness and melancholy but its influence is
slight insignificant. Scientists (Bell at al., 2003) argue cloud cover influenced
spread measures of New-York Stock Exchange during 1994-2004 and
conclude that there is little correlation between these variables.
Chang et al. (2008), argue that it is the intraday weather pattern that
influences investor's behavior. They have found that cloud cover affects the
returns on stocks only at the beginning of the trading day, specifically, only
during the first 12-15 minutes of the working day. They explain this findings
by the fact that traders and investors are impacted by the weather
conditions only on their way to work and, then, while at the office they do not
really feel the weather influence due to the presence of air-conditioners and
lack of windows (as is most probably the case). Hence, the effect of cloud
cover quickly declines.
People become more optimistic, Psychologists also say that, during sunny
weather and more pessimistic during rainy or cloudy days (Eagles, 1994,
Rind, 1996). Good mood and positive outlook in turn positively affect the
perception of reality and future. (Herren et al., 1988) Such a positive feeling
affects people's decisions that are in a good mood in accord with their mood
(Schwarz, 1990). Thus, investors that are in a good mood are inclined to
invest in riskier projects as they believe in a success of their ventures
(Herren et al. 1988).
Several studies in psychology show that weather has a significant effect on
human behavior and moods. Saunders (1993) was the first to study the
effects of cloud cover on stock returns. He uses daily returns on the Dow
Jones Industrial Average over 1927-1989, and daily returns on value and
equal-weighted market indices over 1962-1989. As a proxy for weather
Page 4
conditions, Sanders uses the 'percentage if cloud cover from sunrise to
sunset' according to the New York weather station closest to Wall Street.
Recent research in behavioral economics, for instance Loewestein (2000, p.
246), argues that emotions 'propel behavior in directions that are different
from that dictated by a weighing of the long-term costs and benefits of
disparate actions.' One area of decision making where emotions and feelings
are relevant is equity pricing. Behavioral finance researchers have recently
begun to investigate whether investors' emotions influence their decision
making and if such an impact on behavior has significant economic
outcomes. One area of research pertinent to the topic of this paper is mood
misattribution. This area considers the effect of environmental factors such
as weather and social settings on equity pricing. This literature suggests that
supposedly rational investors are affected by feelings, which are at times
induced by un-related events in their surroundings, and the effect of feelings
on behavior influences investment decisions and market outcomes.
We contribute to the literature by testing whether local weather conditions
affect market capitalization of KSE-100 index. Our study differs from these
previous papers in that we look at trading of market capitalization, not
holdings. Our evidence, while consistent with earlier results on holdings,
does not preclude the possibility that holdings are evenly disrupted
geographically but investors turn over holdings in local companies more
rapidly than other holdings.
Page 5
6. RESEARCH METHOD
A. HYPOTHESIS
The following version of model is proposed and used to investigate the effect
of different weather conditions on the market capitalization of KSE-100
index.
Xmc = f (T, H, PP, VV, V, RA)
Where, Xmc is the values for market capitalization, T is mean temperature in
Kelvin, H is mean humidity in %, PP is precipitation amount in mm, VV is
mean visibility, V is mean wind speed in km/h, RA is occurrence of rain or
drizzle. This model holds the following regression form;
Xmc = alpha + Beta (T) + Beta (H) + Beta (PP) + Beta (VV) + Beta
(V) + Beta (RA) + ET (Standard error)
To investigate the above relation model following hypothesis is developed
and tested,
H1: Temperature in Kelvin, mean humidity, precipitation amount, mean
visibility, mean wind speed and occurrence of rain or drizzle has significant
positive impact on market capitalization of KSE-100 index.
B. SAMPLE (DATA)
Sample of 2255 observations have been taken from the period of 10 years
from 1/1/2002 to 5/31/2011 to find the relation of the model. This huge
sample is taken in order to ensure that the results of sample are close to the
population.
C. RESULT & SUMMARY
In order to have an effective investigation the sample of data is examine and
tested through the regression function, following results were obtained. We
confine our attention to KSE-100 index as we believe their determinants are
likely to be affected by the weather condition.
Every table has different aspects of results explained below, as the table
suggests the results.
Page 6
Notes (Table 1)
Output Created29-Jul-2011 17:06:54
Comments
InputDataG:\RM\RM.sav
Active DatasetDataSet1
Filter<none>
Weight<none>
Split File<none>
N of Rows in Working Data File2255
Missing Value HandlingDefinition of MissingUser-defined missing values are treated as
missing.
Cases UsedStatistics are based on cases with no missing
values for any variable used.
SyntaxREGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT TV
/METHOD=BACKWARD T H PP VV V
RA.
Page 7
ResourcesProcessor Time0:00:00.032
Elapsed Time0:00:00.031
Memory Required3532 bytes
Additional Memory Required for
Residual Plots
0 bytes
Variables Entered/Removed (Table 2)
ModelVariables EnteredVariables RemovedMethod
1Dummy - Occurrence of
Rain, Mean visibility
(Km), Temp in Kelvin,
Precipitation amount
(mm), Mean wind speed
(Km/h), Mean humidity
(%)a
.Enter
2.Dummy - Occurrence of
Rain
Backward (criterion: Probability of F-to-
remove <= .100).
3.Precipitation amount (mm)Backward (criterion: Probability of F-to-
remove <= .100).
a. All requested variables entered.
b. Dependent Variable: Market Capital
Model Summary (Table 3)
ModelRR Square
Adjusted R
Square
Std. Error of the
Estimate
1.209a.044.0419.44766E9
Page 8
2.209b.044.0419.44566E9
3.209c.044.0429.44379E9
a. Predictors: (Constant), Dummy - Occurrence of Rain, Mean
visibility (Km), Temp in Kelvin, Precipitation amount (mm), Mean
wind speed (Km/h), Mean humidity(%)
b. Predictors: (Constant), Mean visibility (Km), Temp in Kelvin,
Precipitation amount (mm), Mean wind speed (Km/h), Mean
humidity(%)
c. Predictors: (Constant), Mean visibility (Km), Temp in Kelvin,
Mean wind speed (Km/h), Mean humidity(%)
ANOVA (Table 4)
ModelSum of SquaresDfMean SquareFSig.
1Regression8.924E2161.487E2116.663.000a
Residual1.952E2321878.926E19
Total2.041E232193
2Regression8.917E2151.783E2119.989.000b
Residual1.952E2321888.922E19
Total2.041E232193
3Regression8.905E2142.226E2124.963.000c
Residual1.952E2321898.919E19
Total2.041E232193
a. Predictors: (Constant), Dummy - Occurrence of Rain, Mean visibility (Km), Temp in Kelvin,
Precipitation amount (mm), Mean wind speed (Km/h), Mean humidity(%)
Page 9
b. Predictors: (Constant), Mean visibility (Km), Temp in Kelvin, Precipitation amount (mm),
Mean wind speed (Km/h), Mean humidity(%)
c. Predictors: (Constant), Mean visibility (Km), Temp in Kelvin, Mean wind speed (Km/h), Mean
humidity(%)
d. Dependent Variable: Market Capital
Coefficients (Table 5)
Model
Unstandardized
Coefficients
Standardized
Coefficients
BStd. ErrorBetatSig.
1)Constant(-1.125E91.830E9.-615.539
Temp in Kelvin506679.484205911.121.0632.461.014
Mean humidity(%) 4.670E71.572E7.0772.971.003
Precipitation amount
(mm)
9520751.58
5
3.232E7.006.295.768
Mean visibility (Km)9.578E82.153E8.0964.448.000
Mean wind speed (Km/h)-3.673E83.871E7.-243-9.488.000
Dummy - Occurrence of
Rain
2.084E87.675E8.006.272.786
2)Constant(-1.110E91.828E9.-607.544
Temp in Kelvin500069.099204423.939.0622.446.015
Mean humidity(%) 4.779E71.520E7.0793.144.002
Page 10
Precipitation amount
(mm)
1.148E73.150E7.008.365.716
Mean visibility (Km)9.535E82.147E8.0954.441.000
Mean wind speed (Km/h)-3.662E83.847E7.-243-9.517.000
3)Constant(-1.102E91.828E9.-603.547
Temp in Kelvin497651.202204275.822.0622.436.015
Mean humidity(%) 4.888E71.490E7.0803.281.001
Mean visibility (Km)9.487E82.142E8.0954.428.000
Mean wind speed (Km/h)-3.673E83.835E7.-243-9.577.000
a. Dependent Variable: Market Capital
Coefficients (Table 6)
Model
Collinearity Statistics
ToleranceVIF
1Temp in Kelvin.6751.482
Mean humidity(%) .6561.525
Precipitation amount (mm).9081.101
Mean visibility (Km).9461.057
Mean wind speed (Km/h).6651.504
Dummy - Occurrence of
Rain
.8371.194
Page 11
2Temp in Kelvin.6841.461
Mean humidity(%) .7011.426
Precipitation amount (mm).9561.046
Mean visibility (Km).9511.051
Mean wind speed (Km/h).6731.487
3Temp in Kelvin.6851.459
Mean humidity(%) .7291.371
Mean visibility (Km).9551.047
Mean wind speed (Km/h).6771.478
a. Dependent Variable: Market Capital
Excluded Variables (Table 7)
ModelBeta IntSig.Partial Correlation
2Dummy - Occurrence of
Rain
.006a.272.786.006
3Dummy - Occurrence of
Rain
.008b.346.729.007
Precipitation amount (mm).008b.365.716.008
a. Predictors in the Model: (Constant), Mean visibility (Km), Temp in Kelvin, Precipitation
amount (mm), Mean wind speed (Km/h), Mean humidity(%)
b. Predictors in the Model: (Constant), Mean visibility (Km), Temp in Kelvin, Mean wind
speed (Km/h), Mean humidity(%)
Page 12
c. Dependent Variable: Market Capital
Excluded Variables (Table 8)
Model
Collinearity Statistics
ToleranceVIF
Minimum
Tolerance
2Dummy - Occurrence of
Rain
.8371.194.656
3Dummy - Occurrence of
Rain
.8811.135.667
Precipitation amount (mm).9561.046.673
Table1. Shows that variables precipitation amount (mm) and occurrence of
rain or drizzle does not fulfilling the criterion of backward probability of f to
remove >= 100.
Therefore the variables entered were temperature, Mean humidity, mean
wind speed, mean visibility, precipitation amount and occurrence of rain or
drizzle. But now model should be analyzed on the values of the variables
temperature, wind speed, humidity and visibility,
Table2. Shows that the relation between the variables now entered and the
dependent variable through the adjusted r-square is 0.42 therefore it
indicates that the intercept alpha plus the variables explains only 4.2% of the
dependent variable.
Table3. Shows for model 3 that result of the regression model are significant
with f 24.963 and significance level less than 5%.
Page 13
Table4. and Table5 Shows the following information for model three with
dependent variable is market capital and independent variable as humidity,
wind speed, temperature and mean visibility;
Xmc = alpha + Beta (T) + Beta (H) + Beta (VV) + Beta (V)
Xmc = -1.102e9 + 497651.202(T) + 4.888e7 (H) + 9.487e8 (VV) – 3.673e8
(V)
So, this suggests that temp in Kelvin has a beta of 497651.202, humidity has
a beta of 4.88e7, visibility has a beta of 9.487e8, wind speed has a beta of -
3.673e8 and the intercept is negative 1.102e9.
Therefore this shows that all the variables except mean wind speed are
directly proportional to the market capitalization in KSE-100 index, and are
significant since the significance level is less than 5%.
The independent variable’s betas and the intercept explaining only 4.2% of
the market capitalization variable of KSE-100 index.
Although the explaining is very low but results are significant, this shows that
weather conditions have an impact on the market capitalization of KSE-100
index.
The only variable mean wind speed has a negative relation with the market
capitalization of KSE-100 index.
Table6 shows the co linearity diagnostics of variables remained in the
model.
Table7 shows the non-significance of the removed variables.
Table8 shows the co linearity diagnostics of removed variables.
7. CONCLUSION
It is concluded as this research was based to find the impact different
weather condition variables on the market capitalization of KSE-100 index.
The main idea of this research is to investigate whether the change in
weather affects the mood or behavior of a person about investment.
Therefore it is been found that weather does have an impact on the mood
and behavior of a person about investment activity.
Page 14
The results if this research indicates that the variables of weather conditions
have positive and negative effect as well on the market capitalization of KSE-
100 index.
Variable like mean wind speed shows a negative impact on the market
capitalization. The regression relation between these variable (Dependent
and independent) is very low means the alpha and the variables explaining
only 4.2% of the dependent variable (Market Capitalization) but these results
are significant with significance level less than 5%.
8. REFERENCES
Saunders, E. “Stock Prices and Wall Street Weather.” American Economic
Review, 83 (1993), 1337-1345.
A. Bell, T. Greene, J. Fisher, and A. Baum. 2003. Environmental Psychology.
Publisher Belmont, Wadsworth.
C. Chang, S.-S., Chen, R. K. Chou and Y.-H.Lin. (2008). ‘’Weather and
intraday patterns in stock returns and trading activity’’. Journal of Banking
and Finance. Doi:10.1016/j.jbankfin.2007.12.007.
M. Eagles. 1994. The relationship between mood and daily hours of sun light
in rapid cycling bipolar illness. Biological Psychiatry 36: 422-424.
Herren, H. Arkes, and A. Isen. 1988. The role of potential loss in the influence
of affect on risk taking behavior. Organizational behavior and human
decision making processes. No. 42: 181-193.
Schwarz and G. L. Clore. 1983. Mood, misattribution and judgments of well-
being: indirect functions of affective states. Journal of Personality and Social
Psychology. Vol 45: 513-523
Page 15
Loewenstein George. “Emotions in Economic Theory and Economic
Behavior.”American Economic Review 65 (2000): 426-432
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