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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 Page 0

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Page 1: Weather Affect on KSE-100 Index

“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

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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

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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.

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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.

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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

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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.

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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.

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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.

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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

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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(%)

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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

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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

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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(%)

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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%.

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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.

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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

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Loewenstein George. “Emotions in Economic Theory and Economic

Behavior.”American Economic Review 65 (2000): 426-432

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