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International Journal of Economics, Commerce and Management United Kingdom Vol. IV, Issue 12, December 2016

Licensed under Creative Common Page 1

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ASYMMETRIES BETWEEN STOCK RETURNS AND

CONSUMER CONFIDENCE: EVIDENCE FROM

TURKISH STOCK MARKET DATA

Emel Siklar

Anadolu University, Dept. of Business Administration, Turkey

[email protected]

Ilyas Siklar

Anadolu University, Dept. of Economics, Turkey

[email protected]

Abstract

According to general theoretical beliefs and empirical findings there is a positive relationship

between stock prices and aggregate consumption expenditures especially toward increment

direction. The conventional explanation of this phenomenon is based upon the “wealth effect”

concept. However, stock market movements can also affect the consumption expenditure in an

indirect way. Bullish market conditions may cause customer to be more optimistic regarding the

future conditions of the economy and, therefore, to increase their spending. This study

investigates the presence of the consumer confidence channel of equity prices in Turkey during

2004-2015 period. Current study uses the Consumer Confidence Index composed by the

Turkish Statistical Institute in a cooperation with the Central Bank of the Republic of Turkey as a

proxy for the consumer confidence while Istanbul Stock Exchange main index (BIST100) is

used to represent stock market conditions. Obtained empirical results indicate that there is

strong confidence channel in Turkey whereas empirical findings also support the view that stock

price changes have some asymmetric effect on consumer confidence. In other words,

decreases in the stock exchange index, compared to increases, create more powerful and

statistically meaningful effects on consumer confidence.

Keywords: Stock prices, consumer confidence, wealth effect, price asymmetry, Turkey

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INTRODUCTION

After 2008 global financial crisis a sophisticated perception of the relationships between

monetary and production sectors of the economy is settled to the agenda of policy decision

makers and scientific studies. In general terms, this study focuses on the connection between

consumption behavior and stock market. Traditionally stock market directly affects consumption

expenditures via wealth effect. When stock prices increase, as wealth effect predicts, the value

of the financial wealth and consequently the share of sources for consumption also increases.

This results in an increase in consumption expenditures. However movements in stock market

can indirectly affect consumption expenditures by affecting consumer confidence. Specifically

this study aims to search the existence and principal characteristics of this confidence channel

indirectly created by stock price changes.

This mechanism which we shortly call as confidence channel depends in a great part on

the idea that share prices can be used as a leading indicator about economic activities. By

causing consumers to be optimistic about the future of the economy, bull market conditions

cause consumers to increase their expenditures. Confidence channel is a different mechanism

from wealth channel. Higher stock prices via higher value of wealth can undoubtedly influence

consumer confidence. However, the important point for the confidence channel is not the

consumers’ belief that they have a higher value of financial wealth, contrary it is the belief that

good days in the economy are coming. If an independently working confidence channel is

present, variations in share prices can influence consumption conclusion of, not only, the

shareholders but also non-shareholders. In emerging markets with a characteristic of limited

shareholding confidence channel consequently carries a different importance

Confidence channel on stock prices was first put forward by the well-known paper of

Romer (1990) on Great Depression. According to Romer, collapse of financial markets 1929

caused to increase uncertainties about the future of the economy. Therefore it is a critical factor

to explain the U.S. consumer expenditures in 1929 – 1932 period. More recent studies

regarding U.S. and European economies conclude that there are some evidence in favor of

existence of confidence channel other than wealth effect. For instance, Jansen – Nahuis (2003)

and Otoo (1999) identify a causal relationship running from stock price changes to consumer

confidence changes while Jansen – Nahuis (2004), Bram – Ludvigson (1998) and Carol

et.al.(2004) determine a positive contribution of consumer confidence changes in improving

estimates of consumer expenditure changes. Contribution of our study in this context is to

analyze whether confidence channel is valid in an emerging stock market.

International Journal of Economics, Commerce and Management, United Kingdom


Past studies conducted for Turkey show that the second phase of the confidence channel is

valid, that is, consumer confidence does affect the consumer expenditures. For instance,

Gormus – Gunes (2010) find a causal relationship running from macroeconomic variables to

consumer confidence and conclude that the reverse is not present in 2002 – 2008 period.

Bolaman – Mandaci (2014) investigate the relationship between consumer confidence and stock

exchange index under tight financial conditions for 2003 – 2012 period. Obtained results show

that macroeconomic factors should be evaluated as a critical factor depending their effects on

consumer confidence. Karasoy (2015) emphasizes that financial volatility besides the other

macroeconomic variables has an important effect on consumer confidence. He also concludes

that this situation can be accepted as an extra transmission mechanism for consumption

expenditures especially in financially distressed times. Investigating the relationship between

consumer confidence and stock returns by using the VAR methodology and Granger causality

tests, Canbas – Kandir (2010) reach the conclusion that stock returns do affect the consumer

confidence. However, they find that consumer confidence is not an efficient variable to estimate

stock returns in Turkey. Celik et. al. (2010) analyze the link between consumer confidence and

financial markets under economic turbulence conditions and conclude that consumer

confidence should be accepted not only as a variable indicating the sensitivity of consumers

about the future of the economy but also as a variable with endogenous characteristic.

Our results support the working mechanism of confidence channel in the context of

Turkish data. Obtained results show that stock price changes have an important effect on

expectations for future of the economy in general terms. However a change in share prices has

a little contribution on the formation of personal financial position. In this study two different

hypotheses about asymmetric outcomes of variations in share prices on consumer assurance

are tested. We analyze whether a downward change in capital market price index makes

economic units more doubtful than trust generated by positive changes. Second, we investigate

whether a greater variation in share prices has a greater effect on consumer confidence than a

smaller change. While our results support the first hypothesis, they do not favor the second one.

We also found that the mentioned asymmetries are valid for both wealth and confidence

channels.

Organization of the remaining part of the study can be summarized as: Part 2 expresses

and summarizes fundamental characteristics of data while Part 3 discusses the estimation

method and deals with empirical outcomes about the structure of relationships among variables.

Finally the last part summarizes the paper and draws attention to the fundamental results

obtained.

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RESEARCH METHODOLGY

Measurement of consumer confidence is done by Turkish Statistical Institute (TSI) together with

the Central Bank of the Republic of Turkey (CBRT) and published by TSI. With a monthly

questionnaire, it is aimed that to measure how consumers evaluate their financial situation and

the current economic environment. This survey also tries to collect data on how consumers

assess the future of the economy (expectations) and think of the possibility of saving.

Assessment of the current position, expectations and tendencies related for the economy are

carried out with the following headings:

Personal financial status: Evaluation of consumers’ financial status of their household

during past 12 months, next 12 months and current situation, probability of credit usage

during next 3 months.

General economic situation: Evaluation of consumers about the general economic

conditions during past 12 months, next 12 months and current status, expectations

about the number of unemployed people over the next 12 months, opinion of consumers

currently whether it is time to buy durable goods and to save, opinion of consumers

about consumer price changes during past 12 months and next 12 months, expectations

about changes in wages over the next 12 months.

Spending and saving tendencies: Opinion of consumers to spend for semi-durable

goods over the next 3 months, for durable goods over the next 12 months, probability of

buying, constructing or renovating of an house over the next 12 months, probability of

saving over the next 12 months.

This questionnaire is applied to a family member who is over 16 in the representative household

with the distinction of rural and urban areas. The target area of the survey covers all the

settlements throughout the country and, in this context, the settlements whose population are

20,001 and over are considered as urban, 20,000 and less are considered as rural regions. The

questionnaire is applied between the first and fifteenth day of each month and published in the

last week of related month. Indices compiled carry a value between 0 and 200. A value greater

than 100 shows the consumer optimism, less than 100 indicates the consumer pessimism.

Two questions in the consumer confidence survey carry weight with assessment of the

relationship between share prices and tendencies of consumers. These are the question

number 2 and number 4. In the rest of the paper we are going to use the overall consumer

confidence index and the sub-indices constructed by TSI considering mentioned questions. The

overall index for consumer confidence will traditionally be shown as CCI and the sub-indices



related with questions 2 and 4 will be represented as IND2 and IND4, respectively. These

questions are as follows in questionnaire:

Question 2: What is your expectation about personal financial status over the next 12 months?

Question 4: What is your expectation about the general economic status of the country over

the next 12 months?

Question 2 is about the assessment of financial situation of the household over the next 12

months. Answers to this question are informative about the strength of wealth effect stemming

from the impact of changes in share prices on consumer confidence. Question 4 is about the

assessment of expectation of household for future economic conditions in the country. If there

exists an independent confidence channel it is expected that answers should be affected by

stock price changes. Therefore indices IND2 and IND4 will be used to assess the prominence of

wealth and confidence channels.

Stock returns will be calculated in terms of Istanbul Stock Exchange 100 Index (ISE100).

This price index is the fundamental indicator for stock market in Turkey and data for that

indicator is published by the management of Istanbul Stock Exchange in semi-daily basis.

Monthly data is constructed from daily series by considering the last trading day closing value.

This reduces the possibility of erroneous correlation and causality among time series for related

variables. Following figures show tendencies of the variables mentioned above for the period

under investigation:

Figure 1: ISE100 Index (2004:01 – 2016:09)

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

2004M01

2004M07

2005M01

2005M07

2006M01

2006M07

2007M01

2007M07

2008M01

2008M07

2009M01

2009M07

2010M01

2010M07

2011M01

2011M07

2012M01

2012M07

2013M01

2013M07

2014M01

2014M07

2015M01

2015M07

2016M01

2016M07

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Figure 2: Developments in Consumer Confidence (2004:01 – 2016:09)

Consumer confidence data are compiled in Turkey since the begging of 2004 while ISE data is

present from 1986. Therefore investigation period begins with 2004-January and covers the

period until 2016-September in monthly basis consisting of 153 observations as sample size.

Descriptive statistics of time series used are given in Table 1.

Table 1: Descriptive Statistics of Time Series (153 Observations)

ISE100 CCI IND2 IND4

Mean 53986,66 76,17 88,56 90,53

Median 54596,81 76,20 89,90 91,50

Maximum 90094,56 98,70 108,30 117,60

Minimum 17041,50 55,70 68,10 62,20

Skewness -0,126 0,223 -0,537 -0,458

Kurtosis 1,818 2,802 3,990 3,152

Jarque-Bera 9,319 1,524 13,604 5,496

(Probability) (0,009) (0,467) (0,001) (0,064)

ESTIMATION RESULTS

Diagnostic Tests

In order to go further in analysis we first have to identify the order of integration in time series

used. For this purpose all the time series are transformed to logarithmic form and the series with

their levels and first differences are subjected to two different unit root tests (Augmented Dickey-

50

60

70

80

90

100

110

120

130

2004M01

2004M06

2004M11

2005M04

2005M09

2006M02

2006M07

2006M12

2007M05

2007M10

2008M03

2008M08

2009M01

2009M06

2009M11

2010M04

2010M09

2011M02

2011M07

2011M12

2012M05

2012M10

2013M03

2013M08

2014M01

2014M06

2014M11

2015M04

2015M09

2016M02

2016M07

CCI IND2 IND4



Fuller (ADF) and Kwiatowski-Phillips-Shcmit-Shin (KPSS) tests) to determine the degree of

integration. In ADF tests the lag length is selected by using Akiake Information Criteria, in KPSS

tests, band width is determined by using Newey-West Criteria. Both ADF and KPSS tests

indicate that all the log series are not stationary in their levels, but stationary in the first

differences of log levels (see Table 2). Since this shows us that time series present an I(1)

characteristic, in the rest of the paper we are going to use these stationary series unless

otherwise stated.

Table 2: Unit Root Test Results

Variable ADF Lag Probability KPSS Band

ise100 -2,127 1 0,235 1,264 10

Δise100 -9,442 0 0,000 0,108 5

cci -3,041 1 0,135 1,211 4

Δcci -10,559 0 0,000 0,159 5

ind2 -2,607*

0 0,278 1,238 10

Δind2 -11,322 0 0,000 0,272 8

ind4 -2,628 2 0,269 1,632 9

Δind4 -10,179 1 0,000 0,256 5

Note: * indicates trend inclusion. For KPSS LM test, the critical

value is 0,463 at 5% level of significance.

To see whether a long-run relationship between stock returns and one of the confidence

indicators exists, we conducted cointegration tests. Results of Johansen multi-variate

cointegration tests with 4 months lag length and a constant are shown in Table 3. It is clear that

there is a cointegrated vector between ISE100 index representing the stock market and each of

the three indices representing the consumer confidence. This shows us that there is a long-run

relationship between changes in stock prices and changes in consumer confidence.

Table 3: Johansen Cointegration Test Results

Null Hypothesis Variable Trace Test Maximum Eigen Value Test

r = 0

Δcci 50,348 31,445

Δind2 54,277 35,286

Δind4 50,062 31,944

Note: Critical values for trace and maximum Eigen value test at 5% level of

significance are 15,495 and 14,265, respectively. r is the number cointegrated

vector.

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The first differences of log levels (in other words, percentage changes) of overall, IND2 and

IND4 indices are presented in Figure 3 below. Correlation coefficients among these percentage

changes are presented as Table 4. By considering the figure and table it is possible to say that

answers to individual questions in the confidence questionnaire are changed independently.

The lowest correlation value between sub-indices IND2 and IND4 supports this conclusion.

Figure 3: Percentage Changes in Confidence Indices

Table 4: Correlations among Confidence Indices

Variable Δcci Δind2 Δind4

Δcci 1,000 0,897 0,932

Δind2 0,897 1,000 0,877

Δind4 0,932 0,877 1,000

Correlation Analysis

Table 5 below shows simultaneous correlations among variables. All of the calculated

coefficients have a positive sign indicating a change with the same direction. Another

remarkable point in the table is that the correlation between percentage change in IND4

(representing the expectations about general economic status) and change in ISE100

(representing stock returns) is stronger than the correlation between percentage change in IND2

(representing the expectations about personal financial status) and percentage change in

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

1 7

13

19

25

31

37

43

49

55

61

67

73

79

85

91

97

103

109

115

121

127

133

139

145

151

IND2 IND4 CCI



ISE100. This indicates the existence of a powerful confidence channel differing from the

traditional wealth channel. All the correlation coefficients calculated are different from zero at

1% significance level. The t statistic regarding the null hypothesis that the correlation coefficient

is zero is calculated as:

𝑡 = 𝑟 𝑁 − 2

1 − 𝑟2

Where, r represents the correlation coefficient. Results show that the null hypothesis of there is

no correlation between changes in stock returns (Δise100) and three indices representing the

changes in consumer confidence (Δcci, Δind2 and Δind4) are rejected in all cases.

Table 5: Correlations between Stock Returns and Consumer Confidence

Variable Coefficient t statistic Probability

Δcci 0,362 4,760 0,000

Δind2 0,312 4,130 0,000

Δind4 0,374 4,948 0,000

Granger Causality Tests

The next step in analyzing the relationship between consumer confidence and stock returns is

to test the causality among variables by estimating the preceding equations:

∆𝑐𝑜𝑛𝑡 = 𝛼𝑐𝑜𝑛 + 𝛽𝑐𝑜𝑛 𝑖 ∆𝑐𝑜𝑛𝑡−𝑖

𝐿

𝑖=1

+ 𝛾𝑐𝑜𝑛 𝑖 ∆𝑖𝑠𝑒𝑡−𝑖

𝐿

𝑖=1

+ 𝜀𝑐𝑜𝑛 ,𝑡 (1)

∆𝑖𝑠𝑒𝑡 = 𝛼𝑖𝑠𝑒 + 𝛾𝑖𝑠𝑒 𝑖 ∆𝑐𝑜𝑛𝑡−𝑖

𝐿

𝑖=1

+ 𝛽𝑖𝑠𝑒 𝑖 ∆𝑖𝑠𝑒𝑡−𝑖

𝐿

𝑖=1

+ 𝜀𝑖𝑠𝑒 ,𝑡 (2)

Where, con represents a measure for consumer confidence (overall confidence index (cci),

index representing the expectations about future personal financial status (ind2) and index

representing expectations about general economic status (ind4)), ise shows a measure of stock

prices (ISE100 index) and L indicates the lag length. The term ε in equations shows the error

term with classical properties of zero mean and normal distribution. If the lagged values of a

variable X have an important predictive power to estimate a variable Y, it is accepted that the

variable X is a Granger cause of the variable Y. Table 6 below summarizes the results of

performed Granger causality tests where the sign → refers the direction of causality. The null

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hypothesis that there is no causality is rejected at the marginal significance level for F test given

in probability column.

Table 6: Granger Causality Test Results

Variable Lag Δise → Δcon Δcon → Δise

F Test Probability F Test Probability

Δcci 2 3,457 0,034 1,963 0,088

Δind2 4 2,277 0,064 0,930 0,449

Δind4 1 5,319 0,023 1,066 0,304

Results obtained for overall consumer confidence index (Δcci) point out that there is a two-way

Granger causality. When we consider two sub-indices which are included in the model beside

the overall index to represent wealth and confidence channels, results substantially differ. For

both indices, causality runs from stock prices to consumer confidence but the reverse is not true

(the null hypothesis that there is no causality cannot be rejected). Consequently stock prices

cause Δind2 for wealth channel and Δind4 for confidence channel and one-way causality is

accepted. This one-way causality should be accepted as an indicator for the existence of both

wealth and confidence channels. For additional evidence, therefore, we have to investigate

closely the results for sub-indices in order to understand the relative importance of wealth and

confidence channels.

Current data for both ind2 and ind4 supports the one-way causality running from share

prices to consumer confidence. In other words, stock prices are taken into account as a variable

to evaluate the personal financial status and the future economic conditions. However

compared to expectations about personal financial status and future economic conditions are

strongly affected by stock price changes. These outlined results confirm together that consumer

confidence is influenced by wealth and confidence channels independently.

Asymmetric Effects

Results of former studies indicate that wealth changes in capital market asymmetrically

influence consumption expenditures, i.e. changes in consumer expenditures respond more

potently to downward capital market variations than the upward variations with the same

amount. In other words, a negative change in stock market more strongly affects consumption

expenditures than a positive change does. In principal, asymmetric effects can emerge for both

channels. To assess the existence of asymmetric effects we are going to consider two different

specifications. The first one can be written as follows:



∆𝑐𝑜𝑛𝑡 = 𝛽1,𝑖∆𝑐𝑜𝑛𝑡−𝑖

𝑘

𝑖=1

+ 𝛽2,𝑖∆𝑖𝑠𝑒𝑡−𝑖

𝑘

𝑖=1

+ 𝜃+𝐷𝑈𝑀+ + 𝜃−𝐷𝑈𝑀− + 𝜀𝑡 (3)

Where, DUM+ is a dummy variable which takes the value of 1 if stock market index increases in

period t, otherwise it has a value of zero. Similarly DUM– is a dummy variable which takes the

value of 1 if stock market index decreases in period t, otherwise it has a value of zero. With

respect to equation (3) the existence of any asymmetric effect will be captured by the constant

term that points out shifts in consumer confidence based on the stock market change.

Estimation results of equation (3) specified to determine asymmetric effects are

summarized in Table 7. In estimation process, the k value which maximizes the adjusted R2 is

taken into account and determined as 3. To save space and make easy to follow up results, we

give only the coefficient estimates for dummy variables. In the context of overall confidence

index, θ+ and θ- coefficients have expected signs and are statistically significant. The negative

coefficient is always twofold of the positive one showing that the existence of asymmetric shifts

in consumer confidence against the stock market changes. When we consider sub-indices,

estimated coefficients of dummy variables have also expected sign and are statistically

significant. However the null hypothesis that θ+ = θ- is rejected for all the indices representing

consumer confidence. In absolute terms, coefficient of θ- is almost twofold of coefficient of θ+ for

all three cases.

Table 7: Asymmetric Effects for Consumer Confidence – I

Confidence Index θ+

ρ θ- ρ 𝑅 2 H0: θ

+= θ

- ρ

Δcci 0,883 0,103 -1,713 0,000 0,553 χ2(1) = 13,214 0,000

Δind2 0,621 0,201 -1,405 0,091 0,218 χ2(1) = 9,262 0,001

Δind4 1,111 0,090 -2,618 0,000 0,654 χ2(1) = 8,004 0,002

Findings reported in Table 7 lay emphasis on three important points: First, downward stock

market changes create more pessimism than optimism created by upward changes. Second,

this negative effect is almost twofold of positive effect. Third, although positive changes are

more frequently faced and have big size, the emerging of asymmetric effects proves that there

is strong confidence channel. In order to see this fact we draw the squares of changes in

ISE100 index. Figure 4 below shows together the series Δise and (Δise)2:

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Figure 4: Course of Asymmetric Effects

Note: Δise is presented with red line and measured on the left axis while Δise2 is presented with

black line and measured on the right axis.

The second part of asymmetric effects is related with the magnitude of effects. In this sense we

should investigate whether the effect of large downward changes in share market is a bigger

effect on consumer confidence than the small ones. To see this point we consider the second

specification:

∆𝑐𝑜𝑛𝑡 = 𝛽0 + 𝛽1,𝑖∆𝑐𝑜𝑛𝑡−𝑖

𝑘

𝑖=1

+ 𝛽2,𝑖∆𝑖𝑠𝑒𝑡−𝑖

𝑘

𝑖=1

+ 𝛽3,𝑖𝐷𝑈𝑀− ∗

𝑘

𝑖=1

∆𝑖𝑠𝑒𝑡−𝑖 + 𝜃−𝐷𝑈𝑀− + 𝜀𝑡 (4)

Fundamental difference between equations (3) and (4) is that the latter carries a slope dummy

(DUM-×Δiset-i) besides constant one. This equation makes possible to assess whether past

negative changes in stock market affect the current changes in consumer confidence. Table 8

summarizes the results obtained to test following null hypotheses regarding asymmetry among

variables:

(1) There is no asymmetry working with both slope and constant dummies (that is,

𝛽3,𝑖 = 0, 𝜃− = 0𝑘𝑖=1 )

(2) There is no asymmetry working with slope dummy (that is, 𝛽3,𝑖 = 0𝑘𝑖=1 )

(3) There is no asymmetry working with constant dummy (that is, 𝜃− = 0)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

2004M02

2004M07

2004M12

2005M05

2005M10

2006M03

2006M08

2007M01

2007M06

2007M11

2008M04

2008M09

2009M02

2009M07

2009M12

2010M05

2010M10

2011M03

2011M08

2012M01

2012M06

2012M11

2013M04

2013M09

2014M02

2014M07

2014M12

2015M05

2015M10

2016M03

2016M08



Table 8 includes test statistics and marginal significance levels for each null hypothesis. In

general obtained results for equation (4) are consistent with already obtained results for

equation (3). First, all of the null hypotheses for overall and sub-indices related for consumer

confidence are rejected. Second, the null hypotheses that there isn’t any asymmetric slope

effect should be accepted for all of the confidence criteria. Third, the null hypotheses that there

is no asymmetric constant effect are rejected for all confidence measures. These results show

that large downward variations in capital market have no large influence on confidence criteria.

However, negative changes in stock market generate asymmetric level shifts in consumer

confidence indices. These types of effects work with both wealth and confidence channels. The

last point we should mention about the results inferred from Table 8 is that the explanatory

power of equations in terms of adjusted R2 are similar. However the equation with ind4 that

represents the consumer confidence channel in our analysis has slightly higher explanatory

power than other indices.

Table 8: Asymmetric Effects for Consumer Confidence – II

Confidence Index H0: 𝛽3,𝑖 = 0, 𝜃

− = 0𝑘𝑖=1 H0= 𝛽3,𝑖 = 0𝑘𝑖=1 H0:𝜃

− = 0 𝑅 2

F(2,138) ρ χ2(1) ρ χ

2(1) ρ

Δcci 9,362 0,002 1,482 0,210 6,144 0,018 0,321

Δind2 4,521 0,027 0,451 0,399 3,218 0,037 0,223

Δind4 6,033 0,013 0,233 0,718 6,205 0,010 0,587

CONCLUSION

Price changes in stock market can affect consumption tendencies by directly changes in value

of wealth and indirectly changes in consumer confidence. Rationale behind the second channel

depends on the fact that movements in stock market affect consumer confidence; this, in turn,

affects consumption tendencies of economic agents. This study investigates the link between

variations in capital market and consumer confidence for Turkish case for January 2004 –

September 2016 period.

Obtained results indicate that variations in capital market and consumer confidence

present a correlation in positive direction. Variations in capital market Granger cause variations

in consumer assurance related with wealth and confidence channels. Downward variations in

capital market create significant, in statistical term, effects on consumer confidence but upward

variations do not. This is an evidence about the existence of asymmetric effects. Our results

also show that confidence channel can intensify effects of stock market changes on consumer

expenditures.

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Due to unavailability of the data regarding the attitudes of shareholders and non-shareholders

distinction in consumer confidence survey we couldn’t conduct the related tests on basis of

stock market participants. Current data should be extended to cover this issue in order to obtain

more satisfactory results. Consequently, by using this type of detailed data further research

should focus on the relationships between components of consumer confidence and economic

fundamentals in order to evaluate more accurately the size and importance of confidence

channel in Turkey.

REFERENCES

Apergis, N. - Miller, S. M. (2006), “Consumption Asymmetry and the Stock Market: Empirical Evidence”, Economics Letters, Vol.93, No.3, pp.337-342.

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