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1 Consumer Sentiment Index and Stock Price: Industry Level Study with Korean data * August 12, 2011 Seung-Nyeon Kim ** and Wankeun Oh *** This paper analyses the relationship between movements in consumer sentiment index and industry level stock prices in Korea. In theory, both stock prices and consumer sentiment are considered as leading indicators of general economic conditions, but the causation between the two variables could go either way. In an empirical study with Korean data, Kim and Oh (2009) found that the general stock index returns Granger- cause consumer sentiment, not vice versa. In this paper, we use industry level data and examine whether the relationships between consumer sentiment and stock price are be different among industries. More specifically, we examine if the industry of consumer goods and service is be more closely related with consumer sentiment than other industries. However, our empirical study does not reveal a close relationship between consumer sentiment and consumer goods industry stock prices. It appears that the consumer sentiment index in Korea does not provide valuable information to stock market. __________________________ * Presented at the conference of Korea and the World Economy X ** Department of Economics, Hankuk University of Foreign Studies, [email protected] *** Corresponding author, Department of Economics, Hankuk University of Foreign Studies, [email protected].

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Page 1: Consumer Sentiment Index and Stock Price: Industry Level …2011)/16.full.pdf · This paper analyses the relationship between movements in consumer sentiment index and industry level

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Consumer Sentiment Index and Stock Price:

Industry Level Study with Korean data*

August 12, 2011

Seung-Nyeon Kim**

and Wankeun Oh***

This paper analyses the relationship between movements in consumer sentiment index

and industry level stock prices in Korea. In theory, both stock prices and consumer

sentiment are considered as leading indicators of general economic conditions, but the

causation between the two variables could go either way. In an empirical study with

Korean data, Kim and Oh (2009) found that the general stock index returns Granger-

cause consumer sentiment, not vice versa. In this paper, we use industry level data and

examine whether the relationships between consumer sentiment and stock price are be

different among industries. More specifically, we examine if the industry of consumer

goods and service is be more closely related with consumer sentiment than other

industries. However, our empirical study does not reveal a close relationship between

consumer sentiment and consumer goods industry stock prices. It appears that the

consumer sentiment index in Korea does not provide valuable information to stock

market.

__________________________

* Presented at the conference of Korea and the World Economy X

** Department of Economics, Hankuk University of Foreign Studies, [email protected]

*** Corresponding author, Department of Economics, Hankuk University of Foreign

Studies, [email protected].

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I. Introduction

Both consumer sentiment index (CSI) and stock price could be leading indicators for

future consumer spending. The CSI provides consumer’s perception on current and

future economic condition, so CSI is considered to an indicator of future business cycles

and future consumer spending in particular. A lot of previous studies tried to find the

predictability of CSI for consumer spending with various country data. A partial list

includes Carroll et al. (1994) and Bram and Ludvigson (1998) with the U.S. data,

Acemoglu and Scott (1994) with the U.K. data, Kim and Goo (2005, 2008) and Jo and

Hwang (2009) with Korean data.

Since stock price incorporates expected profitability of business firms, stock price is

also correlated with future business cycles and consumer spending. Moreover, an

increase (or decrease) in stock price could have positive (or negative) impact on

consumer spending through the wealth effect. Previous studies in the literature generally

find positive relation between stock price and consumer spending (for example,

Mankiw and Zeldes (1991) and Ludvigson and Steindel (1999) with the U.S. data, Choi

and Lee (1999), Kim and Moon (2001) with Korean data)

A related question is about the relationship between CSI and stock price. Since CSI

reveals consumer sentiment on general economic conditions, stock price might respond

on the news on CSI announcement. In this case, CSI leads stock price. At the same time,

consumers may take optimistic sentiment with increasing stock price. This may be

because of signaling effect or wealth effect of stock market to consumer sentiment. Thus,

the relationship between CSI and stock price is mainly an empirical issue which needs

to be discovered through statistical analysis.

There have been several studies in the literature on this issue. According to Otoo (1999)

and Jansen and Nahuis (2003), who studied with the U.S. and EU data, respectively,

higher stock price leads higher consumer sentiment.1 On the contrary, Charoenrook

(2005) reports that consumer sentiment predicts stock returns in the U.S. data. With

Korean data, Park (2005) and Kim and Oh (2009) find some of the leading roles of

stock price to consumer sentiment. Although the empirical literature does not form a

consensus on the relationship between consumer sentiment and stock price, more

1 As an opposite case, Fisher and Statman (2003) report that low stock returns are followed by

high consumer confidence.

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studies have found that stock price leads consumer sentiment.

In the previous studies on the relation between consumer sentiment and stock price, the

generally used stock prices are composite stock price indices. Then, a possible extension

of the previous studies is to analyze whether the relation would be the same among

different industries. Companies producing consumer goods may have different linkage

with consumer sentiment when compared with industrial goods producing companies.

We explore this issue with industry level data by focusing on consumer goods industry.

Thus, the purpose of this paper is to find the relationship between consumer sentiment

and industry level stock prices, and analyze if stock prices of consumer goods are more

closely related to consumer sentiment. This study should provide better understanding

on CSI which is widely used as a leading indicator of consumer behavior.

Park (2005) also analyzed with Korean industry stock index data and found some

leading role of consumer goods stock price to consumer sentiment. He used the

consumer sentiment data from the Statistics Korea and consumer goods stock prices

based on three broadly defined indices such as retail sector index, consumer staples

sector index, and consumer discretionary sector index. His sample period was from

1999 to 2003. His main methodology was Granger causality analysis and OLS

regression.

Our paper used more detailed industry level data, a longer sample period, and various

CSI data. Industries are divided into ten broad industries, while two of them are

consumer goods industries; consumer products closely related to business cycles and

consumer necessities. The consumer goods industries are again divided into 7 sub-

industries. The industry grouping is followed by GICS (Global Industry Classification

Standard) and the industry stock indices are from WISEfn database. Our sample period

is from January 2000 to May 2011. The monthly data provided by the Statistics Korea

ended in August 2008 and the new monthly data by the Bank of Korea were published

from July 2008. The quarterly consumer sentiment data are provided by the Samsung

Economic Research Institute (SERI) and also another kind of quarterly data used to be

reported by the BOK until the second quarter of 2008. We investigate the relationship

between consumer sentiment and stock price with both monthly and quarterly data. We

analyze cross correlation and Granger causality among the variables. News effect from

the announcement of new CSI will be also examined with our data.

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The paper is organized as follows. Section II introduces the data. In section III,

empirical results are reported and explained. Section IV contains the conclusion.

II. Data

In Korea, the CSI data can be collected from three sources; the Statistics Korea (The

SK), the Bank of Korea (BOK), and Samsung Economic Research Institute (SERI).

Among various CSI data, we consider the composite CSI data which show overall

perception of consumers regarding to general economic condition and household

financial situation. The composite CSI’s are the average of indices from several

questions on current and future economic conditions as shown in Table 1. The SK did

not publish an overall composite CSI, but it reported two separate composite CSI’s such

as the present condition CSI (The SK PI) and the expectations CSI (The SK FI).

Table 1. Survey Questions in the Composite CSI

Questioned Issue The SK

PI

The SK

FI

BOK

CSI

SERI

CSI

Current household financial conditions ○ ○ ○

Current general economic situation ○ ○ ○

Expected household financial

conditions ○ ○

Expected general economic situation ○ ○ ○

Expected household consumption

spending ○ ○

Expected household income ○

Current durable goods purchase

sentiment

Note: ○ if this question is included

The SK CSI data are available until August 2008, and then combined to the BOK survey.

Since the 1996, the BOK CSI was published quarterly, but its frequency was changed to

monthly in July 2008.2 In this analysis we use both monthly and quarterly data. Thus,

our monthly CSI data set includes the SK data from February 2000 to August 2008 and

the BOK data from July 2008 to June 2011.3 The monthly CSI’s from the SK and the

2 The monthly data were officially published from September 2008. 3 The SK data start from December 1998, but because of the sample period of industry stock

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BOK are shown in Figure 1 and 2. Our quarterly data are from the BOK (from the first

quarter of 2000 to the second quarter 2008) and the SERI (from the first quarter of 2000

to the first quarter of 2011), and they are shown in Figure 3. Note that the base number

of the CSI for the SK and the BOK are 100, while it is 50 for the SERI.

Figure 1. The CSI from the Statistics Korea (Monthly)

50

60

70

80

90

100

110

120

00 01 02 03 04 05 06 07 08 09 10

The SK PI The SK FI

Figure 2. The CSI from the Bank of Korea (Monthly)

80

85

90

95

100

105

110

115

120

00 01 02 03 04 05 06 07 08 09 10

prices we use the data from January 2000. The Samsung Economic Research Institute (SERI)

also publishes quarterly CSI from 1991.

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Figure 3. The CSI from the Bank of Korea and the Samsung Economic Research

Institute (Quarterly)

80

85

90

95

100

105

110

115

120

32

36

40

44

48

52

56

60

64

00 01 02 03 04 05 06 07 08 09 10

BOK CSI SERI CSI

The stock prices in the analysis are the KOSPI and ten broad industry stock price

indices as shown in Table 2. Two industries are more related to consumer goods than

others. Those two industries are the industry of consumer products closely related to

business cycles (G25) and the industry of consumer necessities (G30). These two

industries are more divided into eight sub-industries to check if there are any differences

among consumer good industries. The changes of KOSPI and stock price indices of the

two broad consumer good industries are shown in Figure 4. They move in similar

patterns, but there are some variations among them.

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Table 2 Industrial Level Stock Price Indices

Composite

Index Broad Industry Index Sub-Industry Index

KOSPI

Energy (G10)

Materials (G15)

Industrial products (G20)

Consumer products closely

related to business cycles (G25)

Automobile and parts (G2510)

Consumer durables (G2520)

Hotel and restaurants (G2530)

Media (G2540)

Retail (G2550)

Education service (G2560)

Consumer necessities (G30)

Food, beverage, and tobacco (G3020)

Household and individual products

(G3030)

Health management (G35)

Financial service (G40)

IT (G45)

Electric and Telecommunication

service (G50)

Utility (G55)

Note: Industry codes in parentheses

Source: WISEfn database

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Figure 4. Stock Price Indices (Monthly)

0

1,000

2,000

3,000

4,000

5,000

00 01 02 03 04 05 06 07 08 09 10

KOSPI G25 G30

Note: G25 = Stock price index of the industry sector of consumer products closely related to

business cycles, G30 = Stock price index of the industry sector of consumer necessities

Source: WISEfn database

We test the existence of unit root with the Augmented Dickey-Fuller method. The

Schwartz information Criterion is used to choose optimal lag lengths. For all of the five

CSI’s, the null hypothesis of unit root can be rejected for the levels of the CSI’s. On the

other hand, a unit root can be rejected for the first difference for all of stock price

indices but not the levels at 5 percent significant level. Thus, in our empirical analysis,

we use the level of CSI and the log differenced stock price indices which is equal to

stock returns.

III. Empirical Results

1. Cross Correlation

With cross correlation we can examine the lead and lag between variables. Cross

correlations between CSI and stock return are shown in Table 3. In this analysis, stock

returns generally lead consumer sentiments. The monthly stock returns lead the SK

expectations index and current condition index by five to six months, while the BOK

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CSI lead stock indices by four to five months. With quarterly data, stock returns again

generally lead the CSI’s from the BOK and the SERI by a quarter.

Among the various industries, energy (G10) and utility (G55) industries reveal

relatively low correlation with the CSI’s. In quarterly data, the highest absolute

correlation for the two industries indicates negative correlation and lagging stock price

indices which are different from most of the other industries. For consumer good

industries, stock prices are leading the CSI and correlations are relatively high. This

finding is similar with more narrowly defined consumer goods industries. However, we

do not find any unique property of the consumer goods industry from the cross

correlation analysis.

Table 3. Cross Correlation of CSI with Leading and Lagged Stock Returns

Panel A. The SK Expectations Index

-6M -5M -4M -3M -2M -1M 0 1M 2M 3M 4M 5M 6M

KOSPI 0.37* 0.37* 0.33 0.35 0.34 0.23 0.13 -0.11 -0.13 -0.07 -0.09 -0.16 -0.24

G10 0.16 0.25 0.24 0.23 0.19 0.07 0.05 -0.07 -0.11 -0.16 -0.24 -0.32

-

0.36*

G15 0.42* 0.38 0.34 0.31 0.25 0.14 0.04 -0.15 -0.17 -0.09 -0.07 -0.12 -0.18

G20 0.38* 0.35 0.26 0.25 0.26 0.19 0.12 -0.10 -0.15 -0.11 -0.13 -0.22 -0.29

G25 0.28 0.29* 0.20 0.23 0.27 0.20 0.10 -0.13 -0.18 -0.15 -0.16 -0.23

-

0.29*

G2530 0.31 0.35* 0.32 0.33 0.30 0.16 0.12 -0.02 -0.03 -0.06 -0.13 -0.21 -0.29

G2550 0.34* 0.31 0.25 0.28 0.34* 0.32 0.26 0.00 -0.12 -0.14 -0.18 -0.24 -0.29

G30 0.32 0.31 0.29 0.34 0.35* 0.25 0.19 0.01 -0.06 -0.06 -0.15 -0.25 -0.32

G35 0.27 0.33* 0.26 0.21 0.16 0.05 -0.02 -0.16 -0.14 -0.08 -0.13 -0.22 -0.25

G40 0.36* 0.28 0.21 0.19 0.22 0.15 0.07 -0.17 -0.18 -0.08 -0.04 -0.11 -0.21

G45 0.24 0.30 0.28 0.35 0.40* 0.31 0.19 -0.05 -0.12 -0.09 -0.10 -0.12 -0.15

G50 0.24 0.27 0.31* 0.26 0.16 0.08 0.00 -0.15 -0.04 0.03 0.01 -0.06 -0.10

G55 0.16 0.16 0.20 0.19 0.15 0.06 0.02 -0.10 -0.08 0.00 -0.07 -0.20

-

0.24*

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Panel B. The SK Current Condition Index

-6M -5M -4M -3M -2M -1M 0 1M 2M 3M 4M 5M 6M

KOSPI 0.31 0.32* 0.28 0.25 0.20 0.14 0.04 -0.12 -0.14 -0.09 -0.08 -0.13 -0.15

G10 0.12 0.15 0.12 0.10 0.04 -0.02 -0.04 -0.17 -0.22 -0.26 -0.30

-

0.34*

-

0.34*

G15 0.34* 0.32 0.28 0.25 0.19 0.12 0.03 -0.12 -0.12 -0.06 -0.03 -0.07 -0.10

G20 0.31* 0.28 0.23 0.22 0.19 0.14 0.06 -0.10 -0.13 -0.10 -0.09 -0.16 -0.18

G25 0.26* 0.25 0.20 0.19 0.17 0.12 0.00 -0.17 -0.22 -0.19 -0.14 -0.16 -0.18

G2530 0.27* 0.27* 0.25 0.23 0.17 0.11 0.06 -0.03 -0.05 -0.07 -0.10 -0.17 -0.18

G2550 0.32* 0.30 0.28 0.28 0.26 0.23 0.13 -0.06 -0.14 -0.15 -0.13 -0.17 -0.17

G30 0.26* 0.24 0.23 0.25 0.20 0.16 0.09 -0.08 -0.13 -0.13 -0.16 -0.21 -0.22

G35 0.18 0.20* 0.15 0.10 0.04 -0.02 -0.07 -0.17 -0.13 -0.09 -0.10 -0.16 -0.18

G40 0.32* 0.29 0.24 0.20 0.15 0.11 0.02 -0.14 -0.15 -0.09 -0.05 -0.11 -0.15

G45 0.20 0.23* 0.21 0.22 0.20 0.13 0.02 -0.12 -0.14 -0.10 -0.06 -0.06 -0.05

G50 0.20 0.23* 0.23* 0.15 0.08 0.06 0.00 -0.07 -0.03 -0.01 -0.03 -0.07 -0.08

G55 0.12 0.17 0.17 0.13 0.05 -0.01 -0.05 -0.13 -0.09 -0.06 -0.12

-

0.23* -0.22

Panel C. The BOK Monthly CSI

-6M -5M -4M -3M -2M -1M 0 1M 2M 3M 4M 5M 6M

KOSPI 0.43 0.58 0.62* 0.47 0.47 0.51 0.39 0.09 0.05 0.08 -0.13 -0.36 -0.35

G10 0.44 0.53* 0.52 0.31 0.27 0.28 0.22 0.04 0.02 0.12 0.04 -0.03 0.02

G15 0.36 0.50 0.56* 0.44 0.38 0.42 0.33 0.08 0.04 0.11 -0.16 -0.38 -0.33

G20 0.39 0.52* 0.51 0.36 0.33 0.34 0.29 0.04 0.03 0.16 0.03 -0.12 -0.08

G25 0.45 0.61 0.65* 0.58 0.60 0.61 0.47 0.15 0.09 0.05 -0.08 -0.26 -0.32

G2530 0.48 0.60 0.61* 0.55 0.50 0.48 0.34 0.16 0.23 0.13 -0.15 -0.22 -0.11

G2550 0.38 0.57* 0.55 0.44 0.48 0.47 0.35 0.06 0.01 0.05 -0.21 -0.40 -0.35

G30 0.17 0.38 0.43 0.30 0.34 0.56* 0.48 0.18 0.23 0.24 0.02 -0.10 -0.11

G35 0.49 0.57* 0.45 0.29 0.21 0.11 -0.03 -0.31 -0.27 -0.18 -0.50 -0.49 -0.38

G40 0.31 0.49 0.54* 0.40 0.46 0.50 0.35 0.06 0.00 0.02 -0.17 -0.51 -0.46

G45 0.48 0.54 0.59* 0.46 0.43 0.42 0.27 -0.01 -0.07 -0.13 -0.26 -0.39 -0.44

G50 0.11 0.00 -0.06 -0.18 -0.15 0.17 0.18 0.04 0.12 0.28* 0.16 0.11 -0.04

G55 0.19 0.38* 0.36 0.17 0.18 0.34 0.22 0.03 0.04 0.12 -0.19 -0.37 -0.32

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Panel D. The BOK Quarterly CSI

-4Q -3Q -2Q -1Q 0 1Q 2Q 3Q 4Q

KOSPI -0.16 -0.04 0.24 0.53* 0.15 -0.16 -0.26 -0.36 -0.29

G10 -0.14 -0.15 -0.20 0.14 -0.13 -0.21 -0.43 -0.44* -0.20

G15 -0.08 -0.08 0.26 0.50* 0.18 -0.15 -0.15 -0.29 -0.18

G20 -0.12 0.02 0.18 0.42* 0.09 -0.14 -0.23 -0.36 -0.29

G25 -0.04 0.11 0.32 0.40* 0.07 -0.27 -0.29 -0.34 -0.33

G2530 0.00 0.13 0.39 0.46* 0.14 -0.17 -0.20 -0.42 -0.37

G2550 -0.01 0.27 0.45 0.47* 0.23 -0.14 -0.26 -0.40 -0.33

G30 -0.22 0.07 0.22 0.44* 0.14 -0.23 -0.39 -0.40 -0.30

G35 -0.07 -0.03 0.12 0.22 -0.10 -0.31 -0.23 -0.34 -0.36*

G40 0.04 0.12 0.45 0.56* 0.10 -0.26 -0.27 -0.39 -0.39

G45 -0.15 -0.05 0.07 0.36* 0.16 -0.05 -0.19 -0.22 -0.16

G50 -0.08 -0.03 0.14 0.48* 0.09 -0.15 -0.33 -0.27 -0.13

G55 -0.30 -0.11 0.17 0.28 -0.07 -0.33 -0.35* -0.35* -0.24

Panel E. The SERI CSI

-4Q -3Q -2Q -1Q 0 1Q 2Q 3Q 4Q

KOSPI -0.16 0.04 0.41 0.55* 0.25 -0.19 -0.44 -0.38 -0.20

G10 -0.16 0.00 0.21 0.26 0.02 -0.33 -0.51* -0.26 0.08

G15 -0.14 0.07 0.41 0.52* 0.20 -0.16 -0.36 -0.31 -0.13

G20 -0.09 0.07 0.38 0.42* 0.16 -0.21 -0.41 -0.26 -0.09

G25 -0.05 0.14 0.31 0.45* 0.20 -0.16 -0.41 -0.36 -0.20

G2530 -0.10 0.12 0.42 0.50* 0.28 -0.15 -0.39 -0.37 -0.19

G2550 0.07 0.21 0.37 0.42* 0.29 -0.19 -0.41 -0.44 -0.18

G30 -0.18 0.08 0.31 0.45* 0.16 -0.21 -0.44 -0.34 -0.23

G35 -0.02 0.14 0.33 0.33 -0.08 -0.34 -0.44* -0.31 -0.17

G40 -0.10 0.12 0.47 0.50 0.18 -0.19 -0.41 -0.52* -0.31

G45 -0.08 -0.04 0.24 0.52 * 0.30 -0.18 -0.39 -0.31 -0.18

G50 -0.09 0.01 0.37* 0.31 -0.08 -0.14 -0.19 -0.10 -0.16

G55 -0.16 0.11 0.37 0.29 0.04 -0.15 -0.46* -0.33 -0.38

Note: * for the highest absolute correlation

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2. Granger Causality Test

We use the Schwartz Information Criterion to select the lag length for the Granger

Causality test. In most of the cases, the optimal lag is selected to be one. Table 4 shows

the Granger causality test results with various CSI’s and stock returns. We summarized

the test results with the number of significant causality cases in Table 5.

As with the previous studies by Jansen and Nahuis (2003) and Kim and Oh (2009),

stock returns generally Granger cause consumer sentiment. The KOSPI Granger causes

all of the five CSI’s. Each stock price indices Granger causes CSI’s at 42 cases out of 65

cases. Among the consumer goods industries, the industry with more closely related to

business cycles (G25) is stronger that consumer necessities (G30) in this relation. As in

the case of cross correlation, Granger causality relation is weak in energy (G10) and

utility (G55) industries.

The Granger causality from the CSI to stock returns is not as strong as the opposite

direction, but we still see 27 significant cases out of total 65 cases. The CSI’s from the

SK and SERI Granger cause the KOSPI, while the BOK CSI’s do not show any

significant causality. With the consumer goods industries, we do not find a substantial

Granger causality from the CSI to stock returns. This result implies that even the stock

prices of consumer goods industry are not responding significantly to the changes in

consumer sentiment.

Table 4. Test for Granger Causality (p-values)

Panel A. The SK Expectations Index

Stock Return to CSI CSI to Stock Return

KOSPI 0.007** 0.028*

G10 0.316 0.129

G15 0.040* 0.017*

G20 0.064 0.025*

G25 0.032* 0.040*

G2530 0.081 0.090

G2550 0.043* 0.141

G30 0.087 0.145

G35 0.091 0.010**

G40 0.110 0.033*

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G45 0.001** 0.039*

G50 0.067 0.042*

G55 0.523 0.305

Panel B. The SK Current Condition Index

Stock Return to CSI CSI to Stock Return

KOSPI 0.001** 0.034*

G10 0.167 0.022*

G15 0.015* 0.042*

G20 0.014* 0.040*

G25 0.005** 0.027*

G2530 0.036* 0.044*

G2550 0.007** 0.099

G30 0.040* 0.042*

G35 0.057 0.006**

G40 0.034* 0.096

G45 0.000** 0.024*

G50 0.041* 0.097

G55 0.432 0.235

Panel C. The BOK Monthly CSI

Stock Return to CSI CSI to Stock Return

KOSPI 0.018* 0.661

G10 0.301 0.871

G15 0.037* 0.812

G20 0.212 0.800

G25 0.002** 0.486

G2530 0.002** 0.890

G2550 0.012* 0.529

G30 0.057 0.960

G35 0.072 0.067

G40 0.012* 0.727

G45 0.034* 0.364

G50 0.762 0.837

G55 0.144 0.912

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Panel D. The BOK Quarterly CSI

Stock Return to CSI CSI to Stock Return

KOSPI 0.009** 0.091

G10 0.113 0.141

G15 0.004** 0.085

G20 0.014* 0.073

G25 0.049* 0.023*

G2530 0.007** 0.016*

G2550 0.058 0.115

G30 0.057 0.138

G35 0.022* 0.012*

G40 0.005** 0.021*

G45 0.187 0.303

G50 0.002** 0.760

G55 0.108 0.197

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Panel E. The SERI CSI

Stock Return to CSI CSI to Stock Return

KOSPI 0.001** 0.033*

G10 0.048* 0.033*

G15 0.001** 0.131

G20 0.009** 0.051

G25 0.010** 0.069

G2530 0.001** 0.028*

G2550 0.050* 0.016*

G30 0.010** 0.036*

G35 0.001** 0.027*

G40 0.019* 0.032*

G45 0.004** 0.038*

G50 0.002** 0.354

G55 0.080 0.106

Note: ** and * are significant at 1 percent and 5 percent, respectively.

Table 5. Number of Significant Granger Causality Cases

Panel A. The SK Expectations Index

Stock Return to CSI CSI to Stock Return

KOSPI 5 3

G10 1 2

G15 5 2

G20 3 2

G25 5 3

G2530 4 3

G2550 4 1

G30 2 2

G35 2 4

G40 4 3

G45 4 3

G50 3 1

G55 0 0

Significant cases/Total 42/65=0.65 29/65=0.42

Note: Number of significant cases at 5 percent level

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3. News Effect of the Consumer Sentiment on Stock Price

The publication of new CSI could provide new information on consumers’ perception

on general economy and their financial situation. If the news of CSI contains any

valuable information, the stock market would respond substantially. Since CSI is

considered to be related to consumer spending, stock price of consumer industries

would change more than other industry stocks. We examine whether this is the case in

Korean stock market.

We collect the dates of CSI publication from the SK and the BOK. The monthly

publication dates of the SK start from January 2000 through August 2008. The BOK

dates are quarterly from 2000 to June 2008, and monthly from September 2008 to May

2011. The CSI’s considered here are the expectations index from the SK (SK FI) and the

BOK CSI.

To examine the news effect, we test if the stock returns are different between high CSI

and low CSI. Since the index number 100 indicates no change in consumer sentiment,

the high CSI is the case when it is higher than or equal to 100, and the low CSI is when

it is lower than 100. With the SK data, 48 cases are high and 57 cases were low. For the

BOK data, 41 cases are high and 27 cases are low.

We test with the KOSPI and the two broad consumer goods industries (Business Cycle

Related (G25) and Consumer Necessities (G30)). And then two sub-industries (Hotel &

Restaurants (G2530) and Retail (G2550)) are tested. The test results are shown in Table

6. For the SK expectations index, low CSI’s are generally followed by negative stock

returns, while the high CSI’s are followed by high positive stock returns. The difference

of stock returns between high and low CSI’s positive at least for the first two days.

However, we do not find any statistically significant case. As for the BOK data, high

CSI’s are generally followed by positive stock returns, but the difference of stock

returns between high and low CSI’s is generally negative. Again, the difference is not

statistically significant with the BOK data.

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Table 6. News Effect of the CSI on Stock Returns

Panel A. KOSPI

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.103 0.497 0.600 0.107

2 -0.067 0.148 0.215 0.372

3 0.046 -0.026 -0.072 0.713

4 -0.007 -0.034 -0.026 0.873

5 0.048 -0.030 -0.078 0.613

BOK CSI

1 0.075 0.049 -0.026 0.961

2 0.173 0.048 -0.125 0.685

3 0.494 0.136 -0.358 0.191

4 0.438 0.137 -0.302 0.220

5 0.435 0.095 -0.340 0.116

Panel B. Consumer Products closely related to Business Cycles (G25)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.161 0.402 0.563 0.070

2 -0.127 0.131 0.254 0.335

3 0.018 0.040 0.022 0.920

4 -0.014 -0.020 -0.007 0.972

5 0.052 -0.022 -0.075 0.672

BOK CSI

1 -0.408 -0.036 0.372 0.497

2 0.085 0.077 -0.008 0.983

3 0.420 0.230 -0.189 0.058

4 0.363 0.221 -0.142 0.546

5 0.379 0.144 -0.234 0.261

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Panel C. Consumer Necessities (G30)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.035 0.357 0.392 0.131

2 0.015 0.155 0.140 0.474

3 0.161 -0.003 -0.163 0.349

4 0.069 -0.016 -0.085 0.546

5 0.103 0.013 -0.090 0.515

BOK CSI

1 0.081 0.064 -0.017 0.953

2 0.269 0.146 -0.123 0.492

3 0.298 0.206 -0.092 0.532

4 0.286 0.126 -0.160 0.274

5 0.249 0.095 -0.154 0.299

Panel D. Hotel and Restaurants (G2530)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.314 0.168 0.482 0.277

2 -0.316 0.056 0.372 0.241

3 -0.216 0.090 0.306 0.243

4 -0.226 0.025 0.252 0.277

5 -0.105 0.010 0.115 0.571

BOK CSI

1 -0.175 -0409 -0.234 0.695

2 0.117 0.114 -0.103 0.755

3 0.298 0.104 -0.194 0.500

4 0.317 0.081 -0.236 0.336

5 0.372 0.070 -0.301 0.214

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Panel E. Retail (G2550)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.186 0.569 0.755 0.078

2 -0.163 0.322 0.485 0.147

3 -0.065 0.120 0.185 0.538

4 -0.024 0.103 0.127 0.618

5 0.019 0.048 0.028 0.905

BOK CSI

1 0.064 0.225 0.161 0.806

2 0.269 0.402 0.133 0.763

3 0.509 0.327 -0.183 0.630

4 0.452 0.265 -0.187 0.574

5 0.470 0.130 -0.340 0.213

Notes: 1) Days from the publication. 2) Geometric average of daily changes of the stock price

for the days from the publication; Unit: percentage. 3) Null hypothesis: (B) - (A) = 0. & * is

significant at 5%.

We also examined with an alternative definition of the high and low CSI’s for a robust

check. When a CSI is greater than or equal to the previous period CSI, it is considered

to be a high CSI, and when a CSI is lower than the previous period CSI, it is low CSI.

With this definition, we focus on the change of CSI rather than the level of CSI. The test

results with this alternative definition are shown in Appendix. We could find three

significant cases: Five day KOSPI return with the BOK data, first day retail industry

stock return with the SK data, and five day retail industry stock return with the BOK

data. The case with SK data indicates a positive correlation, but the cases with the BOK

data reveal negative correlations. With the few conflicting cases and a lot of

insignificant cases, we cannot infer any consistent and significant news effect from the

alternative definition of high and low CSI’s.

Overall, our empirical test does not found a significant and useful news effect of CSI in

Korean stock market. It appears that the news effect is not present even in consumer

goods industries. Kim and Oh (2009) found a marginal news effect with the BOK CSI

and the KOSPI for the sample period from the second quarter of 1996 to the second

quarter of 2008. We combined the quarterly and monthly CSI from the BOK and the

sample period is from the first quarter of 2000 to May 2011. With this change, the one

case of significant news effect found from the previous study disappeared. We interpret

this result as a very weak or insignificant news effect of CSI toward stock market in

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Korea.

IV. Conclusion

We examined the relationship between consumer sentiment and industry level stock

price in Korea. We consider all of the three consumer sentiment indices published in

Korea by the SK, the BOK, and the SERI. We use industry level stock price indices

which divide the KOSPI into ten broad industries. Among the ten industries, we are

particularly interested in two consumer goods industries which are the industry of

consumer products closely related to business cycles and the industry of consumer

necessities. Since CSI reveals consumer’s perception on their financial situation and

general economic condition which could be related to consumer expenditures, CSI

might be more closely related to stock prices of consumer goods, and the announcement

of new CSI data could affect stock prices, particularly the stock prices of firms

producing consumer goods.

In our cross correlation analysis, stock returns generally lead consumer sentiments. The

monthly stock returns lead the consumer sentiment by four to six months, while the

quarterly stock returns lead the consumer sentiment by a quarter. The correlation of

stock price indices producing consumer goods is one of the high correlated industries,

but it does not show any unique feature that is different from other industries or the

KOSPI.

In the Granger causality analysis, we find both directions of causality between stock

returns and the CSI, but a lot more cases indicate Granger causality from the stock

returns to the CSI. This is the same for the industry of consumer goods. We do not find

any substantial evidence that the changes in CSI are followed by the changes stock

prices of firms producing consumer goods.

Although monthly or quarterly data may not reveal the relation between stock price and

the CSI, daily data especially around the announcement days of the CSI could provide

any sign of significant value in the information of the CSI data. However, we could not

find any significant news effect from the SK and the BOK data. It appears that the news

of the CSI is broadly announced through the media, but stock market does not consider

it as new valuable information in Korea.

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The consumer sentiment index is widely used in economic analysis in that it is

considered to provide useful information on future consumption expenditure which is

more than half of GDP in a country. If this is the case, then the CSI may need to have

some influence on stock prices. The finding in our analysis does not support the case.

Our result may indicate that the stock market is efficient enough to perceive the CSI as

known information before the actual announcement. Consumers may be easily

influenced by the recent stock market situation, so the CSI does not provide any

predictive power, but just follow the lead of stock prices.

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Appendix. News Effect of the CSI on Stock Returns with Alternative Definition of

the High and Low CSI’s

Panel A. KOSPI

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 0.070 0.274 0.204 0.584

2 0.057 0.005 0.051 0.830

3 0.063 -0.039 -0.102 0.599

4 -0.020 -0.018 0.002 0.989

5 0.072 -0.049 -0.120 0.432

BOK CSI

1 0.103 0.018 -0.085 0.870

2 0.285 -0.079 -0.036 0.224

3 0.523 0.048 -0.474 0.075

4 0.453 0.071 -0.382 0.111

5 0.462 0.012 -0.451 0.032*

Panel B. Consumer Products closely related to Business Cycles (G25)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 0.016 0.178 0.162 0.603

2 -0.031 0.017 0.048 0.855

3 0.034 0.023 -0.011 0.961

4 -0.008 -0.026 -0.018 0.924

5 0.084 -0.049 -0.124 0.451

BOK CSI

1 -0.345 -0.031 0.313 0.560

2 0.198 -0.031 -0.228 0.515

3 0.466 0.154 -0.312 0.263

4 0.413 0.149 -0.263 0.250

5 0.431 0.055 -0377 0.063

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Panel C. Consumer Necessities (G30)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.044 0.335 0.379 0.143

2 -0.016 0.176 0.191 0.327

3 0.089 0.083 -0.007 0.970

4 -0.036 0.097 0.133 0.343

5 0.045 0.078 0.033 0.811

BOK CSI

1 0.036 0.104 0.068 0.810

2 0.267 0.127 -0.140 0.424

3 0.277 0.213 -0.060 0.680

4 0.254 0.139 -0.105 0.464

5 0.218 0.098 -0.121 0.405

Panel D. Hotel and Restaurants (G2530)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.347 0.165 0.512 0.247

2 -0.314 0.026 0.340 0.282

3 -0.189 0.040 0.229 0.382

4 -0.203 -0.017 0.186 0.421

5 -0.101 -0.002 0.099 0.625

BOK CSI

1 -0.341 -0293 -0.048 0.935

2 0.213 -0.094 -0.306 0.342

3 0.364 0.009 -0.354 0.207

4 0.286 0.069 -0.217 0.368

5 0.299 0.087 -0.212 0.372

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Panel E. Retail (G2550)

Days 1)

Low CSI (A) 2)

High CSI (B) 2)

(B) - (A) p-value 3)

SK FI

1 -0.306 0.634 0.940 0.027*

2 -0.256 0.379 0.635 0.056

3 -0.202 0.245 0.448 0.133

4 -0.168 0.240 0.409 0.104

5 -0.095 0.162 0.256 0.275

BOK CSI

1 0.117 0.202 0.084 0.895

2 0.368 0.331 -0.037 0.931

3 0.617 0.194 -0.423 0.253

4 0.591 0.102 -0.488 0.131

5 0.510 0.035 -0.475 0.037*

Notes: 1) Days from the publication. 2) Geometric average of daily changes of the stock price

for the days from the publication; Unit: percentage. 3) Null hypothesis: (B) - (A) = 0. & * is

significant at 5%.

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