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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, snkim@hufs.ac.kr
*** Corresponding author, Department of Economics, Hankuk University of Foreign
Studies, wanoh@hufs.ac.kr.
2
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.
3
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.
4
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
5
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.
6
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.
7
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
8
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
9
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*
10
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
11
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
12
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*
13
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
14
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
15
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
16
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.
17
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
18
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
19
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
20
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.
21
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.
22
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
23
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
24
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%.
25
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