1
Chinese institutional investors’ sentiment
Gerhard Kling a,*, Lei Gao b
a
b
University of Southampton
School of Business, Shantou University, Shantou 515063, PR China
THIS IS NOT THE FINAL (POST-REVIEW) VERSION
YOU FIND THE FINAL VERSION HERE:
Kling, G. and L. Gao (2008) Chinese institutional investors' sentiment, Journal of
International Financial Markets, Institutions & Money 18(4), 374-387.
We use daily survey data on Chinese institutional investors’ forecasts to measure investors'
sentiment. Our empirical model uncovers that share prices and investor sentiment do not have
a long-run relation; however, in the short-run, the mood of investors follows a positive
feedback process. Hence, institutional investors are optimistic when previous market returns
were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more
sensitively to negative than positive returns. Investor sentiment does not predict future market
movements – but a drop in confidence increases market volatility and destabilizes exchanges.
EGARCH models reveal asymmetric responses in the volatility of investor sentiment;
however, Granger causality tests reject volatility-spillovers between returns and sentiment.
4th April 2007
JEL classification: G14; C22
Keywords: Shanghai stock exchange; Institutional investor; Investors’ sentiment
*
Corresponding author
E-mail addresses: drgaolei@126.com (L. Gao), g.kling@soton.ac.uk (G. Kling).
2
1. Introduction
Research on investors’ sentiment has focused on small shareholders for two main reasons: (1)
data on the sentiment of large investors are not available; (2) small shareholders are supposed
to act like noise traders; thus, the impact of noise trading can be analyzed.1 In contrast, we
measure the sentiment of institutional investors in China by collecting daily survey data on
investors’ forecasts. Hence, we know how many institutional investors expect a positive or
negative change in the Shanghai Stock Exchange (SSE) index on the next day. The
interrelation between institutional investors’ sentiment and stock markets might differ from
former findings, as institutional investors are more professional in analyzing public
information, conduct large-scale transactions, and possess informational advantages.
In China, several authorized closed-end investment funds exist, and open-end funds
were recently introduced. Yet other forms of institutional investment like pension funds or
managed portfolios of insurance companies are negligible. Based on World Bank data for
2001, Chinese institutional investors’ assets reached 19% of GDP; this figure was lower than
the corresponding figures for Hungary (26%), the Czech Republic (32%), and further
developed Asian markets, e.g. South Korea (82%). As Chinese institutional traders are less
important than their US and European counterparts, one can regard our analysis as a lower
estimate of the importance of institutional investors’ sentiment in other markets.
Three questions are at the core of our interest: (1) what determines the forecasts of
institutional investors; (2) can the sentiment index predict future stock market movements; (3)
does a drop in sentiment or a higher volatility of investors' sentiment destabilize stock markets
by triggering higher market volatility? Besides these core issues, we analyze asymmetric
responses of investor sentiment based on EGARCH and TARCH models. In addition,
1
Kelly (1997) showed that the likelihood of being a noise trader declines with household income; thus, the
analysis of noise trading is limited to small investors. We define noise trading in the sense of DeLong et al.
(1990).
3
bivariate GARCH models allow us to explore the time-varying correlation between sentiment
and stock returns.
The literature on investors’ sentiment covers our research questions; however, only the
sentiment of small investors has been studied so far. Our first question, the search for factors
that affect investors’ sentiment, is still under discussion – albeit the formal model of investor
sentiment developed by Barberis, Shleifer, and Vishny (1998) has provided a baseline for
economists. The literature focused mainly on our second question: Neal and Wheatley (1998),
for instance, quantified the predictability of stock returns using different measures of
investors’ sentiment. Lee, Jiang, and Indro (2002) discussed our third question for the US in
analyzing the influence of investors’ sentiment on volatility and excess returns.
The remainder of our paper is organized as follows: section two reviews the recent
literature on theoretical considerations concerning sentiment and market activity, institutional
trading, sentiment of small investors, survey methods, and indirect measures of sentiment.
Section three describes our data sources, followed by our empirical analysis that starts with a
univariate-time series approach followed by unit-root tests and multivariate methods, namely
VAR and bivariate GARCH models. Based on our empirical findings, we discuss
consequences for mature financial markets and peculiarities of the Chinese stock exchange.
2. Literature review
Classical finance theory neglects the role of investor sentiment (see Gomes, Kogan, and
Zhang, 2003), as investors are supposed to be rational. Even if some investors are not rational,
arbitrageurs can exploit their irrational behavior, thus causing prices to reflect future
discounted cash flows. By introducing demand shifts driven by irrational speculation and
binding arbitrage constraints, sentiment can influence stock returns. Hence, a mispricing
triggered by uninformed demand shocks could occur. This is in line with the idea that
sentiment can be regarded as the propensity to speculate and thus reflects the optimism or
4
pessimism of investors. Especially if arbitrage is risky because of subjective valuations, 2 high
volatility, short selling constraints, 3 and thin trading, the impact of sentiment on returns
should be more pronounced. In particular, the situation in China is characterized by the lack
of an earnings history, apparently unlimited growth potential, and unsophisticated investors;
hence, the role of investor sentiment should be tremendous.
As institutional investors account for more than 50% of trading in the US (see Cai and
Zheng, 2004), their influence on stock markets is of considerable interest. Previous studies
have mainly focused on the impact of institutional trading on stock returns, and assessed the
consequences of large-scale transactions, positive-feedback trading, and the herding behavior
of institutional investors (see Lakonishok, Shleifer, and Vishny, 1992). Nevertheless, Bohl
and Brzeszczynski (2006) studied the influence of institutional ownership on volatilities. They
found that institutional investors stabilize exchanges, as volatility drops after institutional
investors become principal shareholders. These studies, however, have in common that they
analyze the effect of institutional trading and ownership, whereas our paper tries to measure
the relevance of institutional investors’ sentiment for stock returns and volatilities. This
requires data on institutional investors’ sentiment, which are usually not available as surveys
or alternative measurement methods focus on small shareholders’ sentiment.
Results on whether the sentiment of small investors is a good indicator of future
market movements are somehow ambiguous. Brown and Cliff (2001) provided only weak
evidence of short-run predictability, but found long-run predictability of returns for a period
of about two to three years. Kenneth and Statman (2000) detected a significantly negative
correlation between US investors’ mood and returns of the S&P500. Apart from the
2
A broad range of valuations increases noise trader risk (see DeLong, Shleifer, Summers, and Waldmann, 1990;
Shleifer and Vishny, 1997).
3
Arbitrage is limited by short selling constraints (see e.g. Jones and Lamont, 2002). In China, the short selling of
stock is hardly possible and derivate instruments are not developed..
5
discussion about whether investor sentiment can predict future market movements, several
studies identified an interrelation with market volatility. By using GARCH models, Lee,
Jiang, and Indro (2002) analyzed the impact of investors’ sentiment on market volatility. They
uncovered that a decline in investors’ sentiment triggers higher volatility.
Several direct and indirect measures of sentiment have been used in the recent
literature. For US studies, the sentiment index provided by Investors’ Intelligence of New
Rochelle based on a weekly survey of 135 independent advisory services, is commonly
applied. 4 Survey methods have provoked the skepticism of many economists. Yet Blinder
(1991) argued that a well-designed survey can provide stylized facts and valuable clues that
go beyond standard models and are not available to econometricians. The so-called indirect
approach serves as an alternative to survey data. For US studies, the fluctuations of closedend fund shares are used as a proxy of sentiment because predominantly private investors
hold these shares.5 Besides observing closed-end funds, Baker and Stein (2004) argued that
market liquidity could be regarded as a proxy of investors’ sentiment. They used bid-ask
spreads and turnovers as a measure of liquidity. This measure refers to the whole market;
thereby, it is impossible to distinguish between different types of investors. Consequently, we
favor a survey approach in order to isolate the opinion of institutional investors and their
market impact from all other market participants.
3. Data
Since April 20, 2001, the Chinese Central Television Station has been surveying 75 leading
institutional investors. On a daily basis, they are asked about their forecast regarding the
Shanghai Stock Exchange Index’s movements on the following day. The survey is conducted
4
Many studies like Siegel (1992) used this index and showed that it can predict market movements.
5
Lee, Shleifer and Thaler (1991) proposed this method, and Neal and Wheatley (1998) showed that fluctuations
of closed-end funds can predict the returns of small caps.
6
at 4pm each day, and the results are published online. 6 As the market closes before 4pm,
institutional investors could use today’s market development as information for their forecast.
The surveyed institutional investors have three choices: bullish, bearish, and tie. The web
page contains the numbers of investors opting for each type of forecast – but individual
opinions remain confidential. Based on these figures, we calculate for each day the percentage
of optimistic institutional investors and set up the daily index of optimism. This index is
constructed in a similar way as the index provided by Investors’ Intelligence of New Rochelle,
which is used for US studies. The sentiment index reaches 0 if everyone expects a decline in
the stock market, and 100 if everyone is optimistic. Henceforth, the sentiment index is
bounded between 0 and 100; however, the variable is normally distributed, as extreme cases
of 0% or 100% optimistic responses are rare.7
Three major aspects make the Chinese daily survey unique: first, the survey is based
on the opinion of institutional investors, which allows the measurement of their sentiment.
Second, it provides daily information on investors’ mood and thus has the highest possible
frequency of information. Sentiment indices for the US are usually on a weekly basis. Third,
the forecasting horizon is only one day, as investors are asked about their forecast regarding
tomorrow’s market movement. Generally, there is no hint that the survey we use contains any
incentives to give false information, as individual forecasts remain confidential.
4. Empirical models
4.1. Univariate analysis of investor sentiment
Daily institutional investors' sentiment fluctuates remarkably over time (see Fig.1). To model
the variance pattern, we use an ARCH (p) model based on the seminal paper of Engle (1982).
6
See http://www.eestart.com/zxzx/20040220/558105.html.
7
Shapiro-Wilk and Shapiro-Francia W tests indicate, with p-values of 0.084 and 0.122, that the null hypothesis
of the sentiment index being normally distributed cannot be rejected at the 95% level of confidence.
7
The log of the sentiment index is labeled yt, and its conditional variance is t2. Hereafter, we
always refer to the logarithmically transformed sentiment index except when stated otherwise.
yt   0   t
(1)
E  t  t   0
Var  t  t    t2   0    j  t2 j
p
j 1
To determine the appropriate lag structure of the ARCH (p), we conduct LM tests by
regressing squared residuals of equation (1) on their lagged values. 8 Justified by these
outcomes, we specify an ARCH (2) process.
Besides dealing with a nonlinear structure in conditional variances, one should capture
the autoregressive behavior of the sentiment index. Accordingly, the next step is to specify an
ARMA (p, q) model and insert this structure into the mean equation (1).9 By looking at the
autocorrelation function and the partial autocorrelation function, we can justify an ARMA (3,
4) specification. A reduction of the lags does not lower information criteria like the Akaike
criterion; thus, we incorporate an ARMA (3, 4) term into our mean equation.
3
4
j 1
j 1
yt   0    j yt  j    j  t  j   t
(2)
2
 t2   0    j  t2 j
j 1
Fig.1 depicts the actual and predicted values of the sentiment index. To ensure that our model
captures all nonlinearities in the time series, we confirm through white noise tests that the
8
Multiplying the number of observations and the R-squares achieved for different lag structures yields a test
statistic that is Chi-squared distributed with p degrees of freedom. It is not possible to reject the null hypothesis
that ARCH terms are jointly insignificant for an ARCH (3) specification and higher orders – but with a p-value
of 0.001 for the Chi-squared test, an ARCH (2) specification has significant coefficients.
9
To reliably estimate an ARMA specification for the sentiment index, the time series has to be stationary. By
looking at Fig.1, one obtains the impression that this requirement is fulfilled. Also, ADF tests indicate that the
time series does not possess a unit root. A subsequent section discusses this issue more thoroughly.
8
residuals exhibit erratic behavior.10 The Barlett’s periodogram-based test procedure fails to
reject the null that the series is white noise, and the Portmanteau Box-Pierce test points in the
same direction.11 How can one interpret these findings? The negative autocorrelation suggests
that a positive forecast is likely to be followed by a negative one. Yet the importance of
former assessments dies out quickly. The following section focuses on the interrelation
between sentiment, stock returns, and volatility.
4.2. The interrelation between sentiment and stock returns: a VAR analysis
Before using more elaborate techniques, a simple cross-correlogram can help to detect
whether returns or forecasts move first. Fig.2 plots the cross-correlation for different lags and
leads. A positive correlation of about 0.4106 between lagged market returns and forecasts
reveals an interesting interdependency. Previous market returns influence the current mood of
institutional investors positively. This is a highly interesting finding, as current market returns
do not affect forecasts despite the fact that the survey is conducted after the market is closed.
Institutional investors could use current returns to improve their forecasts, yet they rely on
past information. To study this time structure and correlation further, our strategy is as
follows. First, by applying unit-root tests, we test whether both time series are stationary.
Second, we model the short-term interrelation by a VAR approach and select the lag structure
based on information criteria. Thereafter, Granger causality tests show whether forecasts
affect market returns or vice versa.
10
Before performing white noise tests, one has to fill two gaps in the time series of the residuals. Because the
problem of missing values is rather limited, we propose a very simple solution for this problem. According to
this, the two missing values in the time series are replaced by the previous values of the residuals.
11
The Barlett’s test statistic reaches 0.950 (p-value: 0.327), and the Portmanteau Q statistic is equal to 39.622 (p-
value: 0.487).
9
We test for stationarity by calculating ADF based tests. 12 Due to the ‘traditionally’
weak explanatory power of ADF tests, we also report the results of KPSS tests that use a
different null hypothesis.13 If both test procedures point in the same direction, the result can
be regarded as informative. Both test procedures point in the same direction and thus confirm
that both time series are stationary.14 As investor sentiment is I(0) and stock prices are I(1),
both time series cannot be cointegrated and a long-run relationship does not exist. However,
we analyze the short-run dynamics by a VAR model and determine the number of lags as
suggested by information criteria, namely Schwarz Bayesian (SBIC), Akaike (AIC), and
Hannan-Quin (HQIC). As expected,15 the SBIC favors the inclusion of two lags, whereas AIC
and HQIC request one additional lag. Hence, we estimate a VAR with two and three lags,
respectively, and carry out Granger causality tests. Table 1 presents the outcomes. The
interpretation is twofold. Only the market return rt-1 of the previous day affects the current
forecasts yt. Moreover, Granger causality tests reveal that investor sentiment does not
influence returns and hence is not a valuable information for the market. It is noteworthy that
a positive market return triggers a positive forecast of the institutional investors. Accordingly,
institutional investors’ sentiment can be interpreted as a positive feedback process with regard
to previous market returns. Variance decompositions highlight that an impulse in returns
determines about 20% of the subsequent innovations in investor sentiment.
12
Elliott, Rothenberg, and Stock (1996) modified ADF tests by a GLS approach that accounts for serial
correlation.
13
Kwiatkowski et al. (1992) constructed this test procedure.
14
The ADF test statistic reaches -4.511 for the sentiment index and -17.451 for market returns. Hence, the null
hypotheses that the series have a unit root can be rejected on the 99% level of confidence. KPSS test statistics are
0.117 for the sentiment index and 0.044 for market returns; hence, the null hypothesis cannot be rejected. The
Schwert criterion determines the lag structure.
15
The SBIC usually favors less complex models.
10
4.3. Asymmetric responses of investor sentiment to changes in past market returns
A widely discussed question in the literature is whether market participants react more
severely if bad news is released.16 Putting this in other words, one should test the hypothesis
that negative market returns influence sentiment more than positive returns. To implement
this test into our ARCH framework, we modify model (2) by inserting an indicator variable
labeled I and an interaction term of the indicator variable with lagged market returns I.rt-1.17
The indicator variable I takes the value one when the previous market return rt-1 is negative,
and the value zero otherwise. Accordingly, we allow an asymmetric response of investor
sentiment to past returns in the mean-equation. To explore whether asymmetric responses also
exist in the variance-equation, we run three different specifications, namely the standard
ARCH(2), EGARCH and TARCH models.
3
4
j 1
j 1
yt   0  1rt 1   2 I   3 I  rt 1    j yt  j    j  t  j   t
(3)
2
ARCH(2):  t2   0    j  t2 j
j 1
2
EGARCH:  t  vt ht ; ln( ht )   0    j
j 1
 t j
0.5
t j
h
2
2
j 1
j 1
2
 t j
j 1
ht0.5j
 j
TARCH:  t  vt ht ; ht   0    j  t2 j    j Dt  j  t2 j
Note that vt is a white noise process with variance 2v = 1, and Dt-j = 1 if t-j < 0. Table 2
shows the outcomes of model (3) with an ARCH(2) specification. To discuss asymmetric
16
Sias (1997) found that the expectations of small investors (compared to institutional investors) react more
sensitively if the market environment changes.
17
Note that market returns are weakly exogenous and Granger-cause sentiment; hence, inserting lagged returns
does not cause an endogeneity bias.
11
responses of investor sentiment in the conditional variance, Table 2 also reports TARCH and
EGARCH model results. 18 The coefficient of the indicator variable I is not significant;
however, the coefficient of the interaction term I.rt-1 is highly significant (p-value = 0.000)
and has a positive sign regardless of whether we use an ARCH, EGARCH or TARCH model.
Consequently, if the previous return rt-1 is negative, investor sentiment exhibits a pronounced
decline. In particular, the magnitude of the impact of past returns is about three times larger in
the case of negative returns compared to increasing stock prices. Based on the threshold
ARCH model (see Chen, 1998), we do not find asymmetric responses in the conditional
variance of forecasts triggered by past market movements. However, the EGARCH model
confirms that negative shocks (measured by -j + j) have a stronger impact on conditional
variance than positive shocks (measured by j + j). This confirms Chen’s (1998) finding that
investor sentiment exhibits a higher variance after negative shocks. Based on our estimates for
the mean and conditional variance equations, we state that negative returns have a pronounced
negative impact on investor sentiment, and that negative shocks increase the conditional
variance of investor sentiment.
4.4. The impact of investor sentiment on market volatility
After confirming that previous stock returns affect sentiment but not vice versa, we focus on
the impact of institutional investors’ sentiment on market volatility. To test the impact of
lagged sentiment yt-1 on volatility and to capture volatility patterns, we apply a GARCH
model of the following form:19
18
We use the EGARCH model developed by Nelson (1991), and the TARCH model based on Glosten,
Jaganathan, and Runkle (1993).
19
Note that sentiment is a stationary variable; hence, taking the first-difference, as done by Lee, Jiang, and Indro
(2002) for US data, is not required and would be a misspecification. We specify a GARCH (1,1) model after
testing for the order of an ARCH (p) model using the LM test as mentioned in a previous section. Because the
12
rt   0   1 yt 1  u t
E u t  t   0
(4)
Var u t  t    ut2   0   1u t21   2 ut2 1   3 yt 1
We find that the sentiment index has a significantly negative impact on the conditional
variance of market returns. Table 3 reports the results of different specifications of model (4);
thus, we estimate a GARCH(1,1) model without any impact of the lagged sentiment index, a
specification with the lagged sentiment index in the mean and variance equations, and a
model that includes the lagged investor sentiment in the variance equation only. Accordingly,
one can confirm that the sentiment has an impact on volatility – but not on returns. If the
sentiment is very low, one should expect a high volatility on the market. 20 Obviously,
statistical significance does not necessarily imply that the impact of sentiment on volatility is
an economically important effect. To evaluate the relevance of institutional investors’
sentiment, we determine the one-sep ahead forecasts of the conditional variance based on our
GARCH(1,1) model with the lagged sentiment. Then, we express the contribution of
sentiment relative to the predicted conditional variance in percentage points. In the case of
overwhelming optimism (95 percentile of the sentiment indicator), market volatility drops by
12.74% on average. If institutional investors are very pessimistic (5 percentile of the
sentiment indicator), volatility rises by 6.60% on average. Consequently, the impact of
institutional investors’ sentiment on market volatility has a considerable economic effect.
Pessimism of institutional investors destabilizes the stock market by triggering higher market
volatility. Having thus confirmed the empirical relevance of our results, do our findings also
make sense theoretically?
lag of the ARCH (p) model would exceed three, one can approximate this complex model by a simpler GARCH
(1,1) specification (see Bollerslev, 1986).
20
In contrast to Lee, Jiang, and Indro (2002) who used an ARCH-in-mean model, we cannot confirm an impact
of the sentiment on stock returns (or excess returns). However, we find an impact of sentiment on volatility,
which they found for the US sentiment index.
13
Theoretical and empirical studies confirm that stocks with a higher volatility are more
sensitive to shifts in investor sentiment (see Baker and Wurgler, 2004), as they exhibit a
broader range of valuation, which make these stocks more vulnerable to noise trading (see
DeLong, Shleifer, Summers, and Waldmann, 1990; Shleifer and Vishny, 1997). However, we
investigate institutional investors’ sentiment and focus on the opposite causal direction. A
theoretical model concerning the linkage between sentiment and market volatility does not
exist, but there is a broad literature on institutional trading behavior and market stability.
Institutional investors can be destabilizing because of positive-feedback trading (see
Lakonishok, Shleifer, and Vishny, 1992), which means that they trade less if past returns were
negative, and trade more if past returns were positive. Our study shows that past returns have
a positive impact on current sentiment; therefore, negative returns trigger a reduction in
investor sentiment. If institutional investors are pessimistic about the future development of
the stock market, they might trade less.21 What we observe is a positive-feedback trading or
“trend chasing” behavior, which destabilizes exchanges as more liquidity is provided in good
times and less in bad times (see Aiken, 1998). As a consequence, market volatility increases
(see DeLong et al., 1990; Cutler, Poterba, and Summers, 1990). Nofsinger and Sias (1999)
argued that lagged stock returns act as a common signal for positive-feedback trading, which
confirms our findings that past returns affect sentiment in the sense of a positive-feedback
process.
4.5. Bivariate GARCH models
So far, we have focused on univariate GARCH models, and our VAR model allowed
interrelations between mean-equations but did not allow volatility-spillovers and correlation
between the error terms of the two-equation system. Accordingly, this section explores
multivariate GARCH techniques that allow volatility-spillovers and time-varying correlation.
21
Note that we cannot observe the trading behavior of Chinese institutional investors directly.
14
We follow the bivariate BEKK model by Engle and Kroner (1995) and construct our basic
model in the following manner:22
3
 rt  j 
r 
VAR:  t   μ   Γ j 
  εt
j 1
 yt 
 yt  j 
(5)
 hr ,t 


Vector GARCH (1,1): vec(H t )  h t  hry,t   C  Avec(ε t 1ε t 1 )  Bh t 1
 h y ,t 


BEKK parameterization: H t  C*C*  A*ε t 1εt 1 A *  B* H t 1 B*
Based on our univariate GARCH models, we know that the conditional volatility of stock
returns can be modeled by GARCH(1,1), whereas the conditional volatility of investor
sentiment follows an ARCH(2) process. Accordingly, we use the standard vector
GARCH(1,1) and apply the BEKK parameterization – but we also explore an extension by
allowing an ARCH(2) process for investor sentiment, GARCH(1,1) for stock returns, and
GARCH(1,1) for the covariance term. 23 For the sake of comparison, we also run a vector
GARCH(1,1) specification without the VAR structure in the conditional mean-equation.
Table 4 shows the results of our maximum-likelihood estimation. To highlight the timevarying correlation coefficients between investor sentiment and stock return, Fig.3 plots
conditional correlation coefficients based on the BEKK parameterization of a VGARCH(1,1)
model. 24 Fig.3 illustrates further that the conditional correlation coefficient fluctuates
tremendously over time, and that on average, the correlation coefficient reaches 0.02. The 5
22
There are a huge variety of different bivariate conditional heteroscedasticity models. Bollerslev, Engle, and
Wooldridge (1988) extended the univariate GARCH model to the vector GARCH – but this model does not
guarantee that the variance-covariance matrix is positively semidefinite (see Lien and Luo, 1993). Hence, we
rely on the BEKK model by Engle and Kroner (1995), which overcomes the problem of negative variance terms
and allows modifications, as it is fairly general.
23
We can extend the vector GARCH to a VGARCH(2,1) and impose parameter restrictions.
24
Note that the two alternative specifications lead to very similar conditional correlation coefficients.
15
percentile is –0.19, and the 95 percentile is 0.21; thus, we can state that the contemporaneous
correlation between the two series is of minor relevance. Nevertheless, Fig.2, the VAR in
Table 1 and the extended VAR in Table 4 point to the fact that lagged returns affect investor
sentiment (in the mean-equation). Based on the three bivariate GARCH specifications in
Table 4, we carry out Granger causality tests to detect volatility-spillovers. Thus far, Table 3
showed that lagged investor sentiment influences the conditional variance of stock returns.
Now, we focus on the alleged impact of the lagged conditional variance of investor sentiment
on the conditional variance of stock returns and vice versa. However, Granger causality tests
reveal that volatility-spillovers cannot be confirmed.
5. Conclusions
In contrast to previous research, we analyze institutional investors’ sentiment and reveal that a
long-run relation between share prices and sentiment does not exist – but short-term dynamics
matter. Granger causality tests show that previous market returns influence investors’
sentiment – but not vice versa. Henceforth, institutional investors’ sentiment does not predict
future stock returns – but stock returns influence sentiment in the sense of a positive feedback
process. Our findings confirm Brown and Cliff (2001) that discovered that the sentiment of
small investors is a poor indicator of future stock returns, at least in the short run. Hence, one
can state that institutional investors do not possess any superior information or above-average
analytical skills to form their expectations about future market developments. An asymmetric
response model emphasizes that institutional investors react by far more sensitively after the
market declines. This is in line with findings for small shareholders (see Sias, 1997).
Lee, Jiang, and Indro (2002) found that the sentiment of small investors and hence
noise-trading increases market volatility – but also increases excess returns. This implies that
the additional risk of noise trading is priced. We confirm for institutional investors that their
sentiment has a tremendous impact on volatilities – but we cannot find any influence on stock
16
returns. As investor sentiment follows a positive-feedback process, we argue that Chinese
institutional investors exhibit a “trend chasing” behavior, which destabilizes exchanges and
hence increases market volatility (see DeLong et al., 1990; Cutler, Poterba, and Summers,
1990).
As survey data on institutional investors’ sentiment is to date only available for the
Chinese market, we must carefully assess which implications can be derived for mature
financial markets from our study. Institutional investors are relatively unimportant in China;
however, their sentiment has a considerable impact on volatility. Henceforth, one could argue
that the discovered impact of institutional investors’ sentiment might be even stronger in
mature markets. Nevertheless, Chinese institutional investors have a short history, as the first
securities firms entered the market in September 1992. Due to this short period of learning,
Chinese institutional investors lack experience, which might partly explain our finding that
institutional investors’ sentiment has similar effects as the sentiment of small shareholders.
17
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