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                       Chinese institutional investors’ sentiment

                                    Gerhard Kling a,*, Lei Gao b
                                      a
                                          University of Southampton
                b
                    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 closed-

end 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                                            (2)
                                yt   0    j yt  j    j  t  j   t
                                             j 1                j 1


                                                           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                     (3)
                   y t   0   1 rt 1   2 I   3 I  rt 1    j y t  j    j  t  j   t
                                                                   j 1                  j 1


                                                                           2
                                     ARCH(2):  t2   0    j  t2 j
                                                                           j 1



                                                                               2     t j         2      t j
                 EGARCH:  t  vt ht ; ln( ht )   0    j                         0.5
                                                                                                  j
                                                                             j 1   h t j        j 1   ht0.5j

                                                                   2                     2
                   TARCH:  t  vt ht ; ht   0    j  t2 j    j Dt  j  t2 j
                                                                  j 1                  j 1



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  ut                                     (4)
                                              E ut  t   0
                        Varut  t    ut   0  1ut21   2 ut 1   3 yt 1
                                         2                         2




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

                                        r          3     rt  j                                            (5)
                                   VAR:  t   μ   Γ j           εt
                                         yt       j 1   yt  j 

                                                 hr ,t 
                                                        
          Vector GARCH (1,1): vec(H t )  h t  hry ,t   C  Avec(ε t 1 ε  1 )  Bh t 1
                                                                              t
                                                 h y ,t 
                                                        

                BEKK parameterization: H t  C C*  A  ε t 1ε  1 A *  B H t 1 B *
                                              *        *         t           *




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 time-

varying 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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