Does investor sentiment predict stock returns? The evidence from Chinese stock market

J Syst Sci Complex (2014) 27: 130–143

DOES INVESTOR SENTIMENT PREDICT STOCK

RETURNS? THE EVIDENCE FROM CHINESE

STOCK MARKET∗

BU Hui · PI Li

DOI: 10.1007/s11424-013-3291-y

Received: 13 April 2012 / Revised: 23 April 2013

c©The Editorial Office of JSSC & Springer-Verlag Berlin Heidelberg 2014

Abstract This paper examines the proxy variables of investor sentiment in Chinese stock market

carefully, and tries to construct an investor sentiment index indirectly. We use cross correlation analysis

to examine lead-lag relationship between the proxy variables and HS300 index. The results show that

net added accounts (NAA), SSE share turnover (TURN), and closed-end fund discount (CEFD) are

leading variables to stock market. The average first day return of IPOs (RIPO) and relative degree of

active trading in equity market (RDAT) are contemporary variables, while number of IPOs (NIPO) is a

lagging variable of stock market. Using the sentiment proxy variables with most possible leading order,

and forward selection stepwise regression method, the empirical results on monthly stock returns reveal

that three leading proxy variables can be used to form a sentiment index. And the out of sample tests

prove that this sentiment index has good predictive power of Chinese stock market, and it is robust.

Keywords Chinese stock market, investor sentiment, return predictability.

1 Introduction

Different theories explain different mechanisms of price determination. According to theclassical economic theory, market fundamentals should be the major factors that determine theprice and drive its volatility. For example, the efficient market hypothesis (EMH) says if financialmarkets are information efficient, then the price of traded assets reflects all known informationabout market fundamentals and, therefore, is unbiased. Since the mid-1980s, there has been aserious attempt to explore the possibility that liquid financial market are not always as orderlyas might be suggested by EMH. This is because classical finance theory often argues investorsare rational, even if some investors are irrational, their demands are offset by arbitrageurs andthus have no significant impact on price. Some anomalies in the market make researchers to

BU Hui · PI Li

School of Economics and Management, Beihang University, Beijing 100191, China. Email : [email protected].∗This research was supported by the National Natural Science Foundation of China under Grant Nos. 71003004

and 71373001.�This paper was recommended for publication by Editor WANG Shouyang.

DOES INVESTOR SENTIMENT PREDICT STOCK RETURNS? 131

examine the key assumption of rational investors. For example, as the “noise trader” theoriesof [1, 2] suggest, if some investors trade on a “noisy” signal that is unrelated to fundamentals,then asset prices will deviate from their intrinsic value. That means there are two different typesof traders, fundamentalists, and noise traders. The group of fundamentalists are characterizedby the fact that they are rational and they have unbiased expectations of an asset’s value,while the group of noise traders trade on different signals and has a bias in its valuation of theasset. There is a growing level of acceptance among scholars that stock prices are driven bytwo types of investors: Noise traders and arbitrageurs[3]. Because classical finance theory leavesno role for investor sentiment, this issue is still need to investigate carefully. This issue is ofgreat interest to not only academics, who are interested in understanding informed trading infinancial markets, but also market practitioners, who are keen to look for reliable market-timingsignals.

What exactly is sentiment? Brown and Cliff[4] pointed out that sentiment represents theexpectations of market participants relative to a norm: A bullish (bearish) investor expectsreturns to be above (below) average, whatever “average” may be. Baker and Wurgler[5] men-tions sentiment is a belief about future cash flows and investment risks that is not justified bythe facts at hand. Hribar and McInnis[6] thought sentiment reflects errors in investors’ expec-tations about future payoffs. Berger and Turtle[7] considered sentiment as general optimismor pessimism towards future returns. Although the expressions of sentiment definition in theliterature are a little different, the essence of them is the same. We find that there are two keypoints in the definitions: One is expectation, which is the investors’ believes and judgmentsabout future trend; the other one is errors in expectations, which means the expectation may bebiased. The definitions deliver the idea that there are two kinds of sentiment in the market, theoptimism or pessimism of fundamentalists should already incorporate into asset prices, whilethe sentiments of noise traders who are bullish (bearish) can also affect the price. Usually,we could find two sentiments in the literature, institutional investor sentiment, and individualinvestor sentiment, which are considered as rational and irrational, respectively.

Investor sentiment can be measured either directly or indirectly through economic variables.The direct approach typically uses survey measures to identify levels of sentiment, such as [8–10]; etc. Besides, Joseph, et al.[11] put forward that there is a growing recognition of thepredictive value of data collected across various digital platforms, and one such rich repositoryof predictive data is online searches. This paper argues that online ticker searches serve as avalid proxy for investor sentiment. The strand of recent research expands the direct measures ofinvestor sentiment to consider aggregate market views regarding sentiment across investor type,including both institutional and individual investors, for examples, Brown and Cliff[4]. For thestudies on US stock market, they often use two surveys. The first is a survey conducted by theAmerican Association of Individual investors (AAII). The association polls a random sample ofits members each week, the sample size varied between 125 and 500, and asks each participantwhere they think the stock market will be in 6 month: Up, down, or the same. AAII thenlabels these responses as bullish, bearish, or neutral, respectively. The second survey is investorintelligence (II), which compiles the weekly bull-bear spread by categorizing approximately 150

132 BU HUI · PI LI

market newsletters. Investor intelligence (II) index is often used as a measure of institutionalsentiment[10, 12].

Contrasting the direct approach, a number of studies use observable economic variables tomeasure levels of sentiment. Zweig[13] and Lee, et al.[14] used the closed-end fund discount. Nealand Wheatley[15] considered the closed-end fund discount, as well as odd-lot sales and mutualfund redemptions to measure individual investor sentiment. Stigler[16] and Ritter[17] consideredthe variables related to IPOs. Jones[18] considered the variable related to liquidity, turnover, andfound that high turnover forecasts low market returns. Baker and Wurgler[19] thought the shareof equities issues in total equity and debt issues is another measure of financial activity thatmay capture sentiment. Baker and Wurgler[20] proposed that another sentiment proxy is thedividend premium. Baker and Wurgler[5, 21] created an aggregate sentiment index based on sixsentiment proxies, including closed-end fund discount, share turnover, number of IPOs, first dayIPO return, share of equity issues relative to debt issues, and dividend premium. Some studiesadopt this index as investor sentiment measure, for example, Berger and Turtle[7]. Besides theeconomic variables mentioned above, another category of variables relates to derivatives tradingactivity. Wang[22] captured investor sentiment based on traders’ positions on futures. Brownand Cliff[4] also examined the trading activity variables of futures and options.

Whether investor sentiment affects stock prices is a question of long-standing interest toeconomists. Numerous authors have considered the possibility that a significant presence ofsentiment-driven investors can cause prices to depart from fundamental values. The classicargument against sentiment effects is that they would be eliminated by rational traders seekingto exploit the profit opportunities created by mispricing. If rational traders cannot fully exploitsuch opportunities, however, then sentiment effects become more likely. More recently, attemptshave been made to measure to what extent historical stock prices have reflected underlyingfundamentals, for example, [23, 24].

Many researches study the predictive power of investor sentiment and its effects on assetpricing. Lee, et al.[12] used the investors intelligence (II) sentiment index and employed a gen-eralized autoregressive conditional heteroscedasticity-in-mean specification to test the impactof noise trader risk on both the formation of conditional volatility and expected return assuggested by De Long, et al.[2]. The empirical results of this paper show that sentiment is asystematic risk that is priced. Many researches focus on the cross-sectional impact of investorsentiment on stock returns, such as [5, 6, 14, 21, 25, 26], and so on. Baker and Wurgler[5]

predicted that a group of investor sentiment had larger effects on securities whose valuationswere highly subjective and difficult to arbitrage. Berger and Turtle[7] found stock opacity andsentiment sensitivities were closely related, both simple and multi-factor risk models did notcapture the variability in these stocks’ returns over time, and there existed an inverse relationbetween ex ante known investor sentiment and the marginal performance of opaque stocks.Joseph, et al.[11] found online search intensity reliably predicts abnormal stock returns andtrading volumes on a weekly horizon. Brown and Cliff[4] investigated investor sentiment andits relation to near-term stock market returns, and found that although sentiment levels andchanges were strongly correlated with contemporaneous market returns, the test showed that


sentiment had little predictive power for near-term futures stock returns. Brown and Cliff[26]

found evidence of a positive contemporaneous relation across sentiment and pricing errors. Inparticular, they found optimism led to overvalued stocks and that high levels of sentiment alsoproduced long run future under-performance. Most of studies use US stock market as studysamples. Studies about emerging stock market are very rare. Beside stock market, some otherstudies use index futures as samples, such as [22, 27], etc.

Studies about investor sentiment in Chinese stock market are limited. One possible reasonmay be the period of Chinese stock market is not long enough and data is limited. In Chinesemarket, there is no completely acceptant direct sentiment measure, although there are foursurveys reported in literature. Studies about the predictive power of economic variables ormarket variables that can be used as a proxy of investor sentiment are also very limited[28–32].Most of these studies explain the relationship between one variable and stock return, or justuse the principal component analysis to get a combination of some economic variables as proxyfor investor sentiment.

The primary goal of this research is to construct an investor sentiment measure and inves-tigate the predict power of investor sentiment for Chinese stock market. We will begin ourempirical analysis with an examination of the predictive power of some economic variables.This means we should determine the relative timing of the variables, that is, if they exhibitlead-lag relationships, some variables may reflect a given shift in sentiment earlier than others.Our interest is in examining the relation between these variables and stock returns more closely.Our paper is not the first to explore the role of investor sentiment in Chinese stock market, butwe try to make it the most comprehensive study to date. Because in the literature, especially inChinese literature about investor sentiment, studies usually form a composite sentiment indexbased on the principal component of some proxies and their first order lags without provid-ing the reason why they choose contemporary and first order lags of some economic variables.We will adopt a new method to construct the investor sentiment index. We will employ verycarefully examinations and tests to explain why a combination of some variables is reasonable.

The remainder of this paper is organized as follows. Section 2 introduces the methodology.Section 3 illustrates the data and makes some preliminary statistic tests. Section 4 providesthe empirical results and discusses the implications. Section 5 is a conclusion.

2 Methodology

In this paper, we will discuss several economic variables, including closed-end fund discount(CEFD), SSE share turnover (TURN), number of IPOs (NIPO), the average first-day returns ofIPOs (RIPO), number of Chinese A shares net added accounts (NAA), relative degree of activetrading in equity market (RDAT). We consider these variables because all of these variableshave implications of other research areas, and we limit to these variables because of dataavailability. Many papers support to use closed-end fund discount as measure of sentiment,such as [2, 5, 13–15, 29]. As Baker and Wurgler[5] stated, the IPO market is often viewedas sensitive to sentiment, with high first-day returns on IPOs cited as a measure of investor

134 BU HUI · PI LI

enthusiasm, and the low idiosyncratic returns on IPOs often interpreted as a symptom of markettiming[16, 17]. Turnover reflects the market liquidity. The net added accounts reflect the cashflow to the stock market, while relative degree of active trading in equity market can also reflectthe cash flow to and liquidity in stock market relative to bond market. Some papers state thatthe bullishness of the stock market is mainly consistent with the quickly increasing number ofparticipants, which are not institutions.

First, we will examine the relations between proxy variables of investor sentiment and stockmarket index. The problem of analyzing the relation between investor sentiment and marketreturns is of course not straightforward — correlation is not causality. Similarly, it is likely thatthere exists feedback between market returns and sentiment measures, further complicatingthe causal relations. What we should do firstly is to investigate the causality more carefully.Therefore, we will study the lead-lag relationships between these variables and stock returns,besides the contemporaneous analysis. We will adopt cross correlation analysis first to determinewhat are leading variables, what are contemporaneous variables, and what are lag variables tostock market.

Second, we will use multivariate regression analysis to study the effect of these economicvariables mentioned above on stock market returns. The independent variables into the regres-sions are the leading variables with suitable lag orders and contemporaneous variables. The lagorder of each leading proxy for the OLS regression is chosen by the results of cross-correlationbetween this variable and stock index.

To make sure the equation is specified correctly, we use forward selection stepwise regres-sion method. Forward selection involves starting with no variables in the model, testing theaddition of each variable using a chosen model comparison criterion, adding the variable (ifany) that improves the model the most, and repeating this process until none improves themodel. When we compare regression models, we choose the criterion of adjust R-square andAkaike information criterion (AIC). Moreover, to ensure the estimation results are effective andunbiased, we test the serial correlation of the residuals and the homoscedasticity of residuals.We use lagrange multiplier (LM) test to examine the serial correlation of the residuals. If thereexists serial correlation, we will bring into an auto-regression (AR) term to eliminate serialcorrelation. If there exits heteroscedasticity, we will adopt White’s heteroscedastic consistentcovariance estimator of coefficient. We will provide the multiple regression estimation process.

Third, we will compare the regression results to determine the best model. Based on thechosen model, we can find a linear combination of these sentiment proxies, which has the mostpredictive power about stock movement. We will record this combination of variables as investorsentiment index. Then, we will test the forecast accuracy of investor sentiment index.

3 Data and Descriptive Statistics

In this paper, we concentrate on market aggregates instead of individual stocks, based onthe data limitation and consideration for simplicity. In Chinese stock market, HS300 indexis a good proxy for the whole market. The closing prices of HS300 index are recorded as Pt,


and returns of HS300 index are calculated as the log price ratio, i.e., Rt = ln(Pt/Pt−1). Dataof HS300 index is from WIND database. Based on the literature and considering the dataavailability of sentiment proxies, this paper discusses the following economic variables. Numberof Chinese A shares net added accounts (NAA) is calculated by the difference between thenumber of Chinese A shares accounts at the end of each month. Data of number of Chinese Ashares accounts is from WIND database. The closed-end fund discount (CEFD) is the value-weighted average discount on the 20 largest closed-end mutual funds. We choose the 20 largestclosed-end mutual funds that last the whole sample period. The discount of each closed-endfund is defined as (NAV−market price)/NAV, where NAV is the net assets value of fund. Thedata of closed-end funds is from RESET database. We take the number of IPOs (NIPO) andthe average first-day returns (RIPO) from RESET database. Turnover of SSE shares (TURN)is based on the ratio of reported share volume to average shares listed from the SSE Fact Book.This data is from the monthly statistics report of Shanghai stock exchange. Although turnoverof SSE shares does not include Shenzhen stock market, this variable is the most convenient toobtain and the bias is sufferable. Relative degree of active trading in equity market (RDAT) isdefined as trading volume of Chinese A and B shares dividing by the sum of trading volume ofChinese A and B shares and trading volume of treasury bonds and corporate bond. The dataof trading volume is from monthly statistics report of Shanghai stock exchange.

Because of the data availability, the total study sample is from January 2006 to December2012, monthly. We take use of the sample from January 2006 to December 2011 as modelingsample, and use the last 12 months data in 2012 as the forecasting sample. From historicaltrends of HS300 index, we know that there exist a long term increasing trend during 2006 and2007, and the HS300 index surged to a record high of above 5877 point. After that, HS300index began to decline, because a financial crisis broke out. Table 1 provides the descriptivestatistics, Jarque-Bera statistic, and unit root test of modeling sample, and also provides thecorrelations among HS300 index and sentiment proxies. From the augmented Dickey-Fuller(ADF) unit root test, we find that the price series and all proxy variables exclude RDAT arenot stationary at 1% significant level, while the return series and RDAT are stationary at 1%significant level. We also take unit root test for change of each sentiment proxy variable, and theresults show that change of each sentiment proxy variable is stationary at 1% significant level.From the correlations between monthly variables, all of these sentiment proxy variables havestrong correlation with HS300 index. The relation between HS300 index and closed-end funddiscount (CEFD) is negative, while the relationships between HS300 index and other variablesare positive.

Table 2 provides the cross-correlation between these proxy variables and HS300 index.Panel A of Table 2 presents cross-correlation between level data, while Panel B presents cross-correlation between change of proxy variable and returns. Figure 1 describes these relationships.From the results, we find that net added accounts (NAA) and SSE share turnover (TURN) areleading indicators of stock market with 4 orders; closed-end fund discount (CEFD) may be aleading indicator of stock market with 1 order. The average first-day return of IPOs (RIPO)and relative degree of active trading in equity market (RDAT) may be a contemporary variable,

136 BU HUI · PI LI

while number of IPOs (NIPO) may be a lagging variable of stock market.

4 Empirical Results and Their Implications

From Figure 2, correlations in Table 1 and the cross-correlations in Table 2, we discusswhether the proxy variable is a leading indicator of stock market, or a contemporary variable,or a lagging variable. We try the leading variables with most possible order and contemporaryvariables into the regression. To discuss more thoroughly, we also try the contemporary orderof leading variables. We adopt forward selection stepwise regression method to estimate theregressions, and provide some results in the estimating process, shown in Table 3.

Because the returns series have some serial correlation, we contain an AR(2) term into theregression to eliminate it. When we first try each variable into the regression, we find thechange of net added accounts (dNAA) is a leading variable to returns with an order of 4, andit provides the most explanation power of stock returns. When the regression includes onlydNAA, the adjusted R-square is 0.3339, shown in column (1) of Table 3. The next variableentering into regression is the change of turnover (dTURN), because it provides the secondmost explanation power of stock returns in the uni-variable regression. We find dTURN as aleading variable with an order of 4 can improve the first model, shown in column (2) of Table 3.If we take dTURN as a contemporary variable, it can improve the model better accompaniedwith dNAA, shown in column (3). If we adopt the model in column (2) or (3) and continue theregression process, the following results shown in the following columns.

When we add the change of closed-end fund discount (dCEFD) into the model shown incolumn (2) or (3), we find that dCEFD as a leading variable with order 1 can improve adjustedR-square or AIC in model (2), while it just only improves adjusted R-square of model (3), butthe coefficients of dCEFD and dTURN are not significant. This results are shown in column(4) and (5). When we take dCEFD as a contemporary variable into the regression, we findthat the results are similar, shown in column (6) and (7). This means model (3) may be not agood specified model. Thus, we choose model (2) to continue this regression process. Becausethe fitness of model (4) is better than model (6), Thus we choose model (4) to continue theregression process.

When we add the change of average first-day return of IPOs (dRIPO) as a contemporaryvariable into model (4), we find it improves the model. This confirms that RIPO is a contempo-rary variable of stock market. The results are shown in column (8). When we add the changeof relative degree of active trading in equity market (dRDAT) as a contemporary variable intomodel (8), we find that it can improve R-square but not AIC, meanwhile the coefficient ofdCEFD−1 becomes insignificant. The results are shown in column (9).

Comparing all of these results, we find the model (8) is the best model, which includes threeleading variables and one contemporary variable. For the explanation purpose, this model isthe best model. For the purpose of forecasting, we choose model (4) that includes only threeleading variables.

From the results of model (8) and (4) in Table 3, we can write the combination of the


explanatory variables on the right side of the equation as the sentiment index, record as Index 1and Index 2. Figure 2 plots these two investor sentiment indexes, comparing with the returnsseries.

Then, we use Index 2 that is from model (4) to do forecasting. We want to get the staticprediction of the forecasting sample from Jan. 2012 to Dec. 2012. We take use of the followingmethods to do the forecast. One way is the static forecasting using estimated model (4) andthe new data of explanatory variables in forecasting sample, recorded as PF1. Another way isthe rolling extending windows static forecasting, recorded as PF2. This means when we get anew data, we add this new data into the modeling sample and re-estimated the coefficients ofmodel (4), and then use it to do the forecast. We provide the forecasted series of HS300 indexin Table 4. According to the accuracy measures of forecast, we find the investor sentimentindex that is implicated by model (4) has a good prediction power of stock market. Also, fromthe forecast results, we find the difference between these two forecast methods is very small,although the rolling extending windows static forecasting is more accurate. This confirms thatinvestor sentiment index coming from model (4) is robust.

5 Conclusion

The primary goal of this research is to construct an investor sentiment index for Chinesestock market. First, the sentiment proxy variables are examined more closely. Besides thecontemporary relations between sentiment variables and stock price movement, we have foundout some lead-lag relationships between the investor sentiment proxy variables and stock price.We investigate the causality carefully. The cross-correlation results tell us net added accounts(NAA), SSE share turnover (TURN), and closed-end fund discount (CEFD) are leading vari-ables to stock market. The average first-day return of IPOs (RIPO) and relative degree ofactive trading in equity market (RDAT) are contemporary variables, while number of IPOs(NIPO) is a lagging variable of stock market.

Next, we try the sentiment proxy variables with most possible lead order into the regression.The empirical results of multiple variables regression confirm our anticipation that net addedaccounts (NAA), SSE share turnover (TURN), closed-end fund discount (CEFD), and theaverage first-day return of IPOs (RIPO) have good prediction power of stock market. Then,we use the combination of the three leading variables to predict the HS300 prices. The resultsprove that our sentiment index has good predictive power of HS300 index, and the index isrobust.

138 BU HUI · PI LI

Table

1D

escr

iptive

statist

ics

ofdata

(the

model

ing

sam

ple

)

Corr

elations

Vari

able

Mea

nStd

.dev

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nes

sK

urt

osis

Jarq

ue-

Ber

aP

rob

AD

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stP

rob

Pt

CE

FD

tT

UR

Nt

RIP

Ot

NIP

Ot

NA

At

Pt

2903.8

1080.4

0.4

540

3.2

201

2.6

191

0.2

699

−0.0

235

0.5

950

1

Rt

0.0

129

0.1

110

−0.6

464

3.4

533

5.6

297

0.0

599

−4.1

563

0.0

001

--

--

--

CE

FD

t0.2

138

0.0

899

0.1

592

2.1

858

2.2

928

0.3

178

−1.5

455

0.1

141−0

.4402

1

TU

RN

t37.3

40

26.7

62

1.2

309

4.0

817

21.6

91

0.0

000

−1.1

138

0.2

384

0.1

220

0.4

844

1

RIP

Ot

0.0

312

0.0

472

2.4

630

9.5

708

202.3

20.0

000

−1.2

525

0.1

915

0.2

831

0.0

978

0.2

132

1

NIP

Ot

9.3

750

7.1

983

0.3

003

2.1

019

3.5

016

0.1

736

−0.7

364

0.3

940

0.3

282

−0.5

999−0

.3492−0

.1554

1

NA

At

126.7

9102.8

81.6

364

5.5

506

51.6

49

0.0

000

−1.6

471

0.0

937

0.7

160

−0.0

656

0.5

824

0.3

079

0.1

254

1

RD

AT

t0.9

769

0.0

263

−3.5

851

19.8

06

1001.6

0.0

000

−0.0

048

0.6

808

0.4

330

−0.1

310

0.2

697

0.2

083

0.1

678

0.3

646

Note

:a.

“P

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Table

2T

he

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-corr

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pro

xy

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sand

HS300

index

Panel

A:H

S300

stock

price

and

Lev

elofpro

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variable

s,i.e.

,P&

X(i

)

iN

AA

(+i)

NA

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i)

TU

RN

(+i)

TU

RN

(−i)

CEFD

(+i)

CEFD

(−i)

RIP

O(+

i)

RIP

O(−

i)

NIP

O(+

i)

NIP

O(−

i)

RD

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(+i)

RD

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(−i)

00.7

160***

0.7

160***

0.1

220

0.1

220*

−0.4

402***

−0.4

402***

0.2

831***

0.2

831***

0.3

282***

0.3

282***

0.4

330***

0.4

330***

10.6

004***

0.7

299***

−0.0

269

0.2

224**

−0.4

399***

−0.4

068***

0.2

619***

0.2

072**

0.3

297***

0.2

905***

0.3

472**

0.4

314***

20.4

318***

0.7

302***

−0.1

861**

0.3

038***

−0.4

520***

−0.3

465***

0.2

569***

0.2

372**

0.3

123***

0.2

479**

0.2

139*

0.4

200***

30.2

929***

0.7

563***

−0.2

854***

0.4

084***

−0.4

495***

−0.2

684***

0.1

832**

0.2

203**

0.2

873***

0.1

866**

0.1

107

0.4

166***

40.1

489*

0.7

745***

−0.3

672***

0.5

104***

−0.4

221***

−0.2

043**

0.1

176*

0.2

068**

0.2

549***

0.1

647**

0.0

282

0.3

771***

50.0

209

0.6

731***

−0.4

148***

0.5

222***

−0.3

740***

−0.1

562**

0.0

783*

0.1

084*

0.1

920**

0.1

220*

−0.0

230

0.3

555***

6−

0.1

248*

0.5

662***

−0.4

709***

0.5

304***

−0.3

396***

−0.0

856*

0.1

408*

0.1

071*

0.1

231*

0.0

878*

−0.1

859**

0.3

178***

7−

0.2

183**

0.4

555***

−0.4

846***

0.5

358***

−0.2

742***

−0.0

033

0.0

743*

0.0

783*

0.0

791*

0.0

570*

−0.2

233**

0.2

550***

8−

0.3

016***

0.3

394***

−0.5

160***

0.5

220***

−0.1

977**

0.0

599*

0.0

103

0.0

237

0.0

555*

0.0

575*

−0.2

333**

0.1

639**

9−

0.3

851***

0.1

680**

−0.5

171***

0.4

589***

−0.1

121*

0.1

161*

−0.0

474*

−0.0

073

−0.0

352

0.0

243

−0.3

153***

0.1

688**

10

−0.4

519***

0.0

120

−0.5

084***

0.3

727***

−0.0

533*

0.1

792**

−0.1

020*

0.0

026

−0.1

361*

−0.0

153

−0.3

275***

0.1

257*

Panel

B:re

turn

sofH

S300

and

change

ofpro

xy

variable

s,i.e.

,R

&dX

(i)

idN

AA

(+i)

dN

AA

(−i)

dT

UR

N(+

i)

dT

UR

N(−

i)

dC

EFD

(+i)

dC

EFD

(−i)

dR

IPO

(+i)

dR

IPO

(−i)

dN

IPO

(+i)

dN

IPO

(−i)

dR

DAT

(+i)

dR

DAT

(−i)

00.3

194***

0.3

194***

0.2

612***

0.2

612***

−0.0

273

−0.0

273

0.1

723**

0.1

723**

0.0

256

0.0

256

0.1

040*

0.1

040*

10.2

619***

0.0

864*

0.1

060*

0.1

594**

0.0

822*

−0.2

598***

−0.0

145

−0.1

774**

0.0

825*

−0.0

080

0.2

662***

0.0

948*

2−

0.0

840*

−0.1

132*

−0.2

607***

−0.0

932*

−0.1

229*

−0.1

452*

0.1

363*

0.0

735*

0.0

475

0.0

604*

−0.1

734**

−0.0

400

30.0

671*

0.0

730*

0.0

080

0.0

394

−0.1

422*

0.0

902*

−0.0

222

−0.0

175

0.0

200

−0.0

742*

0.0

593*

0.1

139*

40.0

325

0.4

936***

−0.0

742*

0.4

479***

−0.0

881*

0.1

609**

−0.0

125

0.1

182*

0.1

025*

0.0

996*

−0.0

537*

−0.0

286

50.1

199*

0.0

153

−0.0

187

−0.0

092

0.1

200*

−0.2

004**

0.0

395

−0.1

206*

0.0

577*

−0.0

790*

0.3

081***

0.0

554*

6−

0.2

450**

−0.0

403

−0.2

993***

−0.0

059

−0.2

319**

−0.0

832*

0.0

950*

0.0

160

−0.0

504*

−0.0

123

−0.3

099***

0.0

595*

7−

0.0

746*

0.0

714*

0.0

318

0.1

180*

−0.1

244*

0.1

728**

−0.0

358

0.0

752*

0.0

582*

−0.0

590*

−0.0

897*

0.0

761*

80.0

136

0.2

928***

−0.1

096*

0.3

110***

−0.0

377

0.1

505*

0.0

155

−0.0

561*

0.1

401*

0.1

174*

0.1

522*

−0.2

284**

9−

0.1

027*

−0.0

373

−0.0

938*

0.0

512*

0.1

472*

−0.0

211

−0.0

269

−0.0

718*

0.0

409

0.0

378

−0.0

902*

0.1

378*

10

−0.1

389*

−0.1

154*

−0.0

353

−0.0

979*

−0.1

384*

−0.0

449*

−0.0

339

0.0

071

−0.0

962*

−0.0

123

0.0

382

0.0

670*

Note

:X

(i)

isth

ela

gi

ord

erof

X,i.e.

,X

(i)

=X

t+

i.

d(X

t)

isth

efirs

tord

erdiffe

rence

ofvariable

Xt,i.e.

,d(X

t)

=X

t−

Xt−

1,and

we

just

reco

rdth

efirs

tdiffe

rence

ofvariable

as

dX

for

sim

plici

ty.

The

ast

eris

k(*

)den

ote

ssi

gnific

ance

.***

(**,*)

den

ote

signific

ance

at

1%

(5%

,10%

)le

vel

.

140 BU HUI · PI LI

Table

3T

he

estim

ate

dre

sults

ofm

ultiv

ari

ate

regre

ssio

ns

on

HS300

index

retu

rns

(the

model

ing

sam

ple

)

Vari

able

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

C0.0

043

0.0

049

0.0

050

0.0

022

0.0

029

0.0

034

0.0

039

0.0

041

0.0

045

(0.8

104)

(0.7

949)

(0.7

966)

(0.9

027)

(0.8

791)

(0.8

503)

(0.8

387)

(0.8

190)

(0.8

073)

dN

AA

(−4)

0.0

008

0.0

005

0.0

007

0.0

004

0.0

006

0.0

005

0.0

007

0.0

005

0.0

004

(0.0

000)

(0.0

183)

(0.0

000)

(0.0

488)

(0.0

002)

(0.0

250)

(0.0

000)

(0.0

293)

(0.0

357)

dT

UR

N(−

4)

0.0

020

0.0

019

0.0

025

0.0

020

0.0

021

(0.0

649)

(0.0

688)

(0.0

232)

(0.0

524)

(0.0

367)

dT

UR

N0.0

017

0.0

013

0.0

015

(0.0

383)

(0.1

086)

(0.0

931)

dC

EFD

(−1)

−0.5

970

−0.4

449

−0.5

964

−0.5

192

(0.0

741)

(0.2

005)

(0.0

670)

(0.1

096)

dC

EFD

−0.5

735

−0.4

333

(0.0

934)

(0.1

794)

dR

IPO

0.3

351

0.3

291

(0.0

327)

(0.0

329)

dR

DAT

0.4

376

(0.2

023)

AR

(2)

0.3

649

0.3

952

0.4

256

0.4

009

0.4

219

0.3

784

0.4

148

0.3

944

0.4

187

(0.0

027)

(0.0

012)

(0.0

005)

(0.0

010)

(0.0

006)

(0.0

022)

(0.0

008)

(0.0

014)

(0.0

008)

Adju

sted

R2

0.3

339

0.3

591

0.3

665

0.3

820

0.3

734

0.3

782

0.3

751

0.4

185

0.4

246

AIC

−1.8

735

−1.8

978

−1.9

096

−1.9

202

−1.9

064

−1.9

141

−1.9

091

−1.9

673

−1.9

643

DW

1.8

384

1.8

442

2.0

004

1.9

004

2.0

052

1.9

117

2.0

336

1.9

421

1.9

968

Note

:T

he

num

ber

inth

epare

nth

esi

sis

the

p-v

alu

e.


0

1000

2000

3000

4000

5000

6000

0

100

200

300

400

500

600

2006 2007 2008 2009 2010 2011

P RICE NAA

P anel A

0

1000

2000

3000

4000

5000

6000

0

20

40

60

80

100

120

2006 2007 2008 2009 2010 2011

P RICE TUR N

P anel B

0

1000

2000

3000

4000

5000

6000

7000

.05

.10

.15

.20

.25

.30

.35

.40

2006 2007 2008 2009 2010 2011

P RICE C EFD

P anel C

0

1000

2000

3000

4000

5000

6000

7000

-.04

.00

.04

.08

.12

.16

.20

.24

2006 2007 2008 2009 2010 2011

P RICE R IP O

P anel D

0

1000

2000

3000

4000

5000

6000

0

5

10

15

20

25

30

2006 2007 2008 2009 2010 2011

P RICE NIP O

P anel E

0

1000

2000

3000

4000

5000

6000

0.80

0.84

0.88

0.92

0.96

1.00

1.04

2006 2007 2008 2009 2010 2011

P RICE R DAT

P anel F

Figure 1 The sentiment proxy variables and HS300 index

-.4

-.3

-.2

-.1

.0

.1

.2

.3

2006 2007 2008 2009 2010 2011

R ETUR N INDEX1 INDEX2

Figure 2 The combination index of sentiment proxy variables

142 BU HUI · PI LI

Table 4 The out of sample forecast of HS300 and the prediction accuracy

Date HS300 PF1 Forecast Error PF2 Forecast Error

2012.01 2464.26 2210.851 −253.409 2286.035 −253.409

2012.02 2634.14 2413.756 −225.462 2461.174 −220.384

2012.03 2454.90 2758.928 300.9953 2687.111 304.0278

2012.04 2626.16 2517.257 −105.998 2455.872 −108.903

2012.05 2632.04 2515.915 −130.138 2599.285 −116.125

2012.06 2461.61 2787.712 329.7774 2722.608 326.1019

2012.07 2332.92 2510.334 180.4323 2499.009 177.414

2012.08 2204.87 2220.214 −7.4955 2295.572 15.34365

2012.09 2293.11 2179.843 −119.822 2231.943 −113.267

2012.10 2254.82 2243.791 −12.0929 2275.894 −11.0287

2012.11 2139.66 2216.459 80.21948 2215.899 76.79929

2012.12 2522.95 2204.071 −325.13 2210.633 −318.879

RMSE 204.4978 RMSE 201.897

MAE 172.5810 MAE 170.1402

MAPE 6.9961 MAPE 6.8998

Note: RMSE means to root mean squared error; MAE refers to mean absolute error;

MAPE is mean absolute percent error.

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Documents

Does investor sentiment predict stock returns? The evidence from Chinese stock market