asset pricing with investor sentiment: evidence from chinese stock markets

ASSET PRICING WITH INVESTOR SENTIMENT: EVIDENCEFROM CHINESE STOCK MARKETS*manc_2260 1..32

byYIHAN XU

International Financial Research Institute, Bank of Chinaand

CHRISTOPHER J. GREEN†Department of Economics, Loughborough University

We study the impact of investor sentiment on stock returns in China,using as a benchmark the three-factor Fama–French model, and distin-guishing between normal and positive sentiment. Sentiment helps explainthe mis-pricing component of returns in the Fama–French model and thetime variation in the factors themselves. Factor loading patterns noted byFama-French are evident in China, but they can be equally well modelledby sentimental factors. Fama–French factors are less significant if factorsare conditioned by sentiment, suggesting that in China sentiment affectsboth the way investors judge risks as well as portfolio returns directly.

1 Introduction

Traditional finance theory argues that asset prices are determined purely byinvestors’ unbiased cognitive evaluation and maximization of expectedutility, and leaves no role for investor sentiment. Assuming rational in-vestors and efficient markets, expected asset returns are equal to a linearcombination of expected premia on factor loadings. The Capital AssetPricing Model (Sharpe, 1964; Lintner, 1965), the Inter-temporal CapitalAsset Pricing Model (ICAPM) (Merton, 1973) and the Fama–French (FF)model (Fama and French, 1992, 1993, 1996) are examples. De Long et al.(1990) challenged this approach with the argument that arbitrageurs haveshort-time horizons and are subject to risks and costs, which implies thatthere are limits to their abilities to arbitrage away price anomalies. Theyalso argued that prices would be determined in part by noise traders: inves-tors whose decisions are not based on an analysis of fundamentals or arbi-trage opportunities, but more on sentiment and possibly ‘irrational’ beliefs.In addition, noise traders may tend to trade in concert rather than to diver-sify because of the effects of common background emotions and feedbacksfrom their social interactions (Kumar and Lee, 2006). Kahneman has char-acterized financial markets in this way:

* Manuscript received 14.10.09; final version received 30.7.10.† We thank an anonymous referee for some very useful comments on an earlier draft of this

paper.

The Manchester School Vol •• No. •• ••–•• •• 2012doi: 10.1111/j.1467-9957.2011.02260.x

© 2012 The AuthorsThe Manchester School © 2012 Blackwell Publishing Ltd and The University of ManchesterPublished by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK, and 350 Main Street, Malden, MA 02148, USA.

1

. . . the market has a psychology, more specifically it has a character. It hasthoughts, beliefs, moods, and sometimes stormy emotions. The main character-istic of the market is extreme nervousness. It is full of hope one moment and fullof anxiety the next moment. . . . In short, the market closely resembles a stereo-typical individual investor. (Shefrin, 2005, pp. 203–204)

Baker and Wurgler (2007) define investor sentiment as ‘. . . a beliefabout future cash flows or investment risks that is not justified by the factsat hand’. Thus, investor sentiment is not derived from fundamental changesin stock markets but from emotional reactions to available information.Sentiment influences expected future cash flows and investment risks andthus affects investment decisions and stock returns. Positive sentimentinduces investors to be more confident about their abilities to evaluate situ-ations and more willing to take risks. Negative sentiment usually has theopposite effects (Kuhnen and Knutson, 2008).

Empirically, investment sentiment may affect both the aggregatemarket and the cross-section of returns. In aggregate, periods of high posi-tive sentiment should yield contemporaneously high returns, followed laterby low returns as sentiment eventually reverts to a more normal level.Many previous papers have broadly confirmed such a pattern in aggregatemarket returns: they are typically positively autocorrelated over short timehorizons (one to two months), and negatively autocorrelated over thelonger run (three to five years).1 In cross-sections of shares, we might alsoexpect sentiment-based behaviour to vary among firms: the dot-com bubblebeing one possible example. Baker and Wurgler (2006) find that sentimentparticularly drives excess returns for stocks that are: small, young, highlyvolatile, unprofitable, non-dividend-paying, distressed or with extremegrowth prospects. The crucial characteristic of stocks that are more easilyinfluenced by sentiment is the difficulty and subjectivity of determiningthese stocks’ true valuation. For example, compared with firms with a longearning history and stable dividend payments, small and young firmsusually have less available information or at least information that is morecostly to access. Currently unprofitable but potentially highly profitablefirms imply a greater degree of deferred consumption, and are thereforemore difficult to value. Extreme growth and distressed stocks also involve agreater degree of uncertainty than others.

In this paper, we investigate the effects of sentiment in the Chinese stockmarket using as our framework the three-factor model of Fama and French(1996). The Chinese market has not been greatly studied either for its con-formity with traditional asset-pricing theory or for sentiment effects, eventhough it is the world’s largest developing country financial market and onethat has been subject to some sharp movements since its liberalization in the

1See De Bondt and Thaler (1985), Hirshleifer (2001) and Barberis and Thaler (2003).

The Manchester School2

© 2012 The AuthorsThe Manchester School © 2012 Blackwell Publishing Ltd and The University of Manchester

1990s.2 We use the FF model as a benchmark because it is widely agreed to bea useful description of stock returns, although by no means the last word(Grauer, 2003).

We begin by testing the FF model allowing for the existence of a pos-sible break in the data in 2001 when the B-share market was opened todomestic investors. We find that the model does explain a substantial pro-portion of the variation in Chinese stock returns but, in contrast to FF’sUS results, the exact three-factor pricing model is rejected. We thereforeinvestigate the role of sentiment. This can be thought of in several ways,but here we focus on two hypotheses. First, sentiment can be viewed as anadditional risk factor if it directly helps explain the mis-pricing componentof returns, i.e. the part of the return series not explained by a traditionalasset-pricing model such as FF. Second, sentiment may influence assetprices indirectly via its effect on the risk factors themselves in a traditionalasset-pricing model. We test these hypotheses first by augmenting the FFmodel with measures of sentiment and checking their ability to improve theexplanatory power of the model. Second, we test if sentiment helps explainthe time variation in the FF factors. In both cases we find that sentimenthas significant power: to help explain mis-pricing in the FF model, and toexplain the FF factors themselves. Therefore, we estimate a more generalasset-pricing model including the FF factors and sentimental factors and inwhich the FF factors are conditioned on sentiment. In these estimates wefind clear evidence that the conditioning effect of sentiment is significant,and that, when allowance is made for this effect, the apparent break in 2001in the basic FF model largely ceases to be significant, suggesting that this isnot a break in the model itself but rather a change in sentiment, possiblydue to the opening of the B market.

Overall, this paper contributes to the behavioural literature in the fol-lowing ways. First we use and test a cognitive-based asset-pricing model asbenchmark (FF). This enables us to examine how far sentiment can contrib-ute directly to asset-pricing theory. Second, we depart from most previousresearchers who have used a single index of sentiment. Instead, we testindividual sentiment proxies directly to reveal differences in sentimentaleffects, depending on how sentiment is measured, and to avoid the problem ofreplication over time that tends to occur when principal components are usedto estimate a single index. Third, we measure and test positive and negativesentimental effects separately. Finally, notwithstanding its importance, theChinese market has not been extensively studied for either asset-pricingtheory or sentiment.

The remainder of the paper is organized as follows. Section 2 providesthe theoretical background and develops the motivation for combining sen-timent with a conditional beta pricing model; Section 3 describes the data;

2For an exception, see Burdekin and Redfern (2009). However, their data set begins only in 2003.

Asset Pricing with Investor Sentiment 3


Section 4 provides the basic results of the FF model and the impact ofsentiment; in Section 5 we report the estimation of the sentiment-basedconditional asset-pricing model; some concluding remarks are contained inSection 6.

2 Methodology: Emotional Decision Making in Stock Markets

2.1 Sentiment-based Decisions

Traditional asset-pricing models use a standard pricing equation derivedfrom expected utility maximization based on purely cognitive judgements inrisky situations. Agents make ‘rational’ decisions using essentially exogenousinformation and rational forecasting; emotion or sentiment has no role toplay. ‘Economics ignores passions like greed . . . by transmuting them toallegedly more predictable, less emotional and completely rational motivesof self-interest’ (Pixley, 2002). An alternative schematic description of adecision-making process that specifically allows for the impact of emotion issuggested inter alia by Loewenstein et al. (2001) (Fig. 1).

This suggests that agents form their beliefs and make decisions not juston the basis of a cognitive evaluation of anticipated outcomes and probabi-lities, but also on the affective evaluations from the information at hand andthe states that aroused emotional regularities. In certain situations, emotionarises from information about outcomes and probabilities, as well as fromemotional regularities. Psychological theories show that the strength of pes-simistic emotion increases when agents face ambiguous situations butdecreases when the anticipated outcomes are described or represented in a

(1)

(2)Behaviour

Outcomes(emotions)

Affectiveevaluation

Cognitiveevaluation

Anticipatedoutcomes

Subjectiveprobabilities

Emotionalstates (time-dependence,socialinteraction, self-esteem)

Fig. 1 Emotions in the Decision-making Process (Edited from Loewenstein et al., 2001)



way that is mentally vivid (Nisbett and Ross, 1980; Einhorn and Hogarth,1986). In addition, people learn to adhere to social norms. Zafar (2008) findsthat individuals like to conform: they gain emotional well-being simplyby making the same decision as those with whom they make their socialcomparisons.

Emotional reactions or feelings result in people behaving in ways thatare biased rather than the unbiased decisions postulated by expected utilitytheory. Thus, even if they do seek to maximize expected utility, they may use‘wrong’ information. In addition, they may update their beliefs according totheir affective evaluation of emotional well-being as well as or instead of theircognitive evaluation of consumption well-being. Emotions matter in thatthey determine agents’ behaviour directly and indirectly by affecting theircognitive evaluations. Applied to asset pricing, this suggests that investorsentiment may be a direct determinant of asset prices and also of the riskfactors that investors recognize cognitively.

2.2 Theoretical Hypotheses for Sentiment

According to behavioural finance theory, we would expect measures of in-vestor sentiment to have certain specific effects in stock markets. We first testthe purely cognitive FF model, and then propose the following main hypoth-eses regarding sentiment:

H1: Sentiment helps explain the mis-pricing component of returns in the FFthree-factor asset-pricing model.H2: Sentiment helps explain the FF factors.H3: Sentiment affects aggregate and cross-sectional returns. In aggregate,sentiment helps explain the FF factors; in cross-sections, the sentimentaleffect is stronger for small stocks, and for growth or distressed stocks (low orhigh book-to-market, respectively).H4: Positive sentiment has a different effect on asset returns from negativesentiment.H5: Sentiment affects asset pricing through its impact on variations in therisk loadings of pricing factors, including the FF factors.

H1 is a test of whether sentiment has direct explanatory power for assetreturns insofar as it helps explain any mis-pricing in the FF model. H2 isabout the indirect effects of sentiment on stock returns through its impacton the risk factors in the FF model. H3 characterizes in more detail thedistinction between the aggregate (market) and cross-sectional effects ofsentiment, following Baker and Wurgler (2006). H4 postulates the existenceof a non-linearity in the response of returns to variations in sentiment fol-lowing, for example, Odean (1998). H5 postulates that sentiment may con-ditionally affect the loadings of risk factors in a conventional asset-pricingmodel.



2.3 Testing Sentimental Effects

We proceed following Fama and French (1996) by first forming the samplestocks into (N) portfolios differentiated according to size and book-to-market ratios,3 and then regressing each portfolio excess return (ri,t) on thethree FF factors: the excess return on a market portfolio (RM); the differencebetween returns on small stock portfolios and those on big stock portfolios(SMB); and the difference between returns on high book-to-market stockportfolios and those on low book-to-market portfolios (HML):

r ei t i i t i t i t i t, , , , ,= + + + +α β β β1 2 3RM SMB HML (1)

where ai, bi,j ( j = 1, 2, 3) and ei are the intercept, coefficients and errors in thetime-series regression for the ith portfolio, i = 1, . . . , N. Following FF, we testthe multi-beta version of the Sharpe–Lintner asset-pricing model usingShanken’s extension (Shanken, 1992) of the Gibbons–Ross–Shanken test(Gibbons et al., 1989).4 We abbreviate this as the ‘GRS test’; it is given by

QT N K

N R RF N T N K= − − ′

+ ′− −

−

−

α αˆ

ˆ ~ ( , )Σ 1

11 Δ(2)

where T = number of observations in the time series; K = number of factors;a = (a1, . . . , aN)′; S = the estimated residual covariance matrix from the Nestimates of (1); R is the 3 ¥ 1 vector of time means of RM, SMB, HML; andD is their covariance matrix.

Next, we investigate H1 by regressing the portfolio returns on the FFfactors and three measures of sentiment (Sentj, j = 1, . . . , 3):5

ri t i i t i t i t

i j t jj

i j

, , , ,

, , , , ,

= + + ++ +∑

α β β βγ γ

M S HRM SMB HML

Sent Se0 1 nnt Sent SentH Ht jj

i j t tj

i j t tj

i tD D e− − −∑ ∑ ∑+ + +1 0 1 1 1, , , , , , , ,λ λ(3)

where DH,t is a dummy variable equal to unity if sentiment is positive, andzero otherwise. gi,j,k measures the impact of current (k = 0) or recent (k = 1)investor sentiment on portfolio returns, and li,j,k measures the impact ofsentiment on returns when sentiment is positive (‘high’). Mis-pricing of any ri,t

is tested using the standard F test for groups of the sentimental variables. Ifthe gi,j,k or li,j,k are significant, H1 can be accepted, and investor sentimentexplains at least part of the portfolio returns that are not explained by the FFfactors. If gi,j,k and li,j,k are both significant, this also provides evidence for H4which allows for positive sentiment to have a different marginal effect onstock returns than negative sentiment.

3The construction of the portfolios is described in Section 3 below.4Velu and Zhou (1999) give a comprehensive overview of these statistics.5These are set out in Section 3 below. An alternative method is to regress the FF pricing errors

on the sentimental variables. However, in this framework it is impossible to test the betaasset-pricing model directly. We thank an anonymous referee for this point.



H2 asserts that sentiment may have an indirect impact on asset pricing inthat it helps explain the FF factors. To investigate this we estimate

Fact Sent Sent

Sent

N N

H H

t j t jj

j t jj

j t tD

= + +

+

∑ ∑ −δ δ δ

δ

0 0 1 1

0

, , , , , ,

, , ,jj

j t tj

tD u∑ ∑+ +− −δ , , ,H HSent1 1 1

(4)

for each of the FF factors: Factt = RMt, SMBt, HMLt. dj,N,k and dj,H,k representthe normal and positive sentimental effects on the factor portfolios; k = 0, 1correspond to the current and lagged values of Sent. These regressions modeltime-series and cross-sectional effects of sentiment. For the time series, if dj,N,k

and dj,H,k are significant, H2 can be accepted, indicating that investor senti-ment may affect asset prices indirectly by affecting the cognitive risk factorsof the pricing model. In the cross-section, following the idea of Fama andFrench (1996), differences among coefficients across the N portfolios providea test of H3. For example, if small firms are regarded as more speculative thanbig firms, dj,N,k and dj,H,k will be positive in the SMB regressions, reflecting thatpositive sentiment increases returns on small firms more than on large firms,while negative sentiment reduces returns on small firms more than on largeones. H4 implies that there is a different marginal effect as between positiveand negative sentiment, and this can be checked by investigating the size andsignificance of dj,N,k and dj,H,k.

If investor sentiment helps explain stock returns even when the FFfactors are included in the regression, it suggests that sentiment can be vieweddirectly as an omitted factor in the model. In addition, if investor sentimentis a determinant of the pricing factors themselves, sentiment also drives assetreturns indirectly by affecting the cognitive pricing factors that are the fun-damental measures of risk in classical finance. Thus, sentiment has a condi-tioning effect on the pricing model which, following Baker and Wurgler(2006), can be measured by the interaction between factors and sentiment. Inthis extended model, portfolio returns are linearly related to the uncondi-tional cognitive factors, the direct sentimental factors and the conditionalsentiment-driven cognitive factors:

r Di t i i t i t i t i j t jj

i j t, , , , , , ,= + + + + +∑α β β β γ λM S HRM SMB HML Sent Sent HH

HSent Fact Sent Fact

,

, , , , , , , ,

tj

i j k t j t kkj

i j k t t t kkj

D

∑

∑∑ ∑∑+ +ϕ θ(5)

As before, bi are the sensitivities to the FF factors, and gi,j and li,j pick up thefactor effect of sentiment. Conditional sentiment effects are captured by theji,j,k for normal sentiment and qi,j,k for positive sentiment.6 The null of H5 is

6Lagged sentiment is omitted from equation (5) as lagged sentimental effects turn out to be lesssignificant than current effects in equations (3) and (4). See the discussion in Section 4below.



that ji,j,k � 0 and qi,j,k � 0 if the conditional sentiment effect is significant;under H4 we may also expect that qi,j,k � 0 as positive sentimental effects aredifferent from normal effects. Finally, we check H3 by again investigatingdifferences in response to sentiment among the N different portfolios.

2.4 Diagnostic Tests

We performed the usual diagnostics on the estimates, especially tests forautocorrelation which may be indicative of unused information or sentiment-based decisions. In addition, there have evidently been substantial changes inthe Chinese stock market over the last two decades: too many to model eachone exhaustively. However, we do systematically check for general structuraldeficiencies in the estimates with the Reset test (Ramsey, 1969). Althoughthere may have been many structural breaks over the 11 years of our data,arguably the most important, given that our analysis is concerned with Ashares, is February 2001, when the B-share market was opened up to do-mestic investors.7 Therefore we systematically checked this date for a struc-tural break in the models using a Chow test (Chow, 1960).

3 Data

We use monthly data from the Shanghai and Shenzhen stock markets fromJanuary 1997 to December 2007, giving 132 observations for each variable.The number of shares in the sample increases from 459 in 1997 to 1354 in2007, and includes all A shares listed on the two exchanges.8 Portfolios areconstructed by weighting returns by listed market values, calculated as theproduct of the total number of listed (tradable) shares and the market priceof the share.9 Exceptionally, for the size portfolio, the weighting is by totalmarket capitalization. It seems reasonable to argue that the non-tradable partof each issue would not contribute directly to pricing the share; whereas itclearly does contribute to the size of the company. As a check however, themodels were also estimated using listed market value weights throughout andthen using total market value weights throughout. Results for the formerwere generally more plausible than the latter, apart from the size effect; andtherefore we report only our initial results using listed market value weights,apart from the construction of the size portfolio. Total market index andreturns are the S&P/CITIC A-Share Composite Index extracted from their

7A shares are those of mainland China companies quoted in Renminbi and can be traded only bymainlanders and selected foreign institutional investors. B shares are those of mainlandChina companies quoted in foreign currencies. Until February 2001 only foreigners wereallowed to trade B shares.

8The data period could begin earlier, but at a cost in a sharp reduction in the number of sharesimplying that the data may be dominated by idiosyncratic return movements.

9The listed market value is the component of total market value that is actually tradable on theexchange: it is the market value of the ‘free float’ for each share.



website (Standard and Poors/CITIC, 2009). This is a value-weighted indexbased on all Shanghai and Shenzhen A shares. The risk-free rate was taken tobe the three-month bank deposit rate, extracted from Datastream (2009). Therest of the data were obtained from the Wind Financial Database (WindDB,2009).

3.1 Construction of the Tested Portfolios

Following Fama and French (1993, 1996) and others, the data were formedinto 25 portfolios sorted simultaneously by size and book-to-market. In eachyear, stocks were sorted into five separate size groups from small (S1) to big(S5), and five book-to-market groups from low (B1) to high (B5). The 25portfolios are constructed by finding the intersection between each size andbook-to-market group: the intersection of the smallest size (S1) and lowestbook-to-market (B1) is identified as portfolio S1B1, and so on. This poolingreduces the noise generated by individual stocks and helps to generate norm-ally distributed portfolio returns. In addition, since the FF portfolios havebecome a benchmark in tests of asset-pricing models, using these 25 port-folios makes it easier to compare our results with other studies.

3.2 Cognitive Factors

Following Fama and French (1996), Davis et al. (2000), L’Her et al. (2004)and others, the three cognitive factors are constructed to mimic risk relatedto: the aggregate market, company size and book-to-market (distress effect).The excess market return (RM) is the return on the S&P/CITIC A-ShareComposite Index less the risk-free rate. To construct the size and distresspremia, all stocks were first ranked separately by their size (total marketvalue) and book-to-market ratio. Then, two size and three book-to-marketportfolios were formed using a 50 per cent breakpoint for size (S and B) and30 per cent and 70 per cent breakpoints for book-to-market (L, M and H).Lastly six value-weighted portfolios were formed from the intersections of thesize and book-to-market groups. The SMB factor (Small minus Big) is theequally weighted average of the difference between returns on small-sizestock portfolios and returns on big size portfolios, balanced so as to beneutral with respect to book equity. Similarly, the HML factor (High minusLow) is the equally weighted average of the difference between returns onhigh book-to-market stock portfolios less returns on low book-to-marketportfolios, balanced so as to be neutral with respect to size.

3.3 Sentiment Proxies

The behavioural pricing literature suggests various proxies for sentimentindicators, but according to Baker and Wurgler (2006), ‘. . . there are nodefinitive or uncontroversial measures . . .’. Brown and Cliff (2004) give a



comprehensive overview of different proxies, but our choice is limited to aconsiderable extent by the availability of data. We use the following senti-ment indicators: market turnover (TURN); the ratio of advancing to declin-ing issues (ADVDEC); and the dividend premium (DPNP), comparingpayers with non-payers.10

Turnover is the ratio of total reported A share trading volume inShanghai and Shenzhen to the value of shares listed in the two markets.Baker and Stein (2004) argue that, under a short-sales constraint, marketliquidity can be a barometer of sentiment: low sentiment drives investors toquit the market as they cannot short-sell. Optimism on the other hand canbe reflected in buy transactions. Therefore, sentiment is positive if turnoverincreases in comparison with some reference point, defined here as the two-period backwards moving average, and negative if it decreases. TURN isthe difference between the turnover ratio and its past two-period movingaverage.

The ratio of the number of advancing issues to declining issues capturesthe relative strength of the market in terms of buying–selling imbalance(Brown and Cliff, 2004). When sentiment is high, there are more buyingcommissions than selling as investors enter the market and are more willingto purchase stocks at higher prices. This means that more stocks close athigher prices. Thus, an advances/declines ratio greater than unity shows thatthere are more buying than selling commissions during the month, andindicates positive sentiment, and vice versa. In general, the greater isADVDEC, the more broad-based is any upward movement and therefore thestronger the underlying market sentiment. We define the number of advancesas the total number of Shanghai and Shenzhen A shares that close at month-end above their beginning-month opening prices. Declines correspond to thenumber of issues that close at month-end below their beginning-monthopening prices.

The dividend premium is the difference between the return on dividend-paying shares and that on non-payers. Dividend-paying stocks are on averagethose of larger, more profitable firms with lower growth opportunities (Bakerand Wurgler, 2004). When sentiment is negative, investors become moreanxious about future. This increases time preference so that immediateincome from dividend-payers is preferred over deferred income from capitalgains from non-payers. This increases the price and reduces the returnpremium on dividend-payers. Thus the dividend premium captures investorsentiment in the sense of time-dependent emotions: a decrease in the premium

10Baker and Wurgler (2006) use the closed-end fund discount but this is available for China onlyfrom October 1998, 21 months after the beginning of our sample data. We checked theclosed-end discount as a sentiment proxy using the shortened data set. Our estimatessuggest that it is not a significant determinant of the risk factors, and therefore we do notreport these results.



indicates increased caution and therefore a decrease in investor sentiment.11

Sentiment is defined to be positive when the current dividend premium islarger than the past two-month moving average, and vice versa.

Studies of sentiment commonly construct a single composite measure ofsentiment from different indicators, using methods such as principal compo-nents (Brown and Cliff, 2004; Baker and Wurgler, 2006). This conformsintuitively to the idea of a unified concept of ‘sentiment’. However, sincesentiment is not directly observed, the weightings in any empirical measure ofsentiment and therefore the whole time series of sentiment are vulnerable tochange as new observations of its components become available. In this paperinstead we do not construct a single measure of ‘sentiment’ but use itspostulated components directly in the regressions. This permits the weight oneach component to vary across portfolios and makes it possible to check onthe robustness of these variables as sentiment measures. In analysing theresults therefore, we focus particularly on F tests to check the joint signific-ance of groups of the sentiment proxies. We do not claim that this method is‘better’ than the received approach, but that it is an important alternativewhich deserves exploration. Indeed, a first look at the data shows that, apartfrom RM and ADVDEC, there is a relatively low correlation among thecognitive and sentimental factors, giving some confidence that all the senti-ment measures can rightfully be interpreted as independent factors (Table 1and Fig. 2).

11Note that this is the reverse of Baker and Wurgler (2004), as they use market–book ratios ratherthan returns.

Table 1Correlations among Cognitive and Sentimental Factors

RM SMB HML TURN ADVDEC DPNP

RM 1.000SMB 0.234 1.000HML 0.197 0.281 1.000TURN 0.589 0.238 0.050 1.000ADVDEC 0.783 0.445 0.263 0.502 1.000DPNP 0.149 -0.420 0.332 0.126 0.035 1.000

Notes:RM = the return on the S&P/CITIC A-Share Composite Index less the three-month bank deposit rate.SMB = the equally weighted average of the difference between the returns on small-size stock portfolios andthose on large-size stock portfolios, balanced so as to be neutral with respect to the book–market ratio.HML = the equally weighted average of the difference between the returns on high book-to-market stockportfolios and those on low book-to-market stock portfolios, balanced so as to be neutral with respect to size.TURN = the difference between the turnover ratio and its past two-period moving average. The turnover ratiois the ratio of A share trading volume in Shanghai and Shenzhen to the total value of shares listed in the twomarkets.ADVDEC = the ratio of the number of advancing issues to declining issues. Advances are the total number ofShanghai and Shenzhen A shares that close at month-end above their beginning-month opening prices.Declines are the number of shares that close at month-end below their beginning-month opening prices.DPNP = the difference between the return on dividend-paying shares and that on non-payers.



4 The FF Model and Investor Sentiment

4.1 Tests of FF

We begin by testing the three-factor FF model (Table 2). We report onlydiagnostics to economize on space and because the main interest is in devel-oping the sentimental variables. Although the model fits well as is to beexpected, and the Lagrange Multiplier (LM) tests show little sign of autocor-relation, the structural diagnostics are of more concern with rejections in 18Reset tests and 13 Chow tests at the date of the opening up of the B market.It should be emphasized that there is abundant evidence that variables otherthan the three FF factors help explain stock returns in a statistical sense (e.g.Clare and Thomas, 1994). In this respect, the Reset tests merely confirm thispoint. Of more substantive interest is the possibility of a structural break, andthat the GRS test clearly rejects the beta asset-pricing model. Fama andFrench (1996) found that the GRS test accepted the three-factor model as a

TURN

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5

1.0

1.5

2.0TURN

ADVDEC

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

1.0

1.5

2.0

2.5ADVDEC

DPNP

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0

5

DPNP

Fig. 2 Investor Sentiment, 1997–2007Notes: This figure shows monthly proxies of investor sentiment.

TURN is the difference between the turnover ratio and its past two-period moving average.The turnover ratio is the ratio of total reported A share trading volume in Shanghai and

Shenzhen to the value of shares listed in the two markets.ADVDEC is the ratio of advancing issues over the month to declining issues over the month.Advancing (declining) issues are the total number of stocks in Shanghai and Shenzhen stock

exchanges that closed above (below) the opening prices of the month.DPNP is the dividend premium measured by the difference in returns between portfolios of

companies that paid dividends in the previous year and those that did not pay.



beta asset-pricing model in their US data set. Finally, although we do notreport coefficients, these are broadly consistent with FF in that we do findthat small stocks have greater sensitivity to SMB; indeed the loadings in thelargest stock portfolios (S5Bi) are mostly negative. Likewise, we replicateFF’s negative loadings on HML for low book-to-market portfolios (SiB1),implying a premium for high book-to-market portfolios that FF attributed toa distress effect.

Table 2Three-factor FF Model (Equation (1))

LM Prob Reset Prob Chow Prob R2

S1B1 0.204 0.903 0.310 0.734 3.911** 0.010 0.831S1B2 2.176 0.337 2.302 0.104 0.602 0.615 0.849S1B3 4.129 0.127 7.893*** 0.001 1.443 0.233 0.894S1B4 1.390 0.499 6.734*** 0.002 1.785 0.154 0.919S1B5 2.394 0.302 4.579** 0.012 1.199 0.313 0.792S2B1 12.15*** 0.002 6.530*** 0.002 1.876 0.137 0.869S2B2 19.26*** 0.000 2.573* 0.080 0.949 0.419 0.908S2B3 0.960 0.619 2.298 0.105 0.217 0.885 0.936S2B4 2.196 0.334 12.59*** 0.000 2.215* 0.090 0.952S2B5 1.148 0.563 0.174 0.841 2.866** 0.039 0.930S3B1 2.778 0.249 4.082** 0.019 4.852*** 0.003 0.867S3B2 5.338* 0.069 2.273 0.107 1.918 0.130 0.892S3B3 0.347 0.841 4.362** 0.015 2.788** 0.043 0.899S3B4 0.320 0.852 8.631*** 0.000 5.540*** 0.001 0.953S3B5 1.354 0.508 1.287 0.280 6.386*** 0.001 0.942S4B1 12.11*** 0.002 12.79*** 0.000 0.439 0.725 0.874S4B2 0.136 0.934 7.641** 0.001 4.455*** 0.005 0.919S4B3 0.439 0.803 8.414*** 0.000 1.005 0.393 0.904S4B4 0.177 0.915 9.651*** 0.000 3.087** 0.030 0.915S4B5 12.28*** 0.002 3.307** 0.040 4.312*** 0.006 0.934S5B1 1.237 0.539 46.08*** 0.000 5.524*** 0.001 0.859S5B2 2.482 0.289 15.43*** 0.000 0.736 0.532 0.897S5B3 0.602 0.740 3.113** 0.048 2.611* 0.054 0.884S5B4 0.003 0.988 1.279 0.282 5.400*** 0.002 0.776S5B5 1.222 0.543 8.862*** 0.000 0.161 0.922 0.913

Rejects 5 18 13GRS 4.931*** 0.000

Notes: This table reports summary statistics for equation (1): the three-factor FF model estimated over1997:02–2007:12. S1 to S5 are size-sorted portfolios from small to big; B1 to B5 are book-to-market sortedportfolios from low to high. The 25 SiBj portfolios are the intersections of the size and book-to-marketportfolios. The three factors are: RM = market excess return; SMB = difference between returns on small andlarge firm-size stock portfolios; HML = difference between returns on high book-to-market portfolios andthose on low book-to-market portfolios.LM is the Breusch/Godfrey LM test for second-order autocorrelation distributed as c2(2).Reset is Ramsey’s misspecification test based on a regression of the residuals on the explanatory variables andsquares and cubes of the fitted values (SiBj); distributed as F(2,125).Chow is the Chow test for parameter stability split at 2001:01/02. We test only the slope coefficients, imposingthe same intercept across the two periods. If the intercept differs across periods, the zero-intercept betaasset-pricing model is necessarily rejected. The test is distributed as F(3,124).Rejects gives the total of portfolios for which each test is rejected at the 5 per cent level.GRS is the Gibbons–Ross–Shanken test for the zero-intercept beta asset-pricing model distributed asF(25,103).Prob gives the p values of each test: *significant at 10 per cent; **significant at 5 per cent; ***significant at 1per cent.



4.2 Sentiment as Factors

We next test whether investor sentiment helps explain the mis-pricing com-ponent of returns in the FF model by regressing the portfolio returns on theFF factors and sentiment proxies (equation (3)). The diagnostics are onlymarginally improved (Table 3(i)), but it is also clear that our measures ofsentiment do help explain the part of Chinese stock returns not explained by

Table 3(i)Sentiment in the FF Model: Summary Statistics (Equation (3))


S1B1 1.727 0.422 0.423 0.656 5.347*** 0.002 0.843S1B2 0.774 0.679 1.607 0.205 0.549 0.650 0.861S1B3 10.83*** 0.004 8.863*** 0.000 1.252 0.294 0.900S1B4 6.133** 0.047 0.451 0.638 5.162*** 0.002 0.931S1B5 4.284 0.117 2.911* 0.059 2.767** 0.045 0.849S2B1 6.946** 0.031 13.75*** 0.000 1.510 0.216 0.902S2B2 8.478** 0.014 0.624 0.538 1.194 0.315 0.921S2B3 5.855** 0.054 2.006 0.139 0.191 0.902 0.944S2B4 1.832 0.400 5.068*** 0.008 2.345* 0.077 0.962S2B5 0.547 0.761 0.754 0.473 1.896 0.134 0.940S3B1 0.891 0.640 1.750 0.179 5.546*** 0.001 0.892S3B2 2.862 0.239 1.321 0.271 0.960 0.414 0.898S3B3 1.581 0.454 3.410** 0.037 1.093 0.355 0.911S3B4 0.657 0.720 3.455** 0.035 2.829** 0.042 0.963S3B5 1.680 0.432 4.175** 0.018 5.330*** 0.002 0.961S4B1 8.398** 0.015 7.056*** 0.001 0.585 0.626 0.887S4B2 0.770 0.681 5.000*** 0.008 2.801** 0.043 0.939S4B3 0.476 0.788 4.811*** 0.010 0.958 0.415 0.915S4B4 0.079 0.961 9.625*** 0.000 2.063 0.109 0.923S4B5 17.77*** 0.000 7.630*** 0.001 2.279* 0.083 0.937S5B1 0.344 0.842 20.985*** 0.000 8.947*** 0.000 0.916S5B2 3.323 0.190 12.780*** 0.000 0.844 0.473 0.927S5B3 2.028 0.363 2.267* 0.108 2.019 0.115 0.896S5B4 0.382 0.826 3.297** 0.041 6.162*** 0.001 0.826S5B5 0.270 0.874 6.596*** 0.002 0.558 0.644 0.919

Rejects 7 17 11GRS1 1.792** 0.025 GRS2 3.100*** 0.000

Notes: This table reports summary statistics for equation (3) estimated over 1997:02–2007:12. The FF modelis augmented by symmetric and positive sentiment proxies: market turnover (TURN), buying–selling imbal-ance (ADVDEC) and dividend premium (DPNP). D = 1 if sentiment is positive, or 0 otherwise. As for equation(1), the 25 SiBj portfolios are the intersections of the five size and five book-to-market portfolios.LM is the Breusch/Godfrey LM test for second-order autocorrelation distributed as c2(2).Reset is Ramsey’s misspecification test based on a regression of the residuals on the explanatory variables andsquares and cubes of the fitted values (SiBj); distributed as F(2,125).Chow is the Chow test for parameter stability split at 2001:01/02 distributed as F(3,112). We test only the slopecoefficients, imposing the same intercept across the two periods.Rejects gives the total of portfolios for which each test is rejected at the 5 per cent level.GRS1 is the Gibbons–Ross–Shanken test for the zero-intercept beta asset-pricing model, treating the FFfactors and all sentiment proxies as priced portfolios; F(25,88).GRS2 is the GRS test, treating just the FF factors and current-dated sentiment proxies as priced portfolios;F(25,94).Prob gives the p values of each test: *significant at 10 per cent; **significant at 5 per cent; ***significant at 1per cent.



the FF factors (Table 3(ii)). Some form of sentiment effect is significant in 16out of the 25 portfolios, although there is considerable variation in thesignificance of different measures. Current proxies are more significant thanthose entering with a one-month lag suggesting that the measures of senti-ment could be interpretable as currently priced ‘irrational’ risk factors,although the GRS test continues to reject the beta asset-pricing model, albeitit at a lower level of significance. However, these results also suggest thatinvestor sentiment has little predictive power over future returns in China.

It is also evident that sentiment-driven mis-pricing is more significantwhen sentiment is positive. In addition, the differences in significance amongthe individual proxies suggest that it is important to identify these separately,as they may be modelling different aspects of investor behaviour. Comparingamong portfolios, all three proxies appear to be particularly important forhigh or low book-to-market portfolios (SiB1, SiB5). This is consistent withthe expectation that sentiment is more important in explaining the mis-pricing of distressed stocks (high book-to-market) and growth stocks (lowbook-to-market) both of which are typically more difficult to value, andtherefore more easily influenced by sentiment-driven demands and supplies.We also expect sentiment to help explain the mis-pricing of small stocks. Thisis broadly true when sentiment is measured by the dividend premium, but theother sentiment measures also affect large stocks. Overall, these preliminaryresults provide encouraging but necessarily tentative support for hypothesesH1, H3 and H4.

4.3 Impact of Sentiment on FF Factors

Next we investigate the impact of sentiment on the FF factors (equation (4)).Here we find strong evidence for H2: even though many of the sentimentproxies are individually insignificant, collectively, they help explain the timevariation in RM, SMB and to a lesser extent HML. Sentiment clearly helpsexplain the risk factors in the FF model (Table 4).

The results for RM are consistent with H3 in that both normal andpositive sentiment contribute separately to explaining aggregate marketreturns. The signs of the sentimental variables in general are positive aspredicted and positive sentiment alone tends to have a further positive impacton the market as we would expect from H4. For SMB we see the signs aremore variable. ADVDEC has the expected positive sign (an increase insentiment tends to increase the return on small stocks relative to large) butmany of the other coefficients are unexpectedly negative. For HML, theimpact of sentiment in general is weaker in terms of significance although thesigns are more uniformly positive indicating that an increase in sentimenttends to have a positive impact on all stocks. However, this effect is attenu-ated when sentiment is positive, and this is consistent with H4, confirmingthat it is important to separate positive and negative sentimental effects.



Ta

bl

e3(

ii)Se

nt

imen

tin

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FM

od

el:F

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ts

(Eq

ua

tio

n(3

))

SE

NT

LA

GC

UR

RN

OR

MP

OS

TU

RN

AD

VD

EC

DP

NP

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

S1B

10.

699

0.75

00.

249

0.95

90.

678

0.66

70.

565

0.75

70.

384

0.88

80.

657

0.62

30.

272

0.89

60.

364

0.83

4S1

B2

1.60

7*0.

099

1.47

70.

192

1.38

90.

225

1.08

00.

379

2.06

2*0.

063

0.76

30.

551

1.25

10.

294

2.74

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2S1

B3

1.16

80.

315

0.84

60.

537

1.63

00.

145

1.29

50.

265

1.42

20.

213

0.97

30.

425

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115

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41.

600

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0.57

32.

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0.07

11.

578

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01.

211

0.30

60.

273

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52.

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51.

700

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2.10

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0.02

20.

500

0.80

72.

017*

0.06

91.

570

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21.

739

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81.

181

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2.25

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42.

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50.

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659

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83.

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40.

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031

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50.

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20.

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B4

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81.

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0.34

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40.

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0.17

60.

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875

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30.

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80.

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258

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0.00

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092

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296

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22.

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60.

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1.78

9*0.

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2.36

4**

0.03

51.

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42.

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Rej

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(nor

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urn(

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(t-

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(6,1

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RN

:tur

n(t)

,tur

n(t

-1)

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turn

(t),

D¥

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(t-

1);F

(4,1

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:adv

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1);F

(4,1

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p(t)

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(4,1

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301]

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SMB

All

-7.0

64**

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815*

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ficie

nts,

sum

mar

yst

atis

tics

and

Fte

sts

for

equa

tion

(4)

esti

mat

edov

er19

97:0

2–20

07:1

2.T

hese

are

regr

essi

ons

ofea

chof

the

thre

eF

Ffa

ctor

son

the

sent

imen

tpr

oxie

s.T

hese

ntim

ent

prox

ies

are

the

sam

eas

for

equa

tion

(3).

tst

atis

tics

are

inbr

acke

ts.

‘F’r

epor

tsF

test

sfo

rth

ejo

int

sign

ifica

nce

ofdi

ffer

ent

grou

psof

sent

imen

tva

riab

les:

SEN

T(a

llse

ntim

ent

prox

ies)

:tu

rn(t

),tu

rn(t

-1)

,D

¥tu

rn(t

),D

¥tu

rn(t

-1)

,ad

vdec

(t),

advd

ec(t

-1)

,D

¥ad

vdec

(t),

D¥

advd

ec(t

-1)

,dp

np(t

),dp

np(t

-1)

,D

¥dp

np(t

),D

¥dp

np(t

-1)

;F(1

2,11

8).

LA

G(l

agge

dpr

oxie

s):t

urn(

t-

1),D

¥tu

rn(t

-1)

,adv

dec(

t-

1),D

¥ad

vdec

(t-

1),d

pnp(

t-

1),D

¥dp

np(t

-1)

;F(6

,118

).C

UR

R(c

urre

ntpr

oxie

s):t

urn(

t),D

¥tu

rn(t

),ad

vdec

(t),

D¥

advd

ec(t

),dp

np(t

),D

¥dp

np(t

);F

(6,1

18).

NO

RM

(nor

mal

sent

imen

t):t

urn(

t),t

urn(

t-

1),a

dvde

c(t)

,adv

dec(

t-

1),d

pnp(

t),d

pnp(

t-

1);F

(6,1

18).

PO

S(p

osit

ive

sent

imen

t):D

H¥

turn

(t),

D¥

turn

(t-

1),D

¥ad

vdec

(t),

D¥

advd

ec(t

-1)

,D¥

dpnp

(t),

D¥

dpnp

(t-

1);F

(6,1

18).

Pro

bgi

ves

the

pva

lues

ofea

chte

st:*

sign

ifica

ntat

10pe

rce

nt;*

*sig

nific

ant

at5

per

cent

;***

sign

ifica

ntat

1pe

rce

nt.



5 The Sentiment-based Conditional Asset-pricing Model

We turn finally to equation (5), where investor sentiment is included in themodel directly as ‘irrational’ risk factors and indirectly as conditioning vari-ables for the three FF factors. Our preliminary results above suggest thatlagged sentiment measures are mostly not very significant in explaining eitherFF mis-pricing or the FF factors themselves. Therefore we include onlycurrent sentiment measures in the estimation of (5).

At first glance, the diagnostics are not much improved with about thesame number of significant LM and Reset tests as before (Table 5(i)).

Table 5(i)Conditional FF Sentiment Model: Summary Statistics (Equation (5))


S1B1 9.505*** 0.009 0.202 0.817 2.831** 0.042 0.857S1B2 3.432 0.180 3.086** 0.050 0.647 0.587 0.904S1B3 1.581 0.454 9.826*** 0.000 0.935 0.427 0.920S1B4 3.036 0.219 4.829*** 0.010 3.363** 0.022 0.931S1B5 5.066* 0.079 21.05*** 0.000 2.635* 0.054 0.853S2B1 3.391 0.184 5.588*** 0.005 0.646 0.587 0.917S2B2 2.369 0.306 0.483 0.618 2.025 0.115 0.939S2B3 3.328 0.189 1.286 0.281 0.041 0.989 0.949S2B4 0.370 0.831 4.651** 0.012 1.820 0.148 0.966S2B5 0.555 0.758 5.034*** 0.008 1.315 0.274 0.935S3B1 4.808* 0.090 3.062* 0.051 1.597 0.195 0.897S3B2 7.702** 0.021 2.780* 0.067 0.958 0.416 0.899S3B3 2.790 0.248 4.710** 0.011 0.890 0.449 0.924S3B4 2.332 0.312 0.511 0.601 1.533 0.211 0.969S3B5 0.059 0.971 2.631* 0.077 0.541 0.655 0.957S4B1 3.105 0.212 1.811 0.169 0.106 0.956 0.901S4B2 0.037 0.982 4.103** 0.019 0.783 0.506 0.946S4B3 0.480 0.786 4.603** 0.012 1.256 0.294 0.915S4B4 0.493 0.782 4.686** 0.011 0.885 0.452 0.942S4B5 16.46*** 0.000 2.084 0.130 1.107 0.350 0.947S5B1 2.327 0.312 2.803* 0.065 1.055 0.372 0.947S5B2 0.845 0.655 12.05*** 0.000 0.467 0.706 0.928S5B3 1.011 0.603 2.331 0.102 1.162 0.328 0.906S5B4 0.851 0.653 4.062** 0.020 2.956** 0.036 0.856S5B5 0.364 0.834 0.876 0.420 0.969 0.411 0.935

Rejects 5 17 4

Notes: This table reports summary statistics for equation (5) estimated over 1997:02–2007:12. The FF modelis augmented by the six current sentiment variables (normal and positive) and the FF factors conditioned by(interacted with) each of the current sentiment variables. As for (1), the 25 SiBj portfolios are the intersectionsof the five size and five book-to-market portfolios.LM is the Breusch/Godfrey LM test for second-order autocorrelation distributed as c2(2).Reset is Ramsey’s misspecification test based on a regression of the residuals on the explanatory variables andsquares and cubes of the fitted values (SiBj); distributed as F(2,125).Chow is the Chow test for parameter stability split at 2001:01/02. We test only the slope coefficients, imposingthe same intercept across the two periods. If the intercept differs across periods, the zero-intercept betaasset-pricing model is necessarily rejected. The test is distributed as F(3,100).Rejects gives the total of portfolios for which each test is rejected at the 5 per cent level.Prob gives the p values of each test: *significant at 10 per cent; **significant at 5 per cent; ***significant at 1per cent.



However, we now observe a substantial reduction in the number of significantChow tests from 13 to just four portfolios. This suggests that sentimentalfactors could have been responsible for the apparent structural break in 2001.It seems plausible to conjecture that the liberalization of the B market had apositive impact on the sentiment of Chinese investors, which impacted in theA market. This in turn should be reflected in our sentiment measures whichtherefore provide a better model for the pricing equation, without the needfor slope dummies around the 2001 break.

From the F tests (Table 5(ii)) we see that the sentiment-conditioned FFfactors have a substantial impact on the pricing equation, being significantin all but five portfolios. This is partly at the expense of the unconditionalsentiment factors, but these are still significant in 12 portfolios. Overall, thisprovides strong support for H5 and, combined with the results from equa-tion (4), clearly suggests that the influence of sentiment on asset pricing inChina has come somewhat more through its indirect impact on the riskloadings of cognitive factors rather than directly through its effect as an‘irrational’ pricing factor. This point is strengthened by the results of theGRS test which accepts the beta asset-pricing model when all the condi-tioning variables are interpreted as priced portfolios, whereas the model isrejected but again at a lower level of significance when only the uncondi-tional risk factors are treated as priced portfolios. Of course, it can beargued that the conditional terms should not be interpreted as priced port-folios, and we would therefore propose that these results are suggestiverather than decisive evidence in favour of the sentiment-based model.However, they clearly imply that the ‘alpha’ in this model is less significantthan in the original FF model, and to this extent the sentiment-based modelmore closely matches the Chinese data.

We turn finally to the factor loadings (Table 5(iii)). Those on the excessmarket return remain significant across all the portfolios. But loadings onSMB and HML become less significant after controlling for sentimentaleffects. In addition, the systematic pattern of size effects observed by FF andbroadly replicated in our estimates of equation (1) is much less clear,although the distress effect in the pattern of HML coefficients does survive.The loadings on SMB for the S5Bi portfolios are mostly negative as before,but so too are those for many of the smaller-size portfolios. Many of theloadings on HML are insignificant when conditional sentiment is introduced,so although distress effect does appear, it is relatively imprecise in magnitude.These findings suggest that, after including sentimental effects, the explana-tory power of the market remains significant, but the other FF factors aresomewhat reduced in importance. However, the size and book-to-marketeffects do remain significant in the sentiment-based conditional componentsof SMB and HML. Therefore we may tentatively conclude that size anddistress risks may equally be interpreted as conditioning variables as well asfundamental risks. This is also consistent with Baker and Wurgler (2006,



Ta

bl

e5(

ii)C

on

dit

ion

al

FF

Sen

tim

ent

Mo

del

:FT

est

s(E

qu

at

ion

(5))

CU

RR

CO

ND

C-N

OR

MC

-PO

SC

-TU

RN

C-A

DV

DE

CC

-DP

NP

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

FP

rob

S1B

11.

347

0.24

33.

145*

**0.

000

1.86

6*0.

065

3.00

3***

0.00

32.

858*

*0.

013

1.66

20.

138

2.10

2*0.

059

S1B

21.

414

0.21

62.

269*

**0.

005

2.42

4**

0.01

52.

139*

*0.

033

2.67

3**

0.01

91.

661

0.13

82.

500*

*0.

027

S1B

32.

281*

*0.

042

3.91

1***

0.00

03.

275*

**0.

002

2.76

0***

0.00

62.

786*

*0.

015

1.42

00.

214

1.02

30.

415

S1B

43.

371*

**0.

004

3.14

7***

0.00

02.

803*

**0.

006

3.57

3***

0.00

11.

746

0.11

82.

913*

*0.

012

0.82

00.

557

S1B

53.

498*

**0.

003

1.25

00.

237

1.16

60.

325

1.36

00.

216

0.71

50.

639

0.83

00.

549

1.63

90.

144

S2B

13.

325*

**0.

005

3.60

8***

0.00

02.

303*

*0.

021

2.68

1***

0.00

82.

395*

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033

1.39

90.

222

1.18

90.

318

S2B

21.

375

0.23

21.

821*

*0.

032

1.62

50.

118

1.12

50.

352

1.56

00.

166

1.78

40.

110

0.88

80.

507

S2B

33.

002*

**0.

010

2.22

1***

0.00

61.

655

0.11

02.

068*

*0.

039

2.96

8***

0.01

01.

354

0.24

01.

423

0.21

3S2

B4

2.29

3**

0.04

11.

968*

*0.

018

1.76

6*0.

084

1.24

90.

274

1.05

50.

394

1.49

70.

186

0.88

20.

511

S2B

52.

845*

*0.

013

0.70

00.

804

0.60

10.

794

0.75

80.

655

0.56

70.

756

0.62

10.

713

0.20

10.

976

S3B

11.

249

0.28

81.

776*

*0.

038

1.05

10.

405

2.70

5***

0.00

71.

850*

0.09

71.

431

0.21

01.

248

0.28

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B2

1.17

60.

325

1.48

10.

112

0.67

80.

727

1.04

60.

409

1.05

50.

395

0.83

90.

543

1.89

2*0.

089

S3B

30.

629

0.70

72.

293*

**0.

005

0.92

40.

507

3.12

3***

0.00

23.

708*

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002

1.08

60.

376

1.63

80.

144

S3B

41.

312

0.25

93.

609*

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000

1.49

00.

161

1.51

70.

152

1.33

80.

247

2.10

3*0.

059

0.90

30.

496

S3B

52.

081*

0.06

23.

321*

**0.

000

3.24

4***

0.00

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164

0.32

61.

094

0.37

11.

041

0.40

31.

627

0.14

7S4

B1

0.87

00.

520

1.43

20.

133

0.45

00.

904

0.59

90.

795

0.84

30.

540

1.62

40.

148

0.77

90.

588

S4B

23.

078*

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008

3.26

3***

0.00

04.

233*

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000

2.30

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642*

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020

2.19

3**

0.04

90.

776

0.59

1S4

B3

0.32

60.

922

0.94

10.

533

0.41

20.

926

0.40

00.

932

0.64

10.

697

0.29

70.

937

0.46

00.

836

S4B

40.

625

0.71

04.

262*

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000

2.45

4**

0.01

42.

202*

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028

2.86

5**

0.01

30.

999

0.43

00.

476

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0.84

60.

537

3.28

9***

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1.48

60.

163

1.35

30.

241

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6*0.

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915

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12.

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310

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000

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0.13

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31.

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0.14

13.

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40.

426

0.41

40.

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1.06

20.

390

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10.

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20.

563

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40.

970

0.44

93.

052*

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000

1.51

30.

153

2.61

0***

0.00

90.

878

0.51

43.

189*

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007

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70.

458

S5B

51.

939*

0.08

13.

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000

1.35

50.

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2.24

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50.

822

0.55

61.

743

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Rej

ects

1220

1112

106

4G

RS1

0.85

80.

658

GR

S21.

640

0.04

6***

Not

es:

Thi

sta

ble

repo

rts

Fte

sts

for

equa

tion

(5)

for

the

join

tsi

gnifi

canc

eof

diff

eren

tgr

oups

ofse

ntim

ent

vari

able

s:C

UR

R(a

llcu

rren

tse

ntim

ent

prox

ies)

:tur

n(t)

,D¥

turn

(t),

advd

ec(t

),D

¥ad

vdec

(t),

dpnp

(t),

D¥

dpnp

(t);

F(6

,103

).C

ON

D(a

llco

ndit

iona

lsen

tim

ent)

:tur

n(t)

¥F

act i,

D¥

turn

(t)

¥F

act i,

advd

ec(t

)¥

Fac

t i,D

¥ad

vdec

(t)

¥F

act i,

dpnp

(t)

¥F

act i,

D¥

dpnp

(t)

¥F

act i,

i=R

M,S

MB

,HM

L;F

(18,

103)

.C

-NO

RM

(nor

mal

sent

imen

t):t

urn(

t)¥

Fac

t i,ad

vdec

(t)

¥F

act i,

dpnp

(t)

¥F

act i,

i=R

M,S

MB

,HM

L;F

(9,1

03).

C-P

OS

(pos

itiv

ese

ntim

ent)

:D¥

turn

(t)

¥F

act i,

D¥

advd

ec(t

)¥

Fac

t i,D

¥dp

np(t

)¥

Fac

t i,i=

RM

,SM

B,H

ML

;F(9

,103

).C

-TU

RN

:tur

n(t)

¥F

act i,

D¥

turn

(t)

¥F

act i,

i=R

M,S

MB

,HM

L;F

(6,1

03).

C-A

DV

DE

C:a

dvde

c(t)

¥F

act i,

D¥

advd

ec(t

)¥

Fac

t i,i=

RM

,SM

B,H

ML

;F(6

,103

).C

-DP

NP

:dpn

p(t)

¥F

act i,

D¥

dpnp

(t)

¥F

act i,

i=R

M,S

MB

,HM

L;F

(6,1

03).

Rej

ects

give

sth

eto

talo

fpo

rtfo

lios

for

whi

chea

chte

stis

reje

cted

atth

e5

per

cent

leve

l.G

RS1

isth

eG

ibbo

ns–R

oss–

Shan

ken

test

for

the

zero

-int

erce

ptbe

taas

set-

pric

ing

mod

el,t

reat

ing

the

FF

fact

ors

and

alls

enti

men

tpr

oxie

sin

clud

ing

the

cond

itio

nals

enti

men

talv

aria

ble

aspr

iced

port

folio

s;F

(25,

79).

GR

S2is

the

GR

Ste

st,t

reat

ing

just

the

unco

ndit

iona

lFF

fact

ors

and

the

unco

ndit

iona

lsen

tim

ent

prox

ies

aspr

iced

port

folio

s;F

(25,

97).

Pro

bgi

ves

the

pva

lues

ofea

chte

st:*

sign

ifica

ntat

10pe

rce

nt;*

*sig

nific

ant

at5

per

cent

;***

sign

ifica

ntat

1pe

rce

nt.



Ta

bl

e5(

iii)

Co

nd

itio

na

lF

FSe

nt

imen

tM

od

el:F

ac

to

rL

oa

din

gs

(Eq

ua

tio

n(5

))

Pan

elA

:U

ncon

diti

onal

fact

orlo

adin

gs

RM

SM

BH

ML

TU

RN

D¥

TU

RN

AD

VD

EC

D¥

AD

VD

EC

DP

NP

D¥

DP

NP

S1B

11.

5530

***

-0.8

293

0.31

224.

0478

-2.4

512

1.14

261.

1196

-0.0

896

-1.1

075

[3.6

41]

[0.8

72]

[0.3

01]

[1.2

59]

[0.8

37]

[0.2

93]

[0.8

16]

[0.0

94]

[0.9

39]

S1B

21.

4218

***

-0.8

481

1.32

921.

2085

-1.8

527

3.56

850.

5105

0.06

73-0

.805

0[4

.943

][1

.082

][1

.372

][0

.715

][0

.949

][0

.985

][0

.500

][0

.172

][1

.301

]S1

B3

0.99

44**

*-0

.342

1-0

.057

43.

0924

*-3

.620

72.

3788

-0.3

246

0.47

96-1

.440

0**

[2.6

39]

[0.4

92]

[0.0

61]

[1.8

36]

[1.4

40]

[0.6

66]

[0.2

59]

[1.5

58]

[2.4

43]

S1B

40.

8028

***

0.15

301.

9592

***

3.55

63**

-0.4

163

1.58

750.

0145

0.83

56**

*-1

.290

8**

[3.5

27]

[0.3

25]

[5.3

28]

[2.4

59]

[0.2

64]

[0.6

60]

[0.0

19]

[2.7

03]

[2.2

37]

S1B

50.

8553

**1.

1179

1.01

2811

.038

9***

-4.6

504*

6.94

36-1

.749

6-0

.101

3-1

.055

2[2

.087

][1

.128

][1

.156

][3

.789

][1

.705

][1

.257

][1

.056

][0

.181

][1

.118

]S2

B1

1.06

26**

*0.

3922

-0.4

375

4.73

38**

*-5

.674

8***

0.49

77-0

.041

7-0

.514

4-0

.151

2[2

.980

][0

.707

][0

.641

][2

.952

][3

.426

][0

.143

][0

.037

][0

.967

][0

.253

]S2

B2

0.70

25**

0.23

940.

4538

4.05

41*

-0.0

565

1.06

61-0

.822

70.

0369

-0.5

776

[2.3

93]

[0.4

29]

[0.8

04]

[1.8

01]

[0.0

28]

[0.4

03]

[1.0

48]

[0.1

21]

[1.0

30]

S2B

30.

7607

***

0.71

730.

8381

**2.

1382

-2.6

119*

2.01

95-0

.134

11.

0242

***

-1.6

774*

**[2

.981

][1

.564

][2

.182

][1

.438

][1

.766

][0

.861

][0

.211

][2

.758

][3

.107

]S2

B4

0.72

23**

*0.

4575

1.28

79**

*0.

7673

2.02

112.

6963

-0.4

822

0.35

99-0

.065

1[4

.895

][1

.325

][3

.613

][0

.466

][1

.363

][1

.586

][0

.665

][1

.268

][0

.125

]S2

B5

0.42

910.

2658

1.33

21**

3.35

621.

2896

-0.0

987

1.42

54-0

.035

0-0

.189

2[1

.217

][0

.418

][2

.434

][1

.468

][0

.627

][0

.036

][1

.434

][0

.083

][0

.281

]S3

B1

0.88

66**

*-0

.225

1-0

.046

63.

2152

*1.

8266

-0.6

480

-0.0

273

-0.5

875

-0.1

578

[3.1

30]

[0.4

40]

[0.0

80]

[1.8

13]

[0.9

91]

[0.2

23]

[0.0

26]

[1.6

30]

[0.2

25]

S3B

20.

7455

***

0.63

620.

3832

4.22

10**

-0.4

333

0.20

62-0

.161

4-0

.418

50.

2365

[2.2

49]

[1.0

81]

[0.7

09]

[2.1

40]

[0.2

50]

[0.0

64]

[0.1

86]

[0.9

95]

[0.3

28]

S3B

30.

9974

***

0.15

010.

7109

*-0

.922

3-2

.284

22.

4275

-0.9

148

-0.1

079

-0.0

654

[5.1

04]

[0.3

31]

[1.8

48]

[0.0

05]

[1.1

35]

[1.0

48]

[1.1

10]

[0.2

90]

[0.1

05]



Ta

bl

e5(

iii)

(Con

tinu

ed)

Pan

elA

:U

ncon

diti

onal

fact

orlo

adin

gs

RM

SM

BH

ML

TU

RN

D¥

TU

RN

AD

VD

EC

D¥

AD

VD

EC

DP

NP

D¥

DP

NP

S3B

40.

8276

***

0.27

990.

6323

3.45

68**

-1.9

940

1.04

31-0

.084

7-0

.047

5-0

.074

2[4

.598

][0

.718

][1

.643

][2

.485

][1

.362

][0

.597

][0

.148

][0

.238

][0

.189

]S3

B5

0.51

43**

-0.0

427

1.25

91**

*3.

1532

**0.

7861

1.78

910.

1727

0.65

61**

-1.0

431*

*[2

.010

][0

.109

][3

.397

][1

.990

][0

.527

][0

.748

][0

.239

][2

.479

][2

.328

]S4

B1

0.29

210.

3745

-0.2

285

1.09

170.

1355

3.83

480.

1462

0.22

05-0

.288

0[1

.142

][0

.776

][0

.437

][0

.347

][0

.047

][1

.453

][0

.139

][0

.446

][0

.408

]S4

B2

0.59

96**

*-0

.042

8-0

.401

33.

2000

***

-1.7

118

0.27

600.

4692

0.37

29-0

.295

9[2

.673

][0

.112

][1

.070

][2

.806

][1

.456

][0

.107

][0

.629

][1

.410

][0

.598

]S4

B3

0.89

92**

*-0

.260

5-0

.031

80.

0547

-0.4

370

1.76

630.

1826

0.30

94-0

.283

4[2

.779

][0

.473

][0

.055

][0

.036

][0

.297

][0

.573

][0

.183

][0

.913

][0

.497

]S4

B4

0.38

94-0

.137

60.

6407

0.34

74-1

.494

62.

7628

0.26

610.

1243

-0.3

496

[1.4

19]

[0.2

90]

[1.3

74]

[0.1

84]

[0.8

71]

[1.1

11]

[0.3

21]

[0.4

23]

[0.6

44]

S4B

50.

4740

-0.6

579

1.20

83**

0.27

17-1

.062

81.

3580

1.11

010.

1267

-0.3

972

[1.6

22]

[1.3

86]

[2.1

69]

[0.1

70]

[0.5

34]

[0.5

54]

[1.3

16]

[0.4

04]

[0.8

53]

S5B

10.

3118

0.36

96-1

.614

9***

0.52

042.

9412

*1.

3173

-0.4

513

0.59

24**

-0.0

967

[1.2

74]

[0.7

80]

[4.8

16]

[0.3

01]

[1.8

37]

[0.5

83]

[0.5

27]

[2.0

44]

[0.2

13]

S5B

20.

9173

***

-0.8

448

0.13

984.

9640

***

-0.9

228

-1.4

477

1.36

13-0

.438

7-0

.649

0[2

.917

][1

.220

][0

.289

][3

.563

][0

.671

][0

.585

][1

.503

][1

.398

][1

.060

]S5

B3

0.85

41**

*-0

.658

9-0

.401

12.

2350

-3.1

337

-1.4

805

0.73

510.

8386

**-0

.779

8[3

.435

][1

.093

][0

.765

][0

.991

][1

.560

][0

.474

][0

.818

][2

.073

][1

.101

]S5

B4

1.60

74**

*-0

.787

1-0

.666

74.

2734

-2.4

341

0.79

33-1

.554

5-1

.385

51.

6078

[3.0

53]

[1.4

57]

[0.8

33]

[1.1

77]

[0.7

96]

[0.1

90]

[1.0

75]

[1.1

74]

[1.3

92]

S5B

51.

1771

***

0.51

741.

0517

*3.

8916

**-1

.512

11.

3967

-0.0

786

0.04

18-0

.866

5[4

.993

][0

.924

][1

.955

][2

.612

][0

.980

][0

.491

][0

.092

][0

.133

][1

.644

]



Ta

bl

e5(

iii)

(Con

tinu

ed)

Pan

elB

:C

ondi

tion

alfa

ctor

load

ings

:R

M

RM

¥T

UR

NR

M¥

D¥

TU

RN

RM

¥A

DV

DE

CR

M¥

D¥

AD

VD

EC

RM

¥D

PN

PR

M¥

D¥

DP

NP

S1B

1-0

.369

60.

2979

-1.0

601*

*0.

6438

**-0

.027

30.

0212

[0.6

83]

[0.6

64]

[2.0

47]

[2.1

68]

[0.3

14]

[0.1

94]

S1B

2-0

.296

70.

4830

**-0

.665

1*0.

1419

-0.8

541

0.07

90[1

.259

][2

.403

][1

.794

][0

.627

][0

.157

][1

.036

]S1

B3

0.22

94-0

.088

8-0

.064

7-0

.030

60.

0342

0.03

20[0

.790

][0

.295

][0

.131

][0

.116

][0

.649

][0

.414

]S1

B4

0.34

52*

-0.3

843*

*-0

.013

60.

0479

-0.0

331

0.09

15[1

.903

][2

.425

][0

.042

][0

.264

][0

.784

][1

.388

]S1

B5

0.84

22*

-0.8

793*

-0.5

625

0.44

840.

1380

*-0

.228

9*[1

.666

][1

.797

][0

.995

][1

.490

][1

.754

][1

.950

]S2

B1

-0.4

066

0.31

000.

0137

0.12

000.

0002

0.03

64[1

.657

][1

.352

][0

.026

][0

.430

][0

.003

][0

.364

]S2

B2

0.36

57-0

.428

80.

2107

-0.0

635

0.02

210.

0067

[1.0

93]

[1.3

01]

[0.6

08]

[0.3

21]

[0.4

92]

[0.0

99]

S2B

30.

1034

0.00

030.

0943

0.09

89-0

.048

20.

0889

[0.4

77]

[0.0

01]

[0.2

83]

[0.5

14]

[0.7

15]

[1.0

93]

S2B

4-0

.925

1-0

.047

50.

1156

0.05

750.

0236

-0.0

101

[0.0

54]

[0.3

17]

[0.5

45]

[0.3

74]

[0.6

96]

[0.1

55]

S2B

50.

2393

-0.3

548

0.36

15-0

.043

00.

0249

-0.0

661

[0.9

36]

[1.4

45]

[0.7

77]

[0.1

79]

[0.3

22]

[0.6

31]

S3B

1-0

.137

10.

0602

-0.0

546

0.18

10-0

.100

40.

1215

[0.6

01]

[0.2

26]

[0.1

48]

[0.6

38]

[1.5

18]

[1.4

74]

S3B

20.

3083

-0.4

575

0.28

460.

0862

-0.0

529

0.20

15*

[1.3

44]

[1.6

07]

[0.6

69]

[0.2

70]

[0.5

81]

[1.7

35]

S3B

3-0

.465

7**

0.34

400.

1171

0.06

80-0

.020

40.

1398

*[2

.125

][1

.364

][0

.422

][0

.310

][0

.339

][1

.870

]



Ta

bl

e5(

iii)

(Con

tinu

ed)

Pan

elB

:C

ondi

tion

alfa

ctor

load

ings

:R

M

RM

¥T

UR

NR

M¥

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DE

CR

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PR

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S3B

40.

0022

-0.0

853

0.01

060.

2341

0.01

49-0

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6[0

.010

][0

.393

][0

.048

][1

.643

][0

.436

][0

.376

]S3

B5

0.20

03-0

.178

20.

3825

-0.1

351

-0.3

566

-0.0

376

[0.8

22]

[0.7

13]

[1.2

10]

[0.7

79]

[0.0

08]

[0.5

58]

S4B

10.

3728

-0.3

995

0.40

300.

0541

0.02

100.

0521

[1.2

60]

[1.4

13]

[1.2

21]

[0.2

43]

[0.2

65]

[0.5

34]

S4B

2-0

.402

2**

0.22

11*

0.47

78-0

.054

40.

0133

-0.0

541

[2.4

15]

[1.6

71]

[1.6

10]

[0.3

14]

[0.4

08]

[1.0

20]

S4B

3-0

.177

30.

1875

-0.0

529

0.10

980.

0294

-0.5

125

[0.6

54]

[0.8

78]

[0.1

28]

[0.4

48]

[0.6

14]

[0.0

07]

S4B

40.

3426

-0.0

258

0.65

65*

-0.3

517

-0.6

074

0.08

00[1

.014

][0

.076

][1

.824

][1

.643

][0

.014

][1

.178

]S4

B5

0.27

50-0

.131

50.

4883

-0.2

737

0.00

060.

0306

[1.1

53]

[0.5

43]

[1.2

80]

[1.2

05]

[0.0

10]

[0.4

17]

S5B

10.

3864

-0.0

925

0.40

900.

0374

0.02

450.

0254

[1.4

14]

[0.4

83]

[1.2

55]

[0.1

87]

[0.7

23]

[0.5

00]

S5B

20.

0418

0.10

20-0

.166

70.

0854

0.01

68-0

.101

8[0

.201

][0

.503

][0

.402

][0

.386

][0

.349

][1

.157

]S5

B3

0.20

00-0

.111

60.

1237

0.05

34-0

.069

40.

1253

[0.6

71]

[0.3

87]

[0.3

81]

[0.2

57]

[1.0

42]

[1.4

05]

S5B

4-0

.243

90.

1836

-0.8

399

0.62

780.

0571

0.01

16[0

.790

][0

.710

][0

.973

][1

.303

][0

.570

][0

.070

]S5

B5

0.03

01-0

.135

7-0

.405

10.

2721

0.03

570.

0397

[0.1

38]

[0.8

28]

[1.2

11]

[1.3

80]

[0.8

82]

[0.6

83]



Ta

bl

e5(

iii)

(Con

tinu

ed)

Pan

elC

:C

ondi

tion

alfa

ctor

load

ings

:S

MB

SM

B¥

TU

RN

SM

B¥

D¥

TU

RN

SM

B¥

AD

VD

EC

SM

B¥

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VD

EC

SM

B¥

DP

NP

SM

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D¥

DP

NP

S1B

1-0

.475

5-0

.170

32.

2819

**-0

.800

6**

0.07

730.

0391

[0.4

95]

[0.3

06]

[2.0

71]

[2.0

82]

[0.6

39]

[0.3

24]

S1B

2-0

.091

40.

0232

1.89

16*

-0.3

380

-0.0

348

0.11

59[0

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.867

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.055

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.334

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.004

]S1

B3

-1.0

461*

0.88

45**

1.30

83-0

.369

10.

0702

-0.0

660

[1.8

96]

[2.0

82]

[1.5

06]

[1.1

83]

[0.7

25]

[0.5

36]

S1B

4-0

.815

80.

4715

0.91

69-0

.177

90.

0165

-0.0

159

[1.6

46]

[1.5

17]

[1.6

00]

[0.7

45]

[0.1

98]

[0.1

58]

S1B

5-0

.529

41.

0505

-0.5

746

0.06

060.

0080

-0.1

514

[0.4

91]

[1.1

87]

[0.4

55]

[0.1

23]

[0.0

70]

[0.6

35]

S2B

10.

5121

0.63

30-0

.368

60.

1914

-0.0

456

-0.0

707

[0.8

29]

[1.3

49]

[0.4

91]

[0.6

35]

[0.4

64]

[0.5

14]

S2B

2-1

.208

3*1.

0992

**0.

7192

-0.2

467

-0.0

172

-0.0

305

[1.8

78]

[2.0

23]

[1.1

47]

[0.8

80]

[0.2

75]

[0.3

05]

S2B

30.

0111

0.09

25-0

.078

40.

0674

-0.0

217

0.11

56[0

.026

][0

.254

][0

.144

][0

.298

][0

.264

][1

.239

]S2

B4

0.63

17*

-0.1

638

-0.0

942

0.13

85-0

.314

30.

0592

[1.6

70]

[0.6

42]

[0.2

41]

[0.7

23]

[0.0

69]

[0.9

82]

S2B

5-0

.568

40.

2338

0.62

39-0

.324

70.

0318

-0.0

486

[0.9

78]

[0.5

41]

[0.8

28]

[1.0

64]

[0.3

11]

[0.4

15]

S3B

10.

6034

-0.3

340

0.70

29-0

.462

7-0

.069

30.

1676

[1.2

60]

[0.8

44]

[1.2

41]

[1.6

23]

[0.7

82]

[1.1

76]

S3B

2-0

.698

60.

6135

-0.3

537

0.23

41-0

.138

20.

0912

[1.2

81]

[1.4

87]

[0.5

48]

[0.6

89]

[0.7

63]

[0.3

55]

S3B

30.

3676

0.48

92-0

.102

40.

2887

-0.0

826

0.10

85[0

.734

][1

.139

][0

.183

][1

.109

][0

.857

][0

.932

]



Ta

bl

e5(

iii)

(Con

tinu

ed)

Pan

elC

:C

ondi

tion

alfa

ctor

load

ings

:S

MB

SM

B¥

TU

RN

SM

B¥

D¥

TU

RN

SM

B¥

AD

VD

EC

SM

B¥

D¥

AD

VD

EC

SM

B¥

DP

NP

SM

B¥

D¥

DP

NP

S3B

4-0

.524

00.

6438

*0.

1145

0.02

39-0

.106

40.

0452

[1.1

89]

[1.8

19]

[0.2

42]

[0.1

33]

[0.0

21]

[0.7

26]

S3B

5-0

.325

10.

2843

0.45

09-0

.135

20.

0772

-0.0

262

[0.7

76]

[0.8

49]

[0.9

90]

[0.6

64]

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2007) who argue that stocks that are more difficult to value are more likely tobe subject to sentiment-driven mis-pricing.

The significance of the unconditional sentiment variables is also reducedby the addition of the conditional factors, but they retain explanatory poweras evidenced by the F tests. An interesting feature of the unconditionalsentiments is that the majority of significant coefficients are on the normalsentiment proxies rather than positive sentiment, suggesting that positivesentiment may have its main effect as a conditioning variable. TURN andDPNP are generally quite significant but ADVDEC much less so. Normalsentiment has a broadly positive impact on returns, apart from DPNP, andpositive sentiment generally tends to attenuate the impact of sentiment. Thisis a more surprising result especially as there is no evident cross-sectionalpattern in the impact of positive sentiment. However, since most of thepositive effects are insignificant it is difficult to place much weight on thisresult.

Notwithstanding the overall significance of the conditioning effects, theindividual conditional factor loadings vary considerably across portfolios insigns and significance. Improvements in sentiment have a broadly positiveeffect on the factor loadings for RM as we would expect, and here, positivesentiment is generally reinforcing of this effect. However, the conditioning onSMB and HML is less clear-cut. In many cases, improvements in sentimentreduce the loading; but in general, positive sentiment tends to increase theloading for SMB and reduce it for HML. This too is consistent with Bakerand Wurgler (2006, 2007) in that optimism particularly increases the loadingon smaller stocks and those with low book-to-market that tend to be stronglygrowth-oriented. More generally, there are relatively few precise patternsof impact on different portfolios. However, the central hypothesis (H5)that conditioning on sentiment matters for share pricing in China is clearlyestablished.

6 Conclusions

Traditional asset-pricing models do not allow a role for investor sentiment.This paper relaxes the assumption that investors are rational and considersthe impact of sentiment on the pricing of Chinese A shares between January1997 and December 2007.

The FF three-factor model does not provide a complete beta asset-pricing explanation of Chinese share returns. Investor sentiment, measuredby turnover, the advances/declines ratio and the dividend premium, helpsprovides a direct explanation for mis-pricing in the FF model. Sentiment alsohelps explain the time series of the FF factors, suggesting that these factorsmay be conditioning characteristics rather than fundamental factors deter-mining stock returns. We find that sentiment affects both the time-series andcross-sectional patterns of share returns. In addition, there is some evidence



that positive sentiment affects the market differently from ‘normal’ senti-ment: in some cases it accentuates the impact of sentiment and leads togreater emphasis on less ‘fundamental’ factors, but in other cases it attenuatesthis impact. Sentiment appears to be particularly important for smaller com-panies and those with ‘extreme’ (high or low) book-to-market portfolios. Thisis consistent with the expectation that sentiment is more important in explain-ing the mis-pricing of stocks which are more difficult to value, and thereforemore easily influenced by sentiment-driven demands and supplies. Theseresults may also help explain some of the cross-sectional patterns observed byFama and French (1996); these patterns could be due in part to ‘irrational’sentiment rather than rational risk-based pricing, as suggested by Lakon-ishok et al. (1994), at least insofar as they apply to China.

Diagnostics suggest that the FF and sentiment-based models both haveshortcomings requiring further research, although an important result of thesentiment-based model is that it effectively eliminates an apparent structuralbreak in the data when the Chinese B market was opened to domestic in-vestors. We conjecture that this move had a direct effect on sentiment andthus on share returns. Finally, when investor sentiment is introduced into themodel as a conditioning variable for the FF factors, alpha, SMB and HMLall become less significant, as to a lesser extent do the direct sentimentaleffects. However, the sentiment-conditioned factor loadings contribute sig-nificantly to explaining share returns. Therefore we conclude that investorsentiment tends to be both a conditional and a direct determinant of assetpricing in China.

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asset pricing with investor sentiment: evidence from chinese stock markets

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