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The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
Contents lists available at ScienceDirect
The Quarterly Review ofEconomics and Finance
journa l homepage: www.e lsev ier .com/ locate /qre f
The impact of individual and institutional investorsentiment on the market price of risk
Rahul Vermaa,1, Gökce Soydemirb,∗
a College of Business, University of Houston-Downtown, 320 North Main Street, Houston, TX 77002, United Statesb College of Business Administration, University of Texas-Pan American, Edinburg, 1201 West University Drive,78539, United States
a r t i c l e i n f o
Article history:Received 19 September 2007Received in revised form 31 October 2008Accepted 17 November 2008Available online 28 November 2008
JEL classification:G12, G14, C3
Keywords:Stock returnsInvestor sentimentVAR model
a b s t r a c t
We examine the effect of individual and institutional investor sen-timent on the market price of risk derived from DJIA and S&P500index returns. Consistent with behavioral asset pricing models, wefind significant positive response of rational sentiment suggestinggreater incentive for rational investors to engage in arbitrage whenthe compensation for taking risk is greater. Further, an increasein irrational optimism leads to a significant downward movement,but an increase in rational sentiment does not lead to a significantchange market price of risk. These results are robust for both marketindexes, DJIA and S&P500 and for both individual and institutionalinvestor sentiment.
© 2008 The Board of Trustees of the University of Illinois.Published by Elsevier B.V. All rights reserved.
1. Introduction
Irrational investor sentiment plays little role in the standard risk-based asset pricing literature. Theissue of investors’ irrationality is ignored due to the central role of rational arbitrageurs who tradeagainst noise traders and bring stock price close to its fundamental value. However, numerous recentstudies have countered this argument and suggested that arbitrage is limited and that stock prices candeviate from the fundamental value due to unpredictability in irrational sentiment. The theoreticalframework describing the role of sentiment in asset pricing is provided by researchers such as Black
∗ Corresponding author. Tel.: +1 956 381 3368.E-mail addresses: [email protected] (R. Verma), [email protected] (G. Soydemir).
1 Tel: +1 713 221 8590.
1062-9769/$ – see front matter © 2008 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.doi:10.1016/j.qref.2008.11.001
1130 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
(1986), Trueman (1988), DeLong, Shleifer, Summers and Waldman (DSSW) (1990, 1991), Shleifer andSummers (1990), Lakonishok et al. (1991), Campbell and Kyle (1993), Shefrin and Statman (1994),Palomino (1996), Barberis et al. (1998), Daniel et al. (1998) and Hong and Stein (1999).
Recent behavioral asset pricing models predict linkages between irrational sentiment and the mar-ket price of risk (MPR) (Abel, 2002; Basak, 2005; Cecchetti et al., 2000; Garrett et al., 2005; Girard etal., 2003; Jouini & Napp, 2005; Li & Zhong, 2005; Yu & Yuan, 2005). Overall, these theoretical studiessuggest that irrational investors and rational arbitrageurs hold opposite beliefs, i.e., when noise tradersare pessimistic, rational arbitrageurs are optimistic. In such scenario, the compensation for bearingrisk should be higher to attract more wealth from rational arbitrageurs, thus adjusting MPR upwards.Conversely, when irrational investors are optimistic, MPR should be lower to deter rational investorsfrom making investments.
Despite the well-documented literature on the importance of investor’s irrationality as a possibledeterminant of fluctuations in MPR, few empirical tests have been undertaken to investigate such rela-tionships. In this study, we use monthly data of investor sentiment at the individual and institutionallevel provided by the American Association of Individual Investors and Investors Intelligence to empiricallytest these theoretical propositions. Specifically, we focus on both rational and irrational componentsof investor sentiment and investigate their relationship with MPR derived from the DJIA and S&P 500returns.
We make the following contributions to the existing literature. First, unlike the previous studieswhich treat sentiment as fully irrational, we focus on both rational and irrational components of investorsentiment and explore how fundamental and noise trading may affect MPR. Second, unlike previousstudies, which treat the two classes of investor sentiment in isolation, we investigate the effects of theindividual and institutional investor sentiment on MPR, jointly in one multivariate model to examinethe dynamics between the two types of sentiment. Shocks originating from sentiment of one class ofinvestors not considered might mistakenly be perceived as a disturbance originating from a class ofsentiment considered in the analysis. Third, unlike previous studies, which capture only the anticipatedchanges in sentiment, we examine the unanticipated component of sentiment on MPR in line withrational expectations theory.
The results of the generalized impulses generated from a vector auto regression (VAR) model revealthe following results. First, consistent with Yu and Yuan (2005), irrational optimism leads to a sig-nificant downward revision in MPR perhaps due to the excess volatility generated. Second, rationalinvestor sentiment have an insignificant effect on MPR suggesting that rational optimism backed bystrong economic fundamentals cause the changes in MPR to stay statistically insignificant. This is con-sistent with Merton’s ICAPM which shows that when investors happen to have correct beliefs, thereturn adjusted for risk does not respond significantly meaning the resulting changes in the MPR arenot significant enough to generate any meaningful response. Third, consistent with Solt and Statman(1988), rational investors are bullish (bearish) when noise traders are bearish (bullish) reflecting thecontrarian investment strategies. Fourth, there are insignificant responses of irrational optimism andpessimism to rational investor sentiment suggesting that irrationality in the market is not likely to bedriven by risk factors. These results are robust for both market indexes, DJIA and S&P500 and for bothindividual and institutional investor sentiment. The empirical results are consistent with the notionthat market’s reaction to volatility is non-homogenous in time depending on different kinds of investorsentiment being generated.
The remainder of this paper is organized as follows: Section 2 reviews the existing literature oninvestor sentiment and MPR while section three presents the model. Section 4 presents the data anddescriptive statistics. Section 5 describes the econometric methodology. Section 6 reports the empiricalfindings. Section 7 provides concluding remarks.
2. Related literature
The link between the behavioral aspects of investors and the fluctuations in MPR stems from thepresence of heterogeneity in sentiment of market participants in the presence of the market imper-fections. Investor’s heterogeneity in beliefs leads to an additional factor implying that standard assetpricing models overestimates/underestimates the equity risk premium depending on investor’s rela-
R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145 1131
tive optimism/pessimism. Recent studies (Buraschi & Jiltsov, 2002; Pavlova & Rigobon, 2003) stronglysupport the notion that difference of opinion among market participants plays an important role inasset pricing.
Basak (2005) suggests that when sentiment is heterogeneous across the market; risk is transferredfrom the more pessimistic to more optimistic investor. This transfer of risk is proportional to the degreeof difference of opinion which brings another factor in the investors’ perceived MPR. As there is anincrease (decrease) in MPR of the overly pessimistic (optimistic) investor.
Jouini and Napp (2005) analyze the impact of heterogeneity in sentiment on the MPR and the riskfree rate. In light of the risk premium and risk-free rate puzzles (Mehra & Prescott, 1985; Weil, 1989),they show that when investors are pessimistic, there is a bias towards a higher MPR and a lowerrisk-free rate than in the standard setting. Also, there is a higher MPR if risk tolerance and investors’pessimism are positively correlated. They argue that the reason why investors’ pessimism increasesthe objective expectation of MPR is not because a pessimistic investor requires a higher MPR. Theinvestor requires the same MPR but his/her pessimism leads him/her to underestimate the averagereturn such that the perceived MPR is greater than the standard MPR.
Yu and Yuan (2005) demonstrate that market’s reaction to volatility is not homogenous throughtime but depends on irrational sentiment. They argue that in the absence of irrationality, MPR is positiveand constant. However, in the presence of irrationality, MPR is a decreasing function of irrationalsentiment, i.e., price of risk is inversely related to irrational sentiment.
Similarly, Abel (2002) proves that investors’ pessimism increases the risk premium when agentshave power utility functions. Likewise Garrett et al. (2005) suggest that fluctuations in investors’ beliefsmay be due to the changes in risk aversion over time. Therefore, MPR can be interpreted as a weightedaverage of investors’ coefficient of relative risk aversion, the weights being investors’ proportion ofwealth. Several studies have documented the effect of heterogeneous beliefs of investors on MPRthrough its effect on the risk premium. For example, Giordani and Soderlind (2003), incorporate het-erogeneous beliefs in the study of pessimism and doubt provide evidence on the role of investors’pessimism in explaining the risk premium. Similarly, Cecchetti et al. (2000) study a standard modelwith distorted subjective beliefs of investors and show that pessimistic sentiment can better matchfirst and second moments of the equity premium and risk free rate than a rational expectation model.
Li and Zhong (2005) find that the predictability of returns from many developed countries’ equitymarkets is explained in part by time varying MPR associated with consumption relative to habit at theworld as well as at local levels. Similarly, Soydemir (2005) links the increase in the price of covariancerisk following the first quarter of 2000, to the bearish investor attitudes and economic slowdown of theU.S. Likewise, Girard et al. (2003) argue that since markets are never fully integrated with the world,and their level of integration with the world portfolio changes over time, the MPR always includes bothcomponents: reward to local variance and reward to world variance. They show that MPR is negativein pessimistic market while positive in optimistic market.
Following the predictions of the behavioral model, several empirical tests have analyzed if investorsentiment play a significant role in asset pricing, effect either used indirect measures or direct mea-sures of investor sentiment. Studies using indirect measure (Baker & Wurgler, 2006; Brown & Cliff,2004, 2005; Chen et al., 1993; Clarke & Statman, 1998; DeBondt, 1993; Elton et al., 1998; Fisher& Statman, 2000; Gemmill & Thomas, 2002; Lee et al., 1991, 2002; Neal and Wheatley, 1998; Siaset al., 2001; Swaminathan, 1996). Overall, these studies provide powerful and consistent empiri-cal support for the hypothesis that stock prices are affected by individual and institutional investorsentiment.
Baker and Wurgler (2006) construct two different investor sentiment indexes by following two-step processes. In their first approach, they employ six indirect measures of investor sentiments (CEFdiscount, NYSE share turnover, number and first day IPO returns, equity shares in new issues, anddividend premium). In the first step, they create an index by estimating the first principal componentof these six proxies. In the second step, they compute the correlation of the index created in the firststep with current and lagged values of these proxies. They form the final sentiment index by taking thedifference between steps one and two. In their second approach (which is similar to our approach),they employ a set of business cycle proxies (in our case we have used 12 rational factors). These proxiesare growth in consumer durables, nondurables, and services, and NBER recession variable. In the first
1132 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
step, they regress each of the six indirect measures of investor sentiments on these business cycleproxies. In the second step, they compute the residuals of these regressions and treat them as betterproxies for investor sentiments.
Our model is very similar to the second approach of Baker and Wurgler (2006). However, thereare two major differences. First, instead of indirect measure of sentiments, we employ survey data;and second, instead of five business cycle variables, we employ 12 rational factors to compute theresiduals. Our motivation and basic premise to split the sentiment variables into rational and irrationalcomponents is derived from these previous studies. However, there are fundamental differences in theapproaches taken by these seminal studies and our research. First, existing papers such as Brown andCliff (2005) and Baker and Wurgler (2006) have focus only on the irrational components of sentiment,whereas in our case we decompose these sentiment into rational and irrational components, so thattheir relative effects can be investigated. Specifically, unlike these studies where only the effect ofnoise trading is considered, we analyze the relative effects of noise and fundamental trading inducedby individual and institutional investor sentiment. Second, these papers analyze the response of stockreturns, while in our case; we investigate the response of market price of risk (Sharpe ratios). Third,we investigate the effects of rational and irrational components of the individual and institutionalinvestor sentiment on MPR in one model to differentiate between the two types of sentiment. Lastly,we examine the unanticipated component of sentiment on MPR in line with rational expectationstheory.
3. Model
Since sentiment partially contain rational expectations based risk factors (Brown & Cliff, 2005;Shleifer & Summers, 1990), it is quite possible that MPR is affected by both fundamental and noisecomponents of sentiment. Hirshleifer (2001) also relates expected returns to both risks and investormis-valuation. When an investor is bullish or bearish, then this could be a rational reflection of futureperiod’s expectation or irrational enthusiasm or a combination of both. We follow the approach ofBaker and Wurgler (2006) to capture the irrational component of investor sentiment by regressingsentiment indicators to a set of risk factors and computing the residuals.
Accordingly, we formulate Eqs. (1), (2) and model rational and irrational effects of fundamentalsand noise respectively on sentiment of individual and institutional investors:
Sentt1t = �0 + �j
J∑
j=1
Fundjt + �t (1)
Sentt2t = �0 + �j
J∑
j=1
Fundjt + ϑt (2)
where �0 and �0 are constants, � j and �j are the parameters to be estimated; �t and ϑt are the randomerror terms. Sentt1t and Sentt2t represent the shifts in sentiment of individual and institutional investorsrespectively at time t. Fundjt is the set of fundamentals representing rational expectations based onrisk factors that have been shown to carry non-redundant information in conditional asset pricingliterature. The fitted values of equations (1) and (2) capture the rational component of sentiment (i.e.Sentt1t and Sentt2t). On the other hand the residual of Eqs. (1) and (2) capture the irrational componentof sentiment (i.e. �t and ϑt).2
Next, we analyze the extent to which MPR is affected by investor sentiment. Sentiment may be irra-tional or rational. We compute MPR based on Elton and Gruber (1991) and Sharpe (1994) measured
2 We follow the same approach used in the previous literature. To cite a few, Barro (1978), Barro and Rush (1980) and Poitras(1997) estimate a model using a two-step procedure: they obtain OLS estimates of the forecasting equation and use the residualand fitted values as regressors in an OLS estimation of the output equation. As Pagan (1984) shows, the two-step estimates arenot efficient, but they are consistent. We thank an anonymous referee for making this point.
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as excess market return per unit of standard deviation. Following Andrew (2002), we compute Sharperatios based on moving average standard deviation for the period t + 1 to t + 12. First, we have com-puted the returns series for both these market indicators. Second, we have calculated moving averagestandard deviation at any time t for the past 12 months. Third, we have computed excess return at timet by subtracting risk-free rate from market return at time t. Lastly, we have computed the Sharpe ratiosby dividing the excess return with the moving average standard deviation at time t. The sentimentvariables are decomposed into the rational and irrational components based on Eqs. (1) and (2) andincluded in the return generating process as
SMt = ˛0 + ˛1Sentt1t + ˛2Sentt2t + ˛3�t + ˛4ϑt + �t (3)
where ˛0 is a constant while ˛1, ˛2, ˛3 and ˛4 are the parameters to be estimated; �t is the random errorterm. Specifically the parameters ˛1 and ˛2 capture the effects of sentiment induced by fundamentaltrading on the part of individual and institutional investors, respectively; while ˛3 and ˛4 capture theeffects of sentiment induced noise trading by individual and institutional investors, respectively.
We also place importance on jointly modeling the sentiment of individual and institutionalinvestors to avoid misspecification. Specifically, shocks originating from sentiment of one class ofinvestors not considered might mistakenly be seen as a disturbance originating from sentiment ofanother class of investors in the analysis.
4. Data and descriptive statistics
We obtain all data in monthly intervals from October 1988 to April 2004. To measure sentiment ofmarket participants, we employ survey data similar to the ones used in the literature. The institutionalinvestors participate in the market for living while the individual investors’ primary line of businessis outside the stock market (Brown & Cliff, 2004). Our choice of individual investor sentiment index isbased on Brown and Cliff (2004), Fisher and Statman (2000) and DeBondt (1993) which use the surveydata of American Association of Individual Investor (AAII). Beginning July 1987, AAII conducts a weeklysurvey asking for the likely direction of the stock market during the next six months (up, down or thesame). The participants are randomly chosen from approximately 100,000 AAII members. Each week,AAII compiles the results based on survey answers and labels them as bullish, bearish or neutral. Theseresults are published as ‘investor sentiment’ in monthly editions of AAII Journal. The sentiment indexfor individual investors is computed as the spread between the percentage of bullish investors andpercentage of bearish investors (Bull–Bear). Since this survey is targeted towards individual investors,it is primarily a measure of individual investor sentiment.
Our choice of institutional investor sentiment index is based on Brown and Cliff (2004, 2005),Lee et al. (2002), Clarke and Statman (1998) and Solt and Statman (1988) which use the survey data ofInvestors Intelligence (II), an investment service based in Larchmont, New York. II compiles and publishesdata based on a survey of investment advisory newsletters. To overcome the potential bias problemtowards buy recommendation, letters from brokerage houses are excluded. Based on the expectedfuture market movements the letters are labeled as bullish, bearish or hold. The sentiment index forthe institutional investor is found by calculating the spread between the percentage of bullish investorsand percentage of bearish investors. Because authors of these newsletters are market professionals,the II series is interpreted as a proxy for institutional investor sentiment.
We employ two different indexes, namely the DJIA and S&P500 to characterize the overall MPR.The DJIA is a price weighted average of 30 large ‘blue chip’ stocks and is the most widely followedand reported stock index. If sentiment is market wide, then its impact should appear in this index. Incontrast, the S&P500 is value-weighted index that reflects the returns of large capitalization stocks.The continuously compounded returns and standard deviation are computed from the both stockmarket indexes obtained from Datastream. The reason we have not analyzed the CRSP index is to avoidpotential misspecification problem since we have included two variables related to the CRSP index asindependent variables (rational factors) in Eqs. (1) and (2). These two variables are the dividend yieldof the value weighted CRSP index for the past 12 months and the excess return on market portfoliowhich is measured as measured as the value-weighted returns on all stocks minus the one-monthTreasury bill rate.
1134 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
Table 1Descriptive statistics. The variables are individual investor sentiment (Sentt1), institutional investor sentiment (Sentt2), returnson Dow Jones Industrial Average (DJIA), returns on S&P 500(S&P500), economic growth(IIP), short term interest rates (T30),economic risk premiums (T90-T30), future economic variables (B10-T30), business conditions (Baa-Aaa), dividend yield (Div.),inflation (INF), excess returns on the market portfolio (Rm), premium on portfolio of small stocks relative to large stocks (SMB),premium on portfolio of high book/market stocks relative to low book/market stocks (HML), momentum factors (UMD), andcurrency fluctuations (USD).
Mean Median Maximum Minimum S.D. Skewness Kurtosis
Sentt1 0.1143 0.1200 0.5100 −0.3500 0.1760 −0.0863 2.6626Sentt2 0.0896 0.1100 0.3640 −0.3420 0.1413 −0.5373 2.9513DJIA 0.0102 0.0137 0.0913 −0.1177 0.0394 −0.5175 3.5256S&P 500 0.0096 0.0147 0.1011 −0.1094 0.0389 −0.5279 3.5607IIP 0.0026 0.0032 0.0199 −0.0121 0.0052 −0.1011 3.2547T30 0.0043 0.0041 0.0080 0.0021 0.0013 0.4793 2.9139T90-T30 0.0004 0.0004 0.0017 −0.0003 0.0004 0.8185 3.7719B10 T30 0.0071 0.0078 0.0549 −0.0440 0.0181 −0.0562 3.1558Baa-Aaa 0.0079 0.0074 0.0144 0.0053 0.0018 1.0835 4.3342Div. 0.0127 0.0153 0.1141 −0.1437 0.0408 −0.4639 3.9027INF 0.0026 0.0023 0.0103 −0.0012 0.0021 0.9335 4.3616Rm 0.0031 0.0077 0.0994 −0.1655 0.0414 −0.7543 4.3240SMB −0.0012 −0.0028 0.2138 −0.1626 0.0382 1.0244 11.0803HML 0.0024 0.0009 0.1367 −0.1205 0.0363 0.4417 5.3273UMD 1.1658 1.3200 18.2100 −25.1300 4.5224 −0.7366 11.7315USD 0.4233 0.3270 4.2894 −3.1701 1.1005 0.3533 4.3973
We include the following variables as fundamentals that have been shown to carry non-redundantinformation in the asset pricing literature:3 (i) economic growth; (ii) short-term interest rates; (iii)economic risk premia; (iv) future economic expectations variables; (v) business conditions; (vi) div-idend yield; (vii) inflation; (viii) excess returns on the market portfolio; (ix) premium on portfolioof small stocks relative to large stocks (SMB); (x) premium on portfolio of high book/market stocksrelative to low book/market stocks (HML); (xi) momentum factor (UMD) (xii)and currency fluctuation.
The data on economic growth, business conditions and inflation are obtained from Datastream;short term interest rates, economic risk premium, future economic variables and currency fluctuationsare obtained from Federal Reserve Bank of St. Louis; dividend yield and excess return on the marketportfolio from CRSP; and SMB, HML and UMD from Kenneth French Data Library at Tuck School of Business,Dartmouth College.
Table 1 reports the descriptive statistics of the above- mentioned variables. The mean of Sentt1and Sentt2 are approximately 11% and 9%, respectively. This suggests both individual and institutionalinvestors have been bullish during most of the sample period. Interestingly, individual investors havebeen more bullish than institutional investors. The mean returns of DJIA and S&P 500 are approximately1.02% and 0.95%, respectively. However, sentiment indicators are measured as the difference betweenpercentage of bullish and bearish investors while stock returns are continuously compounded returns.In order to compare their variability we have now computed their coefficient of variations (CVs) andinterestingly stock returns seems to be more volatile than investor sentiment. The CVs for DJIA and S&P500 are 4.05 and 3.86 respectively, while corresponding coefficients for individual and institutionalinvestor sentiment are 1.54 and 1.58, respectively.
Table 2 reports the cross-correlation between MPR, sentiment variables and the fundamentals. Thecorrelations between the two sentiment variables is approximately 0.50, which is same as Fisher andStatman (2000)’s result of 0.49. Such high correlation indicates possible feedback effects between theindividual and institutional investor sentiment. This further strengthens the approach adopted in thisstudy that modeling them jointly (in a multivariate setting) and then isolating their individual effectson the stock market returns.
3 Fama (1970, 1990), Schwert (1990), Campbell (1987, 1991), Ferson and Campbell (1991), Fama and French (1989, 1993), Famaand French (1988), Keim and Stambaugh (1986), Hodrick (1992), Campbell and Shiller (1988a, 1988b), Sharpe (1964, 2002), Famaand Schwert (1977), Lintner (1965), Jegadeesh and Titman (1993), Elton and Gruber (1991).
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Table 2Cross correlations. The variables are individual investor sentiment (Sentt1), institutional investor sentiment (Sentt2), MPR for Dow Jones Industrial Average (SM1), MPR for S&P 500(SM2),economic growth(IIP), short term interest rates (T30), economic risk premiums (T90-T30), future economic variables (B10-T30), business conditions (Baa-Aaa), dividend yield (Div.),inflation (INF), excess returns on the market portfolio (Rm), premium on portfolio of small stocks relative to large stocks (SMB), premium on portfolio of high book/market stocks relativeto low book/market stocks (HML), momentum factors (UMD), and currency fluctuations (USD).
Sentt1 Sentt2 SM1 SM2 B10-T30 Baa-Aaa IIP HML INF Rm Div. SMB T30 T90-T30 UMD USD
Sentt1 1.00Sentt2 0.49 1.00SM1 −0.11 −0.15 1.00SM2 −0.1 6 −0.19 0.92 1.00B10-T30 −0.04 0.06 0.06 0.03 1.00Baa-Aaa −0.32 −0.04 −0.05 −0.05 −0.01 1.00IIP 0.13 −0.07 0.02 0.00 −0.16 −0.38 1.00HML 0.02 0.04 −0.02 −0.02 0.06 −0.06 0.04 1.00INF −0.31 −0.20 −0.02 −0.06 −0.05 0.16 −0.13 0.01 1.00Rm 0.23 0.18 −0.02 −0.02 0.27 −0.02 −0.09 −0.54 −0.23 1.00Div. 0.22 0.16 −0.04 −0.05 0.33 0.01 −0.10 −0.44 −0.22 0.97 1.00SMB 0.13 0.17 0.07 0.09 −0.19 −0.04 −0.06 −0.52 0.00 0.20 −0.02 1.00T30 −0.09 −0.04 −0.06 −0.04 0.09 0.35 −0.22 −0.05 0.21 −0.12 −0.04 −0.13 1.00T90-T30 −0.12 −0.06 0.03 0.02 0.33 0.27 −0.24 −0.17 0.04 0.12 0.13 0.00 0.17 1.00UMD −0.06 0.00 −0.03 0.01 0.18 −0.08 0.02 −0.24 −0.10 0.07 −0.01 0.21 −0.02 −0.15 1.00USD 0.04 0.02 0.00 −0.01 −0.06 −0.10 0.21 0.18 −0.18 −0.16 −0.16 −0.01 −0.03 −0.09 −0.01 1.00
1136 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
The contemporaneous correlations between the two indexes and individual investor sentimentare higher than those with institutional investor sentiment. This finding gives a priori indication thatindividual investors are more active as noise traders than institutional investors. An alternative inter-pretation of this low correlation is that some of the newsletters are somewhat out of date, dampeningthe correlation for that series. Also, the low correlations among the fundamentals suggest that eachvariable represents the unique risk which is independent from the other. MPRs for both market indexesare negatively correlated with the investor sentiments which a priori may indicate that investor pes-simism leads to increase in MPR. Further, individual investor sentiments seem to have greater effectthan institutional investor sentiment.
5. Econometric methodology
Studies such as Brown and Cliff (2004, 2005) and Lee et al. (2002) suggest that stock market returnsand investor sentiment may act as a system. For this reason, we choose the VAR model by Sims (1980)as an appropriate econometric approach to investigate the postulated relationships. In addition, wetake into consideration the following issues before the estimation stage.
In an efficient financial market, one would expect the reaction of the stock market only to theunanticipated component of explanatory variables. Elton and Gruber (1991) argue all the variables ina multi index model need to be surprises or innovations and therefore should not be predicted fromtheir past values. Thus, asset pricing models such as Arbitrage Pricing Theory (APT) employ the unan-ticipated component (innovations) of explanatory variables. Since, the formulated models are multiindex models; direct estimation in its present form would only give the relationships between theanticipated components. Such estimation would mean ignoring the effect of changes in the unantici-pated components of investor sentiment and stock market returns and therefore could be misleading.To overcome such potential misspecification problems, we use powerful impulse response functions(predicted pattern of surprise changes or innovations) generated from the VAR model.
We express the VAR model as
Z(t) = C +m∑
s=1
A(s)Z(t − m) + ε(t) (4)
where Z(t) is a column vector of variables under consideration, C is the deterministic componentcomprised of a constant, A(s) is a matrix of coefficients, m is the lag length and ε(t) is a vector of randomerror terms. The VAR specification allows the researchers to do policy simulations and integrate MonteCarlo methods to obtain confidence bands around the point estimates (Doan, 1988; Genberg et al., 1987;Hamilton, 1994). The likely response of one variable to a one time unitary shock in another variablecan be captured by impulse response functions.
These impulse response functions are constructed by using the estimated coefficients and makeit possible to trace out the time paths of the effects of pure shocks on a set of series. The y-axismeasured in percentage terms traces the magnitude effect of these shocks i.e., by what percentage aseries increases or decreases in response to one unit shock due to some other series in the system. Thisimplies that the actual unit of these series need not be measured in terms of percentages although. Thex-axis measures the time path of response of a series to shocks. The x-axis is based on the frequencyof data used in the VAR model. In our case, these impulse response functions helps in identifyingby what percentage MPRs change in response to one unit shocks in sentiment on monthly basis. Assuch they represent the behavior of the series in response to pure shocks while keeping the effect ofother variables constant. Since, impulse responses are highly non-linear functions of the estimatedparameters, confidence bands are constructed around the mean response. Responses are consideredstatistically significant at the 95% confidence level when the upper and lower bands carry the samesign.
It is well known theoretically that traditional orthogonalized forecast error variance decomposi-tion results based on the widely used Choleski factorization of VAR innovations may be sensitive tovariable ordering (Koop et al., 1996; Pesaran & Shin, 1996, 1998). To mitigate such potential problemsof misspecifications, we employ the recently developed generalized impulses technique as described
R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145 1137
Table 3Effects of fundamentals on individual investor sentiment. The variables are individual investor sentiment (Sentt1), economicgrowth (IIP), short term interest rates (T30), economic risk premiums (T90), future economic variables (B10), business conditions(Baa), dividend yield (**Div.), inflation (INF), excess returns on the market portfolio (Rm), premium on portfolio of small stocksrelative to large stocks (SMB), premium on portfolio of high book/market stocks relative to low book/market stocks (HML),
momentum factors (UMD), and currency fluctuations (USD) Sentt1t = �0 + �j
∑J
j=1Fundjt + �t .
Dependent variable: Sentt1
Variables Coefficient S.E. t-Statistic Prob.
B10 −0.96 0.88 −1.10 0.27Baa −29.93 8.35 −3.58 0.00IIP 1.30 2.77 0.47 0.64HML 1.44 0.53 2.72 0.01INF −18.28 6.60 −2.77 0.01Rm −6.75 3.29 −2.05 0.04DIV 8.32 3.31 2.51 0.01SMB 2.78 0.80 3.46 0.00T30 7.47 13.57 0.55 0.58T90 −13.11 36.71 −0.36 0.72UMD 0.00 0.00 −0.42 0.68USD 0.00 0.01 −0.32 0.75C 0.29 0.07 3.97 0.00
R-squared 0.30
Akaike info criterion −0.84
Schwarz criterion −0.58
Sum squared resid 3.19Log likelihood 75.94
F-statistic 4.99
Prob (F-statistic) 0.00
by Pesaran and Shin (1998) in which an orthogonal set of innovations which does not depend on theVAR ordering.
6. Estimation results
Before proceeding with the main results, we first check the time series properties of each variable byperforming unit root tests. Table 3 reports the results of unit root tests using Augmented Dickey Fuller(ADF) test (Dickey & Fuller, 1979, 1981). Based on the consistent and asymptotically efficient AIC andSIC criteria (Diebold, 2003) and considering the loss in degrees of freedom, the appropriate numberof lags is determined to be two. In the case of the ADF test, the null hypothesis of non-stationarity isrejected. The inclusion of drift/trend terms in the ADF test equations does not change these results(Dolado et al., 1990).4
Since the focus of this analysis is on the irrational component of sentiment, we follow Baker andWurgler (2006), to decompose the sentiment variables into rational and irrational components basedon fitted and residuals of Eqs. (1) and (2). Specifically, we estimate two separate ordinary least square(OLS) regressions based on Eqs. (1) and (2). Also, the low correlations among the variables related tofundamental (Table 2) suggests that multicollinearity is not an issue.
Table 3 reports that the individual investor sentiment are significantly related to business condi-tions, inflation, dividend yield, excess returns on the market, SMB, and HML. Similarly, Table 4 reportsthat institutional investor sentiments are significantly related to dividend yield, SMB and HML. Theseresults are consistent with the argument of Brown and Cliff (2005) that investor sentiment may containa combination of both rational and irrational components and not necessarily only noise. We generate
4 The unit root rest results are reported in technical Appendix A.
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Table 4Effects of fundamentals on institutional investor sentiment. The variables are individual investor sentiment (Sentiment1) eco-nomic growth (IIP), short term interest rates (T30), economic risk premiums (T90), future economic variables (B10), businessconditions (Baa), dividend yield (Div.), inflation (INF), excess returns on the market portfolio (Rm), premium on portfolio ofsmall stocks relative to large stocks (SMB), premium on portfolio of high book/market stocks relative to low book/market stocks
(HML), momentum factors (UMD), and currency fluctuations (USD) Sentt2t = �0 + �j
∑j
j=1Fundjt + ϑt .
Dependent variable: Sentt2
Variables Coefficient S.E. t-Statistic Prob.
B10 0.49 0.71 0.69 0.49Baa −4.82 8.27 −0.58 0.56IIP −2.22 2.20 −1.01 0.31HML 1.14 0.46 2.48 0.01INF −8.35 6.06 −1.38 0.17Rm −3.60 2.66 −1.35 0.18DIV 4.50 2.71 1.66 0.10SMB 1.99 0.66 2.99 0.00T30 −6.83 11.51 −0.59 0.55T90 −31.70 31.64 −1.00 0.32UMD 0.00 0.00 −0.03 0.98USD 0.00 0.01 0.05 0.96C 0.15 0.07 2.06 0.04
R-squared 0.16
Akaike info criterion −1.09
Schwarz criterion −0.83
Log likelihood 94.78
F-statistic 2.19
Prob (F-statistic) 0.02
the fitted values and residuals for each regression to compute the rational and irrational componentsof individual and institutional investor sentiment.
To analyze the effects of the relative effects of rational and irrational investor sentiment on stockmarket returns, as depicted in Eq. (3), we estimate two separate five variables VAR models with twolags. The first VAR model includes MPR of DJIA while we include MPR of S&P500 in the second VARmodel. The other variables in both these models are rational and irrational sentiment of individualand institutional investors.5
Sims (1980) suggests that autoregressive systems like these are difficult to describe succinctly.Especially, it is difficult to make sense of them by examining the coefficients in the regression equationsthemselves. Likewise, Sims (1980) and Enders (2003) show that the t-tests on individual coefficientsare not very reliable guides and therefore do not uncover the important interrelationships amongthe variables. Sims (1980) recommends focusing on the system’s response to typical random shocksi.e., IRFs. As such, we analyze the relevant IRFs and do not place much emphasis on the estimatedcoefficients of the VAR models and provide the VAR estimation results in the technical appendix B.
We construct the generalized impulse responses from the VAR model to trace the response of onevariable to a one-standard-deviation shock in another variable included in the system. We employMonte Carlo methods to construct confidence bands around the mean response (Doan & Litterman,1986). When the upper and lower bounds carry the same sign, the responses become statisticallysignificant at the 95% confidence level.
Fig. 1a–d plots the impulse responses of MPR variables for DJIA series to one time standard deviationincrease in irrational and rational sentiment of individual and institutional investors. The responsesof MPR to irrational investor sentiment are remarkably similar (Fig. 1a–c), i.e. negative significant in
5 Since, the results of both the VAR models are similar; we report results of the first model only which includes MPR of DJIA.
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Fig. 1. Response of MPR to irrational investor sentiment. (a) Response of MPR of DJIA to irrational sentiment of individualinvestors; (b) response of MPR of DJIA to rational sentiment of individual investors; (c) response of MPR of DJIA to irrationalsentiment of institutional investors; (d) response of MPR of DJIA to rational sentiment of institutional investors. The dashedlines on each graph represent the upper and lower 95% confidence bands. When the upper and lower bounds carry the samesign the response becomes statistically significant. On each graph, “percentage returns” are on the vertical and “horizon” is onthe horizontal axis.
the first month and insignificant thereafter. These results are consistent with the predictions of recentbehavioral asset pricing models on the linkages between irrational optimism/pessimism and rewardto risk ratio. When irrational traders are optimistic, the response of MPR is lower. These results areconsistent with the view that due to lower perceived compensation for bearing a unit amount of risk,there is lesser degree of investment by rational investors. On the other hand, the responses of MPR torational investor sentiment are not statistically significant (Fig. 1b and d). This may perhaps suggest thatwhen everyone on the market holds optimistic beliefs justified by strong economic fundamentals thechanges in MPR are not strong enough to generate a statistically significant response. These findings arealso consistent with economic intuition. For example in Merton’s well-known ICAPM, correct beliefslead to return adjusted for risk to stay positive and constant.6
6 Due to restrictions on short selling it seems there may be an asymmetric relation between irrational sentiment and val-uations. That is, when investors are overoptimistic there is upward pressure on prices that is hard for rational investors toovercome. In the other case of pessimism, it is easier for rational investors to trade against the irrational investors. This suggestsprices are not as likely to deviate below intrinsic value as they are above. In our study we analyze the relative effects of rationaland irrational trading on the market price of risk. Thus any changes in MPR may be due to the numerator or denominator. Ifwe assume the denominator stays constant, than one would conjecture that the market price of risk is not likely to deviatebelow the MPR justified by the fundamental value as they are above. However if the risk accompanies the upward pressureon prices such that the change in risk is greater than the change in prices there may be a decline in MPR. We see an indirectevidence of this from the negative and statistically significant response of MPR to irrational sentiments. We also see a positivebut statistically insignificant response of MPR to rational sentiments. These results may perhaps serve as a partial explanationto this conjecture. On the other hand, recent stock market events unfolding in October 2008 provide further evidence on howextensive such market declines can be. We thank an anonymous referee for this point.
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Fig. 2. Response of rational (irrational) investor sentiment to irrational (rational) investor sentiment. (a) Response of rationalsentiment to irrational sentiment of individual investors; (b) response of rational sentiment to irrational sentiment of insti-tutional investors; (c) response of irrational sentiment to rational sentiment of individual investors; (d) response of irrationalsentiment to rational sentiment of institutional investors. The dashed lines on each graph represent the upper and lower 95%confidence bands. When the upper and lower bounds carry the same sign the response becomes statistically significant. Oneach graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis.
Our results are also consistent with Brown and Cliff (2004), which find weak relationship betweensentiment and market return in the short run but stronger evidence in case of long run. Specifically,Brown and Cliff (2004) have mainly analyzed short term return predictability by using weekly dataand long-term relationship by employing monthly data. In case of weekly data, they find weak rela-tionship. However, in case of monthly data they find significant negative relationship at the 10%level with a monthly lag of one and 5% level in the joint test. In our case also, using monthly datawe find significant negative effect of irrational sentiment on market price of risk during the firstmonth.
Next, we investigate the lead–lag relationships between the rational and irrational sentiment forboth individual and institutional investors. Fig. 2a and b plots the impulse responses of the rationalto irrational sentiment and Fig. 2c and d plots the response of irrational to rational sentiment. Theresponse of rational sentiment to irrational sentiment for both individuals and institutions are negativeand significant in the second month and insignificant thereafter. However, the responses of irrationalto rational sentiment are not significant. These results are consistent with the earlier results that whenirrational traders are optimistic, MPR is lower and hence there is lesser investment by rational investors.The negative response of rational sentiment to irrational sentiment reflects the contrary opinion rules(Solt & Statman, 1988) that a rational investor is contrarian, selling when most of the investors arebullish and buying when they are bearish. However, the response of irrational sentiment to the rationalsentiment is insignificant in both the cases. This implies that irrational traders’ sentiments are notdriven by rational sentiments.
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In summary, the results indicate that irrational sentiment of individual and institutional investorsbear significant negative relationships with both MPR and rational sentiment of arbitrageurs. Thesefindings imply that irrational optimism causes an increase in volatility in the market which decreasesthe mean variance ratio leading to lower MPR. The lower compensation for bearing volatility in turndeters rational investors for making greater investments. Our findings are robust for both individualsand institutions.
The finding of statistically insignificant MPR response to changes in rational sentiment and a sig-nificant negative response to irrational is consistent with the well-known Markowitz mean-varianceportfolio theory (1959). In response to sentiment backed by economic fundamentals, perhaps, the risein the numerator is in equal proportion to the rise in the denominator, hence keeping MPR constant,and displaying the positive relation between mean and variance. In response to irrational trading, therelation between mean-variance is still positive, but the denominator rises faster than the numeratorcausing MPR to decline. Thus, irrational trading can distort the price of risk causing it to move awayfrom the rates justified by economic fundamentals. This may then imply that in times when suchsentiment are high, irrational trading can therefore contribute to the formation of bandwagons andbubbles in equity markets which may eventually lead to a market crisis. Certainly, there is evidence ofa close relation between distortion of the pricing of risk and market crisis. One such evidence is fromthe housing market where some circles have attributed such mispricing of risk to the housing marketcrisis.7,8,9
7. Conclusion
In this study, we investigate the relative effects of rational and irrational sentiment of individualand institutional investors on MPR for DJIA and S&P500. Unlike previous studies which conjectureinvestor sentiment as fully irrational, we find that the individual and institutional investor sentimentsare driven by both rational and irrational factors. Overall, we find the following results after estimatinga six variable VAR model using the generalized impulses. First, consistent with Yu and Yuan (2005),irrational optimism leads to a significant downward revision in MPR perhaps due to the excess volatil-ity generated from such trading activities. Second, unlike studies that find significant stock returnresponses to sentiment, we find that rational investor sentiment have an insignificant effect on MPR.This may perhaps suggest that when everyone on the market holds correct positive beliefs, the result-ing response of MPR is not significant enough to generate any statistically meaningful response. This isconsistent with Markowitz’s mean-variance analysis and Merton’s ICAPM where if traders happen tohave correct beliefs, the return adjusted for risk stays positive and constant. Third, consistent with Soltand Statman (1988), rational investors are bullish (bearish) when noise traders are bearish (bullish)reflecting the contrarian investment strategies. Fourth, there are insignificant responses of irrationaloptimism and pessimism to rational investor sentiment suggesting that indeed irrationality in themarket is not driven by risk factors. These results are robust for both market indexes, DJIA and S&P500and for both individual and institutional investor sentiment. The empirical results are consistent withthe notion that market’s reaction to volatility is not homogenous and is affected by changes in investorsentiment.
These findings are also consistent with the predictions of recent asset pricing models that linkinvestors’ irrationality to MPR and arbitrage. When irrational investors are optimistic, the stock pricesare overvalued but irrational investors believe that prices are undervalued. In such case MPR is lowersince mean of returns is damped down and the prices are pushed up. Due to lower compensation forbearing risk, in such scenario, the incentive to carry out arbitrage is less for rational traders. However,when irrational investors are pessimistic, there is an increase in MPR which attracts greater wealth ofrational arbitrageurs in the market.
The results have several important practical implications for investors and policymakers. Individ-ual investors should place greater priority to trades based on economic fundamentals. Policymakers
7 “IMF chief sees hope in market reckoning,” Financial Times, 10 September 2007.8 “Ways to fix the financial system,” Financial Times, 28 January 2008.9 “What banks can learn from this credit crisis,” Financial Times, February 05, 2008.
1142 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
amongst other indicators should regularly track the pricing of risk and check its consistency witheconomic fundamentals when devising the correct timing and strength of future policies.10
There are some limitations on the use of II survey data as a direct measure of institutional investorsentiment. While professionals “write the newsletters, the primary audience is likely to be individ-ual investors. Given the biases documented in analyst stock recommendations and earnings forecasts,it seems plausible that what the newsletter recommendations say may not react what institutionsactually do. It is quite possible that institutions might not react in line with the newsletter writ-ers’ sentiment. On the contrary, since the primary audiences of these newsletters are individualinvestors, these recommendations might be a proxy for individual investors. This possibly explainssimilar results obtained for both these two sentiment indexes (individual and institutional). However,at the same time presently this is the best available proxy for direct measure of institutional investorsentiment.
Appendix A. Technical Appendix A
Unit root tests. The variables are individual investor sentiment (Sentt1), institutional investor senti-ment (Sentt2), returns on Dow Jones Industrial Average (DJIA), returns on S&P 500(S&P500), economicgrowth(IIP), short-term interest rates (T30), economic risk premiums (T90-T30), future economic vari-ables (B10-T30), business conditions (Baa-Aaa), dividend yield (Div.), inflation (INF), excess returns onthe market portfolio (Rm), premium on portfolio of small stocks relative to large stocks (SMB), pre-mium on portfolio of high book/market stocks relative to low book/market stocks (HML), momentumfactors (UMD), and currency fluctuations (USD).
ADF test results
Sentt1 −5.3961Sentt2 −4.0900SM1 −6.3824SM2 −6.2906IIP −4.3423T30 −2.7501T90-T30 −6.0545B10 T30 −6.1286Baa-Aaa −3.2106Div. −7.0608INF −6.3713Rm −7.1703SMB −8.4732HML −5.5059UMD −6.6781USD −6.8499
Test critical values1% level −3.47485% level −2.880910% level −2.5772
Appendix B. Technical Appendix B
AR estimates. The variables are rational sentiment of individual investors (Sent1 r), irrational senti-ment of individual investors (Sent1 ir), rational sentiment of institutional investors (Sent2 r), irrationalsentiment of institutional investors (Sent2 ir), MPR for Dow Jones Industrial Average (SM1). Note *, **and *** denote significance levels at the 10%, 5% and 1%, respectively. Standard errors are in parentheses.
10 Recently, some practitioners and news commentators have suggested for example that the Federal Reserve’s excessivereliance on interest rate changes should give way to broader approach and that the Federal Reserve should revisit the conven-tional wisdom (“A route back to potency for central banks,” Financial Times, 16 January 2008).
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Sent1 ir Sent2 ir Sent1 r Sent2 ir SM1
Sent1 ir(-1) 0.5947*** 0.0890 −0.0346 −0.1807 −0.1894***(0.1690) (0.1088) (0.2846) (0.1895) (0.0779)
Sent1 ir(-2) 0.1130 −0.0865 −0.0764 −0.0451 0.1562(0.1693) (0.1090) (0.2850) (0.1898) (0.1211)
Sent2 ir(-1) −0.1481 0.2919* −0.0946 0.7111*** −1.3063*(0.2450) (0.1577) (0.4125) (0.2747) (0 6930)
Sent2 ir(-2) −0.0275 0.1179 0.4225 −0.0121 0.9989(0.2594) (0.1670) (0.4367) (0.2908) (0.2496)
Sent1 r(-1) −0.0383 −0.0255 0.2496*** −0.0334 0.5792(0.0551) (0.0355) (0.0928) (0.0618) (0.6903)
Sent1 r(-2) 0.0658** 0.0616* 0.1164 0.1108* −0.0531(0.0550) (0.0354) (0.0926) (0.0617) (0.6889)
Sent2 r(-1) −0.1085 −0.0698 0.2484* 0.4847*** 0.6157(0.0808) (0.0520) (0.1361) (0.0906) (1.0124)
Sent2 r(-2) 0.0371 0.0342 −0.1466 0.2055*** 0.6451(0.0790) (0.0509) (0.1331) (0.0886) (0.9903)
SM1(-1) 0.0028 0.0014 −0.0290** −0.0163* −0.1765*(0.0087) (0.0056) (0.0147) (0.0098) (0.1092)
SM1(-2) −0.0024 −0.0044 0.0103 −0.0015 0.0956(0.0083) (0.0053) (0.0139) (0.0093) (0.1035)
C −0.0507*** −0.0560*** 0.0031 0.0250 −0.7583***(0.0219) (0.0141) (0.0369) (0.0246) (0.2746)
R-squared 0.3652 0.2084 0.1986 0.5083 0.0650
Sum sq. resids 0.8505 0.3526 2.4110 1.0693 133.5074
S.E. equation 0.0822 0.0529 0.1383 0.0921 1.0294
F-statistic 7.2490 3.3170 3.1218 13.0230 0.8763
Log likelihood 153.7136 214.0321 82.3422 138.0341 −192.6256
Akaike AIC −2.0834 −2.9640 −1.0415 −1.8545 2.9726
Schwarz SC −1.8490 −2.7295 −0.8070 −1.6201 3.2071
Mean-dependent −0.1174 −0.0912 −0.0019 −0.0029 −0.6867
References
Abel, A. (2002). An exploration of the effects of pessimism and doubt on asset returns. Journal of Economic Dynamics and Control,26, 1075–1092.
Andrew, L. (2002). The statistics of Sharpe ratios. Financial Analysts Journal, 58(4), 36–52.Baker, & Wurgler. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680.Barberis, N., Shleifer, A., & Vishny, R. W. (1998). A model of investor sentiment. Journal of Financial Economics, 49, 307–343.Barro, R. (1978 August). Unanticipated money, output and the price level in the United States. Journal of Political Economy,
549–580.Barro, R., & Rush, A. (1980). Unanticipated money, and economic activity. In Stanley Fischer (Ed.), Rational Expextations and
Economic Policy (pp. 23–54). University of Chicago Preess.Basak, S. (2005). Asset pricing with heterogenous beliefs. Journal of Banking and Finance, 29, 2849–2881.Black, F. (1986). Noise. Journal of Finance, 41(3), 529–543.Brown, G. (2008, Jan.). Ways to fix the financial system. Financial Times, 28.Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1–27.Brown, G. W., & Cliff, M. T. (2005). Investor sentiment and asset valuation. Journal of Business, 78(2), 405–440.Buraschi, A., & Jiltsov A. (2002). Option volume and difference in beliefs. Working Paper, London Business School.Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18, 373–399.Campbell, J. Y. (1991). A variance decomposition for stock returns. Economic Journal, 101, 157–179.Campbell, J. Y., & Kyle, A. S. (1993). Smart money, noise trading, and stock price behavior. Review of Economic Studies, 60, 1–34.Campbell, J. Y., & Shiller, R. J. (1988a). The dividend-price ratio and expectations of future dividends and discount factors. Review
of Financial Studies, 1, 195–228.
1144 R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145
Campbell, J. Y., & Shiller, R. J. (1988b). Stock prices, earnings and expected dividends. Journal of Finance, 43, 661–676.Cecchetti, S. G., Lam, P.-S., & Mark, N. C. (2000). Asset pricing with distorted beliefs: Are equity retursn too good to be true.
American Economic Review, 90, 787–805.Chen, N., Kan, R., & Miller, M. H. (1993). Are the discounts on closed-end funds a sentiment index? Journal of Finance, 48, 795–800.Clarke, R. G., & Statman, M. (1998). Bullish or bearish? Financial Analysts Journal, 63–72.Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and overreactions. Journal
of Finance, 53, 1839–1886.DeBondt, W. (1993). Betting on trends: Intuitive forecasts of financial risk and return. International Journal of Forecasting, 9,
355–371.De Long, J. B., Shleifer, A. M., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political
Economy, 98, 703–738.De Long, J., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1991). The survival of noise traders in financial markets. Journal of
Business, 64(1), 1–19.Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the
American Statistical Association, 74, 427–431.Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49,
1057–1072.Diebold, F. X. (2003). Elements of forecasting. South Western College Publishing.Doan, T. (1988). RATS user’s manual. Evanston, Illinois: VAR Econometrics.Doan, T. & Litterman, R. (1986). User’s Manual RATS: Version 2.0. Evanston, IL: VAR Econometrics.Dolado, J. J., Jenkinson, T., & Sosvilla-Rivero, S. (1990). Cointegration and unit roots. Journal of Economic Surveys, 4, 249–273.El-Erian, M. (2008, Jan.). A Route Back to Potency for Central Banks. Financial Times.Elton, E. J., & Gruber, M. J. (1991). Modern portfolio theory and investment analysis (4th ed.). John Wiley and Sons Inc.Elton, E. J., Gruber, M. J., & Busse, J. A. (1998). Do investors care about sentiment? Journal of Business, 71, 477–500.Enders, W. (2003). Applied econometrics time series. John Wiley and Sons Inc.Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383–417.Fama, E. F. (1990). Term structure forecasts of interest rates, inflation, and real returns. Journal of Monetary Economics, 25, 59–76.Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22, 3–25.Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics,
25, 23–49.Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33,
3–56.Fama, E. F., & Schwert, G. W. (1977). Asset returns and inflation. Journal of Financial Economics, 5, 115–146.Ferson, W. E., & Campbell, R. H. (1991). The variation in economic risk premiums. Journal of Political Economy, 99, 385–415.Fisher, K. L., & Statman, M. (2000 March/April.). Investor sentiment and stock returns. Financial Analysts Journal, 16–23.Garrett, I., Kamstra, M. J., & Kramer, L. A. (2005). Winter blues and time variation in MPR. Journal of Empirical Finance, 12, 291–316.Gemmill, G., & Thomas, C. D. (2002). Noise trading, costly arbitrage, and asset prices: evidence from closed end funds. Journal of
Finance, 6, 2571–2594.Genberg, H., Salemi, M. K., & Swoboda, A. (1987). The relative importance of foreign and domestic disturbances for aggregate
fluctuations in open economy: Switzerland 1964-1981. Journal of Monetary Economics, 19, 45–67.Giordani, P., & Soderlind P. (2003). Is there evidence of pessimism and doubt in subjective distribution? A comment on Abel. CPER
discussion paper 4068.Girard, E., Rahman, H., et al. (2003). On MPR in Asian capital markets around the Asian flu. International Review of Financial
Analysis, 142, 1–25.Gonzalez, F. (2008, Feb.). What banks can learn from this credit crisis. Financial Times, 05.Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.Hirshleifer, D. (2001). Investor psychology and asset pricing. Journal of Finance, 56, 1533–1597.Hodrick, R. (1992). Dividend yields and expected stock returns: Alternative procedures for inference and measurement. Review
of Financial Studies, 5, 357–386.Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading and overreaction in asset markets. Journal
of Finance, 54, 2143–2184.Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal
of Finance, 48, 65–91.Jouini, E., & Napp, C. (2005). Heterogenous beliefs and asset pricing in discrete time: An analysis of pessimism and time. Journal
of Economic Dynamics and Control.Keim, D. B., & Stambaugh, R. F. (1986). Predicting returns in the bond and stock markets. Journal of Financial Economics, 17,
357–390.Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in non linear multivariate models. Journal of Econo-
metrics, 74, 119–147.Lakonishok, J., Shleifer, A., Vishny, R.W. (1991). Do Institutional investors destabilize stock prices? Evidence on herding and feedback
Trading. Working Paper, NBER 3846.Lee, C., Shleifer, A., & Thaler, R. (1991). Investor sentiment and the closed-end fund puzzle. Journal of Finance, 46, 75–109.Lee, W. Y., Jiang, C. X., & Indro, D. C. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of
Banking & Finance, 26, 2277–2299.Li, Y., & Zhong, M. (2005). Consumption habit and international stock returns. Journal of Banking and Finance, 29, 579–601.Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. Journal of Finance.Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments. New York: Wiley.Mehra, R., & Prescott, E. (1985). The equity premium: A puzzle. Journal of Monetary Economics, 15, 145–162.Neal, R., & Wheatley, S. (1998). Do measures of investor sentiment predict stock returns? Journal of Financial and Quantitative
Analysis, 34, 523–547.
R. Verma, G. Soydemir / The Quarterly Review of Economics and Finance 49 (2009) 1129–1145 1145
Pagan, A. (1984 February). Econometric issues in the analysis of regressions with generated regressors. International EconomicReview, 221–247.
Palomino, F. (1996). Noise trading in small markets. Journal of Finance, 51(4), 1537–1550.Pavlova, A., & R. Rigobon (2003). Asset prices and exchange rates. Working paper, NBER No. W9834.Pesaran, M. H., & Shin, Y. (1996). Cointegration and speed of convergence to equilibrium. Journal of Econometrics, 71, 117–143.Pesaran, M. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58,
17–29.Poitras, M. (1997). Expectations and monetary neutrality: An empirical reexamination. Southern Economic Journal, 63, 920–928.Schwert, G. W. (1990). Stock returns and real activity: A century of evidence. Journal of Finance, 45, 1237–1257.Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 425–442.Sharpe, W. F. (1994). MPR. Journal of Portfolio Management, 21, 49–58.Sharpe, S. A. (2002). Reexamining stock valuation and inflation: The implications of analysts’ earnings forecasts. The Review of
Economics and Statistics, 84, 632–648.Shefrin, H., & Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29(3),
323–349.Shleifer, A., & Summers, L. (1990). The noise trader approach to finance. Journal of Economic Perspectives, 4(2), 19–33.Sias, R. W., Starks, L. T., & Tinic, S. M. (2001). Is noise trader risk priced? The Journal of Financial Research, 24(3), 311–329.Sims, C. (1980). Macroeconomic and reality. Econometrica, 48, 1–49.Solt, M. E., & Statman, M. (1988). How useful is the sentiment Index? Financial Analysts Journal, 45–55.Soydemir, G. (2005). Differences in MPR and the resulting response to shocks: An analysis of Asian markets. Journal of International
Financial Markets, Institutions and Money, 15, 285–313.Swaminathan, B. (1996). Time-varying expected small firm returns and closed-end fund discounts. Review of Financial Studies,
9, 845–887.Thornhill, J. (2007 Sept.). IMF chief sees hope in market reckoning. Financial Times, 10.Trueman, B. (1988). A theory of noise trading in securities markets. Journal of Finance, 43(1), 83–95.Weil, P. (1989). The equity premium puzzle and the risk free rate puzzle. Journal of Monetary Economics, 24, 401–421.Yu, J., & Y. Yuan (2005). Investor sentiment and mean-variance relation. Working paper, Wharton School of University of Pennsyl-
vania.