Rational expectations, irrational exuberance: Linkage between U.S. investors and Pacific-Basin stock returns The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 67 RATIONAL EXPECTATIONS, IRRATIONAL EXUBERANCE: LINKAGE BETWEEN U.S. INVESTORS AND PACIFIC-BASIN STOCK RETURNS Rahul Verma The University of Houston, United States of America Abstract We shed new light on the relevance of rational expectations and irrational exuberance of U.S. individual and institutional investors on Pacific-Basin stock returns. We find insignificant effects of irrational exuberance and significant effect of rational expectations on Asian markets with varying degrees of intensity. There are greater responses of Hong Kong, Malaysia, Philippines, and Singapore while weaker linkages with Taiwan, Thailand, and Korea. Overall evidence suggests that rational expectations of institutional investors are transmitted to a greater extent than those of individual investors. These results are consistent with the view that international effects of the U.S. market can be attributed to rational investor sentiments. Key words: Stock returns, Investor sentiment, VAR model, Asia Pacific markets JEL Classification: G12, G14, C22 1. Introduction The central task in financial economics is to identify the systematic risks that drive asset prices and expected returns (Campbell, 2000; Cochrane, 2000). However, in recent years there has been a growing debate on the possible linkages between the behavioral aspects of investors and stock prices. Financial economics has become more receptive to imperfect rational explanations, and investor psychology has emerged as a major determinant of asset prices. After decades of study, the sources of risk premium in purely rational models are well understood; while, dynamic psychology based asset pricing theories are still in IJBF 68 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 the infancy stage. This debate surrounding asset pricing has identified two prime suspects in setting stock prices: fundamentals and investor sentiments. The theoretical framework describing the role of investor sentiments in determining stock prices is provided by researchers such as Black (1986), Trueman (1988), DeLong, Shleifer, Summers and Waldman [DSSW henceforth] (1991, 1990), Shleifer and Summers (1990), Lakonishok, Shleifer and Vishny (1991), Campbell and Kyle (1993), Shefrin and Statman (1994), Palomino (1996), Barberis, Shleifer and Vishny (1998), Daniel, Hirshleifer and Subramanyam (1998) and Hong and Stein (1999). A direct implication of these studies is certain groups of investors (noise traders), who often do not make investment decisions based on a company’s fundamentals, are capable of affecting stock prices by way of unpredictable changes in their sentiments. Following the ‘noise trader model’ of DSSW (1990), several empirical studies examine the influence of investor sentiments on stock prices (Brown and Cliff, 2004a, 2004b; Lee et al. 2002; Fisher and Statman, 2000; Clarke and Statman, 1998; Solt and Statman 1988; De Bondt, 1993). Overall, these studies provide evidence in favor of strong co-movements between investor sentiment and the stock market returns recognizing the existence of individual investor sentiments, as well as institutional investor sentiments. The previous research mainly focuses on the effect of investor sentiments on the U.S. market while less attention has been given to its relevance in the international context. For example, little has been done to understand the degree of the relationship between the U.S. individual and institutional investor sentiments and Pacific-Basin stock returns. Given strong empirical evidence on the strengthening response of Pacific-Basin stock markets to the U.S. market over time (Soydemir, 2005; Kim, 2003; Ratanapakorn and Sharma, 2002; Janakiramanan and Lamba, 1998; Park and Fatemi, 1993) it is important to analyze whether the expectations of the U.S. investors is an important player in propagating U.S. stock market movements abroad. We shed new light on the relevance of the rational expectations and irrational exuberance of the U.S. investors in determining stock returns of Hong Kong, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Korea. Using the investor sentiments data at the individual and institutional level, provided by American Association of Individual Investors and Investors Intelligence and the vector auto regression (VAR) models we find the following results: first, we do not find any significant effect of irrational exuberance of the U.S. investors on Pacific-Basin stock market returns. Second, we find significant relationship of varying degrees of strength between the rational expectations of the U.S. individual and institutional investors and Asian stock returns except in the cases of Taiwan and Thailand. Third, there are greater effects of rational expectations of the institutional investors than individual investors on theses stock returns. These findings suggest that the international effects of the U.S. stock market can be attributed to fundamental trading and not to noise trading in the U.S. market. These results are consistent with the view that investor sentiment in the U.S. is an important player in propagating U.S. stock market The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 69 movements abroad. Therefore it is important for policymakers to consider such spillover effects in their international policy making decisions and for investors in their portfolio allocation decisions involving stock markets movements. This remainder of this paper is organized as follows: section two reviews the existing literature on investor sentiments and stock prices while section three presents the model. Section four presents the data. Section five reviews the econometric methodology. Section six presents empirical findings. Section seven concludes. 2. Previous work on investor sentiments and stock prices The concept of investor sentiments, noise trading and its role in the financial markets is first given by Black (1986). Black (1986) labels non rational investors as “noise traders”, who have no access to inside information, and act irrationally in response to news that conveys little information about fundamentals. However, there are two opposing views in the literature on the relevance of noise traders in determining stock prices. Based on Friedman (1953) and Fama (1965), it is argued that noise traders are irrelevant and cannot survive since they are driven out of the market by rational arbitrageurs. For example, West (1988) states “there is little direct evidence that trading by naïve investors plays a substantial role in stock price determination”. On the contrary, Black (1986) and Trueman (1988) argue that noise traders induce necessary liquidity in the market, and therefore provide incentives for informed investors to trade. The notable work of DSSW (1990, and 1991) models the influence of noise trading on equilibrium prices. They argue that noise traders falsely believe that they have special information about the future prices and the unpredictability of their sentiments brings an additional risk in the market. They may get pseudo signals from analysts, brokers, consultants and irrationally believe that these signals carry information. Their ‘noise trader’ model shows that a non- fundamental factor exists in the form of investor sentiments that is priced in equilibrium. Furthermore, noise traders as a group can earn expected returns higher than rational investors and can also survive in terms of wealth gain in the long run, due to unpredictability in their sentiments. Campbell and Kyle (1993) model the competitive interaction between noise and informed traders and its consequent effect on stock prices. Shleifer and Summers (1990) present an alternative to the efficient markets paradigm that stresses the role of investor sentiments and limited arbitrage in determining stock prices. They show that the assumption of limited arbitrage is more plausible as a description of risky asset markets than the assumption of complete arbitrage on which market efficiency hypothesis is based. This implies that changes in investor sentiments are not fully countered by arbitrageurs therefore may affect stock returns. Similarly, Shefrin and Statman (1994) show the interaction between noise and informed traders and present the behavioral capital asset pricing theory. They argue that in contrast to information, sentiments of noise traders’ act as a second driver which takes the market away from efficiency. 70 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 Lakonishok, Shleifer and Vishny (1991) find that in small markets institutional investors influence prices. Along the same lines, Palomino (1996) extends the DSSW (1990) model for an imperfectly competitive market and shows that noise traders may earn higher return and obtain higher expected utility than rational investors. Wang (2001) examines the dynamics of non-rational investors find that bullish sentiments can survive while bearish sentiment cannot survive in the long run. Overall, these models suggest that the unpredictability in investor sentiments of noise traders acting as a group can introduce a systematic risk that is priced in markets. Following these predictions, several empirical studies have examined the role of investor sentiments on stock pricing. These studies have either used indirect measures or direct measures of investor sentiments. Studies using indirect measures include the following proxies: close-ended fund’s discount (Gemmill and Thomas, 2002; Baker & Wurgler, 2005; Sias, Starks and Tinic, 2001; Neal and Whitney, 1998; Swaminathan, 1996; Elton, Gruber and Busse, 1998; Chan, Kan and Miller, 1993; Lee, Shleifer and Thaler, 1991); market performance based measures (Brown and Cliff, 2004a); trading activity based measures (Brown and Cliff, 2004a; Neal and Whitney, 1998); derivative variables (Brown and Cliff, 2004a); dividend premium (Baker and Wurgler, 2005); and IPOs related measures (Baker & Wurgler, 2005; Brown and Cliff, 2004a). Overall these studies do not provide a consensus on whether the proxies chosen are appropriate measures of investor sentiment and also show mixed results in their debate on the linkages between sentiments and stock returns. Studies using direct measures employ sentiment surveys data that indicate the expectations of market participants. Research related to individual investors sentiments find strong co-movements with stock market returns (Brown and Cliff, 2004a; De Bondt, 1993) and mixed results regarding its role in short term predictability of stock prices (Brown and Cliff, 2004a; Fisher and Statman, 2000). Similarly, studies examining institutional sentiments find strong co-movements with stock market returns (Brown and Cliff, 2004a) and mixed results regarding its short run implications on stock prices (Brown and Cliff, 2004a; Lee, Jiang and Indro, 2002; Clarke and Statman, 1998; Solt and Statman, 1988). Also, Brown and Cliff (2004b) examine the long run implications of institutional investor sentiments and find strong relationships with long horizon stock returns. Overall, these studies provide powerful and consistent empirical support for the hypothesis that stock prices are affected by individual and institutional investor sentiments. 3. Model Previous studies suggest that some shifts in investor sentiments are fully rational i.e., expectations based on the risk factors, while some are irrational exuberance, induced by the noise (Baker and Wurgler, 2005; Brown and Cliff, The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 71 2004b, Shleifer and Summers, 1990). Hirshleifer (2001) also relates expected returns to both risks and investor mis-valuation. When an investor is bullish or bearish, then this could be a rational reflection of future period’s expectation or irrational enthusiasm or a combination of both. Therefore it is quite possible that international stock returns are affected by both rational (risk based) and irrational (noise) components of the U.S. investor sentiments. We follow the approach of Baker and Wurgler (2005) to capture the irrational component of investor sentiments by regressing sentiment indicators to a set of risk factors and computing the residuals. Accordingly, we formulate equations (1) and (2): (1) (2) where γ 0 and θ 0 are constants, γ j and θ j are the parameters to be estimated; ζ t and t are the random error terms. Sentt 1t and Sentt 2t represent the shifts in sentiments of individual and institutional investors respectively at time t. Fund jt is the set of fundamentals representing rational expectations based on risk factors that have been shown to carry non-redundant information in conditional asset pricing literature. The fitted values of equations (1) and (2) capture the rational component of sentiments (i.e. and ). On the other hand the residual of equations (1) and (2) capture the irrational component of sentiments (i.e. ζ t and t ). Next, we analyze the extent to which Pacific-Basin stock returns are affected by rational expectations and irrational exuberance of the U.S. individual and institutional investors. Accordingly, the sentiment variables are decomposed into the rational and irrational components based on equations (1) and (2) and included in the return generating process as: (3) where α 0 is a constant while α 1 , α 2 , α 3 and α 4 are the parameters to be estimated; ρ t is the random error term. R it is the returns for the ith Pacific- Basin stock market at time t. Specifically, the parameters α 1 and α 2 capture the effects of rational expectations on part of individual and institutional investors respectively; while α 3 and α 4 capture the effects of irrational exuberance of individual and institutional investors respectively. We also place importance on jointly modeling the sentiments of individual and institutional investors to avoid misspecification. Specifically, shocks originating from sentiments of one class of investors not considered might mistakenly be seen as a disturbance originating from sentiments of another class of investors in the analysis. ∂ 1̂tSentt 2̂tSentt ∂ 0 1 1 2 2 3 4 ˆ ˆ it t t t t tR Sentt Senttα α α α ξ α � ρ= + + + + + 1 0 1 J t j jt t j Sentt Fundγ γ ξ = = + +∑ 2 0 1 J t j jt t j Sentt Fundθ θ ϑ = = + +∑ 72 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 4. Data We obtain all data in monthly intervals from October 1988 to April 2004. To measure sentiments of market participants, we employ survey data similar to the ones used in the literature. The institutional investors participate in the market for living while the individual investors’ primary line of business is outside the stock market (Brown and Cliff, 2004a). Our choice of individual investor sentiment index is based on Brown and Cliff (2004a), Fisher and Statman (2000) and DeBondt (1993) which use the survey data of American Association of Individual Investor (AAII). Beginning July 1987, AAII conducts a weekly survey asking for the likely direction of the stock market during the next six months (up, down or the same). 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. These results are published as ‘investor sentiment’ in monthly editions of AAII Journal. The sentiment index for individual investors is computed as the spread between the percentage of bullish investors and percentage of bearish investors (Bull- Bear). Since this survey is targeted towards individual investors, it is primarily a measure of individual investor sentiments. Our choice of institutional investor sentiment index is based on Brown and Cliff (2004a, 2004b), Lee et al. (2002), Clarke and Statman (1998) and Solt and Statman (1988) which use the survey data of Investors Intelligence (II), an investment service based in Larchmont, New York. II compiles and publishes data based on a survey of investment advisory newsletters. To overcome the potential bias problem towards buy recommendation, letters from brokerage houses are excluded. Based on the future market movements the letters are labeled as bullish, bearish or correction (hold). The sentiment index for the institutional investor is found by calculating the spread between the percentage of bullish investors and percentage of bearish investors. Because authors of these newsletters are market professionals, the II series is interpreted as a proxy for institutional investor sentiments. We analyze the response of seven Pacific-Basin stock markets, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand and Korea. The market variable identified for these countries are the major indexes in their respective stock markets. The continuously compounded returns are computed from the local currency denominated stock market indexes obtained from the Datastream. We include the following variables as risk factors that have been shown to carry non-redundant information in the asset pricing literature: (i) Economic growth (Fama, 1970; Schwert, 1990) measured as the monthly changes in the industrial production index (ii) Short term interest rates (Campbell, 1991) measured as the yield on one month U.S. Treasury Bill (iii)Economic risk premia (Ferson and Harvey, 1991; Campbell, 1987) measured as the term structure of interest rates (difference in monthly yields on three month and one month Treasury bills (iv) Future economic expectations variables (Fama, 1990) measured as the term spread (yields spread on the 10 year U.S. Treasury bond The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 73 and three month Treasury bill) (v) Business conditions (Fama and French, 1989; Keim and Stambaugh, 1986) measured as the default spread (difference in yields on Baa and Aaa corporate bonds)(vi) Dividend yield (Hodrick, 1992; Fama and French, 1988; Campbell and Shiller, 1988a, 1988b) measured as the dividend yield for the value weighted Center for Research in Security Prices (CRSP) index over the past 12 months (vii) Inflation (Sharpe, 2002; Fama and Schwert, 1977) measured as the monthly changes in the consumer price index (viii) Excess returns on market portfolio(Lintner, 1965; Sharpe, 1964) measured as the value-weighted returns on all NYSE, AMEX, and NASDAQ stocks minus the one-month Treasury bill rate (ix) Premium on portfolio of small stocks relative to large stocks (SMB) (Fama and French, 1993). SMB (Small minus Big) is the average return on three small portfolios minus the average return on three big portfolios (x) Premium on portfolio of high book/market stocks relative to low book/market stocks (HML) (Fama and French, 1993). This Fama/French benchmark factor is constructed from six size/book-to-market benchmark portfolios that do not include hold ranges and do not incur transaction costs. HML (High minus Low) is the average return on two value portfolios minus the average return on two growth portfolios. (xi) Momentum factor (UMD) (Jegadeesh and Titman, 1993). UMD (Up minus Down) is the average return on the two high prior return portfolios minus the average return on the two low prior return portfolios (xii) Currency fluctuation (Elton and Gruber, 1991) measured as the changes in 15-country trade weighted basket of currencies. 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 fluctuations are obtained from Federal Reserve Bank of St. Louis; dividend yield and excess return on market portfolio 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 Sntt 1 and Sntt 2 are approximately 11% and 9% respectively. This suggests both individual and institutional investors have been bullish during most of the sample period. Interestingly, individual investors have been more bullish than institutional investors. The standard deviations of Pacific-Basin stock markets are very high indicating their extremely volatile nature during the sample period. Among these markets Hong Kong seems to have provided the highest return to investors, while the mean returns in case of Thailand and Korea are negative. Most of the variables relating to the risk factors have shown less variability as compared to the investor sentiments and Asian stock returns. Table 1: Descriptive Statistics The variables are individual investor sentiments (Sentt 1 ), institutional investor sentiments (Sentt 2 ), 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 market portfolio (R m ), premium 74 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 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), currency fluctuations (USD), and stock market returns on Hong Kong (Hong Kong), Malaysia (Malaysia), Philippines (Philippines), Singapore (Singapore), Taiwan (Taiwan), Thailand (Thailand), and Korea (Korea). Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Sentt 1 0.1143 0.1200 0.5100 -0.3500 0.1760 -0.0863 2.6626 Sentt 2 0.0896 0.1100 0.3640 -0.3420 0.1413 -0.5373 2.9513 IIP 0.0096 0.0147 0.1011 -0.1094 0.0389 -0.5279 3.5607 T30 0.0026 0.0032 0.0199 -0.0121 0.0052 -0.1152 3.2783 T90-T30 0.0043 0.0041 0.0080 0.0021 0.0013 0.4793 2.9139 B10_T30 0.0004 0.0004 0.0017 -0.0003 0.0004 0.8185 3.7719 Baa-Aaa 0.0071 0.0078 0.0549 -0.0440 0.0181 -0.0562 3.1558 Div 0.0078 0.0073 0.0144 0.0053 0.0018 1.1020 4.3580 INF 0.0127 0.0153 0.1141 -0.1437 0.0408 -0.4639 3.9027 R m 0.0026 0.0023 0.0103 -0.0012 0.0021 0.9335 4.3616 SMB 0.0031 0.0077 0.0994 -0.1655 0.0414 -0.7543 4.3240 HML -0.0012 -0.0028 0.2138 -0.1626 0.0382 1.0244 11.0803 UMD 0.0024 0.0009 0.1367 -0.1205 0.0363 0.4417 5.3273 USD 1.1658 1.3200 18.2100 -25.1300 4.5224 -0.7366 11.7315 Hong Kong 0.0120 0.0139 0.2645 -0.3482 0.0872 -0.2028 5.0505 Malaysia 0.0005 0.0006 0.2895 -0.2784 0.0991 0.0815 4.2293 Philippines 0.0060 -0.0016 0.3317 -0.2989 0.0974 0.2858 4.6905 Singapore 0.0048 0.0057 0.2484 -0.2107 0.0757 0.0797 4.3745 Taiwan 0.0014 0.0007 0.3324 -0.1746 0.0910 0.7308 4.0350 Thailand -0.0057 -0.0092 0.2843 -0.2817 0.1087 0.2910 3.5018 Korea -0.0015 -0.0097 0.3945 -0.3181 0.0998 0.5246 4.7757 5. Econometric methodology We choose the VAR modeling technique (Sims, 1980) as an appropriate econometric methodology to investigate the postulated relationships. The rationale for doing so lies in the arguments of Brown and Cliff (2004a & 2004b) and Lee et al. (2002) which suggest that stock market returns and investor sentiments may act as a system. Our approach is also consistent with studies such as Soydemir (2005), Kim (2003), Ratanapakorn and Sharma (2002), Janakiramanan and Lamba (1998), and Park and Fatemi (1993) which have employed the VAR models to analyze the linkages between the U.S. and Pacific- Basin stock markets. The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 75 The VAR specification allows the researchers to do policy simulations and integrate Monte Carlo methods to obtain confidence bands around the point estimates (Doan, 1988; Genberg et al. 1987; Hamilton, 1994). The likely response of one variable at time t, t+1, t+2 etc. to a one time unitary shock in another variable at time t can be captured by impulse response functions. As such they represent the behavior of the series in response to pure shocks while keeping the effect of other variables constant. Since, impulse responses are highly non-linear functions of the estimated parameters, confidence bands are constructed around the mean response. Responses are considered statistically significant at the 95% confidence level when the upper and lower bands carry the same sign. Thus VAR model captures the dynamic feedback effects in a relatively unconstrained fashion and is therefore a good approximation to the true data generating process. We express the VAR model as: (4) where, Z(t) is a column vector of variables under consideration, C is the deterministic component comprised of a constant, A(s) is a matrix of coefficients, m is the lag length and ε(t) is a vector of random error terms. Table 2: Cross-correlations of variables relating to fundamentals The variables are economic growth (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 market portfolio (R m ), 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), and currency fluctuations (USD). B10 Baa IIP HML INF R m DIV SMB T30 T90 UMD USD B10 1.00 Baa 0.00 1.00 IIP -0.15 -0.39 1.00 HML 0.06 -0.06 0.05 1.00 INF -0.02 0.16 -0.15 0.00 1.00 R m 0.26 -0.01 -0.10 -0.56 -0.17 1.00 DIV 0.32 0.02 -0.11 -0.47 -0.16 0.97 1.00 SMB -0.20 -0.04 -0.05 -0.50 0.00 0.17 -0.04 1.00 T30 0.14 0.40 -0.28 -0.06 0.26 -0.07 0.02 -0.13 1.00 T90 0.32 0.25 -0.19 -0.15 0.09 0.14 0.15 -0.01 0.19 1.00 UMD 0.20 -0.06 0.00 -0.20 -0.10 0.03 -0.03 0.20 0.01 -0.14 1.00 USD -0.03 -0.09 0.17 0.19 -0.14 -0.16 -0.15 0.00 0.00 -0.04 0.00 1.00 1 m s Z(t) C A(s)Z(t m) (t)ε = = + − +• 76 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 Table 3: Effects of fundamentals on individual and institutional investor sentiments The variables are individual investor sentiments (Sentt 1 ), institutional investor sentiments (Sentt 2 ), economic growth (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 market portfolio (R m ), 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), and currency fluctuations (USD). Variables Sentt 1 Sentt 2 B10 -0.96 (0.88) 0.49 (0.71) Baa -29.93*** (8.35) -4.82 (8.27) IIP 1.30 (2.77) -2.22 (2.20) HML 1.44*** (0.53) 1.14*** (0.46) INF -18.28*** (6.60) -8.35 (6.06) R m -6.75** (3.29) -3.60 (2.66) DIV 8.32*** (3.31) 4.50* (2.71) SMB 2.78*** (0.80) 1.99*** (0.66) T30 7.47 (13.57) -6.83 (11.51) T90 -13.11 36.71 -31.70 (31.64) UMD 0.00 (0.00) 0.00 (0.00) USD 0.00 (0.01) 0.00 (0.01) C 0.29*** (0.07) 0.15** (0.07) R-squared 0.304 0.161 SSR 3.190 2.48 Akaike info criterion -0.839 -1.090 Schwarz criterion -0.578 -0.829 F-statistic 4.989 2.186 Prob(F-statistic) 0.000 0.015 0 1 J it i ij jit it j Sentt Fundλ λ ξ = = + +• The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 77 We first decompose the sentiments variables into rational and irrational components. In particular, we estimate two separate ordinary least square (OLS) regressions based on equations (1) and (2). To check the presence of multicollinearity, we estimate the cross-correlations between the variables related to fundamentals. The results of the cross correlations are reported in Table 2. The low correlations among most of the variables suggest that multicollinearity is not a major issue. Table 3 reports the regression results based on equations (2) and (3). Individual investor sentiments are significantly related to business conditions, inflation, dividend yield, excess returns on market, SMB, and HML. Similarly, the sentiments of institutional investor sentiments are significantly related to dividend yield, SMB and HML. These results are consistent with the arguments of Baker and Wurgler (2005), Brown and Cliff (2004b) and Shleifer and Summers (1990) that investor sentiments may contain a combination of both rational and irrational components and not necessarily only noise. 6. Estimation results Before proceeding with the main results, we first check the time series properties of each variable by performing unit root tests. Table 4 reports the results of unit root tests using Augmented Dickey Fuller (ADF) test (Dickey and Fuller, 1979, 1981) and Kwiatkowski, Phillips, Schmidt, and Shin (1992) (KPSS test). Based Table 4: Unit root test results The variables are rational sentiments of individual investors (Rational 1 ), rational sentiments of institutional investors (Rational 2 ), irrational sentiments of individual investors (Irrational 1 ), irrational sentiments of institutional investors (Irrational 2 ), and stock market returns on Hong Kong (Hong Kong), Malaysia (Malaysia), Philippines (Philippines), Singapore (Singapore), Taiwan (Taiwan), Thailand (Thailand), and Korea (Korea). ADF test KPSS test Rational 1 -4.019 0.112 Rational 2h -5.714 0.107 Irrational 2 -6.337 0.098 Institutional_IR -3.989 0.153 Hong Kong -6.994 0.188 Malaysia -6.129 0.195 Philippines -6.764 0.092 Singapore -6.349 0.126 Taiwan -7.081 0.096 Thailand -6.537 0.114 Korea -6.421 0.134 Test critical values: 1% level -3.469 0.739 5% level -2.878 0.463 10% level -2.575 0.347 78 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 on the consistent and asymptotically efficient AIC and SIC criteria (Diebold, 2003) and considering the loss in degrees of freedom, the appropriate number of lags is determined to be two. In the case of the ADF test, the null hypothesis of nonstationarity is rejected. In the KPSS test, the null hypothesis is that each series in stationary. We fail to reject the null hypothesis in the case of KPSS test. The inclusion of drift/trend terms in the ADF and KPSS test equations does not change these results (Dolado, Jenkinson, and Sosvilla-Rivero, 1990). Given that the series are stationary in nature, we estimate a set of five variable VAR model for each of the seven Pacific-Basin stock countries. Each VAR model is composed of rational and irrational components of the U.S. individual and institutional investor sentiments and the stock returns of Pacific- Basin country being studied1. 6.1 Variance Decomposition The decomposition of variance gives a quantitative measure to the causal relationship indicating how much the movement in one variable can be explained by other variables in terms of the percentage of the forecast error variance. Table 5 (Panels A through G) shows the results of the innovation accounting procedure and reports the 1 through 10 day ahead forecast error variance of Pacific-Basin stock market returns accounted for by innovations in rational and irrational sentiments of the U.S. individual and institutional investors. In the case of Hong Kong (Panel A), total sentiments accounts for approximately 14% of the total forecast error variance. The major portion of such influences is due to rational sentiments of institutional investors which accounts for approximately 9%. However, in the case of Malaysia (Panel B), total sentiments seem to have lesser influence on stock returns as compared to Honk Kong (approximately 11%). Also, the rational sentiments of individual and institutional investors account for approximately 5% and 4% respectively, which are much greater than forecast error variances accounted by irrational sentiments. Panel C reports similar decomposition for Philippines stock market returns. The variance due to the total sentiments averages approximately between 12-13%, of which rational sentiments of institutional investor has the highest contribution (approximately 8%). Similar to the case of Korea, there is less influence of rational sentiments of individual investors. There is relatively very strong effect of rational sentiments of institutional investors in the case of Singapore (Panel D), as it accounts for 18-19% of the total forecast error variance. The effect of other components of sentiments is much less. Panel E reports the forecast error variance in the case of Taiwan. The total sentiments accounts for approximately 8-9%, which is the least among all the Pacific-Basin stock markets. Similar to the earlier findings, the rational sentiments 1 Our approach is similar to the one employed by Park and Fatemi (1993) which estimate a four variable VAR model for each of the seven Pacific-Basin countries instead of one model including all the variables to avoid irrelevant feedback relationships among stock markets. The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 79 of institutional investors account for the maximum variance (approximately 4%). Similar to Malaysia, rational individual investor sentiments accounts for greater proportion of the total variance (approximately 3.5%). The results are very similar for Thailand (Panel F), where the rational institutional and individual investor sentiments account for approximately 4% and 3% respectively. Likewise, in the case of Korea (Panel G), rational sentiments of two classes of investors have approximately similar contribution. Overall, the decomposition of forecast error variances of Pacific-Basin stock returns consistently suggests much higher effect of rational sentiments than the irrational sentiments. Specifically, in all the cases, rational sentiments of institutional investors is the most endogenous variable. These findings imply that the rational expectations of institutional investors are an important player in propagating the U.S. stock market movements in the Pacific-Basin region. Table 5: Decomposition of forecast error variances of Asian stock returns The variables are rational sentiments of individual investors (Rational 1 ), rational sentiments of institutional investors (Rational 2 ), irrational sentiments of individual investors (Irrational 1 ), irrational sentiments of institutional investors (Irrational 2 ), and stock market returns on Hong Kong (Hong Kong), Malaysia (Malaysia), Philippines (Philippines), Singapore (Singapore), Taiwan (Taiwan), Thailand (Thailand), and Korea (Korea). Panel A: Decomposition of Hong Kong By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Sentiments Hong Kong 1 1.9598 0.2250 1.0710 0.3259 3.2558 96.4183 2 1.8287 5.9193 1.1767 1.8388 8.9248 89.2364 3 1.9357 8.1152 1.3355 1.7897 11.3863 86.8240 4 2.1002 8.8842 1.3669 1.7740 12.3513 85.8746 5 2.0978 9.0597 1.3839 1.7761 12.5415 85.6824 6 2.0981 9.0652 1.3898 1.8371 12.5530 85.6099 7 2.0977 9.0693 1.3973 1.8539 12.5643 85.5817 8 2.0982 9.0712 1.4070 1.8601 12.5765 85.5634 9 2.0988 9.0735 1.4151 1.8619 12.5874 85.5507 10 2.0995 9.0747 1.4212 1.8631 12.5954 85.5415 Table continues on the next page 80 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 Panel B: Decomposition of Malaysia By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Sentiments Malaysia 1 0.1231 0.0159 0.0000 0.7613 0.9003 99.0997 2 4.4359 3.4134 0.0581 1.3181 9.2256 90.7744 3 4.1158 4.1651 0.2746 1.5794 10.1349 89.8651 4 4.2303 4.5481 0.5493 1.5791 10.9067 89.0933 5 4.1876 5.0159 0.5658 1.5929 11.3622 88.6378 6 4.2917 5.0893 0.5663 1.5905 11.5377 88.4623 7 4.3044 5.1322 0.5808 1.5904 11.6078 88.3922 8 4.3318 5.1457 0.5806 1.5897 11.6477 88.3523 9 4.3355 5.1576 0.5810 1.5893 11.6633 88.3367 10 4.3402 5.1625 0.5811 1.5893 11.6730 88.3270 Panel C: Decomposition of Philippines By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Sentiments Philippines 1 0.5410 1.1955 0.0040 1.0546 2.2541 97.2048 2 1.0577 7.2477 0.6001 1.0808 8.9286 90.0136 3 2.1692 7.5731 0.8720 1.3869 9.8320 87.9988 4 2.2722 8.0619 0.9584 1.4514 10.4717 87.2561 5 2.2930 8.4228 0.9587 1.4567 10.8382 86.8688 6 2.3287 8.5021 0.9574 1.4576 10.9171 86.7542 7 2.3483 8.5228 0.9583 1.4571 10.9382 86.7135 8 2.3573 8.5282 0.9582 1.4568 10.9432 86.6994 9 2.3608 8.5316 0.9581 1.4569 10.9467 86.6925 10 2.3623 8.5335 0.9583 1.4571 10.9489 86.6888 Panel D: Decomposition of Singapore By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Sentiments Singapore 1 0.1703 0.6844 0.1512 0.0686 1.0745 98.9255 2 1.5614 14.6185 0.1918 1.3938 17.7656 82.2345 3 2.5504 17.6776 0.1820 1.3821 21.7922 78.2078 4 2.6344 18.6832 0.1793 1.3699 22.8668 77.1332 5 2.6584 19.3584 0.2282 1.3560 23.6010 76.3990 Table continues on the next page The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 81 6 2.6557 19.3540 0.2468 1.4530 23.7095 76.2905 7 2.6573 19.3594 0.2711 1.4755 23.7633 76.2367 8 2.6605 19.3557 0.2876 1.4903 23.7941 76.2059 9 2.6625 19.3613 0.3033 1.4928 23.8199 76.1801 10 2.6669 19.3615 0.3142 1.4957 23.8383 76.1617 Panel E: Decomposition of Taiwan By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Senti- ments Taiwan 1 1.7413 0.2104 0.1842 0.5202 2.6561 97.3439 2 3.1608 4.1845 0.6226 0.6517 8.6196 91.3804 3 3.5231 4.1651 0.6250 1.0546 9.3678 90.6322 4 3.5875 4.1551 0.6269 1.0869 9.4564 90.5436 5 3.5893 4.1535 0.6308 1.1201 9.4938 90.5062 6 3.5877 4.1522 0.6413 1.1501 9.5313 90.4687 7 3.5878 4.1512 0.6498 1.1654 9.5541 90.4459 8 3.5876 4.1505 0.6560 1.1757 9.5699 90.4301 9 3.5878 4.1502 0.6602 1.1821 9.5803 90.4197 10 3.5881 4.1500 0.6631 1.1861 9.5873 90.4127 Panel F: Decomposition of Thailand By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Senti- ments Thailand 1 0.8681 0.3833 1.5703 0.0913 0.8681 0.3833 2 1.2603 2.3268 1.5154 1.2825 1.2603 2.3268 3 3.0258 3.8247 1.6956 3.0017 3.0258 3.8247 4 3.3520 4.3644 1.8083 2.9728 3.3520 4.3644 5 3.4016 4.6906 1.8057 2.9724 3.4016 4.6906 6 3.4531 4.7648 1.8053 2.9696 3.4531 4.7648 7 3.4806 4.7984 1.8037 2.9707 3.4806 4.7984 8 3.4954 4.8113 1.8032 2.9698 3.4954 4.8113 9 3.5022 4.8190 1.8037 2.9692 3.5022 4.8190 10 3.5058 4.8228 1.8044 2.9690 3.5058 4.8228 Panel G: Decomposition of Korea By innovations in Period Rational 1 Rational 2 Irrational 1 Irrational 2 Total Senti- ments Korea 1 4.0825 0.9269 0.0120 1.0277 5.0214 93.9509 Table continues on the next page 82 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 2 4.0046 0.8984 0.7324 1.0486 5.6354 93.3160 3 3.8057 3.9798 0.9001 2.6300 8.6855 88.6845 4 3.7764 4.3255 0.9843 3.3019 9.0861 87.6119 5 3.7690 4.4325 0.9838 3.3463 9.1853 87.4684 6 3.7698 4.4903 0.9834 3.3466 9.2435 87.4099 7 3.7687 4.5185 0.9835 3.3475 9.2707 87.3818 8 3.7683 4.5267 0.9837 3.3483 9.2787 87.3731 9 3.7683 4.5292 0.9837 3.3484 9.2812 87.3704 10 3.7683 4.5302 0.9837 3.3483 9.2822 87.3694 6.2 Impulse response function Next, we analyze the impulse response functions to shed light on the significance and duration of the effect of shock in rational and irrational sentiments of individual investors to Pacific-Basin stock returns. It is well known theoretically that traditional orthogonalized forecast error variance decomposition results based on the widely used Choleski factorization of VAR innovations may be sensitive to variable ordering (Pesaran and Shin, 1996; Koop, Pesaran and Potter, 1996; Pesaran and Shin, 1998). To mitigate such potential problems of misspecifications, we employ the recently developed generalized impulses technique as described by Pesaran and Shin (1998) in which an orthogonal set of innovations which does not depend on the VAR ordering. The generalized impulse responses from an innovation to the jth variable are derived by applying a variable specific Cholesky factor computed with the jth variable at the top of the Cholesky ordering. These generalized impulses can capture the effect of unanticipated components and therefore can be regarded as an appropriate choice for this study. Figures 1 a through 1d plot the impulse responses of Hong Kong to rational and irrational sentiments of the U.S. individual and institutional investors. The response to rational sentiments for both individual and institutional are significant in the second month and becomes insignificant thereafter (figures 1 and 1c). However, the effect of the irrational component of sentiments is not significant in both the cases (figures 1 b and 1d). Figures 2a through 2d plot the impulse responses of Malaysia to rational and irrational sentiments of the U.S. investors. Similar to the findings of Hong Kong, the responses to rational sentiments are significant in the second month and become insignificant thereafter (figures 2a and 2c). On the other hand the effect of irrational component of sentiments remains insignificant throughout (figures 2b and 2d). Similarly in the case of Philippines, the responses of stock returns to the rational sentiments are significant The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 83 (figures 3a and 3c) while insignificant to the irrational components (figures 3b and 3c). The results of impulse responses of Singapore are somewhat similar in that rational (irrational) sentiments have significant (insignificant) influences on stock market returns (figures 4a through 4d). Positive significant effect of the shocks of rational sentiments in the case of Hong Kong, Malaysia, Philippines, and Singapore are consistent with earlier studies which find moderate linkages between these markets and the U.S. stock market movements. Figure 1: Response of Hong Kong to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. Figures 5a through 5d plot the responses of Taiwan to the one unit shock in the rational and irrational sentiments of the U.S. individual and institutional investors. Unlike the results of Hong Kong, Malaysia, Philippines, and Singapore - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 1 a R e s p o n s e o f H o n g K o n g t o r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 1 b R e s p o n s e o f H o n g K o n g t o i r r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 1 c R e s p o n s e o f H o n g K o n g t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 1 d R e s p o n s e o f H o n g K o n g t o i r r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s 84 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 there are insignificant effects of rational components of both class of investors. Similarly, the irrational investor sentiments have insignificant effects on stock returns. We find similar results in the case of Thailand, where both the rational and irrational components of sentiments have insignificant effects (figure 6a through 6d). However, in the case of Korea there is significant effect of only rational sentiments of institutional investors (figure 7a). We find insignificant effect of rational sentiments of individual investors (figure 7c). Consistent with our earlier results there are insignificant effects of the irrational sentiments for both class of investors (figures 7b and 7d). These insignificant results in the case of Taiwan, Thailand, and Korea are consistent with earlier studies such as Park and Fatemi (1993) which find little linkages of these markets with the U.S. Figure 2: Response of Malaysia to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 2 a R e s p o n s e o f M a la y s i a t o r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 2 b R e s p o n s e o f M a l a y s i a t o i r r a t io n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 2 c R e s p o n s e o f M a l a y s i a t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 2 d R e s p o n s e o f M a l a y s i a t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 85 Figure 3: Response of Philippines to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. In all, the results of the variance decomposition and impulse responses strongly suggest that irrational exuberance of the U.S. investors, whether individual and institutional are not transmitted to the Pacific-Basin region. However, the rational both classes of investors are transmitted to Asian markets with varying degrees of intensity. Also, there are somewhat greater effects of the rational expectations of the U.S. institutional investors than those of the individual investors. A significant development in emerging markets is that individual investors have increasingly delegated the management of their assets to professional fund managers (Griffith-Jones and Cailloux, 1998). Such institutionalization has increased the sensitivities of emerging markets to the behavior of international - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 3 a R e s p o n s e o f P h i l i p p in e s t o r a t i o n a l s e n t i m e n t s o f i n d iv i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 3 b R e s p o n s e o f P h i l i p p i n e s t o i r r a t i o n a l s e n t i m e n t s o f i n d i v id u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 3 c R e s p o n s e o f P h i li p p i n e s t o r a t i o n a l s e n t im e n t s o f i n s t it u t i o n a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 3 d R e s p o n s e o f P h i l ip p i n e s t o i r r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s 86 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 institutional investors. Moreover, it is much easier for domestic institutional investors engage in herding behavior compared to individual investors since similar information circulates among funds allowing them to follow more easily other institutions’ decisions (Nofsinger and Sias, 1999). Also, due to the high transaction costs of investing in emerging markets, closed-end country funds have emerged as one of the most popular means of international investments by the U.S. individual investors. These factors may explain the greater responses of Pacific-Basin stock markets to the U.S. institutional investor sentiments as compared to the U.S. individual investor sentiments. Figure 4: Response of Singapore to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 4 a R e s p o n s e o f S in g a p o r e t o r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 4 b R e s p o n s e o f S i n g a p o r e t o i r r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 4 c R e s p o n s e o f S i n g a p o r e t o r a t i o n a l s e n t im e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 4 d R e s p o n s e o f S i n g a p o r e t o i r r a t i o n a l s e n t i m e n t s o f i n s t it u t i o n a l i n v e s t o r s The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 87 Figure 5: Response of Taiwan to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 5 a R e s p o n s e o f T a i w a n t o r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 5 b R e s p o n s e o f T a i w a n t o i r r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 5 c R e s p o n s e o f T a i w a n t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 - . 0 2 . 0 0 . 0 2 . 0 4 . 0 6 . 0 8 . 1 0 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 5 d R e s p o n s e o f T a i w a n t o i r r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s 88 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 Figure 6: Response of Thailand to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 . 1 6 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 6 a R e s p o n s e o f T h a il a n d t o r a t i o n a l s e n t im e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 . 1 6 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 6 b R e s p o n s e o f T h a i l a n d t o i r r a t i o n a l s e n t im e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 . 1 6 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 6 c R e s p o n s e o f T h a i l a n d t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 . 1 6 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 6 d R e s p o n s e o f T h a i la n d t o i r r a t io n a l s e n t i m e n t s o f i n s t i t u t io n a l i n v e s t o r s The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 89 Figure 7: Response of Korea to the U.S. individual and institutional investor sentiments 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. * On each graph, “percentage returns” are on the vertical and “horizon” is on the horizontal axis. expectations of 7. Conclusion In this study, we investigate whether the rational expectations and irrational exuberance of the U.S. individual and institutional investors are propagated to Pacific-Basin stock markets, Hong Kong, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Korea. We employ the investor sentiments data at the individual and institutional level, provided by American Association of Individual Investors and Investors Intelligence and the vector auto regression (VAR) models to investigate the postulated relationships. The results of variance decomposition and impulse response functions strongly suggest that there are distinct effects of the rational and irrational investor sentiments on these international stock market returns. - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 7 a R e s p o n s e o f K o r e a t o r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 7 b R e s p o n s e o f K o r e a t o i r r a t i o n a l s e n t i m e n t s o f i n d i v i d u a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 7 c R e s p o n s e o f K o r e a t o r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s - . 0 4 . 0 0 . 0 4 . 0 8 . 1 2 1 2 3 4 5 6 7 8 9 1 0 F i g u r e 7 d R e s p o n s e o f K o r e a t o i r r a t i o n a l s e n t i m e n t s o f i n s t i t u t i o n a l i n v e s t o r s 90 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 67-94 We find strong evidence of insignificant effect of irrational exuberance of the U.S. investors on these set of Pacific-Basin stock market returns. However, the rational expectations of both the U.S. individual and institutional investors have significant effect on these Asian markets with varying degrees of intensity. We find greater effects in the case of Hong Kong, Malaysia, Philippines, and Singapore while a weak linkage with the stock returns of Taiwan, Thailand, and Korea. The overall evidence suggests that institutional investor sentiments are transmitted internationally from the U.S. stock market to a greater extent than the individual investor sentiments; and that the international effects of the U.S. stock market can be attributed to the sentiments induced by fundamental trading and not to noise trading. 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