The Informational Role of Institutional Investors and Financial Analysts in the Market

Journal of Financial Markets 14 (2011) 465493

The informational role of institutional investors and$

with different characteristics. Fifth, good market-wide news diffuses more slowly across securities than

www.elsevier.com/locate/nmar

2007 FMA Annual Meeting in Orlando for helpful comments. Wen-I Chuang gratefully acknowledges the

nancial support from the National Science Council of the Republic of China (NSC 95-2416-H-011-018). The

usual disclaimer applies.n1386-4181/$ - see front matter & 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.nmar.2010.12.001

Corresponding author. Tel.: 886 2 3366 9578; fax: 886 2 2366 0764.E-mail addresses: [email protected] (W.-I. Chuang), [email protected] (B.-S. Lee).does bad market-wide news, and this nding primarily occurs in periods of NBER-dated expansions.

& 2010 Elsevier B.V. All rights reserved.

JEL classification: G14; G20

Keywords: Limited market participation; Information set-up cost; Institutional investors; Financial analysts;

Market-wide information

$The authors are grateful to Eugene Kandel (the editor), an anonymous referee, Yuanchen Chang and seminar

participants at the National Taiwan University, National Chengchi University, National Central University, andnancial analysts in the market

Wen-I Chuanga,n, Bong-Soo Leeb

aDepartment of Finance, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, TaiwanbDepartment of Finance, Florida State University, Tallahassee, FL 32306-1110, USA

Available online 1 January 2011

Abstract

We provide empirical evidence on the impact of limited market participation on the informational role

played by institutions and analysts in the market. Our ndings are as follow. First, the price adjustment of

stocks that are favored by institutions and analysts and associated with low information set-up costs helps

better predict market-wide information. Second, rms that are primarily held by individuals and followed

by fewer analysts tend to respond more sluggishly to market-wide information than do rms that are

primarily held by institutions and followed by more analysts. This nding is partially attributed to public

information generated by the high institutional-ownership and analyst coverage rms with good corporate

governance. Third, high institutional-ownership portfolios and high analyst coverage portfolios play a

complementary role in predicting market returns. Fourth, there is little systematic difference between high

institutional-ownership portfolios and high analyst coverage portfolios in predicting the returns of stocks

1. Introduction

In analyzing the source of contrarian prots, Lo and Mackinlay (1990) uncover a strikingnding that the returns on the portfolio of small stocks are correlated with the lagged returnson the portfolio of large stocks, but not vice versa. Although they attribute this nding to thetendency of small stocks to adjust more slowly to market-wide information than large stocks,they provide little explanation for why rm size per se may be an important determinant of thespeed of price adjustment to information. Considerable research has been conducted to ndfactors that can account for information transmission across securities beyond the size effect.

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493466Candidates that have been put forward to account for information transmission acrosssecurities include the proportion of stocks held by institutional investors (Badrinath, Kale, andNoe, 1995; Sias and Starks, 1997), the number of analysts following a rm (Brennan,Jegadeesh, and Swaminathan, 1993), and trading volume (Chordia and Swaminathan, 2000).1

Badrinath, Kale, and Noe (1995) hypothesize that because of differential informationset-up costs (Merton, 1987) and/or legal restrictions arising from the prudent manregulations, both implying limited market participation, institutional investors gatherinformation about only a subset of stocks. If the information they gather has commoneffects across securities, then the returns on stocks held by institutional investors, who havemore resources to perform systematic investigations into the securities than do individualinvestors, help predict the returns on stocks held by individual investors. Consistent withtheir hypothesis, Badrinath, Kale, and Noe (1995) nd that the returns on the portfolioswith the highest level of institutional ownership lead those with lower levels of institutionalownership. Sias and Starks (1997) report a similar nding. They interpret their nding asconsistent with the hypothesis that institutional trading reects market-wide information,which is incorporated into stocks with low institutional holdings.However, given the implications of limited market participation, institutional trading may

not always be informative about market-wide information. For example, due to informationset-up costs and/or the prudent man rules, institutional investors will prefer investing in largestocks to investing in small stocks, and therefore have more incentive to actively performsystematic investigations into large rms than small rms. Then, the lead-lag relation betweenthe returns on portfolios with different degrees of institutional ownership within large rm sizegroups can be attributable to the effect of information set-up costs and/or the prudent manrules that make institutional trading help better predict market-wide information, which isultimately incorporated into stocks with low institutional holdings.Institutional investors may also invest in small stocks for the purpose of portfolio

diversication or increasing prots.2 However, they will devote less effort to conducting

1There are two additional explanations that have been proposed for cross-autocorrelations among portfolio

returns. The rst group attributes cross-autocorrelations in portfolio returns to time-varying expected returns

(e.g., Conrad and Kaul, 1988). A variation of this explanation claims that cross-autocorrelations are the result of

portfolio autocorrelations and contemporaneous correlations (e.g., Boudoukh, Richardson, and Whitelaw, 1994).

According to this explanation, portfolio cross-autocorrelations should disappear once portfolio autocorrelations

are taken into account. The second group attributes portfolio autocorrelations and cross-autocorrelations to

market imperfections or microstructure biases such as thin trading (e.g., Boudoukh, Richardson, and Whitelaw,

1994).2Bennett, Sias, and Starks (2003) show that over time institutional investors have shifted their preferences

toward smaller stocks because such stocks offer a relatively more attractive trade-off between risk and expectedreturn than do larger stocks.

systematic investigations into small stocks because the costs of information acquisitionfor small rms are higher than for large rms (Merton, 1987). In this circumstance,their trading in small stocks may reveal little market-wide information. Since full-timeprofessional institutional investors tend to pay more attention to market-wide informationthat may have already been propagated for a while in the market than do part-timeindividual investors, the lead-lag relation between the returns on portfolios with differentdegrees of institutional ownership within small rm size groups can be attributable to thefact that institutional investors more closely scrutinize and therefore respond more rapidlyto market-wide information than individual investors.Some theoretical works show that as the number of informed investors increases, the

share price will respond to new information more rapidly (e.g., Kyle, 1985; Holden andSubrahmanyam, 1992; Foster and Viswanathan, 1993). Using the number of analysts as aproxy for the number of informed investors, Brennan, Jegadeesh, and Swaminathan (1993)nd that the returns on the portfolios of rms that are followed by many analysts tend tolead those of rms that are followed by a few analysts, even when the rms are ofapproximately the same size. Although nancial analysts are free from the prudent manregulations, they still have to consider the costs of information acquisition when theychoose rms to cover and analyze. Consequently, as in the case of institutional investors,nancial analysts may not always uncover and disseminate new information to the market,and sometimes they refer to other information sources in order to reduce their costsof collecting information. Indeed, Sant and Zaman (1996) and Easley, OHara, andPaperman (1998) nd some evidence in support of this view.Hong and Stein (1999) develop a dynamic model in which information diffuses gradually

across the investing public, implying that the informativeness of investors trading is adecreasing function of time. That is, investors who receive new market-wide informationrst will revise their valuations of stock prices immediately and their trading will reectthis information, while those who receive the same information later will update theirvaluations with a lag and their trading is not so informative in a timely manner. Combinedwith limited market participation, stocks that are favored by institutional investorsand nancial analysts and incorporate new market-wide information into their pricesfaster than others are more likely to be those with low information set-up costs, such aslarge and liquid stocks. Other stocks with high information set-up costs, although favoredby institutional investors and nancial analysts (to a less extent), may not have such aninformational role.One common observation from Brennan, Jegadeesh, and Swaminathan (1993),

Badrinath, Kale, and Noe (1995), and Sias and Starks (1997) is that high-institutionportfolios and high-analyst portfolios adjust faster to market-wide information than low-institution portfolios and low-analyst portfolios. But they do not analyze whether thisadjustment is informative to the market based on the implications of limited marketparticipation. Our study attempts to ll this void in the literature. In addition, weinvestigate whether there is a complementary or substitution effect between high-institution portfolios and high-analyst portfolios in predicting market returns, and whetherthere is any systematic difference between their informational roles in predicting thereturns of stocks with different characteristics.To address these issues, we form various portfolios and calculate both the daily and

weekly returns of these portfolios. Specically, we form size-institutional ownership and

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 467volume-institutional ownership portfolios, and size-analyst coverage and volume-analyst

coverage portfolios to investigate the informational role played by institutional investorsand nancial analysts in the market. To proxy market-wide information, we use thereturns on both equal- and value-weighted market portfolios. Then, we compare the

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493468relative predictive power of the portfolios with that of the equal- and value-weightedmarket portfolios by performing the Granger causality test.3

Consistent with the previous studies, our results show that within each size or volumegroup, the returns on the portfolios with the highest institutional ownership lead thosewith the lowest institutional ownership, and the returns on the portfolios with the highestanalyst coverage lead those with the lowest analyst coverage. This implies that rms thatare primarily held by individual investors and followed by fewer nancial analysts tend torespond more sluggishly to new market-wide information than do rms that are primarilyheld by institutional investors and followed by more nancial analysts. Moreover, thislead-lag relation is found to be partially attributed to the public information generatedby the high institutional-ownership and analyst coverage rms with good corporategovernance.More importantly, we nd that within the large size and high volume groups, returns on

the portfolios with the highest institutional ownership lead returns on the market portfolio,while within the small size and low volume groups, returns on the portfolios with thehighest institutional ownership lag returns on the market portfolio. The portfoliosassociated with analyst coverage yield similar results. Put together, these results areconsistent with the hypothesis that, due to the effect of limited market participation,institutional investors and nancial analysts collect information more actively about largeand liquid stocks and thus their price adjustment tends to respond to new market-wideinformation in a timely manner, whereas they do less actively about small and illiquidstocks and thus their price adjustment tends to do so with a lag.We further examine whether there is any complementary or substitution effect between

high-institution portfolios and high-analyst portfolios in predicting market returns. Wend that they play a complementary role in predicting market returns. To investigatewhether there is any systematic difference between high-institution portfolios and high-analyst portfolios in predicting the returns of stocks with different characteristics, weconstruct portfolios based on diverse characteristics, such as book-to-market ratio, marketcapitalization, trading volume, return volatility, and age (i.e., the number of years since therms rst appearance in the CRSP databases). We nd little evidence for the systematicdifference. Instead, we nd some evidence that is consistent with the hypothesis thatinstitutional investors tend to predict the returns of stocks with different characteristicsbetter than nancial analysts do.Finally, we nd that stocks with the lowest institutional ownership (analyst coverage)

tend to respond more slowly to good market-wide news emanating from the priceadjustment of stocks with the highest institutional ownership (analyst coverage) than tobad news. This is consistent with the nding of McQueen, Pinegar, and Thorley (1996) thatsmall stocks tend to have more delayed reactions to good market-wide news emanatingfrom the price adjustment of large stocks than to bad news. Moreover, we nd that this

3Previous studies do not consider the relative predictive power of the portfolios and the market portfolio. One

exception is Chordia and Swaminathan (2000). However, their analyses do not provide any evidence on the effect

of limited market participation on the informational role played by institutional investors and nancial analysts inthe market.

asymmetric delayed response occurs primarily during periods of NBER-dated expansions.This is consistent with the hypothesis that investors pay less attention to good market-widenews during periods of expansion and pay more attention to all market-wide news duringperiods of contraction.The balance of the paper is organized as follows. In Section 2, we describe the data and

discuss the empirical frameworks. In Section 3, we present the empirical results, and we

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 469conclude in Section 4.

2. Data and empirical framework

2.1. Data

Our sample consists of all rms listed on the NYSE during the period of January 1982 toDecember 2004. We exclude any rm that is a prime, a closed-end fund, a real estateinvestment trust (REIT), or an American Depository Receipt (ADR). To be included inour sample, a rm must have available information on stock prices, market capitalization,trading volume, the number of shares held by institutional investors, and the number ofnancial analysts following it. Stock prices, market capitalization, and trading volume areobtained from the Center for Research in Security Prices (CRSP) database. Specically, weuse trading turnover, dened as the ratio of the number of shares traded in a given day tothe total number of shares outstanding at the end of the day, as a measure of tradingvolume.4

The number of shares held by institutional investors is obtained from the January, April,July, and October issues of Standard and Poors Security Owners Stock Guides. Theinstitutional holdings they report originate from Vickers Stock Research Corporation.Specically, data in the January issue indicate third-quarter institutional holdings in theprevious year (see also Nofsinger and Sias, 1999). Fractional institutional ownership isdened as the ratio of the number of shares held by institutional investors to the number ofshares outstanding. The number of nancial analysts following each sample rm is takenfrom the IBES tapes. The number of nancial analysts following a particular rm in agiven quarter is dened as the number of nancial analysts making an annual earningsforecast in January, April, July, and October.5

Previous studies document that rm size and trading volume are highly positivelycorrelated with institutional holdings and analyst coverage (e.g., Brennan, Jegadeesh, andSwaminathan, 1993; Badrinath, Kale, and Noe, 1995; Sias and Starks, 1997; Hong, Lim,and Stein, 2000; Naes and Skjeltorp, 2003; Rubin, 2007). These high positive correlationsnaturally lead to the question of whether the size or volume effects are subsumed by theinstitutional ownership or analyst coverage effects or vice versa. To effectively evaluate theinformational role of institutional investors and nancial analysts in the market, we divide

4Lo and Wang (2000) argue that using trading turnover as a measure of trading volume has an advantage in

that it is unaffected by neutral changes of units such as stock splits and stock dividends. Moreover, one problem

with using the number of shares traded as a measure of trading volume is that it is unscaled and, therefore, highly

correlated with rm size. Chordia and Swaminathan (2000) show that the correlation between trading turnover

and rm size is much lower than that between other measures of trading volume and rm size.5We use the number of nancial analysts who make an annual earnings forecast rather than a quarterly

earnings forecast. This is because prior to year 2000 the number of nancial analysts making quarterly earningsforecast is not available for most of sample rms.

our sample of stocks in the following manner. For size-institutional ownership portfolios,six size groups are formed at the beginning of each quarter by ranking all sample stocks bytheir market capitalization. Then, each size group is further classied into six groups basedon the fraction of shares held by institutional investors. Thus, each sample stock isassigned to one of 36 groups. We construct volume-institutional ownership portfolios,size-analyst coverage portfolios, volume-analyst coverage portfolios, analyst coverage-institutional ownership portfolios, and institutional ownership-analyst coverage portfolios

the rm size of size-institutional ownership and size-analyst coverage portfolios are similar for

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493470each size group. A comparable observation is made for each volume group. This indicates thatwe are successful in reducing the association between size and institutional ownership/analystcoverage and between volume and institutional ownership/analyst coverage.The limited market participation implies that institutional investors and nancial

analysts have different incentives in collecting information about stocks with thedifferential information set-up cost. Following the argument by Badrinath, Kale, andNoe (1995), we use rm size as a proxy for the information set-up cost. We also use tradingvolume, a common measure of liquidity in the literature, as another proxy. To gaugewhether different size or volume groups exhibit signicant differences to meet therequirement that they have different characteristics in size or volume, we perform t-testsfor the difference in mean values of size or volume for adjacent two groups. Table 1 showsthat the t-statistics for two portfolios Pi versus Pi1 are statistically signicant at the1% level for all adjacent size or volume groups, illustrating that our sorting algorithm

6The volume-ranked portfolios are based on daily average trading turnover of the sample stocks over the

previous year before the portfolio formation date (see also Chordia and Swaminathan, 2000).7Previous studies document that non-synchronous trading is a more serious problem in daily data than in

weekly data (e.g., Kadlec and Patterson, 1999) and that Wednesday trading volume is higher relative to trading

volume on the other trading days (e.g., Barclay, Litzenberger, and Warner, 1990). To further alleviate the

concerns of the non-synchronous trading and non-trading problems, we also use Wednesday-to-Wednesday

portfolio returns to replicate all empirical tests in the paper. The weekly results show that all reported conclusions

drawn from the results of daily portfolio returns remain virtually unchanged. To conserve space, we do not reportin a similar manner.6 This sorting algorithm ensures that the size-institutional ownershipand volume-institutional ownership portfolios are different in terms of institutionalownership but similar in terms of size and volume, respectively. A similar classicationapplies to other portfolios. For example, size-analyst coverage and volume-analystcoverage portfolios are different in terms of analyst coverage but similar in terms of sizeand volume, respectively.Once portfolios are formed in this manner at the beginning of each quarter, their

composition remains unchanged for the remainder of the quarter. Then, daily equal-weighted portfolio returns are computed for each portfolio by averaging daily the returnsof the stocks in the portfolio. To minimize the effect of non-synchronous trading oncross-autocorrelations, we follow the Chordia and Swaminathan (2000) methodology andexclude stocks that did not trade on date t or t1 when computing portfolio returns fordate t. For each portfolio, we obtain 5,553 observations of daily portfolio returns.7

2.2. Summary statistics

Table 1 reports descriptive statistics on various portfolios. We nd that the mean values ofthe results using weekly portfolio returns. However, they are available from the authors upon request.

Table 1

Summary statistics for various portfolios.

Summary statistics for various portfolios are computed for the sample period from January 1983 to December

2004. For size groups, Pij refers to a portfolio of size i and institutional-ownership or analyst coverage j. i=1, 6

refer to the largest and smallest size portfolios, respectively. h and l refer to the highest and lowest institutional-

ownership or analyst coverage portfolios, respectively, within each size group i. For volume groups, Pij refers to a

portfolio of volume i and institutional-ownership or analyst coverage j. i=1, 6 refer to the highest and

lowest volume portfolios, respectively. h and l refer to the highest and lowest institutional-ownership or analyst

coverage portfolios, respectively, within each volume group i. For analyst coverage groups, Pij refers to a portfolio

of analyst coverage i and institutional ownership j. i=1, 6 refer to the highest and lowest analyst coverage

portfolios, respectively. h and l refer to the highest and lowest institutional ownership portfolios, respectively,

within each analyst coverage group i. For institutional ownership groups, Pij refers to a portfolio of institutional

ownership i and analyst coverage j. i=1, 6 refer to the highest and lowest institutional ownership portfolios,

respectively. h and l refer to the highest and lowest analyst coverage portfolios, respectively, within each

institutional ownership group i. Pem and Pvm refer to the equal- and value-weighted portfolios of all NYSE sample

rms, respectively. The mean size gures are in billions of dollars. The mean volume gures represent the average

percentage of trading turnover. The mean institutional ownership gures are in institutional ownership fraction.

The mean analyst coverage gures represent the average number of analysts following a sample rm. The

t-statistics for Pi versus Pi1 are the result of t-test for the difference in means of group i and group i1 ofportfolio formation criterion 1. For example, for the size-institutional ownership portfolios, portfolio formation

criterion 1 represents the mean market capitalization. The t-statistics for Pih versus Pil are the results of t-test for

the difference in means of groups h and l within each group i of portfolio formation criterion 2. For example, for

the size-institutional ownership portfolios, portfolio formation criterion 2 represents the mean institutional

ownership fraction.

Portfolio returns Size Volume (%) Institutional

ownership

Analyst

coverage

t-Statistics

for Pi vs. Pi1

t-Statistics

for Pih vs. PilMean (%) Std. dev. (%)

Size-institutional ownership portfolios

P1h 0.061 1.101 9.864 0.416 0.806 21.278 11.665nnn 138.306nnn

P1l 0.064 0.857 18.952 0.263 0.324 22.776

P2h 0.061 1.111 2.742 0.482 0.829 17.006 17.014nnn 80.667nnn

P2l 0.051 0.827 2.607 0.270 0.281 16.605

P3h 0.064 1.091 1.228 0.511 0.821 13.991 20.798nnn 91.851nnn

P3l 0.056 0.821 1.140 0.270 0.225 11.971

P4h 0.061 1.085 0.624 0.490 0.805 11.090 17.307nnn 121.557nnn

P4l 0.062 0.855 0.602 0.235 0.190 7.779

P5h 0.057 1.101 0.318 0.435 0.774 8.385 19.225nnn 91.184nnn

P5l 0.061 0.934 0.291 0.250 0.171 5.370

P6h 0.072 1.131 0.132 0.424 0.661 6.094 51.223nnn

P6l 0.105 1.271 0.079 0.265 0.114 3.385

Volume-institutional ownership portfolios

P1h 0.047 1.301 2.250 0.919 0.838 15.721 26.779nnn 46.898nnn

P1l 0.163 1.481 1.325 0.950 0.270 8.444

P2h 0.072 1.067 2.679 0.466 0.819 14.381 28.954nnn 70.534nnn

P2l 0.079 1.254 2.332 0.459 0.282 9.322

P3h 0.076 0.991 3.732 0.340 0.800 14.344 28.945nnn 65.898nnn

P3l 0.081 1.110 2.651 0.334 0.269 9.196

P4h 0.064 0.926 3.971 0.260 0.774 13.436 27.284nnn 70.022nnn

P4l 0.074 1.031 2.165 0.254 0.243 9.523

P5h 0.063 0.913 3.849 0.193 0.730 12.808 23.033nnn 81.477nnn

P5l 0.056 0.946 1.879 0.185 0.195 8.670

P6h 0.053 0.816 2.402 0.115 0.656 9.563 35.926nnn

P6l 0.049 0.880 1.222 0.082 0.114 5.297

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 471

Table 1 (continued )

Portfolio returns Size Volume (%) Institutional

ownership

Analyst

coverage

t-Statistics

for Pi vs. Pi1

t-Statistics

for Pih vs. PilMean (%) Std. dev. (%)

Size-analyst coverage portfolios

P1h 0.048 1.084 24.043 0.383 0.582 33.432 11.505nnn 66.736nnn

P1l 0.065 1.157 9.635 0.403 0.604 12.981

P2h 0.049 1.055 3.619 0.504 0.590 25.218 17.596nnn 57.912nnn

P2l 0.062 1.069 3.052 0.381 0.570 9.110

P3h 0.042 0.971 1.708 0.586 0.586 21.888 23.510nnn 53.728nnn

P3l 0.074 1.102 1.472 0.342 0.507 5.843

P4h 0.066 1.212 0.870 0.560 0.581 17.461 25.221nnn 45.182nnn

P4l 0.047 1.000 0.753 0.360 0.512 3.557nnn nnn

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493472successfully captures the effect of limited market participation or the difference in theinformation set-up cost. Moreover, from the magnitude of the mean values of institutionalholdings and analyst coverage in each size and volume group, we nd that bothinstitutional investors and nancial analysts tend to favor large and high volume stocksover small and low volume stocks.Table 1 also reports the t-statistics for two portfolios Pih versus Pil, where h and l refer to

the highest and lowest institutional-ownership and analyst coverage portfolios, respec-tively, within each size group or each volume group i. They are used to test whetherthe portfolios have different degrees (or mean values) of institutional ownership withineach size or volume group and of analyst coverage within each size or volume group,respectively. The results show that all t-statistics for Pih versus Pil are statisticallysignicant at the 1% level, indicating that size- and volume-institutional ownership (size-and volume-analyst coverage) portfolios have different degrees of institutional ownership(analyst coverage) within each size or volume group, respectively.

P5h 0.079 1.231 0.458 0.509 0.560 13.350 22.921 36.051

P5l 0.047 0.982 0.382 0.296 0.403 2.043

P6h 0.083 1.299 0.198 0.530 0.498 9.504 24.921nnn

P6l 0.081 1.268 0.107 0.324 0.329 1.145

Volume-analyst coverage portfolios

P1h 0.070 1.385 6.046 0.957 0.658 31.245 5.840nnn 45.505nnn

P1l 0.084 1.500 0.603 0.811 0.460 3.188

P2h 0.062 1.135 10.193 0.443 0.636 26.660 28.751nnn 64.244nnn

P2l 0.061 1.241 0.464 0.436 0.480 3.500

P3h 0.058 1.031 14.605 0.329 0.603 27.318 21.676nnn 59.483nnn

P3l 0.079 1.117 0.486 0.326 0.461 3.253

P4h 0.071 1.010 16.991 0.258 0.561 26.955 28.512nnn 63.209nnn

P4l 0.067 1.080 0.496 0.253 0.454 2.962

P5h 0.057 0.984 22.026 0.196 0.498 25.877 21.174nnn 73.806nnn

P5l 0.050 1.022 1.061 0.190 0.407 2.452

P6h 0.060 0.880 21.841 0.134 0.431 20.869 45.233nnn

P6l 0.049 0.845 0.398 0.098 0.291 1.517

Market portfolios

Pem 0.062 0.741

Pvm 0.054 0.938

Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.

2.3. Empirical framework

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 4732.3.1. Vector autoregressions

Following Brennan, Jegadeesh, and Swaminathan (1993), we employ the vector autoregres-sions (VAR) to investigate the lead-lag relation between portfolio returns (see also Sias andStarks, 1997; Chordia and Swaminathan, 2000). Brennan, Jegadeesh, and Swaminathan (1993)demonstrate that the returns of portfolios that are rst to reect market-wide information willpredict the returns of portfolios that reect market-wide information later. To understand therationale behind the VAR, suppose that we want to test whether portfolio B returns leadportfolio A returns. For this, we consider the following bivariate vector autoregressions:

RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk eA;t; 1

RB;t aB XK

k1ckRA;tk

XK

k1dkRB;tk eB;t; 2

where RA,t and RB,t are the returns of portfolios A and B, respectively. The number of lags ineach equation is chosen by considering both the Akaike (1974) information criterion (AIC) andthe Schwarz (1978) information criterion (SIC). In Eq. (1), if the lagged returns of portfolio Bcan predict the current returns of portfolio A, controlling for the predictive power of the laggedreturns of portfolio A, the returns of portfolio B are said to Granger-cause the returns ofportfolio A. Following Chordia and Swaminathan (2000), we also examine whether the sum ofthe coefcients associated with the returns of portfolio B in Eq. (1) is greater than zero.Therefore, this version of the Granger causality tests examines not only for predictability butalso for the sign of predictability (or net effect).Then, we focus on testing formally whether the ability of the lagged returns of portfolio

B to predict the current return of portfolio A is better than vice versa. We test thishypothesis by examining whether the sum of the bk coefcients in Eq. (1) is greater than thesum of the ck coefcients in Eq. (2). If the ability of the lagged returns of portfolio B topredict the current return of portfolio A is better than vice versa, Brennan, Jegadeesh, andSwaminathan (1993) theoretically demonstrate that the sum of the bk coefcients in Eq. (1)should be signicantly greater than the sum of the ck coefcients in Eq. (2).

8

We use the returns on the market portfolio to proxy market-wide information. This proxy isimportant because it helps us identify the relative speed of price adjustment of stocks favored byinstitutional investors and nancial analysts. For example, if the returns on the portfolios withthe highest institutional ownership Granger-cause those with the lowest institutional ownership,this implies that the price adjustment of stocks favored by institutional investors to market-wideinformation is faster than that of stocks favored by individual investors. Then, based on theimplications of limited market participation, two circumstances will occur. First, if the returns onthe highest institutional ownership portfolios with low information set-up costs Granger-causethe returns on the market portfolio, this is consistent with the hypothesis that institutionalinvestors exert more effort to actively collect information about these stocks and thus the price

8Brennan, Jegadeesh, and Swaminathan (1993) show that if the lagged returns of portfolio A predict the current

returns of portfolio B with a negative sign, it is simply a result of the fact that the returns of portfolio A adjustmore sluggishly to market-wide information than the returns of portfolio B.

adjustment of these stocks helps better predict market-wide information. Second, if the returnson the market portfolio Granger-cause the returns on the highest institutional ownershipportfolios with high information set-up costs, this is consistent with the hypothesis thatinstitutional investors devote less effort to conducting systematic investigations into these stocksand thus the price adjustment of these stocks has no predictive power for market-wideinformation.

2.3.2. The complementary and substitution effects

To investigate whether high institutional-ownership portfolios and high analystcoverage portfolios play a complementary or substitution role in predicting marketreturns, we estimate the following four regressions:

Rm;t am XK

k1bkRm;tk

XK

k1gkRA;tk em;t; 3

Rm;t am XK

k1bkRm;tk

XK

k1lkRB;tk em;t; 4

Rm;t am XK

k1bkRm;tk

XK

k1jkRC;tk em;t; 5

Rm;t am XK

k1bkRm;tk

XK

k1dkRD;tk em;t; 6

where Rm,t is the return of the market portfolio, and RA,t, RB,t, RC,t, and RD,t are the returns ofportfolios A, B, C, and D, respectively. Specically, portfolios A and B represent the high andlow institutional-ownership (analyst coverage) portfolios, respectively, and portfolios C and Drepresent the high analyst coverage (institutional-ownership) portfolios within the high andlow institutional-ownership (analyst coverage) groups, respectively. The lag length in Eqs. (3)to (6) is chosen considering both the AIC and the SIC.In these four regressions, we use R-square to measure the predictive power of a portfolio

for market returns. If high analyst coverage portfolios complement (substitute) highinstitutional-ownership portfolios in predicting market returns, the difference between thepredictive power of the high and low institutional-ownership portfolios should be lower(higher) than the difference between the predictive power of the high analyst coveragewithin the high and low institutional-ownership groups. In other words, the differencebetween the R-squares of regressions (3) and (4) should be less (greater) than that betweenthe R-squares of regressions (5) and (6) if high analyst coverage portfolios complement(substitute) high institutional-ownership portfolios in predicting market returns. The testof whether high institutional-ownership portfolios complement (substitute) high analystcoverage portfolios is conducted analogously.

2.3.3. On the systematic difference between institutional investors and financial analysts

As mentioned above, institutional investors are affected by the prudent man rules whilenancial analysts are not. This difference may imply some systematic difference in the market

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493474segments in that the high institutional-ownership and high analyst coverage stocks lead stocks

with different characteristics. For example, the returns on the portfolios of high institutional-ownership stocks may better predict the future returns on the portfolios of value stocks, large

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 475stocks, high volume stocks, low volatility stocks, and old stocks, while the returns on theportfolios of high analyst coverage stocks may better predict the future returns on the portfoliosof growth stocks, small stocks, low volume stocks, high volatility stocks, and young stocks. Toinvestigate this implication, we estimate the following regression:

RA;t aA XK

k1bkRA;tk

XK

k1gkRB;tk

XK

k1lkRC;tk eA;t; 7

where RA,t, RB,t, and RC,t are the daily returns on the portfolios A, B, and C, respectively.Specically, portfolio A represents the portfolio of value stocks or the portfolio of growthstocks, portfolio B the portfolio of high institutional-ownership stocks, and portfolio C theportfolio of high analyst coverage stocks.In Eq. (7), the ability of the lagged returns of portfolio B and that of lagged returns of

portfolio C to predict the current returns of portfolio A, controlling for the predictivepower of the lagged returns of portfolio A, are measured by testing whether gk=0, for all kand whether lk=0, for all k, respectively. The rationale for the test in Eq. (7) is similar tothat for the test in Eqs. (1) and (2). Therefore, if we nd that the sum of the gk coefcientsis signicantly greater than the sum of the lk coefcients in Eq. (7), it implies that theability of the returns of portfolio B to predict the returns of portfolio A is better than thatof the returns of portfolio C.

2.3.4. Asymmetric regression

McQueen, Pinegar, and Thorley (1996) nd that the cross-autocorrelation puzzledocumented by Lo and Mackinlay (1990) is primarily associated with a slow response bysome small stocks to good, but not to bad, market-wide news. A variation of our empiricalframework of the bivariate vector autoregression of Eqs. (1) and (2) can also provideinsight into the cross-autocorrelation between the returns of two portfolios underconsideration. Here, we also investigate whether the cross-autocorrelation of ourportfolios exhibits an asymmetric response to good and bad market-wide news.Following the McQueen, Pinegar, and Thorley (1996) method, we employ the following

asymmetric regression to investigate the asymmetric response of the returns of oneportfolio to positive and negative returns of the other portfolio:

RB;t aB XK

k0bUPB;kRA;tk DA;tk

XK

k0bDNB;k RA;tk 1DA;tk eB;t; 8

where RA,t and RB,t are the returns of portfolios A and B, respectively, and DA;tk is a

dummy variable that takes on a value of one if RA,t is positive and zero otherwise.9 The lag

length in Eq. (8) is determined based on the AIC and the SIC. It can be shown thatportfolio B adjusts more slowly to good market-wide news emanating from portfolio Athan to bad news if and only if the contemporaneous beta associated with the positive

returns of portfolio A, bUPB;0; is less than that associated with the negative returns of

9We have checked the returns of portfolio A and found that none of them is zero over the sample period.

Therefore, it is safe to dene DA;tk as the positive returns of portfolio A and dene 1DA;tk as the negative

returns of portfolio A.

portfolio A, bDNB;0 ; and the sum of the lagged betas in an up market,PK

k1 bUPB;k; is greater

than that in a down market,PK

k1 bDNB;k : In terms of the asymmetric regression in Eq. (8),

this translates into examining whether bUPB;0obDNB;0 andPK

k1 bUPB;k4PK

k1 bDNB;k (see also

McQueen, Pinegar, and Thorley, 1996).10 The rationale behind this result is that if

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493476portfolio B responds more sluggishly to good market-wide news released from portfolio Athan to bad news, it should respond less to todays good market-wide news than to todaysbad news, and respond more to past good market-wide news than to past bad news. Itshould be noted that in order to make a conclusion about the asymmetric response, theabove two conditions should hold simultaneously.Here, we go one step further to investigate whether the asymmetric response is related to

the state of the macro economy. To this end, we modify Eq. (8) as follows:

RB;t ail XK

k0bUP-EXPB;k RA;tk DA;tk DEXPtk

XK

k0bDN-EXPB;k RA;tk 1DA;tk DEXPtk

XM

m0bUP-CONB;m RA;tm DA;tm 1DEXPtm

XM

m0bDN-CONB;m RA;tm 1DiA;tm 1DEXPtm eB;t; 9

where DEXPt is a dummy variable and takes on a value of one during an NBER-datedexpansion and zero otherwise.11 The lag length in Eq. (9) is determined based on the AICand the SIC. As discussed above, to investigate the relation between asymmetric responsesand the state of the macro economy, we examine the relative magnitudes of thecontemporaneous betas and the relative magnitudes of the sum of the lagged betas duringthe period of NBER-dated expansions and contractions.12

3. Empirical results

3.1. The informational role of institutional investors

Table 2 presents the estimation results of the bivariate VAR for the size-institutionalownership and the equal-weighted market portfolios. Specically, Panel A of Table 2presents the estimation results of the bivariate VAR for 12 size-institutional ownershipportfolios, Pih (Portfolio A) versus Pil (Portfolio B), where h and l refer to the highest andlowest institutional-ownership portfolios, respectively, within each size group i. The wbc(1)statistic is employed to measure the relative ability of two portfolios in predicting each

10Chordia and Swaminathans (2000) Dimson beta regressions use the same concept to measure the relative

speeds of adjustment of portfolios to market-wide information.11According to the denition of a business cycle by the NBER, any period should belong to either a period of

expansion or a period of contraction. Thus, our dummy variables should cover all the periods with no period in

between.12We also examine whether market-wide news displays the different patterns of diffusion across securitiesduring the business cycle, but nd no signicant differences.

Table 2

Vector autoregressions for the size-institutional ownership and the market portfolios.

The following bivariate VAR is estimated to examine the relative ability of one portfolio to predict the other

portfolio for the sample period from January 1983 to December 2004:

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 477other. The results show that for each size group, the sum of the ck coefcients is greaterthan that of the bk coefcients. Moreover, the null hypothesis that

Pbk=Pck, which we

test by the wbc(1) statistic, is rejected at conventional signicance levels for all size groups.Consistent with Badrinath, Kale, and Noe (1995) and Sias and Starks (1997), these results

RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk eA;t; 1

RB;t aB XK

k1ckRA;tk

XK

k1dkRB;tk eB;t; 2

where RA,t and RB,t are the daily returns on the portfolios A and B, respectively. Pij refers to an equal-weighted

portfolio of size i and institutional-ownership j. i=1, 6 refer to the largest and smallest size portfolios, respectively.

h and l refer to the highest and lowest institutional-ownership portfolios, respectively, within each size group i. Pemrefers to the equal-weighted portfolios of all NYSE sample rms. The number of lags in each equation is chosen

by considering both the Akaike (1974) information criterion (AIC) and the Schwarz (1978) information criterion

(SIC). The wb(K) and wc(K) statistics obtained from the Wald test are a joint test of the null hypothesis based onthe causality restrictions. The wb(1) and wc(1) statistics obtained from the Wald test are used to test the nullhypothesis that

Pbk=0 and that

Pck=0, respectively. The wbc(1) statistic obtained from the Wald test is used to

test the null hypothesis thatPbk=Pck.

LHS

variable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1) LHSvariable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1)

Panel A: Pih (portfolio A) versus Pil (portfolio B)

R1h,t (4) 14.247nnn 0.180 12.302nnn 11.901nnn R4h,t (5) 12.354nn 0.194 10.699nnn 26.382nnn

R1l,t (4) 10.560nn 0.071 5.765nn R4l,t (5) 73.609

nnn 0.240 50.074nnn

R2h,t (4) 7.899n 0.120 4.467nn 8.064nnn R5h,t (4) 3.918 0.027 0.344 8.784nnn

R2l,t (4) 31.732nnn 0.104 12.162nnn R5l,t (4) 49.288

nnn 0.178 32.233nnn

R3h,t (4) 10.613nn 0.165 8.583nnn 20.934nnn R6h,t (5) 13.533nn 0.080 7.024 11.117nnn

R3l,t (4) 61.926nnn 0.197 42.502nnn R6l,t (5) 90.194

nnn 0.257 51.174nnn

Panel B: Pih (portfolio A) versus Pem (portfolio B)

R1h,t (6) 23.956nnn 0.287 11.769nnn 13.943nnn R4h,t (4) 10.000nn 0.052 0.434 0.181

Rem,t (6) 55.008nnn 0.151 14.027nnn Rem,t (4) 7.810

n 0.100 7.187nnn

R2h,t (6) 15.610nn 0.160 2.731n 5.533nn R5h,t (7) 66.128nnn 0.502 23.372nnn 12.967nnn

Rem,t (6) 54.076nnn 0.160 12.456nnn Rem,t (7) 34.654

nnn 0.015 0.100R3h,t (6) 8.898 0.127 1.733 4.664nn R5h,t (7) 142.337nnn 0.729 61.566nnn 41.025nnnRem,t (6) 27.731

nnn 0.166 13.503nnn Rem,t (7) 34.963nnn 0.068 2.870n

Panel C: Pil (portfolio A) versus Pem (portfolio B)

R1l,t (6) 19.626nnn 0.080 1.227 0.161 R4l,t (4) 77.125

nnn 0.323 37.254nnn 26.686nnn

Rem,t (6) 22.704nnn 0.024 0.254 Rem,t (4) 14.946nnn 0.136 10.120nnn

R2l,t (7) 21.196nnn 0.026 0.192 1.185 R5l,t (9) 145.260

nnn 0.406 24.392nnn 13.305nnn

Rem,t (7) 11.722 0.090 2.666 Rem,t (9) 21.032nn 0.060 1.146R3l,t (5) 52.950

nnn 0.219 15.178nnn 15.511nnn R6l,t (7) 135.110nnn 0.545 64.510nnn 45.289nnn

Rem,t (5) 15.423nnn 0.170 11.723nnn Rem,t (7) 4.847 0.006 0.062


indicate that the ability of the lagged returns on the portfolio with the highest institutionalownership to predict the current returns on the portfolio with the lowest institutionalownership is better than vice versa.Panel B of Table 2 reports the results of the Granger causality tests for the portfolios with the

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493478highest institutional holdings (i.e., Pih for i=1, 2,y , and 6) and the equal-weighted marketportfolio, Pem. Based on the relative magnitudes of the sum of the bk coefcients and that of theck coefcients and the wbc(1) statistics, the ability of the returns of P1h, P2h, and P3h to predictthe returns of Pem is better than vice versa. However, the results show that the ability of thereturns of Pem to predict the returns of P5h and P6h is better than vice versa. Combined with theresults of Panel A of Table 2, these results are consistent with the hypothesis that institutionalinvestors make more effort to actively collect information about large stocks and thus the priceadjustment of large stocks helps better predict market-wide information, while that of smallstocks has little predictive power.Panel C of Table 2 reports the results of the Granger causality tests for the portfolios

with the lowest institutional holdings (i.e., Pil for i=1, 2,y , and 6) and the equal-weighted market portfolio, Pem. Based on the relative magnitudes of the sum of the bkcoefcients and that of the ck coefcients and the wbc(1) statistics, we nd that the ability ofthe returns of Pem to predict the returns of P3l, P4l, P5l, and P6l is better than vice versa.

13

Combined with the results of Panel A of Table 2, these ndings imply that the returns ofthe stocks in which individual investors have greater ownership can be predicted from thereturns of the stocks in which institutional investors have greater ownership and of themarket portfolio.14

3.2. The informational role of financial analysts

Now, we turn our attention to the informational role that nancial analysts play in themarket. Table 3 presents the results of the Granger causality tests for the size-analystcoverage and the equal-weighted market portfolios. Panel A of Table 3 presents theestimation results of the bivariate VAR for 12 size-analyst coverage portfolios, Pih(Portfolio A) versus Pil (Portfolio B), where h and l refer to the highest and lowest analystcoverage portfolios, respectively, within each size group i. It shows that the sum of the bkcoefcients is smaller than that of the ck coefcients for all size groups. Moreover, based onthe wbc(1) statistics, the null hypothesis that the ability of two portfolios to predict eachother is equal is rejected for ve of six size groups. These ndings are consistent withBrennan, Jegadeesh, and Swaminathan (1993) that rms followed by more nancialanalysts react faster to market-wide information than do rms followed by fewer nancialanalysts.Panel B of Table 3 presents the estimation results of the Granger-causality tests for the

portfolios with the highest analyst coverage (i.e., Pih for i=1, 2,y, and 6) and the equal-weighted market portfolio, Pem. We nd that the sum of the bk coefcients is smaller thanthat of the ck coefcients, and the wbc(1) statistics are signicant at conventional levels for

13As a robustness test, we also use the returns on the value-weighted market portfolio as another proxy for

market-wide information in our tests. We nd that all conclusions using this alternative proxy remain unchanged.

For brevity, we do not report these results.14We also perform similar analyses for the volume-institutional ownership and the equal-weighted market

portfolios. Conclusions from the results using these portfolios are the same as what we obtain from the resultsreported in Table 2. For brevity, we do not report these results.

Table 3

Vector autoregressions for the size-analyst coverage and the market portfolios.



W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 479P1h versus Pem. Together with the results of Panel A of Table 3, these results suggest that,because of the low information set-up costs associated with large stocks, some nancialanalysts actively engage in collecting information about them, and thus the priceadjustment of these stocks helps better predict market-wide information. Panel B of

RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk eA;t; 1

RB;t aB XK

k1ckRA;tk

XK

k1dkRB;tk eB;t; 2


portfolio of size i and analyst coverage j. i=1, 6 refer to the largest and lowest size portfolios, respectively. h and l

refer to the highest and lowest analyst coverage portfolios, respectively, within each size group i. Pem refers to the

equal-weighted portfolios of all NYSE sample rms. The number of lags in each equation is chosen by considering

both the Akaike (1974) information criterion (AIC) and the Schwarz (1978) information criterion (SIC). The wb(K)and wc(K) statistics obtained from the Wald test are a joint test of the null hypothesis based on the causalityrestrictions. The wb(1) and wc(1) statistics obtained from the Wald test are used to test the null hypothesis thatPbk=0 and that

Pck=0, respectively. The wbc(1) statistic obtained from the Wald test is used to test the null

hypothesis thatPbk=Pck.

LHS

variable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1) LHSvariable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1)


R1h,t (4) 15.735nnn 0.064 2.291 4.892nn R4h,t (4) 9.645nn 0.078 3.300n 0.746

R1l,t (4) 8.774n 0.110 6.012nn R4l,t (4) 91.419

nnn 0.132 21.305nnn

R2h,t (8) 14.411n 0.049 0.895 3.902nn R5h,t (8) 27.396nnn 0.078 1.761 14.021nnn

R2l,t (8) 14.725n 0.129 6.284nn R5l,t (8) 204.250

nnn 0.220 38.445nnn

R3h,t (4) 66.267nnn 0.037 1.425 3.805n R6h,t (4) 12.866

nn 0.084 6.773nnn 7.506nnn

R3l,t (4) 21.432nnn 0.154 15.906nnn R6l,t (4) 98.875

nnn 0.220 54.391nnn


R1h,t (7) 8.634 0.142 2.819n 5.653nn R4h,t (4) 8.238n 0.109 2.770n 0.126Rem,t (7) 45.586

nnn 0.145 10.698nnn Rem,t (4) 46.817nnn 0.080 9.983nnn

R2h,t (5) 25.732nnn 0.059 0.629 1.995 R5h,t (5) 23.919nnn 0.270 13.626nnn 1.379

Rem,t (5) 32.459nnn 0.092 5.559nn Rem,t (5) 227.316

nnn 0.163 37.509nnn

R3h,t (5) 38.362nnn 0.115 2.627 0.263 R5h,t (5) 61.534

nnn 0.480 39.187nnn 31.851nnn

Rem,t (5) 29.815nnn 0.169 16.092nnn Rem,t (5) 7.832 0.058 4.840nn

Panel C: Pil (portfolio A) versus Pem (portfolio B)

R1l,t (7) 15.771nn 0.108 2.235 1.011 R4l,t (4) 43.064nnn 0.254 16.577nnn 5.615nn

Rem,t (7) 41.153nnn 0.007 0.037 Rem,t (4) 3.401 0.036 1.009

R2l,t (4) 7.775 0.090 2.472 2.324 R5l,t (7) 76.711nnn 0.491 44.959nnn 28.602nnn

Rem,t (4) 9.401n 0.031 1.126 Rem,t (7) 10.854 0.087 3.847nn

R3l,t (4) 8.290n 0.099 2.873n 1.737 R6l,t (9) 181.973

nnn 0.623 57.604nnn 43.130nnn

Rem,t (4) 38.202nnn 0.005 0.037 Rem,t (9) 25.383nnn 0.029 0.928


Table 3 also shows that the sum of the bk coefcients is greater than that of the ckcoefcients, and the wbc(1) statistics are signicant at conventional levels for P6h versus Pem.

whose institutional ownership fraction is lower than their market capitalization

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493480proportion. The high institutional-ownership portfolio is a proxy for informationalreasons for institutional investors to hold these stocks. Then we perform the Grangercausality tests as before.Table 4 reports the estimation results of the Granger-causality tests for the size-

institutional ownership and the equal-weighted market portfolios. Specically, Panel A ofTable 4 presents the estimation results of the bivariate VAR for 12 size-institutionalownership portfolios, Pih (portfolio A) versus Pil (portfolio B), where h and l refer to thehigh and low institutional-ownership portfolios, respectively, within each size group i. Thewbc(1) statistic is employed to measure the relative ability of the two portfolios in predictingeach other. The results show that for each size group, the sum of the ck coefcients isgreater than that of the bk coefcients. Moreover, the wbc(1) statistic, which is used to testthe null hypothesis that

Pbk=Pck, is rejected at conventional signicance levels for all

size groups.Panel B of Table 4 reports the results of the Granger causality tests for the portfolios with

the highest institutional holdings (i.e., Pih for i=1, 2,y , and 6) and the equal-weighted

15We also use the volume-analyst coverage and the equal-weighted market portfolios to conduct similar

analyses. Since all conclusions drawn from the results using these portfolios are virtually the same as those fromTogether with the results of Panel A of Table 3, these results suggest that it is notworthwhile for some nancial analysts to actively collect information about small stocksthat have high information set-up costs, and thus these stocks tend to respond to market-wide information with a lag. These ndings are consistent with Sant and Zaman (1996) andEasley, OHara, and Paperman (1998) that analysts do not always provide newinformation to the market.Panel C of Table 3 presents the estimation results of the Granger causality tests for

the portfolios with the lowest analyst coverage (i.e., Pil for i=1, 2,y, and 6) and theequal-weighted market portfolio, Pem. We nd that the sum of the bk coefcients is greaterthan that of the ck coefcients, and the null hypothesis that

Pbk=Pck is rejected at

conventional signicance levels for P4l versus Pem, P5l versus Pem, and P6l versus Pem. Thisindicates that the ability of the market portfolios to predict these portfolios is better thanvice versa. Combined with the results of Panel A of Table 3, these ndings imply that rmsfollowed by fewer nancial analysts tend to respond sluggishly to new market-wideinformation.15

3.3. The informational motive for institutional investors

An alternative way to examine the informational role of institutional investors inrelation to market-wide information is to nd a proxy for the informational reasons forinstitutional investors to hold their stocks. For this, we rst construct six size and sixvolume portfolios as before, and then we further construct two institutional-ownershipportfolios within each size or volume group: a high institutional-ownership portfoliocomposed of stocks whose institutional ownership fraction is higher than their marketcapitalization proportion, and a low institutional-ownership portfolio composed of stocksthe results reported in Table 3, we do not report these results.

Table 4

Vector autoregressions for the size-institutional ownership and the market portfolios.

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 481market portfolio, Pem. Both the relative magnitudes of the sum of the bk coefcients and thatof the ck coefcients and the wbc(1) statistics indicate that the ability of the returns of P1h, P2h,and P3h to predict the returns of Pem is better than vice versa. Moreover, the results show



RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk eA;t; 1

RB;t aB XK

k1ckRA;tk

XK

k1dkRB;tk eB;t; 2


portfolio of size i and institutional-ownership j. i=1, 6 refer to the largest and smallest size portfolios, respectively.

h and l refer to the institutional-ownership portfolios of stocks whose institutional ownership fraction is higher

and lower than their market capitalization proportion in the portfolio, respectively, within each size group i. Pemrefers to the equal-weighted portfolios of all NYSE sample rms. The number of lags in each equation is chosen

by considering both the Akaike (1974) information criterion (AIC) and the Schwarz (1978) information criterion

(SIC). The wb(K) and wc(K) statistics obtained from the Wald test are a joint test of the null hypothesis based onthe causality restrictions. The wb(1) and wc(1) statistics obtained from the Wald test are used to test the nullhypothesis that

Pbk=0 and that

Pck=0, respectively. The wbc(1) statistic obtained from the Wald test is used to

test the null hypothesis thatPbk=Pck.

LHS

variable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1) LHSvariable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1)


R1h,t (4) 13.242nn 0.111 3.494n 13.825nnn R4h,t (6) 10.133 0.054 1.139 15.550nnn

R1l,t (4) 79.977nnn 0.291 27.233nnn R4l,t (6) 92.355

nnn 0.525 52.938nnn

R2h,t (5) 13.897nn 0.056 1.162 11.030nnn R5h,t (5) 7.258 0.102 5.450

nn 6.703nnn

R2l,t (5) 108.970nnn 0.418 41.694nnn R5l,t (5) 50.313

nnn 0.359 31.591nnn

R3h,t (4) 10.800nn 0.111 2.537 12.426nnn R6h,t (6) 13.834nn 0.085 9.180nnn 17.679nnn

R3l,t (4) 37.719nnn 0.299 33.222nnn R6l,t (6) 113.322

nnn 0.425 48.837nnn


R1h,t (6) 15.964nnn 0.199 8.818nnn 7.469nnn R4h,t (8) 48.584nnn 0.213 3.843nn 0.578

Rem,t (6) 58.533nnn 0.078 3.265n Rem,t (8) 79.992

nnn 0.349 20.722nnn

R2h,t (6) 25.802nnn 0.176 5.525nn 15.099nnn R5h,t (7) 49.461nnn 0.458 15.626nnn 4.374nn

Rem,t (6) 86.013nnn 0.298 29.871nnn Rem,t (7) 34.963

nnn 0.073 0.971

R3h,t (6) 20.946nnn 0.097 0.890 4.000nn R5h,t (7) 92.870nnn 0.526 26.456nnn 15.475nnn

Rem,t (6) 31.504nnn 0.210 14.068nnn Rem,t (7) 19.057

nnn 0.085 2.047Panel C: Pil (portfolio A) versus Pem (portfolio B)

R1l,t (7) 41.448nnn 0.052 0.360 0.014 R4l,t (6) 28.707

nnn 0.325 11.146nnn 4.800nn

Rem,t (7) 41.376nnn 0.068 1.324 Rem,t (6) 10.983

n 0.013 0.059

R2l,t (6) 22.018nnn 0.014 0.021 1.142 R5l,t (5) 84.415

nnn 0.410 26.009nnn 10.990nnn

Rem,t (6) 41.414nnn 0.173 9.177nnn Rem,t (5) 15.099

nnn 0.050 2.017

R3l,t (6) 11.003n 0.042 0.189 0.019 R6l,t (6) 134.777

nnn 0.530 48.691nnn 34.890nnn

Rem,t (6) 0.836 0.019 0.074 Rem,t (6) 16.858nnn 0.015 0.482


that the ability of the returns of Pem to predict the returns of P5h and P6h is better thanvice versa.Panel C of Table 4 reports the results of the Granger causality tests for the portfolios

with the lowest institutional holdings (i.e., P for i=1, 2,y , and 6) and the equal-

reports the estimation results of the bivariate VAR for 12 governance-institutionalownership portfolios, P (portfolio A) versus P (portfolio B), where h and l refer to the

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493482ih il

highest and lowest institutional-ownership portfolios, respectively, within each governancegroup i. Based on the relative magnitudes of the sum of the bk coefcients and that of the ckcoefcients and the wbc(1) statistic that is used to measure the relative ability of twoportfolios in predicting each other, the ability of the returns of Pih to predict the returns ofPil, for i=1, 2, 3, and 4, is better than vice versa at the 5% or 10% signicance levels.Compared to the results in Panel A of Table 2 that the ability of the returns of Pih topredict the returns of Pil, for i=1, 2,y , and 6, is better than vice versa at the 1%

16For brevity, we do not report the estimation results of the Granger-causality tests for the volume-institutional

ownership and the equal-weighted market portfolios. They are very similar to the results reported in Table 3.17See Gompers, Ishii, and Metrick (2003) for details pertaining to the construction of the Governance Index.

The data of the Governance Index are available only from 1990. During the sample period from 1990 to 2004, weil

weighted market portfolio, Pem. We nd that the ability of the returns of Pem to predict thereturns of P4l, P5l, and P6l is better than vice versa based on the relative magnitudes of thesum of the bk coefcients and that of the ck coefcients and the wbc(1) statistics. Taken as awhole, the general pattern observed from the results of Table 4 is virtually the same as thatobserved from the results of Table 2. Therefore, the results of Table 4 provide furtherevidence on the effect of limited market participation on the informational role played byinstitutional investors in the market.16

3.4. Examination of the governance hypothesis

Firms with good corporate governance may produce more public information. Thisinformation production generates more liquidity and trading volume, making their stocksattractive to both institutional investors and nancial analysts. Because of a large amountof free information associated with these stocks, they lead other stocks and aredisproportionally owned by institutional investors and covered by nancial analysts.Built on the above argument, if the observed lead-lag relation between the high and low

institutional-ownership (analyst coverage) portfolios is due to more public informationgenerated by the high institutional-ownership (analyst coverage) rms with goodgovernance mechanisms, then this lead-lag relation should disappear or become weakeronce the degree of corporate governance is controlled for each portfolio. Moreover, it isexpected that corporate governance exerts a larger effect on the lead-lag relation betweenthe high and low institutional-ownership (analyst coverage) portfolios with good corporategovernance than on the lead-lag relation with poor corporate governance. To test thegovernance hypothesis, we use the Governance Index constructed by Gompers, Ishii, andMetrick (2003) to form the governance-institutional ownership and governance-analystcoverage portfolios and then perform the Granger causality tests for these portfolios.17

Table 5 reports the estimation results of the Granger causality tests for the governance-institutional ownership and governance-analyst coverage portfolios. Specically, Panel Amatch 88% of our sample rms with the Governance Index.

Table 5

Vector autoregressions for the governance-institutional ownership portfolios and for the governance-analyst

coverage portfolios.



RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk eA;t; 1

XK XK

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 483signicance level, the results of Panel A of Table 5 therefore imply that corporategovernance exerts some effect on the lead-lag relation between the high and lowinstitutional-ownership portfolios with poor corporate governance and makes this lead-lagrelation become weaker. However, corporate governance exerts a substantial effect on thelead-lag relation between the high and low institutional-ownership portfolios with goodcorporate governance and makes this lead-lag relation disappear.Panel B of Table 5 reports the estimation results of the bivariate VAR for 12 governance-

analyst coverage portfolios, Pih (portfolio A) versus Pil (portfolio B), where h and l refer to thehighest and lowest analyst coverage portfolios, respectively, within each governance group i.

RB;t aB k1

ckRA;tk k1

dkRB;tk eB;t; 2


portfolio of corporate governance i and institutional-ownership (or analyst coverage) j. i=1, 6 refer to the largest

and smallest Governance Index portfolios, respectively. h and l refer to the highest and lowest institutional-

ownership (or analyst coverage) portfolios, respectively, within each governance group i. A large Governance

Index refers to poor governance (dictatorship portfolio), while a small Governance Index refers to good

governance (democracy portfolio). The number of lags in each equation is chosen by considering both the Akaike

(1974) information criterion (AIC) and the Schwarz (1978) information criterion (SIC). The wb(K) and wc(K)statistics obtained from the Wald test are a joint test of the null hypothesis based on the causality restrictions. The

wb(1) and wc(1) statistics obtained from the Wald test are used to test the null hypothesis thatPbk=0 and thatP

ck=0, respectively. The wbc(1) statistic obtained from the Wald test is used to test the null hypothesis thatPbk=Pck.

LHS

variable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1) LHSvariable

(K)

wb(K) orwc(K)

Pbk orPck

wb(1) orwc(1)

wbc(1)

Panel A: Pih (portfolio A) versus Pil (portfolio B) for the governance-institutional ownership portfolios

R1h,t (4) 2.291 0.037 1.085 6.107nn R4h,t (4) 3.542 0.004 0.008 4.177nn

R1l,t (4) 30.024nnn 0.214 19.240nnn R4l,t (4) 18.117

nnn 0.116 14.681nnn

R2h,t (4) 4.821 0.083 3.896nn 4.448nn R5h,t (8) 20.429

nnn 0.027 0.179 0.986R2l,t (4) 36.869

nnn 0.203 27.980nnn R5l,t (8) 34.708nnn 0.111 5.007nn

R3h,t (7) 16.776nn 0.118 5.436nn 2.933n R6h,t (7) 11.186 0.037 0.548 1.274

R3l,t (7) 207.916nnn 0.244 28.957nnn R6l,t (7) 35.570

nnn 0.132 6.460nn

Panel B: Pih (portfolio A) versus Pil (portfolio B) for the governance-analyst coverage portfolios

R1h,t (5) 14.456nn 0.019 0.216 3.998nn R4h,t (8) 3.810 0.022 0.345 3.023n

R1l,t (5) 25.235nnn 0.121 7.389nnn R4l,t (8) 50.354

nnn 0.221 21.664nnn

R2h,t (7) 16.882nn 0.063 2.353 5.284nn R5h,t (8) 51.609nnn 0.058 1.833 3.250n

R2l,t (7) 20.392nnn 0.133 4.488nn R5l,t (8) 28.529

nnnn 0.226 11.617nnn

R3h,t (6) 9.822 0.037 0.661 5.315nn R6h,t (8) 20.250nnn 0.039 0.585 0.164R3l,t (6) 18.845

nnn 0.135 7.832nnn R6l,t (8) 22.651nnn 0.105 4.048nn


Based on the relative magnitudes of the sum of the bk coefcients and that of the ck coefcientsand the wbc(1) statistic, the ability of the returns of Pih to predict the returns of Pil, for i=1,2,y , and 5, is better than vice versa at the 5% or 10% signicance levels. Compared to the

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493484results in Panel A of Table 3, the results of Panel B of Table 5 imply that corporate governanceexerts some effect on the lead-lag relation between the high and low analyst coverageportfolios with poor corporate governance and makes this lead-lag relation become weaker.However, corporate governance exerts a substantial effect on the lead-lag relation between thehigh and low analyst coverage portfolios with good corporate governance and makes this lead-lag relation disappear.Overall, the results of Table 5 imply that some of the observed lead-lag relations in Table 2

and 3 are attributable to the public information generated by the high institutional-ownershipand analyst coverage rms with good corporate governance mechanisms.

3.5. The complementary and substitution effects between high institutional and high analyst

portfolios

Table 6 reports the estimation results of four regression Eqs. (3)(6) used to investigatethe complementary and substitution effects between high institutional portfolios and highanalyst portfolios in predicting market returns. Specically, Table 6 reports D1 and D2which is the measure of the difference in the R-squares between Eqs. (3) and (4) and thatbetween Eqs. (5) and (6), respectively. If there is a complementary (substitution) effect, thedifference in the R-squares between Eqs. (3) and (4) should be less (greater) than thatbetween Eqs. (5) and (6).Panel A of Table 6 reports the results using the returns of the high and low institutional-

ownership portfolios and the returns of the high analyst coverage portfolios within thehigh and low institutional-ownership groups to predict the equal-weighted market returns.Panel A shows that D1 is less than D2, indicating that high analyst portfolios complementhigh institutional portfolios in predicting the equal-weighted market returns.Panel B of Table 6 reports the results using the returns of the high and low analyst

coverage portfolios and the returns of the high institutional-ownership portfolios withinthe high and low analyst coverage groups to predict the equal-weighted market returns.Similar to what we observe in Panel A of Table 6, Panel B of Table 6 shows that D1 is lessthan D2, indicating that high institutional portfolios complement high analyst portfolios inpredicting the equal-weighted market returns. Overall, the results of Table 6 indicate thathigh institutional portfolios and high analyst portfolios complement each other inpredicting the equal-weighted market returns.18

3.6. On the systematic difference between institutional investors and financial analysts

Table 7 reports the estimation results of Eq. (7) that compare the predictive power of thehighest institutional-ownership with that of the highest analyst coverage portfolios withsimilar size in predicting the value and growth portfolios.19 The wbc(1) statistics are used to

18We also estimate Eqs. (3)(6) by excluding the lagged market returns. The results again show that high

institutional portfolios and high analyst portfolios complement each other in predicting market returns.19The results using the highest institutional-ownership versus highest analyst coverage portfolios with similartrading volume are similar to those reported in Table 7.

Table 6

Regressions for the complementary and substitution effects.

The following regressions are estimated to examine the complementary and substitution effects between

institutional investors and nancial analysts in predicting the market returns for the sample period from January

1983 to December 2004:

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 485test the null hypothesis thatPbk=Pck. In Panel A, the returns of the value portfolios are

used as the predicted variables (i.e., RA,t), and the result shows that the sum of the bkcoefcients is greater than that of the ck coefcients, and the wbc(1) statistics reject the nullhypothesis that

Pbk=Pck at conventional signicance levels for P2hi versus P2ha, P3hi

versus P3ha, and P4hi versus P4ha. This provides evidence that the ability of the returns of

Rm;t am XK

k1bkRm;tk

XK

k1gkRA;tk em;t; 3

Rm;t am XK

k1bkRm;tk

XK

k1lkRB;tk em;t; 4

Rm;t am XK

k1bkRm;tk

XK

k1jkRC;tk em;t; 5

Rm;t am XK

k1bkRm;tk

XK

k1dkRD;tk em;t; 6

where RA,t, RB,t, RC,t and RD,t are the daily returns on the portfolios A, B, C, and D, respectively, and Rm,t is the

daily returns on the market portfolio. Pi refers to an equal-weighted portfolio of institutional-ownership or

analyst coverage i. i=1, 6 refer to the highest and lowest institutional ownership or analyst coverage portfolios. Pijrefers to an equal-weighted portfolio of institutional ownership i and analyst coverage j. i=1, 6 refer to the highest

and lowest institutional ownership portfolios, respectively. h and l refer to the highest and lowest analyst coverage

portfolios, respectively, within each institutional ownership group i. The analyst coverage- institutional ownership

portfolios are dened analogously. Pem refers to the equal-weighted portfolios of all NYSE sample rms. The

number of lags in each equation is chosen considering both the Akaike (1974) information criterion (AIC) and the

Schwarz (1978) information criterion (SIC). R2 is the coefcient of determinant. D1 (D2) is the difference in R2

between equations (3) and (4) ((5) and (6)).

Equation (3) (4) (5) (6)

Panel A: Portfolio A (the highest institutional ownership portfolio, P1), Portfolio B (the lowest institutional ownership

portfolio, P6), Portfolio C (the institutional ownership-analyst coverage portfolio, P1h), Portfolio D (the institutional

ownership-analyst coverage portfolio, P6h), and Market Portfolio (Pem)

R2 3.575% 3.400% 3.782% 3.287%

D1 or D2 0.175% 0.495%

Complementarity or substitutability Complementarity

Panel B: Portfolio A (the highest analyst coverage portfolio, P1), Portfolio B (the lowest analyst coverage portfolio,

P6), Portfolio C (the analyst coverage- institutional ownership portfolio, P1h), Portfolio D (the analyst coverage-

institutional ownership portfolio, P6h), and Market Portfolio (Pem)

R2 3.325% 3.285% 3.461% 3.288%

D1 or D2 0.040% 0.173%

Complementarity or substitutability Complementarity


Table 7

Regression for the ability of institutional investors vs. nancial analysts to predict the returns of the value and

growth stocks.

The following regression is estimated to examine the ability of the highest institutional-ownership versus highest

analyst coverage portfolios with similar size to predict the value and growth portfolios for the sample period from

January 1983 to December 2004:

RA;t aA XK

k1akRA;tk

XK

k1bkRB;tk

XK

k1ckRC;tk eA;t; 7

where RA,t, RB,t, and RC,t are the daily returns on the portfolios A, B, and C, respectively. Pv and Pg refer to an

equal-weighted portfolio of value stocks and that of growth stocks, respectively. Pihi refers to an equal-weighted

portfolio of size i and the highest institutional-ownership and Piha refers to an equal-weighted portfolio of size i

and the highest analyst coverage. i=1, 6 refer to the largest and smallest size portfolios, respectively. The number

of lags in Eq. (1) is chosen by considering both the Akaike (1974) information criterion (AIC) and the Schwarz

(1978) information criterion (SIC). The wb(K) and wc(K) statistics obtained from the Wald test are a joint test of thenull hypothesis that bk=0 for all k and that ck=0 for all k, respectively. The wb(1) and wc(1) statistics obtainedfrom the Wald test are used to test the null hypothesis that

Pbk=0 and that

Pck=0, respectively. The wbc(1)

statistic obtained from the Wald test is used to test the null hypothesis thatPbk=Pck. R

2is the adjusted

coefcient of determinant.

Pihi and Piha P1hi and P1ha P2hi and P2ha P3hi and P3ha P4hi and P4ha P5hi and P5ha P6hi and P6ha

Panel A: Ability of Pihi (portfolio B) versus Piha (portfolio C) to predict Pv (portfolio A)

Lag length K 4 4 4 4 7 7

wb(K) 0.613 14.466nnn 9.067n 12.760nn 15.003nn 7.356

[p-Value] [0.962] [0.006] [0.059] [0.013] [0.036] [0.393]Pbk 0.046 0.206 0.178 0.168 0.053 0.023

wb(1) 0.508 4.183nn 5.247nn 4.512nn 0.469 0.107

[p-Value] [0.476] [0.041] [0.022] [0.034] [0.494] [0.744]

wc(K) 12.181nn 3.472 5.276 4.392 5.475 2.459

[p-Value] [0.016] [0.482] [0.260] [0.356] [0.602] [0.930]Pck 0.136 0.127 0.148 0.103 0.063 0.074

wc(1) 4.718nn 1.584 3.243n 1.767 0.292 0.433

[p-Value] [0.030] [0.208] [0.072] [0.184] [0.589] [0.510]

wbc(1) 2.469 2.980n 4.928nn 3.663n 0.455 0.106

[p-Value] [0.116] [0.084] [0.026] [0.056] [0.500] [0.745]

R2 0.061 0.070 0.056 0.059 0.059 0.055

Panel B: Ability of Pihi (portfolio B) versus Piha (portfolio C) to predict Pg (portfolio A)


wb(K) 6.040 29.609nnn 34.620nnn 27.164nnn 32.511nnn 18.042nnn

[p-Value] [0.419] [0.000] [0.000] [0.000] [0.000] [0.006]Pbk 0.018 0.107 0.158 0.162 0.096 0.102

wb(1) 0.191 6.108nn 11.686nnn 11.962nnn 5.533nn 7.990nnn

[p-Value] [0.662] [0.013] [0.001] [0.001] [0.019] [0.005]

wc(K) 33.878nnn 6.707 5.238 7.779 8.390 7.981

[p-Value] [0.000] [0.349] [0.514] [0.255] [0.299] [0.239]Pck 0.135 0.021 0.035 0.045 0.014 0.029

wc(1) 14.765nnn 0.182 0.453 0.840 0.042 0.373

[p-Value] [0.000] [0.670] [0.501] [0.359] [0.838] [0.541]

wbc(1) 5.153nn 1.031 4.683nn 5.929nn 0.714 3.810nn

[p-Value] [0.023] [0.310] [0.030] [0.015] [0.398] [0.051]

R2 0.135 0.130 0.122 0.125 0.123 0.111


W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493486

the highest institutional-ownership portfolios to predict the returns of the value portfoliosis better than that of the returns of the highest analyst coverage portfolios.Panel B of Table 7 reports the results when the returns of the growth portfolios are used

as the predicted variables (i.e., RA,t). The results show that the sum of the bk coefcients isgreater than that of the ck coefcients, and the wbc(1) statistics reject the null hypothesisthatPbk=Pck at conventional signicance levels for P3hi versus P3ha, P4hi versus P4ha,

and P6hi versus P6ha that the sum of the ck coefcients is greater than that of the bkcoefcients, and the wbc(1) statistics reject the null hypothesis that

Pbk=Pck at

conventional signicance levels only for P1hi versus P1ha. These observations suggest thatthe ability of the returns of the highest institutional-ownership portfolios to predict thereturns of the growth portfolios is still better than that of the returns of the highest analystcoverage portfolios. As such, we do not nd any signicant evidence for the hypothesis ofthe systematic difference between institutional investors and nancial analysts in predicting

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 487the returns of stocks with different characteristics such as value and growth portfolios.When we employ various portfolios with other characteristics, such as large and smallsizes, high and low volumes, high and low volatilities, old and young stocks, we do not ndany signicant evidence in favor of the systematic difference either. Instead, we ndevidence that institutional investors tend to predict the returns of stocks with differentcharacteristics better than nancial analysts.20

3.7. The relative speed of the diffusion of good and bad market-wide news across investors

Table 8 reports the estimates of Equation (8). Panel A of Table 8 reports the results forthe portfolios with the highest (Pih) and lowest (Pil) institutional holdings in six size

groups. The dependent variable is Pil for i=1, 2,y , and 6. We nd that bUPil;0obDNil;0 , and

the null hypothesis that bUPi0 bDNi0 is rejected at the 1% level for all size groups. We alsond that

PKk1 b

UPik 4PK

k1 bDNik ; and the null hypothesis that

PKk1 b

UPik PK

k1 bDNik is

rejected at least at the 5% level in all size groups.21 These ndings suggest that theportfolios with the lowest institutional holdings respond more sluggishly to good market-wide news than to bad market-wide news.Similarly, Panel B of Table 8 reports the results for the portfolios with the highest (Pih)

and lowest (Pil) analyst coverage in six size groups. Again, the left-hand-side variable of the

20Several observations for stocks with other characteristics are noted. First, the comparison of the ability of the

returns of the highest institutional-ownership portfolios versus the highest analyst coverage portfolios to predict

the returns of the large size portfolios exhibits no signicant difference, and the ability of the returns of the highest

institutional-ownership portfolios to predict the returns of the small size portfolios is better than that of the

returns of the highest analyst coverage portfolios. Second, the ability of the returns of the highest institutional-

ownership portfolios to predict both the returns of the high volume portfolios and the returns of the low volume

portfolios is better than that of the returns of the highest analyst coverage portfolios. Third, the ability of the

returns of the highest institutional-ownership portfolios to predict both the returns of the high volatility portfolios

and the returns of the low volatility portfolios is better than that of the returns of the highest analyst coverage

portfolios. Fourth, the ability of the returns of the highest institutional-ownership portfolios to predict both the

returns of the old stock portfolios and the returns of the young stock portfolios is better than that of the returns of

the highest analyst coverage portfolios. We do not report these results for brevity.21The observation that the sum of the lagged bDNil;k is signicantly negative suggests an overreaction of the

portfolios with the lowest institutional holdings to bad market-wide news corrected in the following days in

contrast to the partially delayed reaction to good market-wide news (see also McQueen, Pinegar, and Thorley,1996).

Table 8

Asymmetric regression based on the sign of portfolio returns.

The following regression is estimated to examine the asymmetric response of the returns of one portfolio to positive

and negative returns of the other portfolio for the sample period from January 1983 to December 2004:

RB;t aB XK

k0bUPB;kRA;tk DA;tk

XK

k0bDNB;k RA;tk 1DA;tk eB;t; 8

where RA,t and RB,t are the daily returns on the portfolios A and B, respectively, andDA;t is a dummy variable and takes

on a value of one if RA,t is positive and zero otherwise. Pij refers to an equal-weighted portfolio of size i and institutional-

ownership or analyst coverage j. i=1, 6 refer to the largest and smallest size portfolios, respectively. h and l refer to the

highest and lowest institutional-ownership or analyst coverage portfolios, respectively, within each size group i. The

number of lags in the equation is chosen by considering both the Akaike (1974) information criterion (AIC) and the

Schwarz (1978) information criterion (SIC). The w(1) test statistic is used to test the null hypothesis thatPK

k1 bUPik 0

and thatPK

k1 bDNik 0: The w1(1) test statistic is used to test the null hypothesis that bUPi0 bDNi0 : The w2(1) test statistic

is used to test the null hypothesis thatPK

k1 bUPik PK

k1 bDNik :

LHS variable R1l,t R2l,t R3l,t R4l,t R5l,t R6l,t

Panel A: Size-institutional ownership portfolios (Pih=portfolio A and Pil=portfolio B)


bUPil;0 0.531nnn 0.521nnn 0.530nnn 0.532nnn 0.552nnn 0.447nnn

(29.261) (25.029) (24.700) (23.897) (21.421) (14.074)

bDNil;0 0.658nnn 0.640nnn 0.649nnn 0.670nnn 0.707nnn 0.595nnn

(21.955) (24.747) (21.741) (28.479) (24.372) (19.484)

w1(1) 9.727nnn 11.456nnn 8.927nnn 22.430nnn 19.262nnn 9.201nnn

[p-Value] [0.002] [0.001] [0.003] [0.000] [0.000] [0.002]PKk1 b

UPil;k

0.077 0.058 0.074 0.040 0.068 0.285

w(1) 4.799nn 3.769n 8.332nnn 2.469 8.303nnn 33.240nnn

[p-Value] [0.028] [0.052] [0.004] [0.116] [0.004] [0.000]PKk1 b

DNil;k

0.139 0.075 0.028 0.086 0.090 0.053w(1) 4.836nn 5.036nn 3.164n 4.573nn 3.640n 1.744[p-Value] [0.028] [0.025] [0.075] [0.032] [0.056] [0.187]

w2(1) 6.171nn 6.696nnn 6.239nn 5.582nn 7.731nnn 11.769nnn

[p-Value] [0.013] [0.010] [0.012] [0.018] [0.005] [0.001]

Panel B: Size-analyst coverage portfolios (Pih=portfolio A and Pil=portfolio B)


bUPil;0 0.781nnn 0.695nnn 0.658nnn 0.442nnn 0.372nnn 0.393nnn

(26.564) (20.336) (17.756) (17.006) (14.685) (14.227)

bDNil;0 0.896nnn 0.738nnn 0.811nnn 0.607nnn 0.496nnn 0.553nnn

(19.473) (23.154) (19.429) (16.714) (10.848) (17.798)

w1(1) 2.939n 0.614 5.229nn 14.866nnn 6.304nn 14.270nnn

[p-Value] [0.086] [0.433] [0.022] [0.000] [0.012] [0.000]PKk1 b

UPil;k

0.011 0.011 0.105 0.038 0.035 0.161

w(1) 0.178 0.090 6.460nn 3.082n 1.022 32.964nnn

[p-Value] [0.731] [0.764] [0.011] [0.079] [0.312] [0.000]PKk1 b

DNil;k

0.210 0.016 0.121 0.099 0.179 0.065w(1) 17.154nnn 0.105 2.767n 13.425nnn 4.477nn 3.892nn

[p-Value] [0.000] [0.746] [0.096] [0.000] [0.034] [0.049]

w2(1) 9.904nnn 0.131 4.792nn 2.197 2.598 3.764n

[p-Value] [0.002] [0.717] [0.029] [0.138] [0.107] [0.052]


W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493488

regression is Pil for i=1, 2,y , and 6. The observation that bUPil;0obDNil;0 ; with the rejection

W.-I. Chuang, B.-S. Lee / Journal of Financial Markets 14 (2011) 465493 489of the null hypothesis that bUPi0 bDNi0 at conventional signicance levels, and thatPKk1 b

UPik 4PK

k1 bDNik ; with the rejection of the null hypothesis that

PKk1 b

UPik PK

k1 bDNik at conventional signicance levels, can be found in the rst, third, and sixth

size groups. These ndings suggest that good market-wide news travels more slowly acrossinvestors than does bad market-wide news.22

3.8. The relative speed of the diffusion of good and bad news during the business cycle

Table 9 reports estimates of Equation (9). Panel A reports the estimation resul

Documents

The Informational Role of Institutional Investors and Financial Analysts in the Market