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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: wichuang@management.ntu.edu.tw (W.-I. Chuang), blee2@cob.fsu.edu (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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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.
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 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
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 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)
Panel A: Pih (portfolio A) versus Pil (portfolio B)
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
Panel B: Pih (portfolio A) versus Pem (portfolio B)
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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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
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:
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 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)
Panel A: Pih (portfolio A) versus Pil (portfolio B)
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
Panel B: Pih (portfolio A) versus Pem (portfolio B)
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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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.
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 1990 to December 2004:
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
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 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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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)
Lag length K 6 6 6 6 7 6
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
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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)
Lag length K 4 4 3 4 4 4
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)
Lag length K 4 2 2 1 1 2
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]
Note: nnn, nn, and n denote signicance at the 1%, 5%, and 10% levels, respectively.
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
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