Information Asymmetry, Institutional Trading, and Cost of Capital1
Mingshan Zhang
mingshan.zhang@anderson.ucla.edu
110 Westwood Plaza, Suite D402
Anderson School of Management
University of California – Los Angeles
Los Angeles, CA 90095
1
I am especially thankful to my advisor John Hughes for his guidance. I have benefited from
insightful discussions with David Aboody and Jing Liu. All remaining errors are mine.
Information Asymmetry, Institutional Trading, and Cost of Capital
Abstract
Previous empirical research by Aboody, Hughes, and Liu (2004) find evidence of
positive associations between earnings quality as a measure of information asymmetry,
insider trading, and cost of capital. Prompted by their study, in this paper we address the
question whether there exists other set of informed traders who trade in a large magnitude
to drive a measurable risk premium. The definition of informed traders is therefore
broadened to include institutions. Employing a classification scheme by Bushee (1998)
based on turnover and diversification, we first examine the trading profitability for
institutions from each of Bushee’s (1998) classes. Our findings in terms of abnormal
returns suggest that the so-called transient institutions (high turnover, high diversification)
trade on both the idiosyncratic and systematic components of private information similar to
corporate insiders. The evidence is mixed on dedicated institutions (low turnover, low
diversification) and quasi- indexers (low turnover, high diversification) do not appear to
trade on private information. Second, extending the analysis to a setting where R&D is
used to proxy for information asymmetry rather than earnings quality corroborates the
above findings on the likelihood of information-based trading by three institutional classes.
Third, in contrast to the evidence on the systematic component of earnings quality,
asymmetric information risk associated with R&D appears to be diversifiable.
1
1. Introduction
Both theoretical and empirical studies point to the likelihood that information
asymmetry affects cost of capital. The basic intuition offered by Easley and O’Hara [2004]
is that uninformed traders require compensation for bearing the risk that they might be
trading with informed traders. Greater information asymmetry in their model implies
higher cost of capital. A potential caveat to their prediction is that all risk in their model is
idiosyncratic. According to traditional asset pricing theory, idiosyncratic risk is
diversifiable in a large economy. One way to sustain the cost of capital effect of
asymmetric information in a large economy characterized by Hughes, Liu, and Liu [2004]
is to allow for the prospect that informed traders have private information with respect to
systematic factors. Another empirical study on the pricing of asymmetric information risk
provided by Aboody, Hughes and Liu [2004] (AHL [2004] hereafter) links cost of capital
with earnings quality as a measure of information asymmetry and abnormal returns to
corporate insiders. Based on the results that earnings quality risk is priced and that insiders
profit on the priced component, they conclude that insider trading is associated with the
pricing of earnings quality risk.
While corporate insiders are natural informed traders, the impact of insider trading
on order flow is limited due to the relatively small magnitude. Corporate insiders are also
resource constrained. For information asymmetry to drive a discernible risk premium, we
expect the existence of other informed traders besides corporate insiders. In this study, we
broaden the definition of informed traders to include institutions and examine the extent to
which institutions’ trading profitability is associated with measures of asymmetric
information and cost of capital.
2
Institutions have long been regarded as well- informed investors1 , suggesting that
they may enjoy an advantage in acquiring private signals and extracting information from
public signals. In particular, Bushee [1998] classifies institutions that trade actively and
hold diversified portfolios as “transient”. Since then, transient institutional trading has been
linked with the exploitation of post-earnings announcement drift (PEAD) (Ke and
Ramalingegowda [2004]) and the expectation of earnings breaks (Ke and Petroni [2004]).
In this paper, we address transient institutions’ information advantage from a different
perspective — that of the market. Specifically, we examine whether the pricing effect of
information asymmetry is associated with transient institutions’ informed trading.
Our choices of asymmetric information measures are earnings quality and R&D.
While earnings quality related asymmetric information may share common characteristics
across firms within an industry, R&D related asymmetric information is relatively unique
or idiosyncratic to the developing firms. Although previous empirical studies provide
evidence that information risk associated with earnings quality is priced (Francis, LaFond,
Olsson and Schipper [2002], AHL [2004]), it is still an open question whether or not R&D
related information asymmetries are priced. Notwithstanding evidence that insiders exploit
information asymmetries associated with R&D (Aboody and Lev [2000]), it is possible
that such information asymmetries are unsystematic and have no effect on the cost of
capital. Hence, for R&D we consider whether information asymmetries associated with
R&D are priced as well as whether institutions exploit those asymmetries.
We start by examining whether transient institutions profit from asymmetric
information measured by earnings quality. Consistent with our hypothesis, we find
1
See Walther [1997], Balsam, Bartov, and Marquardt [2002], Jiambalvo, Rajgopal, and Venkatachalam
[2002], Utama and Cready [1997].
3
transient institut ions’ profits to be higher when trading on stocks with lower earnings
quality (higher information asymmetry). This difference in profits is statistically significant
when measured from the beginning of trading quarter and from the end of trading quarter.
Our results hold after controlling for known risk factors including earnings quality risk.
Quasi- indexers, the benchmark institutions in our study, don’t seem to exploit information
advantages, consistent with their passive indexing trading strategy.
Transient institutions’ asymmetric information advantage may not be related to the
pricing effect since the idiosyncratic component of asymmetric information may be
diversifiable. Therefore, we proceed to investigate whether transient institutions’ trading
profits are associated with the systematic component of earnings quality risk measured by
firms’ exposure to the earnings quality risk factor. We hypothesize that transient
institutions realize higher trading profits on firms with higher loadings on the earnings
quality risk factor. We constructed a hedged portfolio that is long in the highest quintile of
asymmetric information exposure firms and short in the lowest quintile of asymmetric
information exposure firms. The results support our hypothesis. The hedged portfolio earns
significant monthly abnormal returns ranging from 0.971% to 1.948% even if returns are
measured at the end of trading quarter. The trading profits for quasi- indexers do not appear
to be associated with firm’s exposure to the earnings quality risk. The results for dedicated
institutions are mixed and depend on the quarter over which profits are estimated.
To complement our findings on transient institutions’ asymmetric information
advantage, we move to the R&D related information asymmetries in the second part of the
paper. Similar to the case of earnings quality, we find that transient institutions, as opposed
to other institutional classes, exploit information asymmetries associated with R&D. To
4
test if R&D related asymmetric information risk is systematic (priced), we construct a
factor- mimicking portfolio that is long in R&D firms and short in No-R&D firms, and
analyze whether the loadings on R&D factor are positively associated with the risk
premium excluding an R&D factor. We do not find significant variation of risk premiums
between high R&D exposure firms and low R&D exposure firms, i.e. R&D related
asymmetric information risk does not seem to be priced.
To sum up, this study contributes to literature in three aspects. We find that
transient institutions exploit information asymmetries measured by both earnings quality
and R&D, implying that transient institutions are informed traders. Further analysis shows
transient institutions profit on the systematic component of asymmetric information. And
last, unlike information asymmetries associated with earnings quality, information
asymmetries associated with R&D do not appear to be priced by the market.
The rest of the paper is organized as follows. Section 2 reviews the related
literature. Section 3 develops hypotheses and describes the methodology used in the study.
Section 4 documents results using earnings quality as information asymmetry proxy.
Section 5 corroborates the previous findings using R&D to proxy information asymmetry.
Section 6 discusses some robustness issues and Section 7 concludes.
2. Literature Review
2.1
Theoretical Antecedents
Easley and O’Hara [2004] offer a theory to explain the cost of capital effect of
asymmetric information. Basically, uninformed traders require a premium to invest in risky
assets since they might be trading with informed traders. In their rational expectations
5
model, informed traders make better portfolio rebalancing decisions based on their private
information. Faced with an information disadva ntage, uninformed traders seeking to buy
reduce their demands that in turn depresses price and thereby raises the cost of capital. In
equilibrium, uninformed traders are compensated for assuming more risk of trading with
informed traders. Although asymmetric information in Easley and O’Hara [2004] is firm
specific, it has a systematic effect since there are only a limited number of assets. This
effect may be dissipated as the number of assets becomes large, leaving it as an empirical
question as to whether pricing effects can be detected empirically.
Hughes, Liu and Liu [2004] incorporate systematic factors into informed traders’
private information. They demonstrate that information risk about idiosyncratic factors has
no effect on cost of capital in large economies and information risk about systematic
factors affects cost of capital only through factor risk premiums in such economies.
Although their model doesn’t provide support on the cross-sectional effect of cost of
capital in large economies, they emphasize that private information about systematic
factors could lead to the cost of capital effect of information asymmetry.
2.2
Empirical Antecedents
2.2.1
Information Asymmetry and Cost of Capital
There are a number of studies that have examined the relationship between a firm’s
information environment and its cost of capital. The underlying presumptions are that high
information asymmetry fosters informed trading and uninformed traders require
compensation for bearing asymmetric information risk. Asymmetric information proxies
that have been used in previous studies include the Association for Investment
Management and Research’s assessment (AIMR) (Botosan [1997], Botosan and Plumlee
6
[2002]), dispersion in analysts’ earnings forecasts (Botosan, Plumlee, and Xie [2004]),
analysts’ coverage (Healy, Hutton, and Palepu, [1999]), abnormal accruals in accounting
earnings (Francis, LaFond, Olsson, and Schipper [2002], Aboody, Hughes and Liu [2004]),
and Probability of Informed trading (PIN) (Easley, Hvidkjaer, and O’Hara [2002]). Among
these proxies, earnings quality, i.e., unsigned abnormal accruals, based on accounting
information has an appealing attribute that it can be applied to a large set of firms.
Identified by Aboody and Lev [1998], R&D is another source of information asymmetry
measured by accounting information and leading to insider gains. However, they didn’t
examine if information asymmetries associated with R&D affect firms’ cost of capital.
2.2.2
Institutional Classification and Informed Trading
Institutional investors overall have incentives to search for private pre-disclosure
information when making portfolio choices (El- Gazzar [1998], Brous and Kini [1994])2 .
To improve portfolio performance, institutions maintain close contacts with management
and security analysts (O’Brien and Bhushan [1990]). However, institutional investors are
diverse. Depending on their investment objectives and risk tolerance, different types of
institutions may choose to more actively trade on information or to follow longer-term buy
and hold strategies. What we are interested in this study are those institutions that have
superior information gathering and processing abilities and, as a consequence, can be
reasonably portrayed as informed traders. We conjecture that a classification scheme that
differentiates institutions based on their investment objectives might be suitable.
Past literature groups institutional investors in two different ways. One way is to
2
Investment managers from different institutions expressed that they spend much time and effort on
information collection and in-house analysis to improve portfolio performance and to satisfy their fiduciary
responsibility standard.
7
classify institutional investors based on their legal forms: bank trusts, insurance companies,
investment companies, investment advisers, and pensions and endowment. Lang and
McNichols [1997] and Ali et al. [2002] use this categorization in their studies. Lang and
McNichols [1997] examine the association between quarterly changes in institutional
holdings and firms’ earnings and return performance. Ali et al. [2002] use a three-day
period abnormal return of the subsequent quarterly earnings announcement to test if some
institutional investors trade on information about forthcoming earnings. The commonality
of their findings is that independent investment advisors (which invest client funds into
several mutual funds) and investment companies (primarily mutual funds) are more likely
to have early access or fast reactions to short-term information. However, pension funds,
college and university endowments and private foundations focus more on long-term
horizons and don’t react to short-term information as actively. Compared to their
investigations, this study draws a picture that directly depicts institutions’ short-term
profitability with respect to asymmetric information as specifically measured by earnings
quality and R&D.
In contrast to the institutional types above, Bushee [1998] refines the classification
of institutions by their past investment behavior. He classifies institutions into three
clusters: transient, quasi- indexing, and dedicated. Transient institutions are characterized as
having high portfolio turnover and highly diversified portfolio holdings. Dedicated
institutions are characterized as having low turnover and more concentrated portfolios.
Quasi- indexing institutions are those having low turnover and diversified portfolios. Since
then, several studies have examined transient institutions’ short-term profit driven trading
behavior. Ke and Ramalingegowda [2004] provide evidence that transient institutions trade
8
to exploit PEAD. Ke and Petroni [2004] picked a subset of earnings announcements, i.e.,
earnings breaks, and examine the trading behavior of transient institutional investors in
anticipation of those breaks. Although prior research shows transient institutions’ reactions
to short-term as well as long-term information, none of the studies above link transient
institutional trading with information asymmetry and cost of capital. It is the active trading
behavior of transient institutions that motivates us to study whether they are trading on
asymmetric information systematically and how that affects cost of capital.
Thus, we chose Bushee’s classification for our main analysis. We justify this
choice on two grounds: First, transient institutions’ active trading behavior suggests that
they possess better skills for acquiring, processing, and reacting to information quickly.
Hence, they are better candidates for informed traders that seek to exploit the information
asymmetries within the framework of our research design. Second, holding the information
aspect aside, we want our benchmark institutions to be similar to the target institutions on
some dimension. Quasi- indexing institutions have similar diversification to transient
institutions suggesting their usefulness as a benchmark. We include dedicated institutions
notwithstanding that they may also be seeking to exploit an information advantage. While
dedicated institutions may be trading on private information, we believe that information is
more likely to be long-term in nature and less likely to be detectable using our shorthorizon windows for estimating abnormal returns.
3.
Hypothesis Development and Methodology
3.1
Hypothesis Development
Institutions have incentives to improve their portfolio performance as well as fulfill
their fiduciary duties. While diversification is a measure consistent with a fiduciary’s
9
prudence according to modern portfolio theory, turnover rates indicates that institutions
actively trade on information. Bushee [1998] classifies institutions into three groups based
on these measures. According to Bushee, transient institutions, characterized by high
portfolio turnover and highly diversified portfolio holdings, are more likely to base their
trades on superior knowledge of current earnings or its components. Hence, they are
hypothesized to trade on private information. Quasi- indexers3 have low turnover and
diversified holdings consistent with passive indexing and buy-and- hold strategies. Since
quasi- indexers have similar diversification as transient institutions, we regard them as a
benchmark for non- information based trading. Dedicated institutions have low turnover
and low diversification. As mentioned above, these characteristics suggest that to the
extent trades are information based, the information may be long-term in nature and less
likely to surface within the windows employed in this study for measuring monthly
returns.
The asymmetric information measure in this paper is primarily earnings quality.
Earnings quality proxies defined by Francis et al. [2002] are based on unsigned accruals
and are intended to capture the information uncertainty in future earnings. Compared with
the cash component of earnings, accruals are subjected to management judgment and
manipulation. Therefore, high unsigned discretionary accruals (low earnings quality)
correspond to less private information preempted by public disclosure. As a consequence,
informed traders are left with more of an information advantage. We employ four earnings
quality measured used by AHL [2004] 4 . Specifically, two earnings quality proxies are
3
Quasi-indexers account for over 60% of the total sample, only 10% of institutions have claimed index
strategies (Bushee, 2001). The majority of quasi-indexers doesn’t index but follow buy and hold strategy.
4
We thank Aboody, Hughes and Liu for making their data available. The detailed construction methods of
four earnings quality measures are restated in Appendix for readers’ convenience.
10
based on the modified Jones’ model (Dechow, Sloan, and Sweeney [1995]) and the other
two are based on Dechow and Dichev’s [2002] model.
Apart from whether asymmetric information risks are priced, standard
microstructure theory (e.g., Kyle [1985]) predicts that expected profits from trading on
private information are increasing in both the prior variance of future asset payoffs and the
precision of that information, i.e., the degree of information asymmetry. With earnings
quality as the measure of information asymmetry and transient institutions to proxy
informed traders, we predict:
H1: Transient institutions’ trading profitability is higher when trading lower
earnings quality firms’ shares.
In a multi-assets context, Easley and O’Hara [2004] model uninformed investors as
requiring a risk premium to compensate for the risk of trading with informed traders.
Francis et al. [2002] and AHL [2004] empirically detect the existence of a premium for
asymmetric information risk as measured by a factor-mimicking portfolio based on
earnings quality. If the low earnings quality (high information asymmetry) firms are priced
lower than they would otherwise be without information asymmetry, we would expect H1
to hold with the control for an asymmetric information risk premium.
Easley and O’Hara [2004] and Hughes, Liu and Liu [2004] further indicate that the
price effects of asymmetric information risk are associated with informed traders’
exploitation of their information advantage. AHL [2004] develop a research design for
extracting the systematic (priced) component of earnings quality factor by filtering out the
non-systematic (diversifiable) component. If transient institutions’ trading profit
contributes to the cost of capital effect of information asymmetry, then we expect:
11
H2: Transient institutions’ trading profitability is positively associated with
the priced component of earnings quality risk.
3.2
Methodology
The methodology used in this paper is to construct “copycat” portfolios to mimic
institutions’ trading strategies and use returns on those portfolios to represent institutions’
trading profitability. We further investigate if the trading profitability increases with
information asymmetry. First, for each institutional class, the change in an individual
institution’s holdings in shares for a given firm are aggregated across institut ions.
Beginning and ending holdings are adjusted for splits and stock dividends. Then, for the
sample firms within the same information asymmetry quintile, we construct a buy portfolio
and a sell portfolio at the beginning and the end of each quarter5 according to the sign of
each institutional class’s aggregate holdings change. Portfolio returns could be measured
over contemporaneous or subsequent quarter. Within any buy and sell portfolio, we weight
the return for each stock in proportion to its aggregate institutional holdings change in
dollars6 . After that, we form the copycat portfolio 7 for each institutional class by going
long on the buy and short on the sell portfolios derived earlier. Copycat portfolio returns
are then regressed on Fama-French’s [1993] three factors8 and an asymmetric information
risk factor 9 for each information asymmetry level. The resulted abnormal return is used to
5
Since 13F doesn’t provide the exact trading date, we form portfolio at the beginning and ending of quarter
to proxy the trading profitability for early informed institutions and late informed institutions. Hirshleifer,
Subrahmanyam and Titman (1994) models some informed traders receive private information before others.
The trades of the informed in their model are consistent with oft-cited institutional strategies.
6
Specifically, the weight for a given firm in a portfolio of firms traded by the aggregate institution is
determined by dividing the dollar value of the change in holdings of that firm’s shares by the dollar value of
the aggregate changes in holdings for all firms shares in that portfolio.
7
“Copycat portfolio” is rebalanced every quarter.
8
We thank Fama and French for making their data available.
9
Factor mimicking portfolio is constructed by going long in the low earnings quality firms and short in high
earnings quality firms. To ensure only public information is used, we form factor mimicking portfolio based
12
proxy the total trading profitability. For example, for low earnings quality firms, the
regression is the following:
R L Q, t = Rbuy ( q), t − Rsell ( q), t = α LQ + β LQ MKTt + δ LQ SMB t + σ LQ HMLt + φ LQ EQt + ε t
(1)
where
Rbuy ( q), t =
∑
i∈LQ,∆ ( q )> 0
ωi =
ω i Ri
∆i
∑
j∈LQ ,∆ ( q)> 0
∆j
Rsell( q), t
The portfolio returns in month t for firms that have low
earnings quality and institutions are net buyers in
quarter q; q is 0 when portfolio formation quarter
concurs with return measurement quarter; q is -1 when
portfolio formation quarter is the quarter before return
measurement quarter;
The weight for the a given stock i in a portfolio is
defined by institutional holding change in dollars ? i for
that stock divided by institutional holding change in
dollars for all stocks in that portfolio; beginning quarter
stock price is used to calculate ? ;
Defined in the same way as buy side but with ?<0;
MKTt
The market excess return in month t;
SMBt
The return on the zero investment portfolio in month t
that is long in small stocks and short in large stocks;
HMLt
The returns on the zero investment portfolios in month t
that is long in high book-to- market stocks and short in
low book-to- market stocks ;
EQt
The earnings quality factor mimicking portfolio returns
in month t that is long in low earnings quality stocks
and short in high earnings quality stocks;
Note that within each portfolio we weight the return for each stock in proportion to
its institutional holding change in dollars. This is a refinement of the methodology used in
Aboody and Lev [2000] and AHL [2004] and is more suitable for analyzing institutional
trading. Institutions trade different stocks at the same time. The trading profitability is
characterized not only by the choice of stocks but also by the funding allocation among
on EQ measures of the preceding fiscal year.
13
different stocks. It is reasonable to expect that when trading for informational purposes,
portfolio managers will allocate funds consistent with their private information, taking
larger positions in shares of firms for which they have a greater information advantage than
diversification alone would recommend. Using equally weighted portfolio returns
disregards the information contents contained in the change in holdings. Hence, in our
portfolio construction, we weight stocks by dollar volume change in quarterly holdings.
Under this construction, the copycat portfolio is a zero- investment portfolio of taking long
and short positions similar to institutions’ trading as implied by institutions’ quarterly
holdings change.
To examine the extent to which institutions’ gain differs across firms with different
information asymmetry, we employ an intercept test on regression (2) using the difference
of copycat portfolio returns between low information asymmetry firms and high
information asymmetry firms as the dependent variable. Independent variables still include
Fama-French’s three factors and an asymmetric information risk factor in the form of the
earnings quality factor- mimicking portfolio. A significantly positive intercept term
α p would be evidence in support of Hypothesis 1.
R L Q, t − R HQ , t = α p + β p MKTt + δ p SMB t + σ p HMLt + φ p EQt + ε t
(2)
We need to first extract the priced component of information asymmetry risk before
proceeding to Hypothesis 2. Following the methodology developed by AHL [2004], we
develop our test in two stages. In the first stage, for each firm- month, returns of its past 36
months are regressed on Fama-French’s three factors and a fourth earnings quality
(asymmetric information risk) factor as in equation (3). Earnings quality risk factor
loadings are used to estimate firms’ exposures to the systematic component of asymmetric
14
information risk.
R j , t − R f , t = α j + β j MKTt + δ j S M B t + σ j HMLt + φ j EQ t + ε j , t
(3)
In the second stage regression, instead of partitioning our sample by the raw
earnings quality measures as in the case of testing Hypothesis 1, we first quintile our
sample firms by their exposure to asymmetric information risk φ j each month. The firms in
the highest quintile are labelled Hφ firms and the firms in the lowest quintile of exposure
are labelled Lφ firms. Then, copycat portfolios are formed for each quintile of asymmetric
information risk exposure and hedge portfolios are constructed by going long on the firms
with high exposure and short on firms with low exposure as estimated by first stage factor
loadings. Because firms are initially sorted by their risk exposure, idiosyncratic
(diversifiable) component of information asymmetry associated with earnings quality is
filtered out. The resulting profit, if any, can only be related to the systematic component of
information asymmetry. Regressions are based on the quintile of asymmetric information
exposure as described in (4) and (5). Hypothesis 2 predicts the intercept term α p in (5) to
be significantly positive.
R Lφ , t = Rbuy ( q), t − Rsell ( q), t = α Lφ + β Lφ MKT t + δ L φ SMB t + σ Lφ HMLt + φ Lφ EQt + ε t
R H φ , t − R Lφ , t = α p + β p MKTt + δ p SMBt + σ p HMLt + φ p EQt + ε t
4.
Earnings Quality as a Proxy for Information Asymmetry
4.1
Data and Descriptive Statistics
(5)
Institutional holding data are obtained from CDA/Spectrum. The time frame is
from the first quarter of 1985 through the fourth quarter of 2002. All institutions with
15
(4)
greater than $100 million of securities under management are required to report their
holdings to the SEC on a quarterly basis. On the quarterly report form 13F, all commonstock positions greater than 10,000 shares or $200,000 must be disclosed.
Classification of institutions is obtained from Brian Bushee 10 . The classification
method is described in Bushee [1998] and has been used in many studies (Bushee and Noe
[2000], Bushee [2001], Ke, Huddart, and Petroni [2003], Ke and Petroni [2004], Ke and
Ramalingegowda [2004], Collins et al. [2003]). Later, results using institutions’ legal
forms 11 specified in Spectrum are reported for comparison. Spectrum categorizes
institutions into five types: (1) bank, (2) insurance company, (3) investment company
(mutual funds), (4) investment advisor, and (5) other. “Investment advisor” is comprised of
the large brokerage firms and “O ther” contains pension funds and university endowments.
We also restrict our study to common stocks found in the Center for Research in Securities
Prices (CRSP) monthly files.
Accounting and cash flow data are obtained from COMPUSTAT and price data
from CRSP. Four earnings quality measures are estimated over years 1985-2002 yielding
612,821 firm- month observations for EQ1 and EQ2, 512,049 observations for EQ3 and
329,513 observations for EQ4 (See Appendix for detailed estimation method). Table 1
reports the summary statistics of variables used in the following analysis.
INSERT TABLE 1 ABOUT HERE
Panel A of Table 1 lists the sample distribution of EQ1 to EQ4. EQ1 and EQ2 are
highly skewed due to the deflation effect of total assets when total assets for some firms
10
I thank Brian Bushee for sharing with us his trading orientation classification of institutional investors.
Cross classification check is conducted between Bushee’s classification and institutional types provided in
Spectrum. Results show that these two classification schemes are largely orthogonal. In the discussion
section, we repeat our analysis to use institutional types to proxy informed traders.
11
16
are close to zero. This skewness has little impact on the later part of our study since all
return analyses are done on a portfolio basis. Panel B of Table 1 tabulates the aggregate
institutional holding and quarterly holding change statistics for three institutional classes.
Because all ratio statistics inevitably create outlier problems when denominators are small,
median statistics are reported in Panel B. We observe the following patterns from Panel B.
First, for all three institutional classes, the number of firm- months they buy is significantly
larger than the number of firm- months they sell. This is consistent with the fact that
institutions’ holdings grew dramatically during our sample period. The total institutional
ownership increases from 36.5% of market capitalization in 1985 to over 50% of
outstanding equity in 2000 (Gompers and Metrick [2001]). Second, the institutions’ fast
expansion is also reflected through their quarterly holding changes. The median of ratios of
holding change to holding itself (Change/Holding, column 3) and the median of ratios of
holding change to total shares outstanding (Holding/Outstanding, column 5) are
consistently greater on the buy side than on the sell side for all institutional classes. The
medians of ratios of change of holding to quarter-end holding display different investment
styles for three institutional classes. Transient institutions are the most aggressive and
quasi- indexers are the least aggressive traders among the three classes. Lastly, the median
of ratios of quarter-end holding to total shares outstanding (Holding/Outstanding, Column
4) reflects the diversification aspect of institutions’ trading. All three institutional classes
tend to buy stocks that they hold less of, and sell stocks that they hold more of.
Panel C of Table 1 depicts the pricing implications of earnings quality. We run a
time series regression for each firm in our sample using its latest 36 months’ returns up to
December of 2002. The regressors include Fama-French three factors and the fourth
17
asymmetric information risk factor in the form of the hedged portfolio returns going long
in the lowest earnings quality firms and short in the highest earnings quality firms. The
positive loadings on the asymmetric information risk factor in Panel C are consistent with
previous findings on the pricing of asymmetric information risk (Francis et al. [2002] and
AHL [2004]). To control for the risk premium impact on later measurement of institutions’
information gains, we include the earnings quality factor as an additional factor for the rest
of portfolio regression analyses.
4.2
Institutional Trading Profitability on Raw Earnings Quality
If we had daily data on institutional holdings, it would be easy to measure
institutions’ trading profitability starting from the next day after trading. However,
institutions are only required to report their holdings on a quarterly basis. We need to
fashion our assumptions accordingly. Hirshleifer, Subrahmanyam and Titman [1994]
provide a rational expectations framework for doing so. They model a competitive security
market in which some informed agents receive private information before others. Their
results rationalize oft-cited institutional trading strategies such as profit-taking, followingthe-leader, buying winners and selling losers. Along the same line as Hirshleifer,
Subrahmanyam and Titman [1994], we assume early informed institutions trade at the
beginning of quarter and reverse their trades in a quarter after late-informed institutions’
trades make the price more revealing. Late- informed institutions profit from the remaining
information advantage. In the following two subsections, information advantage for the
early informed institutions and late informed institutions are examined separately.
Depending on the arrival of late informed institutions, the profiting window for early
informed institutions can be thought to span the trading quarter.
18
We use the subsequent quarter as the profiting window for late informed
institutions. The SEC imposes a 45 day deadline for 13F filing following a quarterly
reporting period. We allow another 45 days for the market to fully absorb information
revealed through the delayed disclosure of trades 12 . An advantage of using the subsequent
trading quarter as the profit window for late informed institutions is the comparability of
duration with the window for early informed institutions.
The results based on an window of one month instead of three months are reported
in section 6 under the alternative assumption that sufficient private information revealed in
the first month of subsequent quarter to register detectable abnormal returns. An advantage
of this choice is it does not rely on an assumption as to when within a quarter institutions
might trade; only that their private information is not fully revealed for at least one month
in the subsequent quarter.
4.2.1
Informational Advantage for Early- informed Institutions
For the early- informed institutions, portfolio formation quarter concurs the return
measurement quarter. In particular, each stock return in the copycat portfolio is weighted
by its institutional holding change in dollars over the contemporaneous quarter (Quarter 0).
First, using the classification in Bushee [1998], trading shares (proxied by the change of
holding between adjacent quarter-ends) for each firm-quarter13 are aggregated for three
classes of institutions: transient, quasi- indexing and dedicated. The trade direction for each
12
13(f) authorizes the Commission to delay or prevent the public disclosure of information as it determines
to be necessary or appropriate in the public interest or for the protection of investor. Due to the delay or
prevent the public disclosure of certain information, we allow 45 days after reporting deadline for most of
information reflected on the price.
13
Missing reports in CDA/Spectrum may cause the misalignment of quarterly holding change. To combat
this problem, for missing data of less than 2 quarters, we assume that change of holding spread over the
missing quarters. For missing data of more than 2 quarters, we exclude the reporting quarter from our study
to ensure the accuracy of using change of holdings to proxy institutions’ trades.
19
institutional class and each firm- month is further dichotomized into buy (sell) if that
institutional class increases (decreases) its positions in the contemporaneous quarter.
Second, firm- months are sorted into quintile portfolios based on one of the four earnings
quality measures and then partitioned into buy and sell. The portfolio returns are calculated
by weighting each stock’s monthly returns in proportion to its corresponding quarterly
institutional holding change in dollars. Finally, for each earnings quality quintile, we
construct a copycat portfolio by going long on the buy portfolio and short on the sell
portfolio. We then run time series regression (1) on the copycat portfolio returns based on
180 months of data, from 1/88 to 12/02 14 .
To verify that institutions’ trading profits are positively associated with information
asymmetry, we construct a hedged portfolio long in the lowest quintile and short in the
highest quintile of earnings quality copycat portfolios. A similar hedge portfolio
methodology has been used in prior studies to test corporate insiders’ superior information
advantage. (Aboody and Lev [2000], Piotroski and Roulstone [2004], Ke, Huddart and
Petroni [2003], AHL [2004]). In the test of Hypothesis 1, we expect the hedge portfolio to
earn significant abnormal returns as estimated by the intercept in regression (2); i.e.,
Jensen’s alpha.
INSERT TABLE 2 ABOUT HERE
Table 2 shows how three institutional classes’ trading profits vary with information
asymmetry as measured by earnings quality. We begin our discussion with the results for
transient institutions. The abnormal returns for copycat portfolio are significantly positive
14
The time series is shortened by three years in the reported tables in order to be consistent and comparable
with the later part of the study in examining the systematic component of information risk.
20
for all earnings quality quintiles. The estimated intercepts range from 4.111% to 4.486%
for the highest earnings quality quintile and range from 4.987% to 6.486% for the lowest
earnings quality quintile. To examine if asymmetric information is one aspect of transient
institutions’ information advantage, we find the trading gain is significantly higher for low
earnings quality firms than for high earnings quality firms. The abnormal returns for
hedged portfolio are 1.540% (t = 2.50), 1.134% (t = 1.88), 2.000% (t = 3.13) and 0.840% (t
= 2.08) for EQ1-EQ4, respectively. In contrast to the first three earnings quality measures,
EQ4 is less impressive for capturing the information advantage for institutions possibly
due to a sizable reduction of observations.
Moving to quasi- indexers, we don’t find significant abnormal returns for copycat
portfolios nor for hedge portfolios, consistent with their passive indexing and buy-and-hold
strategies. Dedicated institutions are found to profit significantly more from low earnings
quality firms, although less in magnitude than transient institutions.
To sum up, results in Table 2 support our Hypothesis 1 that trading profits for
transient institutions are positively associated with the information asymmetry measured
by earnings quality. Those profits on asymmetric information for transient institutions are
economically significant. The total annualized profits for transient institutions are 18.482%
based on EQ1, 15.763% based on EQ2, 24.003% based on EQ3 and 10.079% based on
EQ4. Consistent with the inactive trading pattern observed for quasi- indexing institutions,
we do not find that they profit from information asymmetry. Dedicated institutions also
appear to exploit asymmetric information, but in a less significant way. Although the
positive abnormal returns on hedged portfolios for dedicated institutions are consistent
with information-based trading suggested by under-diversification, the mild significance
21
indicates that information possessed by dedicated institutions likely has long-term
characteristics the profits from which are not picked up by our monthly return regressions
over quarter- length windows. While plausible theoretically, empirically, the results above
are based on the unverifiable assumptions that early- informed institut ions trade at the
beginning of quarter and profit from partial revelation of that information by late- informed
institutions’ trades. The fact that there may be informed traders than our institutions makes
return over concurrent quarter a noisy measure for institutions’ information advantage. To
cope with these and other issues mentioned earlier, we examine institutions’ information
advantage by examining the profitability of their trades in the subsequent quarter.
4.2.2
Informational Advantage for Late- informed Institutions
Rule 13f-1 of the 1934 Security Act requires every institutional investment
manager which exercises investment discretion with respect to accounts with an aggregate
fair market value of at least $100 million to file a report on Form 13F with SEC within 45
days after the last day of calendar quarter. The public disclosure of institutions’ trades
provides another channel for information to surface price and hence another way to detect
the information advantage for institutions. Furthermore rule 13f-3 on confidential
treatment authorizes the SEC to delay or prevent the public disclosure of information as it
determines to be necessary or appropriate, and for comparability with early- informed trade
results, we allow another 45 days before public disclosure or trading fully dissipates an
information advantage. Thus, in this section, we measure information advantage for lateinformed institutions revealed through public disclosure of trades over the subsequent
quarter after trading.
Since we assume that the late- informed trades at the end of quarter, trading profits
22
are then measured over the subsequent quarter. For example, a copycat portfolio formed at
the end of March, 1995 is based on the institutional holding change over the first quarter of
1995. But the portfolio returns are measured over the second quarter. We hold this copycat
portfolio for three months before it is rebalanced. Essentially, the portfolio return measured
this way represents the private information revealed through the public disclosure of trades
or revelation through other means.
INSERT TABLE 3 ABOUT HERE
Table 3 parallels Table 2 to demonstrate institutions’ trading profitability as lateinformed traders. Transient institutions profit from low earnings quality firms for three out
of four earnings quality measures, EQ1, EQ2 and EQ3. Comparing results in Table 3 with
those in Table 2, abnormal returns for the copycat portfolios using returns of subsequent
quarter are notably lower than for the trading quarter. This comparison suggests that most,
but not all, of the private information upon which transient institutions may be trading
likely becomes reflected in price through trading. The hedge portfolios’ abnormal returns
are significant for EQ1 and EQ3 at 1.198% (t = 2.37) and 1.464% (t = 2.66) respectively.
The significant abnormal returns for hedged portfolios in the subsequent quarter are
consistent with profits to private information becoming realized as public disclosures of
information based trades are made to the SEC or by other means. Moving to the other two
institutional classes, we don’t observe significant abnormal returns for either copycat
portfolios or hedged portfolios. In other words, these two institutional classes don’t appear
to trade on short-term information detectable from monthly returns.
In sum, results in Table 3 and Table 4 support our hypothesis that transient
institutions trade on information asymmetry measured by earnings quality. Trading
profitability is positively associated with information asymmetry for both early- informed
23
institutions and late- informed institutions. Profits for the former are measured using
contemporaneous quarter returns and profits for the latter are measured over subsequent
quarter. The positive association between information asymmetry and subsequent quarter
abnormal returns is consistent with the theoretical prediction that not all private
information is revealed through informed trading; some is revealed by public disclosure of
trades.
4.3
Institutional Trading Profitability on Systematic Asymmetric Information Risk
Theory predicts that for information risk to be priced by the market, informed
traders must be trading on the systematic component of asymmetric information. In other
words, the informed knowing more than the uninformed with respect to idiosyncratic
(diversifiable) information would not, in theory, have any pricing implication. Having
demonstrated that raw earnings quality measures are effective proxies for information
asymmetries exploited by transient institutions, we now examine whether institutions profit
from just the systematic component of their private information.
Considering the exposure to asymmetric information risk as the basis for market
compensation for the uninformed to bear the risk of trading with informed traders, we
would expect informed traders to profit more from stocks that require more compensation
for risk. In other words, we expect, a priori, that transient institutions profit more from
stocks that have higher asymmetric information risk exposure than what the premium on
that risk alone would recommend.
4.3.1
Informational Advantage for Early-Informed Institutions
Table 4 inherits the structure of Table 2 except that the raw earnings quality
measure is replaced by asymmetric information risk exposure as indicated by loadings on
24
the earnings quality factor. To get asymmetric information risk exposure for each firmmonth, we use its past 36 months of returns to estimate the factor loadings through
monthly time series regressions given by (3). Again, the four regressors are Fama and –
French’s three factors and the earnings quality factor. In the same spirit as the market
exposure β in CAPM, the resulted loading φ j on the factor- mimicking portfolio return
defines the asymmetric information risk exposure for firm j and it changes over time.
For purposes of the second stage time-series regression on portfolio returns, all
sample firms are sorted into quintiles each month based on their exposures to information
risk and further partitioned into buy and sell portfolios by the corresponding institutional
holding change in contemporaneous quarter. The rest of analysis is structured the same
way as the case of raw earnings quality. Since the diversifiable (non-priced) component is
filtered out in the first stage regression, the information advantage is now only related to
the systematic component of asymmetric information.
INSERT TABLE 4 ABOUT HERE
Table 4 presents strong evidence that transient instit utions exploit the systematic
component of information asymmetry. Intercepts for copycat portfolios range from 4.323%
to 5.205% for firms with low asymmetric information risk exposure, while for high
information risk exposure firms, intercepts range from 6.919% to 7.834%. The difference
of profitability on high and low information risk exposure firms is examined through hedge
portfolio returns. Hedge portfolios make significant abnormal returns of 2.626% (t = 2.68)
for EQ1 exposure, 2.149% (t = 2.22) for EQ2 exposure, 3.350% (t = 3.67) for EQ3
exposure and 3.426% (t = 3.90) for EQ4 exposure. Comparing this set of results with those
reported in Table 2, we observe greater magnitudes and significance levels on hedge
25
portfolios’ abnormal returns associated with systematic component of asymmetric
information than with raw asymmetric information. This difference could be caused by the
considerable noise contained in the raw earnings quality measures. This noise is filtered
out when information risk exposure is used to quintile the portfolios. Turning to the two
other classes, although results suggest that quasi- indexers do not profit from the systematic
component of asymmetric information, dedicated institutions appear able to exploit such
information asymmetry sys tematically.
4.3.2
Informational Advantage for Late-Informed Institutions
As indicated earlier, by being early- informed traders, it is plausible that some
transient institutions can exploit information to greater advantage than others. Information
acquired later is less valuable even though it has not yet been fully revealed. Profit for lateinformed institutions is realized through public disclosure of trades and public revelation
of private information in which they trade. In Table 5, we repeat the analysis above using
subsequent quarter return to examine if systematic component of asymmetric information
is still valuable for late-informed institutions.
Results in Table 5 again favor viewing transient institutions as informed traders.
The abnormal retur ns for high information risk exposure firms are significantly positive at
2.108% (t = 2.28) for EQ1 based exposure, 1.619% (t = 1.89) for EQ2 based measure,
2.392% (t = 3.03) for EQ3 based exposure and 2.042% (t = 2.45) for EQ4 based measure.
Abnormal returns for hedged portfolios are significantly positive for EQ3 and EQ4 based
exposure measures. For exposure based on EQ1 and EQ2, abnormal returns are less
statistically significant. With regards to the other two institutional classes, the evidence
does not indicate that they profit on asymmetric information systematically.
26
In sum, the results from our tests of a positive association between firms’
asymmetric information risk exposure and transient institutions’ trading profitability offer
supporting evidence that pricing effect of asymmetric information risk is at least partially
attributable to transient institutions’ trading. In both cases, as either early- informed or lateinformed traders, transient institutions profit abnormally more on firms with high
systematic exposure to asymmetric information risk. Quasi- indexers and dedicated
institutions don’t appear to profit from the systematic component of private information
and hence don’t contribute to the asymmetric information premium found in prior research.
5.
R&D Intensity as a Proxy for Information Asymmetry
The analysis in Section 4 supports our hypothesis that transient institutions are a
suitable proxy for informed traders and exploit asymmetric information as measured by
earnings quality. The focus of this section is on another potential source of information
asymmetry, namely research and development (R&D) activities. R&D was identified as a
measure of information asymmetry by Aboody and Lev [2000] in their investigation of
corporate insiders’ trading gains. Compared with the asymmetric information as measured
by earnings quality, R&D related asymmetric information is relatively unique. Therefore,
one can infer little information about one firm’s R&D outcome from observing the R&D
performance of another firm.
5.1
Data and Descriptive Statistics
INSERT TABLE 6.1 ABOUT HERE
The sample consists of 1,248,203 firm- months. We group the sample firm- months
into R&D and No-R&D sub-samples depending on whether the company reported any
R&D expenditure during the sample period from 1985 to 2002. As shown in Panel A,
27
market value of the R&D sub-sample on average is much larger and more skewed than NoR&D sample since big firms are more likely to conduct R&D projects. The R&D subsample has bigger variance on its monthly returns, consistent with our assumption that
R&D firms have higher information asymmetry. Panel B provides the institutional
ownership statistics for our three classes of institutions. Among them, transient institutions
trade the most aggressively, especially when they are trading R&D stocks. In the next
section, we look at how their trading profits vary with firms’ information asymmetry.
5.2.
Institutions’ Information Advantage
If institutional investors are privately informed about planned changes in R&D
budgets and trade on that advance knowledge, we would expect institutions to profit more
on firms engaged in R&D activities than firms without R&D. Similar to the case of
earnings quality, R&D firms and No-R&D firms are further divided into portfolios based
on transient institutional holding change in dollars, leading to the construction of four
portfolio time series. (1) RDb u y(q ),t for firms engaged in R&D while transient institutions
are net buyers. (2) RDsell( q), t for firms engaged in R&D while transient institutions are net
sellers. (3) NORDbuy ( q), t for firms without R&D while transient institutions are net buyers.
(4) NORDsell ( q), t for firms without R&D while transient institutions are net sellers.
Portfolio returns are measured over concurrent or subsequent quarter. Then, copycat
portfolios for R&D firms and No-R&D firms are constructed by going long in buy
portfolios and short in sell portfolios and returns on those portfolios are regressed on
Fama-French’s three factors. The regressions are described as in equations (6) and (7):
RDt = RDbuy ( q),t − RDsell ( q), t = α RD + βRD MKTt + δRD SMBt + σRD HMLt + εt
28
(6)
NORDt = NORDb u y(q ),t − NORDsell ( q), t = αNORD + β NORD MKTt + δNORD SMBt + σNORD HMLt + εt
(7)
To investigate if institutions’ profits vary with information asymmetry, returns on a
hedge portfolio that is long in the RD portfolio and short in the No-RD portfolio are then
regressed on Fama-French’s three factors as in equation (8).
RDt − NORDt = α + β ( Rm , t − R f ,t ) + δ SMBt + σ HMLt + ε t
(8)
INSERT TABLE 6.2 ABOUT HERE
As expected, results in Table 6.2 show that transient institutions’ trading profits are
significantly higher for R&D firms than for No-R&D firms. Specifically, the abnormal
returns on hedge portfolios for transient institutions are 1.963% (t = 7.39). However, for
the other two institutional classes, trading in R&D firms doesn’t appear to give them an
information edge. Judging from the estimated intercepts of copycat portfolios, quasiindexer and dedicated institutions don’t profit in either R&D and No-R&D groups. Panel B
of Table 6.2 provides results when profits are measured over subsequent quarter. Transient
institutions’ profits still remain significantly positive as late- informed traders. The
evidence that transient institutions, as opposed to other institutions, realize abnormal
returns of 0.502% (t = 2.23) on hedged portfolios corroborates their superiority from
trading R&D related asymmetric information.
5.3
Is Information Asymmetry Associated with R&D Priced?
Using earnings quality as information asymmetry proxy, Francis et al [2002] found
that firms’ return significantly loads on the asymmetric information factor. AHL [2004]
extend their findings and show that pricing evidence on Jensen’s alpha is weaker than that
suggested by factor loading estimates. They further extrapolate systematic component of
29
information risk as measured by earnings quality and link it with insider trading. Facing
another source of information asymmetry—R&D, we consider whether R&D related
information asymmetry is priced by the market.
We start by building a R&D risk mimicking portfolio by going long in R&D firms
and going short in No-R&D firms. To estimate the risk exposure for each firm- month, we
then run time series regression as in equation (9) on Fama-French’s three factors and the
R&D risk factor based on the firm’s past 36 months returns. Following the first stage
regression, the sample firms are quintiled into portfolios based on their exposure to R&D
risk. We further construct a hedged portfolio of long high risk exposure firms, Hφ , and
short low risk exposure firms, Lφ . Note that our objective is to verify if R&D risk is priced
in this subsection. Hence, the portfolio construction is unrelated to institutions’ trading.
Finally, we conduct a time-series regression in equation (10) on the hedged portfolio and
test if the estimated intercept is significantly positive.
R j ,t − R f , t = α j + β j MKTt + δ j SMBt + σ j HMLt + φ j RD t + ε j , t
H φ p , t − Lφ p , t = α p + β p MKTt + δ p SMBt + σp HMLt + εt
(9)
(10)
INSERT TABLE 6.3 ABOUT HERE
The results in Table 6.3 indicate that R&D risk is not priced by the market.
Jensen’s alpha on hedged portfolio is insignificantly different from zero. In other words,
there appears to be no systematic component of information risk associated with R&D.
This insignificance could be explained by the relative uniqueness (idiosyncrasy) of R&D.
asymmetric information about R&D outcomes does not appear to represent a source of
systematic risk. Rather, it seems that uninformed traders can avoid R&D information risk
30
by holding diversified portfolio. We further attribute the information gains found in Table
6.2 as purely idiosyncratic and as having no impact on companies’ cost of capital. The
absence of pricing effect of R&D risk along with the informed traders’ exploitation call
into question if under diversification is the reason behind the pricing of information
asymmetry.
6.
Robustness Issues and Discussion
6.1
Exploitation of Asymmetric Information by Institutional Types
Ali et al. [2004] document change of institutional ownership during a calend ar
quarter is positively associated with abnormal returns at subsequent earnings
announcements. Using the earnings announcement return as an information asymmetry
leftover, they suggest that some institutions’ trades in the prior-announcement quarter are
based on superior information about forthcoming earnings. Furthermore, they identified
investment companies, investment advisors, and insurance companies as those that seek
and trade their private information about future earnings. One reason that their results hold
in the extreme case of change of institutional ownership might be that next quarter earnings
is only one dimension of institutions’ private information. In order to test if institutions
exploit information advantage in a broader setting of information asymmetry, we rerun our
analysis based on institutional types as Ali et al. Results are reported in Table 7. To save
space, we only tabulate institutions’ trading profitability using EQ1.
Panel A shows that hedged portfolio returns are significantly positive for mutual
funds and brokerage firms if we measure trading profits from the beginning of trading
quarter. This is consistent with the notion that these two types of institutions face greater
competition for clients and, hence, strive to obtain pre-disclosure information and improve
31
their performance (O’Barr and Conley [1992]). But if instead trading gains are measured
over the subsequent quarter, the information advantage diminishes for all institutional
types. On one hand, the diminishing trading profit is consistent with Ali et al.’s finding that
information-based trading can only be detected in the extreme cases of change of
institutional ownership. On the other hand, the result supports our hypothesis that transient
institutions are a better proxy for informed traders than any institutional type based on its
legal form.
Panel B of Table 7 demonstrates how institutions exploit systematic component of
asymmetric information. We find that the hedged portfolio’s Jensen’s Alpha remains
significant only for mutual funds at 2.918% (t = 3.44). But, again, this information
advantage disappears if returns are measured from the end of trading quarter.
In sum, using institutional types to classify institutions, we find mixed results in
terms of exploitatio n of information advantage. Mutual funds and investment advisors
seem to be trading on information asymmetry if trading profit is measured from the
beginning of trading quarter. Judging from the “information asymmetry leftover” over the
subsequent quarter, none of institutional types gain significantly. In terms of exploitation
of systematic component of asymmetric information, mutual funds perform the best.
Again, their private information does not seem to yield significant gains over the
subsequent quarter.
6.2
Information Revelation Through Disclosure of Trades — Profits from the First
Month in the Subsequent Quarter
Provided that there is sufficient information revelation when most of institutions
publicly disclose their trades following each quarter end, we expect transient institutions to
32
profit most from the information revelation during the first month in subsequent quarter.
Table 8.1 and Table 8.2 report institutions’ trading profitability during this period. The
results are broadly consistent with those reported in Table 4 and Table 5. Since the
Jensen’s alpha is obtained through a time-series regression of portfolio returns, two thirds
of observations are lost if only the first month returns are used in regression. The
significance of abnormal returns is lost as a result. This insignificance suggests information
revealed through disclosure of trades is randomly distributed over the subsequent quarter.
On one hand, the randomness could be caused by the 45 days deadline imposed by SEC for
13-F filing and the potential delay due to the confidential treatment of holdings report. On
the other hand, it is hard to identify transient institutions without investigating institutions’
past investing behavior. Hence, it may take a longer time for the market to fully absorb
information revealed through the disclosure of the transients’ trades.
6.3
Abnormal Returns vs. Transaction Cost
We now consider whether profits to private information are sufficient to cover the
transaction costs. Ke and Ramalingegowda [2004] use Wermer’s [2000] method to
estimate the ex post transaction costs (both direct and indirect) for institutions’ trading and
found that the average transaction cost for transient institutions is about 0.81% with a
range of 0.65% to 1.64%. Any of our copycat portfolio returns intended to proxy transient
institutions’ trading profits are higher than the average transaction cost. It is also worth
noting that the hedge portfolio is constructed to compare the trading profits across different
information asymmetry levels, not for trading purpose. Hence, transaction costs don’t
necessarily apply to hedge portfolios.
6.4
Potential Bias due to Offsetting Trades
33
The focus this paper is to test if transient institutions, as a group, could proxy
informed traders. If the majority of trades are offsetting trades, then aggregation will not
reflect the informativeness of the whole group, but rather just that of some institutions.
This concern is alleviated by examining the median statistics of holding changes as
reported in Panel B of Table1. Transient class buys 18.65% of its holdings and sells 8.32%
of its holdings, on average, each quarter. The high percentage is not likely to be caused by
only a few institutions. The existing offsetting trades within the group are regarded as
noise and are zeroed out once trades are aggregated. Another potential bias could be
caused by transient institutions, as a group, trading in opposite directions during a quarter.
Existence of such opposite trading directions would only induce conservative bias to our
results. Suppose that good and bad news arrive sequentially within a quarter, the holding
changes would be less sensitive to information arrival compared to actual trades. The
resulted trading profit would be biased downward.
6.5
Potential Bias due to Window-Dressing
Window dressing refers to that institutional investors buy past winners and sell past
losers immediately before quarter-end reporting dates. If transient institutions are window
dressing, then positive portfolio returns calculated from the beginning of quarter might
overstate institutions’ trading profits.
We address the potential bias due to window-dressing from the following aspects.
First, our institutional holding change is based on aggregate level. Therefore, windowdressing would affect the aggregation only when majority of institutions trade for this
reason. However, there is little evidence in literature that institutions systematically
window dress their portfolios. Second, although window dressing could be used to argue
34
that profit measured from the beginning of quarter overstates institutions’ actual trading
profits, it would not explain the trading profitability measured over subsequent quarter.
Finally, our test mainly focuses on the abnormal returns for hedge portfolios. If the
tendency that institutions window dress their portfolios is uncorrelated with information
asymmetry, then the net effect on hedge portfolio would cancel out. Overall, we don’t
think window-dressing could explain our findings.
7. Conclusion
Information asymmetry has been associated with cost of capital in both theoretical
and empirical studies. Generally speaking, models assume that uninformed traders are
aware that they may be trading against informed traders and seek to protect themselves
from the information disadvantage through price. This paper examines the role that
institutional investors play in exploiting the information asymmetries captured by earnings
quality and R&D.
Using the classifications of institutional traders proposed by Bushee [1998], the
evidence indicates that transient institutional investors appear to exploit information
asymmetries and, more importantly from a cost of capital perspective, the systematic
component of information asymmetries as measured by earnings quality. As a
consequence, they realize larger abnormal profits through trading in firms with lower
earnings quality as well as in firms with high exposure to asymmetric information risk.
This result is robust to whether trading gains are measured from the beginning or from the
end of the trading quarter. In contrast to the common characteristics of asymmetric
information as measured by earnings quality across firms within an industry, asymmetric
information associated with R&D is more unique to developing firms. Extending the
35
analysis of institutional trading profitability to R&D setting where private information may
have idiosyncratic nature corroborates the conclusion that transient institutions exploit
information asymmetry. However, unlike the pricing effect of earnings quality risk
identified by previous studies, asymmetric information risk related to R&D is not fo und to
be priced by the market, suggesting private information characteristics also matter for the
pricing effect of information asymmetry.
36
Appendix
A: Earnings quality measures construction methods
The first two measures are generated by the Modified Jones’ model. Based on the
Modified Jones’ model, accruals are separated into a non-discretionary part correlated with
firms’ operation and a discretionary part. In contrast to the signed discretionary accruals in
earnings management research, the unsigned discretionary accruals are used to measure
earnings quality. The other two measures of earnings quality are constructed using Dechow
and Dichev’s [2002] model. Basically, total working capital accruals are mapped into a
normal part correlated with past, current, or future cash flows from operation, and an
abnormal part. Earnings quality is then measured from the unsigned abnormal working
capital accruals. In sum, all four earnings quality measures are based on the absolute value
of the abnormal component. The larger absolute value indicates the poorer earnings
quality.
To start with, total accruals TA j, t , total current accruals TCAj, t , and cash flow from
operations CFO j ,t for firm j and year t are calculated using information from the balance
sheet and income statement (indirect approach) as the following:
37
TAj , t = ∆CAj, t − ∆CL j, t − ∆CASH j ,t + ∆STDEBT j, t − DEPN j ,t ;
TCAj ,t = ∆CAj, t − ∆CLj ,t − ∆CASH j, t + ∆STDEBTj , t ;
CFO j , t = NIBE j, t − TAj, t ;
where :
∆CAj ,t : firm j's change in current assets(Compustat #4) in year t;
∆CLj , t : firm j's change in current liabilities (Compustat #5) in year t;
∆CASH j, t :firm j's change in cash (Compustat #1) in year t;
∆STDEBTj , t : firm j's change in short-term debt (Compustat #34) in year t;
DEPN j, t : firm j 's depreciation and amortization expense (Compustat #14) in year t;
NIBE j, t : firm j's net income before extraordinary items (Compustat #18) in year t.
1. EQ1:
The first earnings quality measure EQ1 is the absolute value of discretionary total
accruals AAj , t , calculated using Modified Jones’ model.
First, for each of Fama and French’s [1997] 48 industry groups with at least 20
firms in each year, we do the following cross-sectional regression:
TAj, t
1
∆REVj , t
PPEj , t
= k1, t
+ k2,t
+ k3, t
+ εj , t
Asset j, t − 1
Asset j, t− 1
Assetj , t − 1
Assetj , t − 1
where
∆REV j, t : firm j's change in revenue (Compustat #12) in year t;
(A1)
PPE j, t : firm j's gross value of property, plant and equipment (Compustat #7) in year t;
Asset j ,t −1: firm j's total asset in year t-1.
Second, to estimate firm-specific non-discretionary accruals as a percentage lagged
total assets NA j , t for firm j in year t, we use the industry-year parameter estimates from
(A1) on this specific firm; i.e.
38
∧
NAj, t = k 1, t
∧
1
( ∆REVj , t − ∆ΑRj , t ) +k∧ PPEj, t
+ k 2,t
3,t
Asset j, t − 1
Asset j, t − 1
Asset j, t − 1
where
∆AR j, t : firm j's change in accounts receivable (Compustat #2) in year t.
Finally, discretionary accruals AAj , t for firm j in year t are the difference between
total accruals and non-discretionary accruals. The larger the unsigned value AAj ,t , the
lower the earnings quality.
AA j, t =
TAj , t
− NAj, t
Asset j, t − 1
.
2. EQ2:
The second earnings quality measure EQ2 is the absolute value of the discretionary
current accruals rather than discretionary total accruals. First, we follow a modified
procedure to estimate the following cross-sectional regression for each of Fama-French 48
industry groups with at least 20 firms in year t.
TCAj , t
1
∆REVj, t
= γ1,τ
+γ 2,τ
+ vj , t
Asset j, t − 1
Assetj , t − 1
Asset j, t − 1
(A2)
Again, the parameter estimates from equation (A2) are used to calculate each
specific firm’s non-discretionary current accruals as a percentage of lagged assets,
∧
NCAj , t = γ1,t
∧
1
( ∆REVj , t − ∆ΑRj, t )
+ γ2, t
Asset j, t − 1
Asset j, t − 1
,
And then the discretionary current accruals for firm j year t are calculated as the
difference of total current accruals and non-discretionary current accruals. Larger values of
EQ2 suggest poorer earnings quality.
39
ACAj, t =
TCAj , t
− NCA j, t
Asset j, t − 1
.
3. EQ3:
To estimate EQ3, we start with estimating the following Dechow-Dichev model for
each of Fama and French’s [1997] 48 industries with at least 20 firms:
TCAj , t
CFO j, t − 1
CFOj , t
CFO j, t + 1
= θ 0,t + θ1,τ
+ θ2,τ
+ θ3,τ
+ vj , t
Aveasset j, t
Aveassetj , t
Aveassetj , t
Aveasset j, t
where:
(A3)
Aveasset j ,t : firm's j average total assets over years t-1 and t.
The third earnings quality measure EQ3 is defined as the absolute value of the
firm’s residual vˆ j ,t from equation (A3). Larger absolute residuals indicate lower earnings
quality.
4. EQ4:
The fourth earnings quality measure is based on firm-specific time-series
estimations of (A3). EQ4 is the standard deviation of these firm-specific residuals
σ (v j ,t ) calculated using a minimum of five residual observations. Similar to the other
measures, larger standard deviations indicate lower earnings quality.
40
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44
Table 1
Sample Descriptive Statistics
Panel A: Earnings Quality Measures and Market Statistics
N
612,821
612,821
512,049
329,513
612,821
612,821
EQ1it
EQ2it
EQ3it
EQ4it
Rit
MVit
Mean
0.255
0.225
0.071
0.088
1.136
1122.1
Median
0.066
0.049
0.041
0.050
0.000
90.9
Q1
0.028
0.020
0.017
0.028
-8.333
20.4
Q3
0.141
0.108
0.086
0.089
8.333
394.4
The sample time frame is from 1985-2002. EQ1it and EQ2 it are earnings quality measures based on the Modified Jones
Model. EQ1 it is the absolute value of abnormal total accruals and EQ2 it is the absolute value of the abnormal current
accruals. EQ3 it and EQ4 it are calculated the same way as Dechow and Dichev (2002). EQ3 is based on cross-sectional
regressions and EQ4 is based on the time-series regressions. MV it is firm i’s market capitalization in thousands at the
end of calendar month t; R it is the monthly return in percentage for firm i.
Panel B: Institutional Classification and Ownership Statistics
TRANSIENT_BUY
TRANSIENT_SELL
QUASI_INDEX_BUY
QUASI_INDEX_SELL
DEDICATED_BUY
DEDICATED_SELL
N
301,353
124,549
345,366
140,369
237,006
128,163
Change/Holding
18.65%
8.32%
8.04%
3.47%
12.03%
7.26%
Holding/Outstanding
5.63%
6.48%
10.84%
13.38%
6.02%
8.55%
Change/Outstanding
1.09%
0.46%
0.91%
0.44%
0.73%
0.51%
N is the number of firm-months in our sample that have net buy or net sell for three classes of institutions.
Change/Holding is the median ratio of institutional holding change over the quarter to the holding at the beginning of
quarter. Holding/Outstanding is the median ratio of institutional holding at the beginning of quarter to the total shares
outstanding at the end of quarter. Change/Outstanding is the median ratio of change of ownership to the total shares
outstanding.
Panel C: Mean and Median Coefficient Estimates for the Following four-factor model across
8465 firms
EQ1
EQ2
EQ3
EQ4
Rm – RF
Mean
Median
0.89
0.80
0.88
0.78
0.77
0.76
0.87
0.80
Mean
0.69
0.68
0.61
0.62
SMB
Median
0.61
0.60
0.58
0.58
Mean
0.46
0.45
0.30
0.46
HML
Median
0.32
0.33
0.29
0.32
EQ
Mean
0.82
0.75
0.67
0.88
Median
0.32
0.33
0.28
0.35
For each firm j in the sample, we conduct the time series regression using its last 36 months’ returns up to 2002/12.
Monthly returns are regressed on Fama -French three factors and the fourth asymmetric information factor EQt . EQt is
the hedged portfolio return going long in the low earnings quality firms and short in the high earnings quality firms at
time t.
Rj ,t − Rf ,t = α j + β j ( Rm,t − R f ,t ) + δ j SMBt +σ jHMLt + φj EQt + ε j ,t
45
Table 2
Trading Profitability in the Contemporaneous Quarter Characterized by Raw Earnings
Quality Measures
EQ1
HQ1
LQ5
LQ5-HQ1
EQ2
HQ1
LQ5
LQ5-HQ1
EQ3
HQ1
LQ5
LQ5-HQ1
Transient
Quasi-indexer
Dedicated
4.111
10.39
5.651
10.90
1.540
2.50
-0.853
-3.10
-0.558
-1.08
0.295
0.57
0.265
0.86
1.279
2.35
1.014
1.65
4.184
8.45
5.498
10.58
1.314
1.88
-0.502
-1.64
-0.394
-0.67
0.108
0.18
0.453
1.27
1.789
2.54
1.336
1.82
4.486
13.42
6.486
10.88
2.000
3.13
-0.323
-1.37
-1.056
-1.77
-0.733
-1.12
0.490
1.23
2.047
3.10
1.557
1.93
EQ4
HQ1
4.147
-0.726
12.64
-1.93
LQ5
4.987
-1.517
13.72
-3.86
LQ5-HQ1
0.840
-0.791
2.08
-1.60
Time series regression results are obtained using the following four-factor model:
0.301
0.97
0.217
0.54
-0.084
-0.20
R E Q,t = Rbuy (0), t − Rsell (0),t = α EQ + β EQ MKT t + δ EQ SMB t + σ EQ HMLt + φ EQ EQt + ε t
where Rbuy (0),t andRsell (0), t are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
according to institutions’ contemporaneous quarterly (Quarter 0) holding change. Stocks in the buy and sell portfolios
are then weighted by the holding change in dollars to get portfolio returns. The return interval is monthly. MKTt , SMBt
and HMLt are the Fama-French (1992) excess market returns, size and market-to-book factor returns; EQ t is the
earnings quality factor-mimicking portfolio returns that long lower earnings quality firms and short high earnings
quality firms. Factor loadings are estimated using a time-series regression based on 180 months of data, from 1/88 to
12/02. Intercepts and t-statistics are reported and t-statistics is below the coefficient estimates and in italics. Hedge
portfolio regression is defined as the following model and independent variables definitions are the same as above.
RLQ , t − RH Q, t = α p + β p MKTt + δ p SMBt + σ p HMLt + φ p EQt + ε t
46
Table 3
Trading Profitability in the Subsequent Quarter Characterized by Raw Earnings Quality
Measures
EQ1
HQ1
LQ5
LQ5-HQ1
EQ2
HQ1
LQ5
LQ5-HQ1
EQ3
HQ1
LQ5
LQ5-HQ1
Transient
Quasi-indexer
Dedicated
-0.066
-0.23
1.132
2.48
1.198
2.37
-0.614
-2.87
-0.255
-0.65
0.359
0.86
0.321
1.23
0.096
0.20
-0.225
-0.47
0.573
1.80
1.214
2.32
0.641
1.18
-0.349
-1.36
-0.282
-0.66
0.067
0.14
0.318
1.19
-0.105
-0.19
-0.423
-0.77
0.218
0.81
1.682
3.01
1.464
2.66
0.008
0.03
0.137
0.29
0.129
0.24
0.468
1.47
0.309
0.59
-0.159
-0.26
EQ4
HQ1
0.211
0.321
-0.167
0.65
1.15
-0.64
LQ5
-0.105
-0.963
-0.024
-0.24
-2.32
-0.05
LQ5-HQ1
-0.316
-1.284
0.143
-0.68
-2.74
0.28
Time series regression results are obtained using the Fama-French three-factor and earnings quality factor model:
R E Q,t = Rbuy ( − 1),t − Rsell (− 1), t = α EQ + β EQ MKTt + δ EQ SMB t + σ EQ H M Lt + φ EQ EQ t + ε t
where Rbuy(− 1),t andRsell( −1),t are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
according to institutions’ contemporaneous quarterly (Quarter -1) holding change. Stocks in the buy and sell portfolios
are then weighted by the holding change in dollars to get portfolio returns. The return interval is monthly. MKTt , SMBt
and HMLt are the Fama-French (1992) excess market returns, size and market-to-book factor returns; EQ t is the
earnings quality factor-mimicking portfolio returns that long lower earnings quality firms and short high earnings
quality firms. Factor loadings are estimated using a time-series regression based on 180 months of data, from 1/88 to
12/02. Intercepts and t-statistics are reported and t-statistics is below the coefficient estimates and in italics. Hedge
portfolio regression is defined as the following model and independent variables definitions are the same as above.
RLQ , t − RH Q, t = α p + β p MKTt + δ p SMBt + σ p HMLt + φ p EQt + ε t
47
Table 4
Trading Profitability in the Contemporaneous Quarter Characterized by Systematic (Priced)
Component of Asymmetric Information Risk
Transient
Quasi-indexer
Dedicated
5.205
11.76
7.834
8.60
2.629
2.68
-0.820
-1.92
0.145
0.18
0.965
1.04
-0.224
-0.51
1.555
1.97
1.779
2.09
4.770
12.49
6.919
7.54
2.149
2.22
-0.922
-2.34
-0.481
-0.61
0.442
0.47
-0.174
-0.39
1.872
2.45
2.046
2.42
4.350
11.76
7.700
9.10
3.350
3.67
-0.898
-2.21
-0.232
-0.29
0.667
0.80
0.075
0.18
0.995
1.54
0.921
1.30
EQ1
Lφ
Hφ
H φ − Lφ
EQ2
Lφ
Hφ
H φ − Lφ
EQ3
Lφ
Hφ
H φ − Lφ
EQ4
Lφ
4.323
-1.000
0.440
12.49
-2.81
1.07
Hφ
7.759
1.010
2.513
9.49
1.40
2.90
H φ − Lφ
3.436
2.010
2.073
3.90
2.57
2.66
Firms’ exposure to asymmetric information risk is estimated by the following four-factor model:
R j ,t − R f ,t = α j + β j MKTt + δ j SMBt + σ j HMLt + φ j EQt + ε j,t
where Rj ,t is firm stock return. MKTt , SMBt and HMLt are the Fama-French (1992) contemporaneous excess market
returns, size and market-to-book factor returns; EQ t is the earnings quality factor-mimicking portfolio returns that long
lower earnings quality firms and short high earnings quality firms. Return window is monthly, and factor loadings are
estimated for every 36 months using a time series regression. The regression coefficient φ j measures the firm’s
exposure to asymmetric information risk. The firms are then sorted into quintiles using the estimated exposure to
asymmetric information risk. In the second stage regression, we apply four-factor model on the time-series difference of
institutions’ buying and selling portfolios for all earnings quality exposure measures.
R Lφ ,t = Rbuy (0),t − R sell (0), t = α Lφ + β Lφ MKTt + δ Lφ SMB t + σ Lφ HMLt + φ Lφ EQt + ε t
where Rbuy(0),t andRsell(0),t are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
according to institutions’ contemporaneous quarterly (Quarter 0) holding change. Stocks in the buy and sell portfolios
are then weighted by the holding change in dollars to get portfolio returns. The return interval is monthly. Factor
loadings are estimated using a time-series regression based on 180 months of data, from 1/88 to 12/02. Intercepts and tstatistics are reported and t-statistics is below the coefficient estimates and in italics. Hedge portfolio regression is
defined as the following model and independent variables definitions are the same as above.
RH φ ,t − R Lφ ,t = α p + β p MKTt + δ p SMBt + σ p H M Lt + φ p EQ t + ε t
48
Table 5
Trading Profitability in the Subsequent Quarter Characterized by Systematic (Priced)
Component of Asymmetric Information Risk
Transient
Quasi-indexer
Dedicated
0.683
1.85
2.108
2.28
1.425
1.43
0.185
0.62
-0.758
-0.98
-0.943
-1.16
0.037
0.13
0.146
0.19
0.110
0.14
0.647
2.01
1.619
1.89
0.971
1.11
-0.126
-0.44
-0.414
-0.56
-0.289
-0.36
-0.100
-0.35
0.180
0.25
0.280
0.38
0.880
2.58
2.392
3.03
1.511
1.97
0.090
0.38
-0.549
-0.88
-0.639
-0.99
0.308
0.90
-0.106
-0.17
-0.414
-0.66
EQ1
Lφ
Hφ
H φ − Lφ
EQ2
Lφ
Hφ
H φ − Lφ
EQ3
Lφ
Hφ
H φ − Lφ
EQ4
Lφ
0.093
-0.087
-0.022
0.29
-0.31
-0.07
Hφ
2.042
-0.821
-0.173
2.45
-1.02
-0.24
H φ − Lφ
1.948
-0.734
-0.151
2.25
-0.88
-0.20
Firms’ exposure to asymmetric information risk is estimated by the following four-factor model:
R j ,t − R f ,t = α j + β j MKTt + δ j SMBt + σ j HMLt + φ j EQt + ε j,t
where Rj ,t is firm stock return. MKTt , SMBt and HMLt are the Fama -French (1992) contemporaneous excess market
returns, size and market-to-book factor returns; EQ t is the earnings quality factor-mimicking portfolio returns that long
lower earnings quality firms and short high earnings quality firms. Return window is monthly, and factor loadings are
estimated for every 36 months using a time series regression. The regression coefficient φ j measures the firm’s
exposure to asymmetric information risk. The firms are then sorted into quintiles using the estimated exposure to
asymmetric information risk. In the second stage regression, we apply four-factor model on the time-series difference of
institutions’ buying and selling portfolios for all earnings quality exposure measures.
RLφ ,t = Rbuy ( −1),t − R sell ( −1), t = α Lφ + β Lφ MKTt + δ L φ SMBt + σ Lφ HMLt + φ Lφ EQt + ε t
where Rbuy(− 1),t a n d Rsell( −1),t are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
according to institutions’ contemporaneous quarterly (Quarter 0) holding change. Stocks in the buy and sell portfolios
are then weighted by the holding change in dollars to get portfolio returns. The return interval is monthly. Factor
loadings are estimated using a time-series regression based on 180 months of data, from 1/88 to 12/02. Intercepts and tstatistics are reported and t-statistics is below the coefficient estimates and in italics. Hedge portfolio regression is
defined as the following model and independent variables definitions are the same as above.
RH φ ,t − R Lφ ,t = α p + β p MKTt + δ p SMBt + σ p H M Lt + φ p EQ t + ε t
49
Table 6.1
Sample Descriptive Statistics
Panel A: Sample and Market Statistics
All Sample
Rit
MVit
No-R&D subsample
Rit
MVit
R&D subsample
Rit
MVit
N
Mean
Median
Q1
Q3
1,248,203
1.16
1,063.9
0.00
85.3
-6.48
17.8
6.99
367.0
797,606
1.04
773.5
0.00
83.2
-5.26
15.9
6.17
340.2
450,597
1.36
1,576.8
0.00
89.2
-8.96
20.8
8.89
426.0
The sample time frame is from 1985-2002. MV it is firm i’s market capitalization in thousands at the end of calendar
month t; R it is the firm i’s monthly return in percentage at month t.
Panel B: Institutional Classification and Ownership Statistics
N
R&D
TRANSIENT_BUY
TRANSIENT_SELL
QUASI_INDEX_BUY
QUASI_INDEX_SELL
DEDICATED_BUY
DEDICATED_SELL
233,222
95,691
263,716
110,826
178,243
99,295
NoR&D
354,888
162,720
440,119
182,792
275,703
155,950
Change/Holding
R&D
NoR&D
19.47% 17.64%
8.53%
8.41%
8.27%
7.19%
3.63%
3.24%
12.54% 10.90%
7.57%
6.74%
Holding/Outstanding
R&D
NoR&D
5.75%
3.64%
6.82%
4.60%
10.02%
8.66%
12.73%
11.31%
5.97%
4.70%
8.65%
7.16%
Change/Outstanding
R&D
NoR&D
1.15%
0.69%
0.48%
0.32%
0.88%
0.62%
0.44%
0.33%
0.75%
0.52%
0.54%
0.40%
N is the number of firm-months in our sample that have net buy or net sell for three classes of institutions.
Change/Holding is the median ratio of institutional holding change over the quarter to the holding at the beginning of
quarter. Holding/Outstanding is the median ratio of institutional holding at the beginning of quarter to the total shares
outstanding at the end of quarter. Change/Outstanding is the median ratio of institutional holding change over the
quarter to the total share outstanding at the end of quarter.
50
Table 6.2
Asymmetric Information Advantages Characterized by R&D Intensity
Contemporaneous
Quarter
No-R&D
R&D
R&D-No-R&D
Subsequent Quarter
No-R&D
R&D
R&D-No-R&D
Transient
Quasi-indexer
Dedicated
2.973
17.12
4.935
17.23
1.963
7.39
-1.077
-4.53
-0.620
-2.52
0.457
1.37
-0.151
-0.83
0.383
1.27
0.534
1.79
-0.096
-0.67
0.406
1.84
0.502
2.23
-0.305
-2.52
0.097
0.53
0.402
1.98
0.091
0.66
-0.137
-0.73
-0.228
-1.06
Time series regression results are obtained using the following four-factor model:
R R D, t = R buy ( q) , t − R sell ( q ), t = α RD + β RD M K T t + δ RD S M B t + σ RD H M L t + ε t
R N O R D, t = R b u (y q), t − R s e l l( q ), t = α N O R D + β N O R D M K Tt + δ N O R D S M B t + σN O R D H M Lt + εt
where Rb u (y )q,t a n d Rs e (l )l ,q t are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
according to institutions’ contemporaneous quarterly holding change (Quarter 0) and previous quarterly holding change
(Quarter -1). Stocks in buy and sell portfolios are then weighted by the holding change in dollars to get portfolio returns.
The return interval is monthly. MKTt , SMBt and HMLt are the Fama -French (1992) excess market returns, size and
market-to-book factor returns. Factor loadings are estimated using a time-series regression based on 216 months of data,
from 1/85 to 12/02. Intercepts and t-statistics are reported and t-statistics is below the coefficient estimates and in italics.
Hedge portfolio regression is defined as the following model and independent variables definitions are the same as
above.
RR D,t − R N O R D, t = α p + β p MKTt + δ p SMBt + σ p HMLt + ε t
51
Table 6.3
Premium on Exposure to the R&D Related Asymmetric Information Risk
Intercept
Rm_Rf
SMB
HML
Low F Portfolio 1
0.076
0.745
0.739
0.364
t-stat
0.50
20.60
15.97
8.76
Portfolio 2
0.171
0.652
0.582
0.364
t-stat
1.62
26.04
18.19
12.65
Portfolio 3
0.085
0.630
0.525
0.293
t-stat
0.85
26.73
17.44
10.82
Portfolio 4
-0.036
0.888
0.916
0.347
t-stat
-0.28
29.18
23.53
9.92
High F Portfolio 5
-0.182
1.207
1.484
0.148
t-stat
-0.63
17.73
17.05
1.90
Hedged portfolios HF5-L F 1
-0.258
0.462
0.745
-0.216
t-stat
-0.79
5.94
7.49
-2.41
Two stages of regressions are done for the results of this table.
In the first stage, firms’ exposure to R&D related asymmetric information risk is estimated by the following four-factor
model:
R j ,t − R f ,t = α j + β j MKTt + δ j SMBt + σ j HMLt + φ j RDt + ε j,t
where Rj ,t is firm stock return, R f ,t is risk free rate, measured as one-month treasury bill rate; MKTt , SMBt and HMLt
are the Fama -French (1992) excess market returns, size and book to market factor returns; RDt is the hedged portfolio
return going long in R&D firms and going short in No-R&D firms. Return window is monthly. Factor loadings are
estimated for every 36 months using a time series regression. The regression coefficient φ j measures the firms’
exposure to R&D related asymmetric information risk.
For the second stage regression, firms are sorted into quintiles using the estimated exposure to R&D related asymmetric
information risk. Then we apply three-factor model on the quintiled portfolios and the hedged portfolio which is long
HF firms and short LF firms.
Hφ p ,t − Lφ p ,t = α p + β p MKTt + δ p SMB t + σp HMLt + εt
where Hφp ,t and Lφ p, t are equally-weighted portfolio stock returns in the highest and lowest quintile of exposure to R&D
risk. The return interval is the monthly and all factors are defined the same way as the first stage regression. Factor
loadings are estimated using a time -series regression based on 216 months of data, from 1/85 to 12/02. Intercepts and tstatistics are reported and t-statistics is below the coefficient estimates and in italics.
52
Table 7
Comparison of Five Types of Institutional Investors’ Exploitation of Information Advantage
Panel A: Characterized by Earnings Quality Measures
Contemporaneous
quarter
Banks
Insurance
Mutual Funds
Brokerage
Pension Funds
HQ1
-0.177
0.594
1.812
1.414
-0.299
-0.74
1.95
6.13
4.79
-0.83
0.477
0.219
3.095
2.741
-0.222
1.26
0.47
7.33
5.30
-0.47
0.654
-0.376
1.283
1.327
0.077
1.48
-0.72
2.64
2.37
0.15
-0.276
-0.509
-0.250
-0.407
0.164
-1.38
-1.71
-1.07
-2.03
0.56
-0.146
0.056
0.068
0.381
1.44
-0.40
0.12
0.14
0.85
0.020
0.130
0.565
0.319
0.788
0.05
0.31
1.24
0.61
1.60
-0.145
LQ5
LQ5-HQ1
Subsequent
quarter
HQ1
LQ5
LQ5-HQ1
Panel B: Characterized By Earnings Quality Risk Exposures
Contemporaneous
quarter
Banks
Insurance
Mutual Funds
Brokerage
Pension Funds
Lφ
0.093
0.27
-0.267
-0.80
2.724
6.71
2.180
5.17
-0.582
-1.19
Hφ
0.645
0.97
-0.138
-0.18
5.724
7.25
3.856
4.15
0.13
0.022
0.552
0.74
0.129
0.16
2.918
3.44
1.676
1.60
0.02
0.604
Lφ
-0.004
-0.01
-0.443
-1.29
0.252
0.63
0.154
0.43
-0.052
-0.14
Hφ
-1.178
-1.59
-0.537
-0.73
1.321
1.51
0.002
0.00
1.40
1.082
H φ − Lφ
Subsequent
quarter
H φ − Lφ
-1.174
-0.094
1.040
-0.152
1.31
-1.36
-0.12
1.11
-0.16
1.134
The intercepts of time series portfolio regression for five institutional types are presented in this Table.
The contemporaneous quarter results in Panel A are obtained through the same procedure as described in Table 1;
The subsequent quarter results in Panel A are obtained through the same procedure as described in Table 2;
The contemporaneous quarter results in Panel B are obtained through the same procedure as described in Table 3;
The subsequent quarter results in Panel B are obtained through the same procedure described in Table 4.
53
Table 8.1
Trading Profitability in the First Month of Subsequent Quarter Characterized by Raw
Earnings Quality Measures
EQ1
HQ1
LQ5
LQ5-HQ1
EQ2
HQ1
LQ5
LQ5-HQ1
EQ3
HQ1
LQ5
LQ5-HQ1
EQ4
HQ1
LQ5
LQ5-HQ1
Transient
Quasi-indexer
Dedicated
-0.041
-0.08
0.964
1.10
0.168
0.40
0.263
0.43
0.653
1.80
0.702
0.81
1.004
1.01
0.094
0.13
0.048
0.06
0.383
0.68
0.237
0.28
-0.146
-0.15
0.689
1.67
0.843
1.02
0.154
0.18
0.050
0.12
-0.480
-0.59
-0.531
-0.60
0.133
0.31
0.454
0.88
-0.138
-0.43
1.260
1.38
1.127
1.30
0.069
0.08
-0.385
-0.39
0.624
0.76
0.763
0.87
-0.171
-0.29
0.511
0.87
-0.347
-0.88
-0.855
-1.13
-0.684
-0.84
-0.179
-0.30
-0.690
-0.79
0.169
0.30
0.516
0.81
Time series regression results are obtained using the following four-factor model:
R E Q,t = Rbuy ( − 1),t − Rsell (− 1), t = α EQ + β EQ MKTt + δ EQ SMB t + σ EQ H M Lt + φ EQ EQ t + ε t
where Rbuy(− 1),t a n d Rsell (1),
are weighted buy and sell portfolio returns. Stocks are classified into buy and sell portfolios
− t
according to institutions’ previous quarterly (Quarter -1) holding change. Stocks in the buy and sell portfolios are then
weighted by the previous quarterly holding change in dollars to get portfolio returns. MKTt , SMBt and HMLt are the
Fama-French (1992) contemporaneous excess market returns, size and market-to-book factor returns; EQ t is the
earnings quality factor-mimicking portfolio returns that long lower earnings quality firms and short high earnings
quality firms. Only the first month return for each quarter is used to estimate factor loadings. Then factor loadings are
estimated using a time-series regression based on 60 months of data, from 1/88 to 10/02. Intercepts and t-statistics are
reported and t-statistics is below the coefficient estimates and in italics. Hedge portfolio regression is defined as the
following model and independent variables definitions are the same as above.
RLQ , t − RH Q, t = α p + β p MKTt + δ p SMBt + σ p HMLt + φ p EQt + ε t
54
Table 8.2
Trading Profitability in the First Month of Subsequent Quarter Characterized by the
Systematic (priced) Component of Asymmetric Information Risk
Transient
Quasi-indexer
Dedicated
0.947
0.57
1.068
1.58
0.121
0.07
-0.040
-0.10
-0.079
-0.07
-0.038
-0.03
-0.261
-0.57
-0.567
-0.48
-0.306
-0.25
0.385
0.62
0.568
0.35
0.183
0.12
-0.014
-0.04
0.900
0.89
0.914
0.82
-0.778
-1.63
-0.008
-0.01
0.770
0.73
0.518
0.86
2.672
1.77
2.154
1.51
0.122
0.33
0.999
0.96
0.876
0.84
-0.469
-0.92
-0.099
-0.11
0.370
0.38
EQ1
Lφ
Hφ
H φ − Lφ
EQ2
Lφ
Hφ
H φ − Lφ
EQ3
Lφ
Hφ
H φ − Lφ
EQ4
Lφ
0.200
-0.476
-0.684
0.29
-1.00
-1.25
Hφ
1.808
0.720
-1.714
1.54
0.71
-1.59
H φ − Lφ
1.608
1.196
-1.030
1.40
1.15
-1.00
Firms’ exposure to asymmetric information risk is estimated by the following four-factor model:
R j ,t − R f ,t = α j + β j MKTt + δ j SMBt + σ j HMLt + φ j EQt + ε j,t
where Rj ,t is firm stock return. MKTt , SMBt and HMLt are the Fama-French (1992) contemporaneous excess market
returns, size and market-to-book factor returns; EQ t is the earnings quality factor-mimicking portfolio returns that long
lower earnings quality firms and short high earnings quality firms. Return window is monthly, and factor loadings are
estimated for every 36 months using a time series regression. The regression coefficient φ j measures the firm’s
exposure to asymmetric information risk. The firms are then sorted into quintiles using the estimated exposure to
asymmetric information risk. In the second stage regression, we apply four-factor model to run time series regression on
the difference of institutions’ buying and selling portfolios for all earnings quality exposure measures.
R Lφ ,t = Rbuy ( −1),t − R sell ( −1), t = α Lφ + β Lφ MKTt + δ L φ SMB t + σ Lφ HMLt + φ Lφ EQt + ε t
where Rbuy(− 1),t andRsell( −1),t are weighted buy and sell portfolio returns. Stocks are classified to buy and sell portfolios
according to institutions’ previous quarterly (Quarter -1) holding change. Stocks in the buy and sell portfolios are then
weighted by the holding change in dollars to get portfolio returns. The return interval is monthly. Factor loadings are
estimated using a time-series regression based on 60 months of data, from 1/88 to 12/02. Intercepts and t-statistics are
reported and t-statistics is below the coefficient estimates and in italics. . Hedge portfolio regression is defined as the
following model and independent variables definitions are the same as above.
RH φ ,t − R Lφ ,t = α p + β p MKTt + δ p SMBt + σ p H M Lt + φ p EQ t + ε t
55