Journal of Banking & Finance 34 (2010) 1225–1236
Contents lists available at ScienceDirect
Journal of Banking & Finance
journal homepage: www.elsevier.com/locate/jbf
Aggregate insider trading: Contrarian beliefs or superior information?
Xiaoquan Jiang a, Mir A. Zaman b,*
a
b
Department of Finance, College of Business Administration, Florida International University, Miami, FL 33199, United States
Department of Finance, College of Business Administration, University of Northern Iowa, Cedar Falls, IA 50614, United States
a r t i c l e
i n f o
Article history:
Received 3 May 2009
Accepted 14 November 2009
Available online 20 November 2009
JEL classification:
G11
G12
G14
G19
Keywords:
Insider trading
Return decomposition
Discount rate news
Cash-flow news
Contrarian investment strategy
a b s t r a c t
We decompose realized market returns into expected return, unexpected cash-flow news and unexpected discount rate news to test the relation between aggregate market returns and aggregate insider
trading. We find that (1) the predictive ability of aggregate insider trading is much stronger than what
was reported in earlier studies, (2) aggregate insider trading is strongly related to unexpected cash-flow
news, (3) market expectations do not cause insider trading contrary to what others have documented,
and (4) aggregate insider trading in firms with high information uncertainty is more likely to be associated with contrarian investment strategy. These results strongly suggest that the predictive ability of
aggregate insider trading is because of insider’s ability to predict future cash-flow news rather than from
adopting a contrarian investment strategy. These results hold even after we control for non-informative
trades and information uncertainty.
Ó 2009 Elsevier B.V. All rights reserved.
1. Introduction
Recent studies on aggregate insider trading have documented
that insiders are able to predict future market movements and that
they are able to time the market (Seyhun, 1988; Lakonishok and
Lee, 2001). However, it is not clear from the evidence what the
source of this predictability of market returns is. One possibility
is that insiders are contrarian investors (Rozeff and Zaman, 1998;
Lakonishok and Lee, 2001; Jenter, 2005). It is also possible that
managers are better informed about their firms’ future prospects
and this informational advantage explains their market timing
ability (Ke et al., 2003). Finally, insiders could have informational
advantage and are also contrarian investors (Piotroski and
Roulstone, 2005).
There is substantial evidence that corporate officers and directors are able to discern apparent mispricing in their firms’ securities based on firm related information and are able to profitably
* Corresponding author. Tel.: +1 319 273 2579; fax: +1 319 273 2922.
E-mail addresses: jiangx@fiu.edu (X. Jiang), [email protected] (M.A. Zaman).
0378-4266/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.jbankfin.2009.11.016
trade on this.1 If this information is related to future economy-wide
activity, then aggregate insider trading should predict future market
movements and the market timing ability of insiders would be based
on information unanticipated by the market (see Seyhun, 1988). We
differentiate this from the contrarian investment strategy of insiders
and define it as superior information hypothesis, which is related to
unexpected changes in future cash flow and discount rate news. If
insiders are motivated to trade because of perceived mispricing, it
is also conceivable they may react to market returns. It is possible
that noise traders may drive market prices away from intrinsic values even in the absence of new information. Hence, a stock that was
trading roughly at its intrinsic value could decline (rise) significantly
because of such noise trading. Corporate insiders may then perceive
the stock to be undervalued (overvalued) and buy (sell) it. To the
extent that noise trading is a market-wide phenomenon, we would
1
Previous studies based on US data unanimously documented that insiders are
better informed and earn abnormal returns (Lorie and Niederhoffer, 1968; Jaffe, 1974;
Seyhun, 1986; Rozeff and Zaman, 1988; Lakonishok and Lee, 2001). Using Oslo Stock
Exchange data Eckbo and Smith (1998) show that insiders do not earn abnormal
returns while Jeng et al. (2003) show that abnormal returns earned by insiders are
restricted only to purchases. Aktas et al. (2008) find that even though the financial
markets do not respond strongly in terms of abnormal returns, price discovery in the
market is hastened on days that insiders are trading.
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
expect market returns to ‘predict’ aggregate insider transactions (see
Rozeff and Zaman, 1998; Chowdhury et al., 1993; Lakonishok and
Lee, 2001; Jenter, 2005). Such a relationship would be viewed as
insiders following a contrarian investment strategy and aggregate
insider trading should be related to market expectations about future cash flows and future returns. On the other hand, if mispricing
is firm specific then insiders’ transactions in each firm would cancel
out and aggregate insider trading should not be related to market returns. Even though under both contrarian strategy and superior
information scenarios insider trading is related to market returns,
the key distinction is that if insiders trade on the basis of superior
information aggregate insider trading will predict future market returns while the contrarian strategy implies insider trading is a reaction to market returns.
Other related studies of managerial decisions also suggest that
insiders are better informed about their companies’ future prospects. For example, Ikenberry et al. (1995) find positive abnormal
returns earned by shareholders of companies that have announced
open market share repurchases. These abnormal returns persist for
some time after the announcement. One of the main motivations
for repurchases seems to be that insiders perceive the company’s
stock as being undervalued. Chan et al. (2007) uses a comprehensive sample of US repurchase announcements to look at whether
executives possess market timing skills when announcing certain
corporate transactions. Their results are consistent with the notion
that managers possess market timing ability in the context of share
repurchases. However, Ginglinger and Hamon (2007) using repurchases made by French firms show that share repurchases largely
reflect contrarian trading rather than managerial timing ability.
Loughran and Ritter (1995), on the other hand, observe a prolonged
underperformance by companies following seasoned equity offerings. This is in line with the hypothesis that companies tend to issue seasoned equity when they perceive the market to be too
optimistic about the prospects of their company. Baker and Wurgler (2000) find that the share of equity issues in total new equity
and debt issues increases right after a year of high market returns
and has been a stable predictor of US stock market returns between
1928 and 1996. The paper also provides evidence of issuing firms
preferring equity finance before periods of low market returns
and shunning equity in favor of debt before periods of high market
returns. Overall, the results add to a growing body of evidence that
managerial decisions are in response to or in anticipation of market
conditions (see also Baker et al., 2006; Adams et al., 2009, among
others).
A related line of research on insider trading has focused on
whether aggregate insider trading can predict market movements
and could be used as a tool to time the market. Even though Givoly
and Palmon (1985) found no relation between insider trading and
subsequent information events, Seyhun (1988) provides evidence
suggesting that some of the mispricing observed by insiders in
their own firms’ securities is caused by unanticipated changes in
economy-wide activity. Using a single-equation regression analysis
he finds aggregate insider trading is correlated to market return in
the subsequent 2 months following the trading activity. In a subsequent paper, Seyhun (1992) finds that aggregate insider trading is
positively related to future real activity as measured by growth
rates of after-tax corporate profits, the Index of Industrial Production and the Gross National Product. However, in the paper he concludes ‘. . .both changes in business conditions as well as
movements away from the fundamentals contribute to the information content of aggregate insider trading’.
Chowdhury et al. (1993) find that stock market returns Granger-cause insider transactions, while the predictive content of
aggregate insider transactions for subsequent market returns is
slight. Lakonishok and Lee (2001) also provide evidence in support
of the predictive ability of aggregate insider trading and market
movement. They conclude that this ability is partially because
insiders act as contrarian investors.
Previous studies simply examine the relationship between realized market return and some metric of insider trading without
explicitly considering the source of predictability. Piotroski and
Roulstone (2005) is an exception; their paper attempts to differentiate the source of the predictability and finds that insider trades are
related to the firm’s future earnings performance. However, they
use the change in accounting returns as proxies for future cash
flows. Cohen et al. (2002) point out that the change in accounting
returns is not a good measure to proxy future cash flows.
Both conclusions of contrarian strategy of investing by insiders
and insiders’ ability to predict unanticipated future economy-wide
activity rely on insider trading being positively related to subsequent realized market returns. These studies, however, make no attempt to determine whether the apparent predictability of market
returns by aggregate insider trading is because insiders follow contrarian strategy or are better able to predict market-wide activities.
For example, Rozeff and Zaman (1998) show that insiders predominantly buy (sell) shares in value (glamour) firms and interpret this
as evidence of insiders trading against the market’s over-reaction
to past performance. Such trading behavior is consistent with
insiders purchasing (selling) securities with high (low) expected
returns or the greatest amount of undervaluation (overvaluation).
In a related paper, Jenter (2005) provides further evidence that a
top manager tends to have contrarian views with respect to valuation of his own company’s stock. Based on a methodology similar
to Rozeff and Zaman (1998), Jenter finds that insiders are more
likely to be net sellers in growth firms (low book-to-market ratio)
and net buyers in value firms (high book-to-market ratios) and ascribes this trading pattern to contrarian views of insiders on stock
valuation. To strengthen this claim Jenter controls for non-information based motives, like diversification and portfolio rebalancing, of insider trading. Using variables to control for the effect of
stock ownership and equity based compensation (measures more
likely to lead to trading for diversification and portfolio rebalancing
purposes) he finds that book-to-market has a strong and significant
effect on insider trading and concludes that insider trading is more
likely due to contrarian beliefs. In both these papers, the reported
pattern of trading across cash flow-price or book-to-market portfolios could reflect insiders trading on market pricing errors (e.g.,
over-reaction to past performance), but it could also reflect insiders’ superior knowledge of future earnings performance. La Porta
et al. (1997) show that, on average, value (growth) firms tend to
have positive (negative) future earnings announcement period returns. Because returns on earnings announcement tend to be correlated with actual changes in performance both Rozeff and
Zaman (1988) and Jenter (2005) findings cannot differentiate trading on the basis of contrarian beliefs from trading on the basis of
superior information about future cash flows.
The purpose of this paper is to re-examine the ability of aggregate insider trading to predict market-wide movement using return decomposition in a vector autoregressive (VAR) model
framework. Such a re-examination is called for because of mixed
results reported in previous papers. Moreover, it is important for
the capital markets to be able to distinguish between these two
sources of predictability. If insiders are trading based on contrarian
strategy, then in aggregate, such trading would not provide any
‘new’ information about the future economy-wide activity. Aggregate insider trading would in this case imply market overreaction
(under reaction) and subsequently lead to market correction. However, if insiders are trading on the basis of information related to
unanticipated (from outside investors) changes in future cash
flows, then aggregate insider trading will predict future real economic activities and future market returns. In order to distinguish
between these two sources of predictability we closely follow
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Campbell (1991) and Hecht and Vuolteenaho (2006) method of
decomposing aggregate market return into expected return, unexpected cash-flow news and unexpected discount rate news. We argue that insider trading based on new information will result in a
positive relation between aggregate insider trading and unexpected cash-flow news (aggregate insider trading will lead the positive future unexpected returns). On the other hand, if insiders are
trading based on a contrarian investment strategy then we expect
a negative relation between insider trading and expected return
(aggregate insider trading will react on the expected return). Using
this decomposition, a regression of market returns on insider trading measures is then decomposed into three component regressions. We find the following: (1) the predictive ability of
aggregate insider trading is much stronger than what was reported
in earlier studies, (2) aggregate insider trading is strongly related to
unexpected cash-flow news, (3) market expectations do not cause
insider trading, contrary to what others have documented, and (4)
aggregate insider trading in firms with high information uncertainty is more likely to be associated with contrarian investment
strategy. These results strongly suggest that the predictive ability
of aggregate insider trading is because of insiders’ ability to predict
unanticipated changes in economy and market-wide factors as opposed to following a contrarian investment trading strategy.
Our contribution is twofold. First, this paper provides definitive
evidence into the debate of whether insider trading based on perceived mispricing is a result of contrarian investment strategy or
whether it is based on insiders’ access to information about future
cash-flow news. By decomposing realized market returns into expected returns, unexpected cash-flow news and unexpected
changes in discount rate news, this paper directly tests the sources
of the insider trading predictability. Second, this paper contributes
to the existing literature on the importance of the relation between
corporate transactions and insiders ability to time the market.
When the firm’s securities are mispriced and insiders are able to
identify this mispricing, then this ability affects the financing,
investment and other corporate transactions.
The paper is organized as follows. Section 2 discusses reasons to
believe that insider trading can predict future market returns and
develops the framework and formulates the hypotheses, Section
3 describes the data and provides summary statistics. Results are
reported and discussed in Sections 4–6 while the last section contains a summary and interpretation of the results.
2. Framework and hypotheses
There are a number of compelling and competing reasons to believe that aggregate insider trading can predict future market returns. Assume that company executives and directors know their
businesses more intimately than analysts (investors) following
their stocks. They know when demand for their goods and services
is increasing, when inventories are piling up, when production
costs are increasing or profit margins declining, etc. Given their
knowledge about their firm, insiders should be able to predict
when the firm’s future cash flows would increase, and would then
buy stocks in their firms. If the predicted increase in cash flows by
insiders is strictly the result of some firm-specific improvement
(e.g. profit margin) there should be no relation between aggregate
insider trading and market return. On the other hand, if the cash
flows are related to economy-wide activity such as increases in
aggregate demand of goods and services, then subsequently when
the increase in economy-wide activity is recognized by the market,
stock prices will rise. This will result in a positive relation between
insider buys and market return. We call this the superior information hypothesis and test this by examining the relationship between insider trading and unanticipated cash-flow news.
A competing hypothesis regarding aggregate insider trading
relies on the contrarian strategy of investing. If stock prices are
affected by the trading of both informed and uninformed (noise)
traders then prices can diverge from fundamental values (Shiller,
1984; DeLong et al., 1990). According to this view noise traders
may drive market prices away from current fundamental values.
However, in the long run prices would revert back to fundamental
values. Wang (2010) develops a model whereby an informed investor aggressively trades on her information and takes a large, opposite position against the noise trader. If the contrarian strategy is
employed by insiders at the firm specific level then there should
be no relation between aggregate market returns and insider trading. On the other hand, if noise trading is a market-wide phenomenon then a relation between aggregate insider trading and market
return should exist. In such a scenario, market returns would ‘predict’ insider trading behavior.
In order to test the relation between aggregate stock returns
and insider trading we use the standard log-linear present value
model developed by Campbell (1991).
2.1. Log-linear present value model framework and insider trading
Campbell (1991) decomposes the realized return on equities
into following three components:
Rtþ1 ¼ Et Rtþ1 þ ðEtþ1 Et Þ
1
X
qj DDtþ1þj ðEtþ1 Et Þ
j¼0
¼ Et Rtþ1 þ NCF;tþ1 NDR;tþ1 ;
1
X
qj Rtþ1þj
j¼1
ð1Þ
where R is the log return on equities, DD is dividend growth, q is the
discount factor, Et(Rt+1) is the one-period expected return, NCF,t+1 is
the cash-flow news, and NDR,t+1 is the discount rate news. This equation states that the realized return must be associated with the expected return, the changes in expectations of future cash flows, and/
or the changes in the expectations of future discount rates. As
emphasized by Campbell (1991), Eq. (1) is really nothing more than
a dynamic accounting identity relating the current return innovation to revisions in expectations.
Hecht and Vuolteenaho (2006) apply this method to measure
the relative importance of these three effects in regressions of
returns on cash flow proxies. Based on Eq. (1), the explanatory
power of cash flow proxies may arise from the correlation of cash
flow proxies (predictors) with one-period expected returns, cashflow news, and/or expected return news. They argue that ‘‘if
expected-return variation is responsible for the high explanatory
power of the aggregate regressions, these R2 should not be interpreted as evidence of cash-flow news driving the returns. Similarly,
if expected-return news is highly variable and positively correlated
with cash-flow news, the low R2s in regressions of firm-level
returns on earnings do not necessarily imply that earnings are a
noisy or delayed measure of the cash flow generating ability of
the firm. Even if earnings are a clean signal of cash-flow news, expected return effects (due to variation in risk-adjusted discount
rates and/or mispricing) can garble the earnings-returns relation.”
In a similar spirit, we apply Campbell’s decomposition to estimate and test the dynamic relation between market returns and
aggregate insider trading. This method uniquely helps us to distinguish whether the relation between market returns and aggregate
insider trading is due to contrarian strategy or superior knowledge.
Consider a typical forecast regression of returns on insider trading,
Rtþ1 ¼ a þ bIT t þ etþ1 ;
ð2Þ
where IT is a measure of insider trading. Seyhun (1988) uses a similar methodology to show a weak relationship between insider trading and market returns and concludes that insider trading predict
market return. As analyzed above, it is difficult to interpret the
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
coefficient b, and more importantly, using regression (2) we cannot
distinguish whether the relation between market returns and insider trading is because insiders trade based on contrarian strategy or
they are better able to predict unanticipated changes in marketwide factors due to changes in future cash-flow news.
Using Campbell’s (1991) decomposition, however, we can rewrite the regression (2) as following:
Et Rtþ1 ¼ a þ bER IT t þ eER;tþ1 ;
ð3aÞ
NCF;tþ1 ¼ a þ bCF IT t þ eCF;tþ1 ;
ð3bÞ
NDR;tþ1 ¼ a þ bDR IT t þ eDR;tþ1 :
ð3cÞ
Since the sum of the left-hand-side in regression (3) is the realized return and the independent variables in regression (3) are the
same, regression (2) can also be expressed as:
Rtþ1 ¼ a þ ðbER þ bCF þ bDR ÞIT t þ ðeER;tþ1 þ eCF;tþ1 þ eDR;tþ1 Þ:
ð4Þ
Regressions (3) and (4) show that there are three sources driving the relation between market return and insider trading: oneperiod expected return, cash-flow news, and discount rate news.
We also consider the following regression:
IT tþ1 ¼ a þ cRt þ utþ1 ;
ð5aÞ
IT tþ1 ¼ a þ cER Et ðRtþ1 Þ þ uER;tþ1 ;
ð5bÞ
IT Tþ1 ¼ a þ cCF NCF;t þ uCF;tþ1 ;
ð5cÞ
IT tþ1 ¼ a þ cDR ðNDR;t Þ þ uDR;tþ1 :
ð5dÞ
Eq. (4) shows, if expected return variation is responsible for the
high explanatory power of the aggregate regressions, these R2
should not be interpreted as evidence of superior information driving the returns. Similarly, if expected return news is highly variable
and positively correlated with cash-flow news, the low R2s in
regressions of market returns on insider trading do not necessarily
imply that insider trading is a noisy or delayed measure of the cash
flow generating ability of the firm. Even if insider trading is a clean
signal of cash-flow news, expected return effects (due to variation
in risk-adjusted discount rates and/or mispricing) can garble the
insider trading-returns relation. We use regressions (3) and (5) to
estimate the relation between market return and insider trading,
and distinguish whether the relation is attributed to insiders’ possessing superior knowledge to predict market-wide movements
(as evidenced in Seyhun (1988)) or contrarian strategy as evidenced in Rozeff and Zaman (1998), Chowdhury et al. (1993), Lakonishok and Lee (2001) and Jenter (2005).
Assume that insiders are better able to predict future cash-flow
news of the firm than outside investors. If these cash flows are related to economy-wide activity then subsequent to aggregate insider buying (selling) in stocks of their firm the market returns
should increase (decrease). It may be argued that if insiders have
information about their firm’s future cash-flow news which is related to economy-wide activity then it is likely they may be better
off trading in options or other derivative securities than trading in
stocks of their firm. However, given Seyhun’s (1986) evidence of
passive as well as active trading by insiders around firm-specific
nonpublic information, insiders would also be expected to trade
in stocks of their firms. If the superior information hypothesis is
true, we expect positive and significant coefficients for bCF and
bDR. In contrast, if insider trading does not reveal information
about future economy-wide activity then the coefficients bCF and
bDR will be insignificant. Furthermore, under this hypothesis if
insiders know more about their firms’ cash-flow news and in the
aggregate, cash-flow news do not cancel out but rather are proxies
of aggregate market cash-flow news, then the coefficient bCF should
dominate bDR.
The contrarian strategy hypothesis states that outsiders make
valuation errors through the application of inferior valuation mod-
els and/or the incorporation of biased judgments. Based on the perceived mispricing, insiders trade against outside investors’
sentiment. If the contrarian strategy drives the relation between
market return and aggregate insider trading, we would expect that
cER, cCF, and cDR in Eq. (5) to be significantly negative. For instance,
if outside market expectation Et[Rt+1] is positive, and if inside traders perceive this expectation to be incorrect, insider traders will
sell their stocks, i.e., cER is negative. It is also possible that insiders
are contrarians with respect to future cash-flow news and discount
rate news. If insiders perceive that the markets valuation of future
cash-flow news is too high relative to cash flow fundamentals, then
insiders will sell their stocks. We formulate the following
hypotheses:
Hypothesis 1. If insider trading is not informative (in terms of
cash-flow news) then the coefficients bCF, bDR in Eq. (3) are
indistinguishable from zero; otherwise they are positive.
Hypothesis 2. If insider trading is not informative (in terms of contrarian strategy) then the coefficients cER, cCF, and cDR in Eq. (5) are
indistinguishable from zero; otherwise they are negative.
2.2. Estimating one-period expected returns, cash-flow news and
discount rate news
We follow Campbell (1991) and Campbell and Vuolteenaho
(2004) to estimate the one-period expected return, cash-flow
news, and discount rate news series using a vector autoregressive
(VAR) model. We assume that the data are generated by a first-order VAR model
Z tþ1 ¼ A0 þ AZ t þ utþ1 :
ð6Þ
where Zt+1 is a vector of excess log market returns, the term yield
defined as the yield difference between 10-year constant-maturity
taxable bonds and short-term taxable notes, the earnings growth
rate from S&P 500 index, and small-value spread,2 describing the
economy at time t + 1, A0 and A are vector and matrix of constant
parameters, and ut+1 is a vector of shocks. With the VAR expressed
in this form, the components of identity (1) can be obtained by
Et Rtþ1 ¼ e10 ðA0 þ AZ t Þ;
ð7aÞ
1
0
NCF;tþ1 ¼ ½e3 qAðI qAÞ utþ1 ;
0
1
NDR;tþ1 ¼ ½e1 ðI qAÞ utþ1 ;
0
ð7bÞ
ð7cÞ
0
where e1 ¼ ½ 1 0 0 , e3 ¼ ½ 0 0 1 0 0 , and I is an
identity matrix. Eq. (7) expresses EtRt+1, the one-period expected return as fitted value of Zt+1 based on VAR model in Eq. (3), NCF,t+1, the
cash-flow news, and NDR,t+1, the discount rate news as linear functions of the t + 1 shock vectors. Here in Eq. (7b), we directly measure
the cash-flow news based on earnings growth ðNcf ¼ ðEtþ1 Et Þ
P1 j
j¼0 q DEtþ1þj Þ, which is different from the Campbell and Vuolteenaho (2004) residual approach (see Chen and Zhao (2009)).3
3. Data and summary statistics
3.1. Insider trading data
We collect insider trading information from the Securities
Exchange Commission (SEC) Ownership Reporting System (ORS).
The ORS data starts in 1975 and ends in 2000 and contains all
insider transaction data that are subject to disclosure by the
Securities Exchange Act of 1934. Section 16(a) of the Act requires
2
For details of data construction, see Campbell and Vuolteenaho (2004).
We also performed all of the analyses using cash flow measure from the residual
method used in Campbell and Vuolteenaho (2004). The results are qualitatively
similar and are available from the authors.
3
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Table 1
Summary statistics. This table summarizes the statistics of insider trading data and VAR state variables. In Panel A, insider trading data are from all open market purchases and
sales of NYSE/AMEX and Nasdaq CRSP- and Compustat-listed common shares (CRSP share code 10 or 11) during 1978:Q1 to 2000:Q4. We report average quarterly number of buys
and sells per firm of our sample. IT is the average of IT from Eq. (8). We exclude all option transactions and transactions less than 100 shares. We define ‘‘Management: as CEOs,
CFOs, and Chairmen of the Board, Directors, Officers, Presidents, and Vice-Presidents. ‘‘Large shareholders” are those who own more than 10% of shares and are not in
management. ‘‘Others” are all those who are required to report their trading to the SEC but neither managers nor large shareholders. Large, medium, and small firms are firms
based on the sample firms’ quintile cutoff points at the market value in previous quarter. The log excess market return (R), term yield spread (TY), 10-year moving average
earnings growth (GE), and small-stock value spread (VS). In Panel B, we report the descriptive statistics of the VAR state variables estimated from the full sample period 1978:Q1
to 2000:Q4, 92 quarterly data points. It includes the log excess market return (R), term yield spread (TY), 10-year moving average earnings growth (GE), and small-stock value
spread (VS). ‘‘Std Dev.” denotes standard deviation and ‘‘Auto.” denotes the first-order autocorrelation of the series.
Management
Buys
Sales
Panel A: Summary statistics for insider trading
All
0.778
1.432
Small firms
0.977
0.796
Medium firms
0.748
1.332
Large firms
0.622
2.122
Variables
Mean
Panel B: Summary statistics for state variables
R
0.019
TY
0.566
GE
0.016
VS
1.497
Large shareholders
Sales
IT
Buys
Sales
IT
Buys
Sales
IT
data
0.255
0.073
0.275
0.542
1.210
1.203
1.155
1.276
0.794
0.562
0.780
1.053
0.212
0.325
0.186
0.123
1.121
1.182
1.064
1.113
1.108
0.638
1.080
1.629
0.004
0.250
0.027
0.222
1.017
1.110
0.973
0.967
1.118
0.670
1.062
1.621
0.020
0.210
0.042
0.228
Std Dev.
Min
Max
Auto.
0.080
0.747
0.007
0.154
0.276
1.320
0.001
1.236
0.182
2.720
0.027
2.045
0.054
0.760
0.963
0.822
BuysQ ;i SellsQ ;i
:
BuysQ ;i þ SellsQ ;i
ð8Þ
To construct an average of the IT time series we define
IT Q ¼
N
X
IT Q ;i =N;
Total
Buys
that open market trades by corporate insiders be reported to SEC
within 10 days after the end of month in which they took place.
For the purposes of this reporting requirement, ‘‘corporate insiders” include officers with decision making authorities over the
operations of the company (CEOs, CFOs, Other Officers, Presidents,
Vice-Presidents, etc.), all members of the board of directors, and
beneficial owners of more than 10% of the company’s stock. These
reports filed on the SEC’s Form 3, 4 and 5 are the source of insider
trading data. From the reported transactions we exclude all transactions that are less than 100 shares and only focus on open market purchases and sales by insiders.
Using the ORS data we classify insiders into three groups. The
first group, Management, includes Chairmen of the board, CEO,
CFO, Officers, Directors, Presidents, and Vice-Presidents and is assumed to have direct access to information about the firm’s future
prospects. ‘Large shareholders’ are those who are not management
but own 10% or more of shares and are assumed to have no direct
access to inside information. The third group, ‘others’ are all investors who are required to report their trades to SEC but are neither
managers nor large shareholders.
We define a measure of aggregate insider trading activity, IT in
the following manner. For each quarter in our sample from January
1978 to December 2000 we find the total number of insiders buying (Buys), and the total number of insiders selling (Sells), stocks in
their companies. We then define the following insider trading
measure for each firm i in quarter Q:
IT Q;i ¼
Others
IT
ð9Þ
i¼1
where N is the number of firms with insiders trading in each
quarter.
In Table 1, we present summary statistics of the trading behavior of insiders during our sampled period. On average, for the total
sample, there are 1.017 insiders buying stocks per firm-quarter and
1.118 insiders selling per firm-quarter and the average of the insider trading per firm-quarter, IT is 0.020 (this is the mean of the IT
measure in Eq. (9) over the sample period from 1978 to 2000).
Recall that the firm-quarter IT measure is the ratio of net buys to
total trades (buys + sells) over the quarter. The management group
averages 0.778 buys and 1.432 sells per firm-quarter and the average IT measure is 0.255. For the large shareholder group, there
are on average 1.210 buys and 0.794 sells per firm-quarter, and
the average IT measure is 0.212. When we look at the trading
behavior across the size of the firms we notice a monotonic decrease in buys and a monotonic increase in sales for the management group. Buys decrease from 0.977 per firm-quarter in the
small firms to 0.622 per quarter in the large firms for the management group. Sales range from 0.796 per firm-quarter in the small
firms to 2.122 per quarter in the large firms. The mean of the IT
measure also reflects this behavior with a mean of 0.073 for the
smallest size firms to 0.542 for the large firms, implying smaller
firms’ insiders are net buyers and larger firms’ insiders are net sellers. These results are in line with previous evidence of insiders
buying more heavily in smaller firms and selling heavily in larger
firms4 (Seyhun, 1986; Rozeff and Zaman, 1988; Jenter, 2005; Aktas
et al., 2008).
3.2. VAR data
In order to decompose the realized return into expected return,
cash-flow news and discount rate news using VAR approach, we
need to specify variables to be included in the state vector. Following Campbell and Vuolteenaho (2004), we choose a model with the
following four state variables. The excess market return (R) is measured as the log excess return on the Center for Research Security
Prices (CRSP) value-weighted index over log risk-free rate. The
risk-free-rate data are constructed by CRSP from Treasury bills
with approximately 3-month maturity. The term yield spread between long-term and short-term bonds (TY) is measured as the difference between 10-year constant-maturity taxable bond yield and
the yield on short-term taxable notes. The market’s earnings
growth (EG) is the log growth of a 10-year moving average of the
S&P 500 earnings. The small-stock value spread (VS) is measured
as the difference between the log book-to-market ratios of small
value and small growth stocks. Based on Campbell and Vuolteenaho (2004), the small-stock value spread (VS) is constructed in
the following way. The portfolios, which are constructed at the
4
For example, in Jenter (2005), going from the lowest book-to-market decile to the
highest book-to-market decile the number of net sellers falls from 68% to 33% while
number of net buyers increase from 24% to 54%. A similar pattern in buys and sells
can be discerned in our sample as one goes from large firms to small firms.
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
end of each June, are the intersections of two portfolios formed on
size (market equity, ME) and three portfolios formed on the ratio of
book equity to market equity (BE/ME). The size breakpoint for year
t is the median NYSE market equity at the end of June of year t. BE/
ME for June of year t is the book equity for the last fiscal year end in
t 1 divided by ME for December of t 1. The BE/ME breakpoints
are the 30th and 70th NYSE percentiles. At the end of June of year t,
the small-stock value spread is constructed as the difference between the log(BE/ME) of the small high-book-to-market portfolio
and the log(BE/ME) of the small low-book-to-market portfolio,
where BE and ME are measured at the end of December of year t
1. For months from July to May, the small-stock value spread is
constructed by adding the cumulative log return (from the previous June) on the small low-book-to-market portfolio to, and subtracting the cumulative log return on the small high-book-tomarket portfolio from, the end-of-June small-stock value spread.5
Asset pricing literature finds that these state variables are able
to forecast and track market returns.6 In Panel B, we provide summary statistics of the state variables for the sample period 1978–
2000. Our data description is a little different from the one in
Campbell and Vuolteenaho (2004) due to data frequency (we use
quarterly data while they use monthly data) and sample periods
(our data is from 1978 to 2000, while they use data from 1928 to
2001, including in the sample period the Great Depression and
World War Two). Our data show that all state variables except
for earnings growth are fairly volatile, since we use 10-year moving
average S&P 500 earnings. Quarterly log excess market return
shows negative autocorrelation (0.054). Log earnings growth,
log small-stock spread and log term yield spread are all quite persistent as expected, consistent with the result in Campbell and
Vuolteenaho (2004).
Table 2
VAR parameter estimates. This table shows the OLS parameter estimates for a firstorder VAR model including a constant, the log excess market return (R), term yield
spread (TY), 10-year moving average earnings growth (GE), and small-stock value
spread (VS). Each set of three rows corresponds to a different dependent variable. The
first five columns report coefficients on the five explanatory variables, and the sixth
column shows adjusted R2. Panel B reports the correlation among the state variables.
We report descriptive statistics of cash-flow news based on residual (Ncf1), cash-flow
news based on earning growth (Ncf), and discount rate news (Ndr) in Panel C. We also
report the Newey–West adjusted t-statistics in parentheses. Sample period for the
dependent variables is 1978:Q1–2000:Q4.
4. Results
Torous et al., 2004). Panel B provides correlation among the state
variables. It seems that excess return is positively correlated with
other state variables. In Panel C, we report the descriptive statistics
for the discount rate news and the cash-flow news from return
decomposition. Consistent with Campbell and Vuolteenaho
(2004), the volatility of cash-flow news is larger than the volatility
of discount rate news.
We regress realized market excess returns (defined as the CRSP
value-weighted return minus 3-month T-Bill rates) and its three
estimated components (one-period expected market excess return,
cash-flow news and negative of discount rate news) individually
on lagged values of aggregate insider trading measure, IT. The
return decomposition is based on the VAR system in Eq. (7). We
report the estimates, t-statistics, adjusted R2, sum of c and
Granger-causality test in Table 3. t-Statistics are computed using
Newey–West heteroskedastic-robust standard errors with 5 lags,
and are list below each estimate in parentheses. F-test is the Granger-causality test that the coefficients of all lagged insider trading
are zero.7 The p-value is listed below the F-test in bracket parenthesis. In Panel A, we report results for all insiders. The first row of Panel A shows that trading by all insiders has no explanatory power
in explaining the variation in realized market returns. The F-statistic is 5.377 with a p-value of 0.146. Note that the F-statistic is used
for the Granger-causality test of whether the coefficients of lagged
IT explain the variation in Rt. Furthermore, none of the individual
coefficients of lagged IT are significant at the 5% confidence level.
This suggests that there is no relation between realized market return and insider trading in the sample period. A possible reason
The evidence presented in this section uses a VAR model to
examine the relationship between aggregate insider trading and
market return. In Table 2 Panel A, we report parameter estimates
for the VAR model. Each row of the table corresponds to a different
equation of the model. The first five columns report coefficients on
the five explanatory variables: a constant and lags of the excess
market return, term yield spread, earnings growth, and small-stock
value spread. Newey–West adjusted t-statistics are reported in
parentheses below the coefficients. Finally, we report the adjusted
R2 for each of the estimated equations. The first row of Table 2
shows that only small-value stock spread (VS) in our VAR state
variables significantly predicts excess market return. The smallstock value spread negatively predicts the return, consistent with
Eleswarapu and Reinganum (2004) and Brennan et al. (2001).
Overall, the adjusted R2 of the return forecasting equation is about
1.2%, which is similar to Campbell and Vuolteenaho (2004) and a
reasonable number for a quarterly model. The remaining rows in
Panel A summarize the dynamics of the explanatory variables. All
explanatory variables are approximately an AR(1) process. The
earnings growth is highly persistent, with a root close to unity. It
is well known that estimates of persistent AR(1) coefficients are
biased downwards in finite samples, and that this causes bias in
the estimates of predictive regressions for returns if return innovations are highly correlated with innovations in predictor variables
(Stambaugh, 1999). There is an active debate about the effect of
this on the strength of the evidence for return predictability (Ang
and Bekaert, 2007; Campbell and Yogo, 2006; Lewellen, 2004;
5
All data for VAR are kindly provided by Tuomo Vuolteenaho.
6
We do not incorporate insider trading into the VAR on purpose, because our null
hypothesis is that inside trading is not informative.
Constant
Rt
Panel A: VAR estimates
Rt+1
0.173
0.042
(2.465)
(0.458)
TYt+1
0.434
0.815
(0.781)
(1.235)
GEt+1
0.001
0.003
(0.249)
(1.542)
VSt+1
0.314
0.098
(3.213)
(1.434)
TYt
GEt
VSt
R2
0.006
(0.559)
0.564
(4.436)
0.000
(0.372)
0.017
(1.112)
0.844
(0.546)
35.140
(4.063)
0.984
(28.197)
0.620
(0.314)
0.114
(2.666)
0.262
(0.773)
0.000
(0.156)
0.805
(12.721)
0.012
TYt+1
Rt+1
Panel B: Correlation of the state variables
1.000
0.067
TYt+1
1.000
GEt+1
VSt+1
Mean
Std
Min
Max
0.925
0.669
GEt+1
VSt+1
0.012
0.662
1.000
0.065
0.041
0.023
1.000
Autocorrelation
1
Panel C:
Ncf1
Ncf
Ndr
0.630
2
3
Descriptive statistics of cash-flow news and discount rate news
0.000
0.067
0.238
0.130
0.055
0.055
0.051
0.000
0.339
0.698
1.222
0.138
0.073
0.060
0.000
0.045
0.099
0.139
0.005
0.003
0.064
7
For the sake of brevity, we only report estimates of coefficients of variables which
are of interest. For example, in Table 2, we only report the estimates of lagged IT, the
ci’s. The adjusted R2 reported is for the full model which includes the lagged X
variables.
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Table 3
Regression of market excess return and its components on insider trading. This table
shows the results of the regressions between insider trading and market return (its
components) over the period 1978:Q1–2000:Q4. ITt denotes the insider trading
measure defined in Eq. (9), Rt denotes the realized market excess return, Et1[Rt]
denotes the expected market excess return, NCFt denotes the cash-flow news, NDRt
denotes the discount rate news, NEWS denotes the sum of cash-flow news and
discount rate news and D is a dummy variable to control for seasonality. The return
decomposition is based on the VAR system in Eq. (7). We report the estimates, tstatistics, adjusted R2, sum of c(b) in Panel A (B), and Granger-causality test. Note that
the adjusted R2 is for the full model, whereas in the table we only report the
coefficients of lagged IT. t-Statistics are computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in
parentheses. F-test is the Granger-causality test that the coefficients of all lagged
insider trading are zero. The p-value is listed below the F-test in bracket.
Xt ¼ a þ
3
X
bi X ti þ
i¼1
3
X
ci IT ti þ
i¼1
ITt1
Panel A: All insiders
0.042
Rt
(1.251)
0.086
Et1[Rt]
(0.557)
0.005
NDRt
(0.201)
NCFt
0.030
(0.198)
0.038
NEWSt
(0.217)
4
X
/k Dk þ et
Tt ¼ a þ
k¼1
3
X
i¼1
ITt2
ITt3
0.004
(0.100)
0.315
(1.877)
0.031
(1.281)
0.282
(1.699)
0.313
(1.659)
0.036
(0.926)
0.094
(0.625)
0.016
(0.965)
0.109
(0.787)
0.132
(0.857)
Panel B: Management
0.036
0.017
Rt
(1.130)
(0.576)
Et1[Rt]
0.045
0.331
(0.298)
(2.120)
0.001
0.037
NDRt
(0.050)
(1.682)
0.002
0.309
NCFt
(0.013)
(2.028)
NEWSt
0.004
0.345
(0.023)
(2.007)
Panel C: Large shareholders
0.035
0.023
Rt
(0.997)
(0.464)
0.127
0.372
Et1[Rt]
(0.651)
(1.734)
0.013
0.033
NDRt
(0.422)
(1.086)
NCFt
0.067
0.311
(0.348)
(1.449)
0.083
0.344
NEWSt
(0.379)
(1.412)
Table 4
Regression of insider trading on market excess return and its components. This table
shows the results of the regressions between insider trading and market return (its
components) over the period 1978:Q1–2000:Q4. ITt denotes the insider trading
measure defined in Eq. (9), Rt denotes the realized market excess return, Et1[Rt]
denotes the expected market excess return, NCFt denotes the cash-flow news, NDRt
denotes the discount rate news, NEWS denotes the sum of cash-flow news and
discount rate news and D is a dummy variable to control for seasonality. The return
decomposition is based on the VAR system in Eq. (7). We report the estimates,
t-statistics, adjusted R2, sum of c(b) in Panel A (B), and Granger-causality test. Note
that the adjusted R2 is for the full model, whereas in the table we only report the
coefficients of lagged X. t-Statistics are computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in
parentheses. F-test is the Granger-causality test that the coefficients of all lagged
return are zero. The p-value is listed below the F-test in bracket.
R2
P3
c
i¼1 i
0.003
0.081
0.033
0.324
0.003
0.043
0.039
0.361
0.033
0.407
0.010
(0.339)
0.021
(0.137)
0.002
(0.121)
0.025
(0.182)
0.034
(0.222)
0.005
0.063
0.041
0.307
0.007
0.040
0.049
0.332
0.043
0.375
0.056
(1.162)
0.106
(0.684)
0.022
(1.193)
0.138
(0.987)
0.168
(1.075)
0.014
0.068
0.038
0.351
0.006
0.042
0.039
0.383
0.032
0.429
F-test
bi X ti þ
3
X
ci IT ti þ
i¼1
Xt1
4
X
/k Dk þ et
k¼1
Xt3
R2
0.355
0.273
0.343
0.036
0.338
0.559
0.338
0.034
0.337
0.021
0.430
0.458
0.402
0.041
0.397
0.728
0.398
0.038
0.396
0.022
0.401
0.399
0.406
0.046
0.395
0.456
0.401
0.039
0.400
0.026
5.377
(0.146)
8.289
(0.040)
5.298
(0.151)
8.983
(0.030)
8.800
(0.032)
Panel A: All insiders
0.003
X = Rt
(0.007)
0.015
X = Et1[Rt]
(0.205)
0.323
X = NDRt
(0.612)
X = NCFt
0.032
(0.468)
0.021
X = NEWSt
(0.343)
0.244
(0.392)
0.047
(0.611)
0.249
(0.372)
0.032
(0.376)
0.027
(0.360)
0.520
(1.945)
0.068
(1.384)
0.012
(0.038)
0.034
(0.721)
0.027
(0.669)
6.287
(0.098)
10.436
(0.015)
8.718
(0.033)
11.514
(0.009)
11.456
(0.010)
Panel B: Management
0.124
X = Rt
(0.282)
X = Et1[Rt]
0.037
(0.524)
0.288
X = NDRt
(0.560)
0.052
X = NCFt
(0.800)
X = NEWSt
0.038
(0.650)
0.369
(0.605)
0.052
(0.601)
0.241
(0.340)
0.029
(0.311)
0.025
(0.297)
0.702
(2.217)
0.056
(0.980)
0.199
(0.580)
0.014
(0.281)
0.009
(0.202)
Panel C: Large shareholders
0.067
0.003
X = Rt
(0.202)
(0.005)
0.035
0.064
X = Et1[Rt]
(0.495)
(1.032)
X = NDRt
0.107
0.470
(0.207)
(0.935)
0.053
0.060
X = NCFt
(0.795)
(0.920)
0.041
0.052
X = NEWSt
(0.678)
(0.907)
0.463
(1.930)
0.075
(1.857)
0.121
(0.350)
0.046
(1.021)
0.038
(0.961)
3.504
(0.320)
8.721
(0.033)
5.091
(0.165)
9.462
(0.024)
9.284
(0.026)
could be the inclusion of large shareholders in our sample as this
groups trading may be less informative. An alternate explanation,
discussed in Section 2, is that such a lack of relationship does not
necessarily imply insider trading is not informative. The second,
third and fourth rows report results when the realized excess return is decomposed into one-period expected return, cash-flow
news and the discount rate news. The F-statistic for the expected
return, cash-flow news and discount rate news are 8.289, 8.983,
and 5.298, with p-values of 0.040, 0.030, and 0.151, respectively.
Our results suggest that when we consider all insiders their trading
has little effect on realized market excess returns. However, if we
decompose the realized return into three components, insider
trading is significantly related to expected market excess return
and future aggregate cash-flow news. This means insider trading
can explain the variations in realized excess market return which
is due to future unexpected cash-flow news. Also, the F-statistic
for the NEWS regression (sum of cash-flow news and discount rate
news) is 8.80 with a p-value of 0.03 suggesting that the sum of
unexpected cash-flow news and unexpected discount rate news
P3
Xt2
i¼1 bi
F-test
5.830
(0.120)
1.955
(0.582)
0.923
(0.820)
0.739
(0.864)
0.540
(0.910)
8.151
(0.043)
1.154
(0.764)
0.974
(0.808)
0.825
(0.843)
0.491
(0.921)
5.555
(0.135)
3.765
(0.288)
1.445
(0.695)
1.531
(0.675)
1.317
(0.725)
(unexpected returns) experiences significant positive shocks subsequent to insiders buying stocks in their firms.
As defined before, the Management group comprises of insiders
who are assumed to have direct access to information about the
firm’s future prospects. If this is true, the superior information
hypothesis would predict a stronger relation between insider trading and future market returns. Panel B reports results for the Management group. Here the effect of insider trading is more
pronounced as predicted by the superior knowledge hypothesis.
The F-statistics (p-value) for realized return, expected return, discount rate news, cash-flow news, and NEWS are 6.287 (0.098),
10.436 (0.015), 8.718 (0.033), 11.514 (0.009), and 11.456 (0.01).
The two quarter lagged coefficients of IT for the realized return, expected return, cash-flow news, discount rate news and NEWS
regressions are 0.017, 0.331, 0.037, 0.039, and 0.345 with t-statistics of 0.576, 2.120, 1.682, 2.208, and 2.007, respectively. This
provides strong evidence that insiders who are directly related to
the day-to-day activities of the firms are better able to predict
market return. Furthermore, our results show that trading by this
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X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
group of insiders is more likely to be related to unexpected future
cash-flow news two quarters later. Panel C reports results for the
large shareholders group. F-statistics for expected return and
cash-flow news are marginally significant and the lagged coefficient for IT is only significant in the NEWS regression. For this
group there is marginal evidence that trading is positively related
to future unexpected news. Also, the 2 quarter and 3 quarter
lagged coefficients for IT are all insignificant.
In Table 4, we report results of regressions between insider
trading and realized market excess return and the components of
realized market excess return. Here the insider trading variable,
IT is the dependent variable. The motivation here is to investigate
whether the market’s expectation of return drives insider trading.
This would provide evidence in support of Chowdhury et al.
(1993), Rozeff and Zaman (1998) and Jenter (2005) assertion that
insiders follow a contrarian investment strategy. Our interest is
in the relation between insider trading and the lagged values of
one-period expected market excess returns. If the assertion of contrarian strategy is true then we expect a negative relation between
insiders trading and lagged expected return.
In Panel A, we report again results for the overall group of insiders. The F-statistics are 5.830, 1.955, 0.923, 0.739, and 0.540 when
the insider trading variable, IT is regressed on lagged values of realized market excess returns, expected market excess returns, discount rate news, cash-flow news, and NEWS, respectively. The
results in Panel A suggest that we cannot reject the null hypothesis
that realized market excess return and lagged values of expected
market excess return, discount rate news, cash-flow news, and
NEWS do not Granger-cause insider trading. Furthermore, none
of the coefficients of the lagged values of expected market excess
return is statistically significant. The evidence from Panel A does
not support the assertion of insiders following a contrarian investment strategy. Recall that this group includes all insiders and results maybe misleading as the group consist of insiders who may
not have access to information relating to a firm’s future prospects.
In Panel B and Panel C, we report results for the Management
group and the large shareholders group. In Panel B, the F-statistics
(p-value) is 8.151 (0.043) for the regression of realized market excess return on IT. This suggests that aggregate insider trading is related to prior period’s realized return which is consistent with the
evidence provided by Chowdhury et al. (1993), Rozeff and Zaman
(1998) and Jenter (2005). Their sample excludes large shareholders
and is comparable to the Management group in this study. However, the F-statistics for the regression of IT on the lagged values
of expected market excess returns, cash-flow news, discount rate
news, and NEWS are not significant. These results do not support
the contrarian strategy hypothesis as we cannot reject the null
hypothesis of lagged values of expected market excess return do
not Granger-cause insider trading. Evidence presented in this table
clearly demonstrates that market excess return do not cause insider trading hence insider trading is not a manifestation of the contrarian strategy.
These results are quite different from what Chowdhury et al.
(1993),8 Lakonishok and Lee (2001) and Jenter (2005) conjecture
in their papers. These papers suggest that insider trades are more
likely to be a function of contrarian strategy. These studies did not
8
In the context of Chowdury et al. (1993) findings of market returns having a
stronger effect on insider trading (i.e. insiders are contrarians) we offer the following
explanation. Even though methodologically our research is similar there are three
major differences of which any or all could explain the different conclusions. First,
they look at insider buys and sells separately; second, they analyze weekly data and
finally their sample period is 1975–1986. The use of weekly data imposes serious
restriction on making long-term inferences about aggregate insider trading and
market return. As many studies suggest, long-horizon returns differ in many ways
from short horizon returns. For example, Fama and French (1988) suggest that long
horizon returns are more predictable.
address the issue of whether the observed relationship between insider trading and market return could be due to insider’s ability to
predict unanticipated cash-flow news or discount rate news. In this
paper, we directly test both hypotheses by using return decomposition methods. Our results show that insider trading is more likely to
be based on managers’ ability to time the market based on superior
information. Results reported in Table 3 provide evidence that insider trading is related to future cash-flow news and hence is more
likely to be based on the managers’ ability to predict market-wide
activities while results reported in Table 4 shows a lack of relation
between insider trading and lagged expected market excess return
thereby providing no evidence in support of the contrarian strategy.
5. Intensive insider trading criterion
Insiders may trade in stocks of their companies for reasons other
than information about future cash flow realizations.9 Many insiders may trade to diversify and rebalance their portfolios. They may
also trade for liquidity purposes (see Rozeff and Zaman, 1988; Jenter, 2005; Aktas et al., 2008). To reduce the potential noise from
such trades and to isolate trades which are information driven we
develop an intensive trading criterion similar to the one in Rozeff
and Zaman (1988) and apply it to the insider trading metric in Eq.
(8).10 In a given month, we require that at least three insiders take
the same action and that no insiders take an opposing action. Thus,
in a given month, if there are at least three insiders buying stocks
in their company and no insiders selling then the firm is classified
as a ‘‘buy”. For a firm to be classified as a ‘‘sell” three or more insiders
must sell in a given month and no insiders buy it. We then use Eq. (8)
to estimate the IT measure for each quarter across all firms.
In Table 5, we report results of regressions using this new measure of insider trading metric for the Management group. Results
are qualitatively the same as before. Panel A reports regression results when realized market excess returns and its components are
regressed on lagged values of insider trading measure IT. The F-statistics (p-value) for realized return, expected return, discount rate
news, cash-flow news, and NEWS are 3.857 (0.277), 10.339
(0.016), 9.664 (0.022), 9.922 (0.019), and 10.12 (0.018). In Panel B,
we report results of regressions of insider trading on realized market
excess return and the components of realized market excess return.
Here the insider trading variable, IT is the dependent variable. The Fstatistics (p-values) are 12.122 (0.007), 3.627 (0.305), 6.087 (0.107),
4.716 (0.194), and 4.804 (0.187). Panel A and Panel B results clearly
indicate that after controlling for insider trading that may be due to
portfolio diversification and rebalancing purposes, insiders are still
able to predict aggregate cash-flow news and discount rate news.
Trading by insiders is significantly related to unexpected future
cash-flow news two quarters later. On the other hand we find no evidence that insider trading is Granger caused by lagged values of expected market excess return. These results add robustness to our
conclusion that trading by insiders is more likely to be related to
unexpected future cash-flow news and not due to contrarian beliefs.
6. Firm size, information uncertainty and aggregate insider
trading
Jiang et al. (2004) define information uncertainty as the degree
to which a firm’s value can be estimated by the most knowledge9
We would like to thank an anonymous referee for suggesting to control for noninformation motivated trades.
10
We did not follow Jenter’s (2005) methodology for a couple of reasons. First,
Jenter’s methodology for controlling trades due to portfolio diversification and
rebalancing motives rely on annual measures of executive compensation, etc.,
whereas our study looks a quarterly trading. Second, our study is focused on
aggregate trading while Jenter’s study looks at insider trading at the individual level.
1233
X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Table 5
Insider trading and market returns: intensive insider trading measure. This table
shows the results of the regressions between insider trading and market return (its
components) over the period 1978:Q1–2000:Q4. ITt denotes the intensive insider
trading measure, Rt denotes the realized market excess return, Et1[Rt] denotes the
expected market excess return, NCFt denotes the cash-flow news, NDRt denotes the
discount rate news, NEWS denotes the sum of cash-flow news and discount rate news
and D is a dummy variable to control for seasonality. The return decomposition is
based on the VAR system in Eq. (7). We report the estimates, t-statistics, adjusted R2,
sum of c(b) in Panel A (B), and Granger-causality test. Note that the adjusted R2 is for
the full model, whereas in the table we only report the coefficients of lagged IT(X).
t-Statistics are computed using Newey–West heteroskedastic-robust standard errors
with 5 lags, and are list below each estimate in parentheses. F-test is the Grangercausality test that the coefficients of all lagged insider trading are zero. The p-value is
listed below the F-test in bracket.
ITt1
P3
ITt2
R2
ITt3
P3
P3
c
i¼1 i
F-test
Table 6
Regression of market excess return and its components on insider trading for different
size portfolios. This table shows the results of the regressions between insider trading
and market return (its components) over the period 1978:Q1–2000:Q4. Small firms
are the lowest quintile of the sample firms’ market capitalization and large firms is
the highest quintile. ITt denotes the insider trading measure defined in Eq. (9), Rt
denotes the realized market excess return, Et1[Rt] denotes the expected market
excess return, NCFt denotes the cash-flow news, NDRt denotes the discount rate news,
NEWS denotes the sum of cash-flow news and discount rate news and D is a dummy
variable to control for seasonality. The return decomposition is based on the VAR
system in Eq. (7). We report the estimates, t-statistics, adjusted R2, sum of c(b) in
Panel A (B), and Granger-causality test. Note that the adjusted R2 is for the full model,
whereas in the table we only report the coefficients of lagged IT. t-Statistics are
computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and
are list below each estimate in parentheses. F-test is the Granger-causality test that
the coefficients of all lagged insider trading are zero. The p-value is listed below the
F-test in bracket.
P4
Panel A: X t ¼ a þ i¼1 bi X ti þ i¼1 ci IT ti þ k¼1 /k Dk þ et
Rt
0.006
0.007
0.039
0.011
0.038
(0.331)
(0.186) (0.997)
0.204
0.046
0.054
0.043 0.196
Et1[Rt]
(2.299) (0.313) (0.487)
0.027
0.002
0.003
0.001
0.026
NDRt
(2.345)
(0.087)
(0.152)
0.211
0.016
0.023
0.050
0.204
NCFt
(2.342)
(0.100)
(0.188)
NEWSt
0.234
0.019
0.022
0.041
0.231
(2.319)
(0.104)
(0.157)
P3
Xt1
Xt2
Xt3
R2
i¼1 bi
P
P
P
Panel B: IT t ¼ a þ 3i¼1 bi X ti þ 3i¼1 ci IT ti þ 4k¼1 /k Dk þ et
X = Rt
0.518
0.801
1.281
0.554
(1.327)
(1.793)
(3.126)
0.149
0.125
0.020
0.503
X = Et1[Rt]
(1.353)
(1.594) (0.233)
X = NCFt
0.175
1.236
1.028
0.510
(0.246) (2.049)
(1.685)
0.158
0.144
0.067
0.506
X = NDRt
(1.377) (1.796)
(0.758)
0.127
0.130
0.068
0.506
X = NEWSt
(1.279) (1.810)
(0.872)
2.600
0.004
2.089
0.054
0.070
3.857
(0.277)
10.339
(0.016)
9.664
(0.022)
9.922
(0.019)
10.120
(0.018)
F-test
12.122
(0.007)
3.627
(0.305)
6.087
(0.107)
4.716
(0.194)
4.804
(0.187)
able investors at reasonable costs. Using this definition, high information uncertainty firms would be those firms whose expected
cash flows may be difficult to estimate due to their environment
or nature of operations, etc. These firms are likely to have high
information acquisition costs and their fundamental values are
more likely to be unreliable and volatile. If aggregate insider trading is driven by the contrarian strategy then insiders are more
likely to trade in high information uncertainty firms as these are
more likely to have current market values deviating from the ‘true’
fundamental values. It is also true that high information uncertainty firms have greater information asymmetry which can lead
to insiders exploiting their superior knowledge. If this is true then
aggregate insider trading in high information uncertainty firms
would predict market return. On the other hand, low information
uncertainty firms are more likely to have market values equal to
the fundamental values. Insider trading in these types of firms is
more likely to be a manifestation of the insider’s ability to predict
market return based on superior knowledge rather than contrarian
strategy. To the extent that small firms have high information
acquisition costs and are likely to be followed by fewer analysts
we use firm size as a proxy for information uncertainty.
For each quarter in our insider trading sample we form size
quintiles based on the market capitalization value. The first quintile comprises of the smallest firms while the fifth quintile comprises the largest firms. We repeat our earlier analyses on
smaller firms and larger firms but confine it to the Management
group of insiders. In Table 6, we report regression results for the
two groups – small firms and large firms. Realized market excess
returns and its components are regressed on lagged values of IT
Xt ¼ a þ
3
X
bi X ti þ
i¼1
3
X
ci IT ti þ
i¼1
ITt1
4
X
/k Dk þ et
k¼1
ITt2
ITt3
Panel A: Small firms
0.031
Rt
(0.788)
0.186
Et1[Rt]
(1.788)
NCFt
0.015
(1.002)
0.119
NDRt
(1.184)
0.133
NEWSt
(1.183)
0.010
(0.230)
0.394
(2.166)
0.030
(1.145)
0.332
(1.906)
0.360
(1.827)
0.072
(2.079)
0.198
(1.225)
0.048
(2.444)
0.238
(1.554)
0.294
(1.725)
Panel B: Large firms
0.044
Rt
(2.350)
0.001
Et1[Rt]
(0.011)
0.007
NCFt
(0.412)
NDRt
0.044
(0.358)
0.049
NEWSt
(0.351)
0.005
(0.239)
0.131
(1.290)
0.015
(0.970)
0.113
(1.058)
0.127
(1.051)
0.023
(0.926)
0.031
(0.249)
0.003
(0.165)
0.042
(0.352)
0.047
(0.350)
R2
P3
c
i¼1 i
0.015
0.093
0.074
0.407
0.061
0.063
0.076
0.451
0.074
0.520
0.018
0.072
0.017
0.161
0.040
0.024
0.011
0.198
0.019
0.222
F-test
7.240
(0.065)
20.027
(0.000)
15.296
(0.002)
24.188
(0.000)
24.464
(0.000)
7.324
(0.062)
3.191
(0.363)
3.315
(0.346)
4.462
(0.216)
4.396
(0.222)
for the small firm and large firm samples. Panel A reports results
for small firms. The F-statistics (p-values) for realized market excess return, expected market excess return, cash-flow news, discount rate news, and NEWS regressions are 7.24 (0.065), 20.027
(0.00), 15.296 (0.002), 24.188 (0.000), and 24.464 (0.000), respectively. In Panel B results for large firms are reported. Here the F-statistics (p-values) for realized returns, expected return, cash-flow
news, discount rate news, and NEWS are 7.324 (0.062), 3.191
(0.363), 3.315 (0.346), 4.462 (0.216), and 4.396 (0.222), respectively. The results suggest that for both small and large firms, insider trading positively predicts the realized market excess return. In
addition, it is only for small firms we find insider trading and cashflow news are positively and significantly related suggesting that
aggregate insider trading predicts future cash-flow news.
Table 7 reports results when IT is regressed on lagged values of
realized returns, expected returns, cash-flow news, discount rate
news, and NEWS for both the small firm group and the large firm
group. In Panel A, for small firms the F-statistics (p-values) for
the regressions of realized returns, expected returns, cash-flow
news, discount rate news, and total news are 4.436 (0.218),
1.662 (0.645), 1.927 (0.588), 0.855 (0.836), and 0.794 (0.794),
respectively. In Panel B, results are reported for large firms. The
F-statistics (p-values) for the regressions of realized returns, expected returns, cash-flow news, discount rate news, and total news
are 4.454 (0.216), 1.966 (0.580), 1.817 (0.611), 0.845 (0.839), and
1234
X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Table 7
Regression of insider trading on market excess return and its components for different
size portfolios. This table shows the results of the regressions between insider trading
and market return (its components) over the period 1978:Q1–2000:Q4. Small firms
are the lowest quintile of the sample firms’ market capitalization and large firms are
the highest quintile. ITt denotes the insider trading measure defined in Eq. (9). Rt
denotes the realized market excess return, Et1[Rt] denotes the expected market
excess return, NCFt denotes the cash-flow news, NDRt denotes the discount rate news,
NEWS denotes the sum of cash-flow news and discount rate news and D is a dummy
variable to control for seasonality. The return decomposition is based on the VAR
system in Eq. (7). We report the estimates, t-statistics, adjusted R2, sum of c(b) in
Panel A (B), and Granger-causality test. Note that the adjusted R2 is for the full model,
whereas in the table we only report the coefficients of lagged X. t-Statistics are
computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and
are list below each estimate in parentheses. F-test is the Granger-causality test that
the coefficients of all lagged return are zero. The p-value is listed below the F-test in
bracket.
IT t ¼ a þ
3
X
bi X ti þ
i¼1
3
X
ci IT ti þ
i¼1
Xt1
Panel A: Small firms
0.453
X = Rt
(1.281)
0.001
X = Et1[Rt]
(0.013)
X = NCFt
0.119
(0.309)
0.027
X = NDRt
(0.517)
0.019
X = NEWSt
(0.411)
Panel B: Large firms
0.163
X = Rt
(0.346)
0.048
X = Et1[Rt]
(0.511)
0.822
X = NCFt
(1.259)
0.037
X = NDRt
(0.423)
X = NEWSt
0.040
(0.523)
4
X
/k Dk þ et
Table 8
Regression of market excess return and its components on insider trading for number
of analysts following portfolios. This table shows the results of the regressions
between insider trading and market return (its components) over the period
1978:Q1–2000:Q4. Least followed firms are the lowest quintile of the sample firms’
number of analysts and closely followed firms are the highest quintile, ITt denotes the
insider trading measure defined in Eq. (9), Rt denotes the realized market excess
return, Et1[Rt] denotes the expected market excess return, NCFt denotes the cash-flow
news, NDRt denotes the discount rate news, NEWS denotes the sum of cash-flow news
and discount rate news and D is a dummy variable to control for seasonality. The
return decomposition is based on the VAR system in Eq. (7). We report the estimates,
t-statistics, adjusted R2, sum of c(b) in Panel A (B), and Granger-causality test. Note
that the adjusted R2 is for the full model, whereas in the table we only report the
coefficients of lagged IT. t-Statistics are computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in
parentheses. F-test is the Granger-causality test that the coefficients of all lagged
insider trading are zero. The p-value is listed below the F-test in bracket.
Xt ¼ a þ
3
X
bi X ti þ
i¼1
k¼1
Xt2
Xt3
R2
P3
F-test
0.468
(0.832)
0.094
(1.219)
0.459
(0.698)
0.075
(0.868)
0.065
(0.845)
0.137
(0.591)
0.022
(0.539)
0.203
(0.699)
0.000
(0.005)
0.003
(0.098)
0.353
0.784
0.349
0.073
0.340
0.781
0.342
0.048
0.342
0.049
4.436
(0.218)
1.662
(0.645)
1.927
(0.588)
0.855
(0.836)
0.794
(0.851)
0.435
(0.538)
0.040
(0.336)
0.515
(0.484)
0.067
(0.488)
0.061
(0.500)
0.640
(1.890)
0.080
(1.153)
0.113
(0.235)
0.037
(0.560)
0.032
(0.548)
0.270
0.367
0.248
0.073
0.253
0.195
0.245
0.067
0.246
0.053
i¼1 bi
4.454
(0.216)
1.966
(0.580)
1.817
(0.611)
0.845
(0.839)
0.875
(0.831)
0.875 (0.831). The results in Panels A and B clearly suggest that
realized market excess return, expected returns, cash-flow news,
discount rate news, and total news do not Granger-cause insider
trading. In summary, we find little evidence of a contrarian investment strategy employed in aggregate insider trading.
To reinforce our results we use an alternate proxy for information uncertainty. We classify firms into quintiles based on the
number of analysts following a firm.11 Each year we find the number of analysts following a firm from the I/B/E/S tapes and classify
firms in one of five quintiles based on the number of analysts following the firm. Quintile 1 comprises of firms least followed by
analysts and quintile 5 has firms closely followed by analysts. In
Table 8, in Panel A, for least followed firms the F-statistics (p-values) for the regressions of realized returns, expected returns,
cash-flow news, discount rate news, and total news are 9.931
(0.019), 8.52 (0.036), 8.941 (0.03), 10.337 (0.016), and 10.753
(0.013), respectively. In Panel B results for closely followed firms
are reported. Here the F-statistics (p-values) for realized returns,
expected return, cash-flow news, discount rate news, and NEWS
are 7.443 (0.059), 4.852 (0.183), 4.081 (0.253), 6.390 (0.0.094),
and 6.209 (0.102) The results are qualitatively similar when size
is used as a proxy for information uncertainty with one exception.
11
Our choice of number of analysts as a proxy for information uncertainty is based
on earlier studies. For example, Agca and Mozumdar (2008) list analyst following as
one of the factors associated with market imperfections.
3
X
ci IT ti þ
i¼1
ITt1
4
X
/k Dk þ et
k¼1
ITt2
Panel A: Least followed
0.039
0.024
Rt
(1.254)
(0.639)
0.082
0.110
Et1[Rt]
(0.739)
(0.708)
NCFt
0.004
0.002
(0.208)
(0.108)
0.022
0.072
NDRt
(0.212)
(0.490)
0.028
0.074
NEWSt
(0.239)
(0.452)
Panel B: Closely followed
0.039
0.016
Rt
(1.852)
(0.847)
0.007
0.157
Et1[Rt]
(0.060)
(1.676)
0.006
0.018
NCFt
(0.335)
(1.326)
0.035
0.147
NDRt
(0.284)
(1.563)
NEWSt
0.038
0.165
(0.279)
(1.547)
ITt3
0.088
(2.195)
0.335
(1.846)
0.057
(2.688)
0.358
(2.324)
0.420
(2.448)
0.018
(0.799)
0.030
(0.231)
0.002
(0.115)
0.035
(0.290)
0.040
(0.293)
R2
P3
c
i¼1 i
0.042
0.103
0.038
0.362
0.040
0.055
0.049
0.409
0.047
0.466
0.021
0.073
0.009
0.179
0.034
0.026
0.000
0.217
0.008
0.244
F-test
9.931
(0.019)
8.520
(0.036)
8.941
(0.030)
10.337
(0.016)
10.753
(0.013)
7.443
(0.059)
4.852
(0.183)
4.081
(0.253)
6.390
(0.094)
6.209
(0.102)
For firms closely followed by analysts we find weak evidence of
aggregate insider trading being related to unexpected changes in
future discount rate news.
Table 9 reports results when IT is regressed on lagged values of
realized returns, expected returns, cash-flow news, discount rate
news, and NEWS for least followed firms and closely followed
firms. In Panel A, for least followed firms the F-statistics (p-values)
for the regressions of realized returns, expected returns, cash-flow
news, discount rate news, and total news are 8.391 (0.039), 0.808
(0.848), 5.392 (0.145), 1.136 (0.768), and 1.312 (0.726), respectively. In Panel B, results are reported for large firms. The F-statistics (p-values) for the regressions of realized returns, expected
returns, cash-flow news, discount rate news, and total news are
4.850 (0.183), 1.993 (0.574), 2.557 (0.465), 1.016 (0.797), and
1.109 (0.775). For firms which are least followed by analysts, we
find that aggregate insider trading is related to prior realized market excess returns. This can be misinterpreted as evidence of contrarian investment strategy by insiders for this group of firms.
However, we find no evidence of expected returns, cash-flow news,
discount rate news, and NEWS Granger-causing aggregate insider
trading. In summary, we cannot find any evidence of a contrarian
investment strategy employed in aggregate insider trading when
realized market excess returns are decomposed.
Our results from Tables 6–9 confirm what we conjectured earlier regarding information uncertainty and insider trading. We find
that for firms which are high in information uncertainty (small
X. Jiang, M.A. Zaman / Journal of Banking & Finance 34 (2010) 1225–1236
Table 9
Regression of insider trading on market excess return and its components for number
of analysts following portfolios. This table shows the results of the regressions
between insider trading and market return (its components) over the period
1978:Q1–2000:Q4. Least followed firms are the lowest quintile of the sample firms’
number of analysts and closely followed firms are the highest quintile. ITt denotes the
insider trading measure defined in Eq. (9), Rt denotes the realized market excess
return, Et1[Rt] denotes the expected market excess return, NCFt denotes the cash-flow
news, NDRt denotes the discount rate news, NEWS denotes the sum of cash-flow news
and discount rate news and D is a dummy variable to control for seasonality. The
return decomposition is based on the VAR system in Eq. (7). We report the estimates,
t-statistics, adjusted R2, sum of c(b) in Panel A (B), and Granger-causality test. Note
that the adjusted R2 is for the full model, whereas in the table we only report the
coefficients of lagged X. t-Statistics are computed using Newey–West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in
parentheses. F-test is the Granger-causality test that the coefficients of all lagged
return are zero. The p-value is listed below the F-test in bracket.
IT t ¼ a þ
3
X
bi X ti þ
i¼1
3
X
ci IT ti þ
i¼1
Xt1
Panel A: Least followed
0.483
X = Rt
(1.183)
0.002
X = Et1[Rt]
(0.019)
X = NCFt
0.029
(0.061)
0.033
X = NDRt
(0.446)
0.026
X = NEWSt
(0.393)
Panel B: Closely followed
0.325
X = Rt
(0.644)
0.055
X = Et1[Rt]
(0.636)
0.914
X = NCFt
(1.534)
0.052
X = NDRt
(0.646)
X = NEWSt
0.054
(0.756)
4
X
/k D k þ et
k¼1
Xt2
Xt3
R2
P3
F-test
0.104
(0.187)
0.063
(0.787)
0.423
(0.559)
0.058
(0.628)
0.051
(0.615)
0.441
(1.849)
0.019
(0.325)
0.335
(0.845)
0.014
(0.206)
0.016
(0.274)
0.297
0.145
0.277
0.043
0.279
0.786
0.277
0.039
0.277
0.041
8.391
(0.039)
0.808
(0.848)
5.392
(0.145)
1.136
(0.768)
1.312
(0.726)
0.504
(0.663)
0.054
(0.434)
0.610
(0.562)
0.085
(0.599)
0.077
(0.606)
0.691
(2.087)
0.074
(0.972)
0.054
(0.114)
0.030
(0.419)
0.025
(0.408)
0.303
0.512
0.276
0.072
0.284
0.250
0.275
0.062
0.276
0.048
i¼1 bi
4.850
(0.183)
1.993
(0.574)
2.557
(0.465)
1.016
(0.797)
1.109
(0.775)
1235
other hand, if these trades are a result of contrarian beliefs then insider trading should be negatively related to past expected return.
We find strong evidence that aggregate insider trading is positively
related to unexpected cash-flow news for all types of insiders.
When we partition our sample based on insiders who are more
likely to have access to performance related information these results are much stronger and significant. We also examine whether
aggregate insider trading is in response to market expectations. We
find no evidence of aggregate insider trading being caused by market expectations. Our results strongly suggest that insiders are able
to predict market returns because of having superior information
about future cash-flow news. We also control for non-information
based motives of insider trading. Using an intensive trading criterion we isolate trades which are more likely to be based on information and find results to be consistent.
To further substantiate our results we classify firms into high
information uncertainty and low information uncertainty firm
and use firm size and analyst following as proxies for information
uncertainty. If aggregate insider trading is due to contrarian strategy, then insiders are more likely to trade in small firms (or firms
with less analyst following), as these firms are likely to have high
information uncertainty. On the other hand, if insiders are trading
due to superior knowledge then the trades should be concentrated
on larger firms as these trades are more likely related to economywide activity. However, small firms also have greater information
asymmetry which can lead to insiders exploiting their superior
knowledge. We find that the predictive ability of aggregate insider
trading in high information uncertainty firms is due to superior
knowledge. We find weak evidence that aggregate insider trading
in these firms is associated with contrarian strategy of investment.
The fact that insider trading is due to informational advantage
has an important implication. Given that insider trades are driven
by superior information, aggregate insider trading should be construed as a leading indicator of market-wide activities. Furthermore, such trading by insiders will drive prices towards
fundamental values.
Acknowledgements
firms or fewer analysts following firms) insider trading is more
likely due to managers exploiting their superior knowledge about
future cash-flow news, whereas for firms which have low information uncertainty (large firms or larger number of analysts following
firms) aggregate insider trading is not a manifestation of contrarian
strategy or of informational advantage.
We appreciate valuable comments from an anonymous referee,
Gordon Klein, Bong-Soo Lee, Ike Mathur (the editor), Paul Schultz,
seminar participants at the University of Northern Iowa, Florida
International University, Hohai University, and Hunan University,
and presentation participants at the 2007 Financial Management
Association Annual Conference. We alone are responsible for errors. Zaman acknowledges financial support from the College of
Business Administration, University of Northern Iowa.
7. Conclusion
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of superior knowledge about future cash-flow news (e.g., Ke et al.,
2003; Seyhun, 1988). Piotroski and Roulstone (2005) document
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insider trading to predict market-wide movement using return
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insider trading. We argue that if insiders are trading on the basis of
superior information, then aggregate insider trading is more likely
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