Electronic copy available at: http://ssrn.com/abstract=2625614

1

Can Anomalies Survive Insider Disagreements?

Deniz Anginer, Gerard Hoberg and Nejat Seyhun*

July 1, 2015

Abstract

Many studies show that future stock returns are predictable. These findings are consistent with

either mispricing or risk-based asset pricing models that capture the cross section of expected

returns. In this paper, we use a large backward-extended insider trading database from 1975 to

2013 to construct anomaly-specific measures of mispricing that are designed to be unrelated to

risk. We find that the predictive ability of both insider trading and anomalies survives when the

direction of insider trading agrees with the anomaly but the predictive ability of the anomalies

completely disappears when insider trading disagrees with the anomaly. In all cases, insider

trading continues to predict future abnormal returns. We conclude that mispricing is an

important component of the predictive ability of all thirteen anomalies we consider.

JEL Classifications: G11, G12, G14, G17

Keywords: Anomalies, behavioral finance, asset pricing, factor models, market efficiency, and insider trading ______________

* Deniz Anginer, Virginia Tech, danginer@gmail.com; Gerard Hoberg, University of Southern California,

hoberg@marshall.usc.edu; Nejat Seyhun, University of Michigan, nseyhun@umich.edu . We thank Ivana Mrazova

for excellent research assistantship.

Electronic copy available at: http://ssrn.com/abstract=2625614

2

1. Introduction

A large body of literature shows predictable patterns in security returns that appear to

conflict with the central paradigms of market efficiency and commonly used parsimonious asset

pricing models.1 Since there is no commonly accepted explanation, these patterns are called

anomalies.2 In spite of voluminous research on these anomalies, there is little consensus as to

whether these predictable return patterns require a modification to the market efficiency

paradigm or to the asset pricing paradigm.3 In this study, we utilize long time-series and cross-

sectional data on insider trading to test if the sources of 13 anomaly returns are due to

informational inefficiencies.

Fifty years of research on insider trading has established that corporate insiders are

among the most sophisticated investors when it comes to identifying potential mispricing of their

own stock.4 Using both private and public information, insiders can and do accurately identify

times when their own firms common stock becomes over- or underpriced and they trade to

exploit such mispricing. Insiders buy before an abnormal stock price increase and sell before an

abnormal stock price decline. Given the consensus that insiders do identify mispricing in their

own firms, we ask whether previously discovered anomaly returns are due to the same

mispricing identified by insiders, or whether these anomalies exist beyond any information

already identified by insiders. An important advantage of our back-extended insider trading

database is the availability of long time-series (almost 40 years) and universal coverage of all

publicly listed firms.

1 The anomaly literature is large. See for a partial list of anomalies: Basu (1977), Ball (1978), Ohlson (1980), Banz

(1981), Rosenberg, Reid, and Lanstein (1985), Fama and French (1993), Jegadeesh and Titman (1993), Carhart

(1997), Davis, Fama and French (2000), Jegadeesh and Titman (2001), Hirshleifer, Hou, Teoh, and Zhang (2004),

Titman, Wei, and Xie (2004), Livnat and Mendenhall (2006), Daniel and Titman (2006), Cooper, Gulen and Schill

(2008), Fama and French (2008), Pontiff and Woodgate (2008), and Novy-Marx (2012, 2013). 2 Green, Hand and Zhang (2013) report over 330 return predictive signals. For 39 of these that are readily

programmable, they report average correlation to be near zero, indicating that the number of rationally priced risk

factors are likely to be implausibly large. 3 Fama and French (2008), Brav, Heaton and Li (2010), Subrahmanyam (2010), Richardson, Tuna and Wysocki

(2010), Green, Hand and Zhang (2013, 2014), Hanson and Sunderam (2014), Stambaugh, Yu and Yuan (2012,

2014), Harvey, Liu and Zhu (2015) and McLean and Pontiff (2013) 4 Corporate insiders are officers, directors and large shareholders of public corporations. Information content of

insider trading is well established for almost 50 years. See for instance, Lorie and Niederhoffer (1968), Jaffe (1974),

Finnerty (1976), Seyhun (1986, 1988, 1992, 1998), Rozeff and Zaman (1988), Lin and Howe (1990), Bettis,

Vickrey and Vickrey (1997), Lakonishok and Lee (2001), Jeng, Metrick, and Zeckhauser (2003), Scott and Xu

(2004), Piotroski and Roulstone (2005), Jenter (2005), Marin and Olivier (2008), Cohen, Malloy, and Pomorski

(2012).

3

Potential explanations for anomalous stock return patterns can be grouped into two broad

categories, each with different testable implications when compared to insider trading patterns.

The first group of explanations points to inadequacies in measuring risk properly, since theory

gives only limited guidance for measuring risk.5 To the extent that some anomalous forecasting

variables are correlated with an unmeasured true risk variable, it would not be surprising if they

forecast future stock returns. Suppose that a particular stock is shocked with higher distress risk,

and its stock price falls as a result. In this case, the stock will have a lower market capitalization

and a higher book-to-market ratio. Higher future returns for this stock would be consistent with

compensation for the higher ex-post distress risk. In this case both firm size and book-to-market

variables will appear to forecast future stock returns.6

The second group of explanations relies on the premise that investor rationality is

bounded, investor attention is limited, investors have heterogeneous priorities, or investors are

subject to behavioral biases and arbitrage is costly. Consequently, market efficiency will not

always hold.7 Investors can overreact to more salient attention-grabbing information and

underreact to less salient information.8 This hypothesis predicts that stocks become mispriced

from time to time and this mispricing is captured by the anomaly regressions. For instance, a

higher book-to-market ratio can also result if the stock price falls too much due to an

overreaction or market inefficiency. As the mispricing is corrected over time, stock prices will

bounce back and provide higher future returns. Once again, under the second scenario both firm

size and book-to-market variables will also appear to forecast future stock returns.

Our paper contributes to this literature by using a novel method to identify likely

mispricing using a large backward-extended database of insider trading data covering 1975 to

2013. Because asset pricing anomalies are often established using long time series, we view this

database as crucial for providing adequate power to examine the sources of anomaly returns.

Our methodological innovation is that we use insider trading activity to construct anomaly-

specific measures of mispricing that should correlate little with risk. Our objective is to focus on

5 Ball (1978), Berk (1995), and Fama and French (1993, 1996), and Zhang (2005).

6 This argument is formalized in Berk (1995) and Berk, Naik and Green (1999).

7 Delong, et al, (1990), Shleifer and Summers (1990), Harris and Raviv (1993), Lakonishok, Shleifer, and Vishny

(1994), Kandel and Pearson (1995), Shleifer and Vishny (1997), Daniel and Titman (1997), Barberis and Huang

(2001), Hirshleifer (2001), Daniel, Hirshleifer and Teoh (2002), Barberis and Thaler (2003), Hirshleifer, Lim and

Teoh (2009), Hong, Torous, and Valkanov (2007), Hong and Stein (2003), Della Vigna and Pollet (2003), Hong,

Stein and Yu (2007), Baker and Wurgler (2007), and Hanson and Sunderam (2014). 8 Hirshleifer and Teoh (2003) and Peng and Xiong (2006).

4

the ability of insiders to identify and trade on mispricing, which is well established.

Furthermore, our measures are based on both insider sales and purchases, and tag the same

individual stocks as both overvalued and undervalued over time. This allows us to predict when

given stocks will experience anomaly returns, and when they will not. In contrast, we do not

expect firms to frequently switch from high risk to low risk or vice versa.

We also note that an increase in risk (which would be associated with positive future

returns) is not likely to induce undiversified, risk-averse insiders to increase their purchases in

their own employers stock as would be necessary to explain our results through a potential risk

channel. If anything, an increase in risk is likely to induce undiversified investors such as

insiders to trim their positions in their own employers stock, thus predicting insider sales.

Hence for both empirical and theoretical reasons, any traction we find in explaining anomalies

using our anomaly specific mispricing measures likely cannot be explained by systematic risk

exposures.

To further alleviate concerns that insider trading can proxy for risk, we examine the

predictability of anomalies when insiders agree or disagree with the direction of returns implied

by a given anomaly. In particular, our central test asks whether anomalies only produce

abnormal returns when insiders are trading in a direction that agrees with the direction of the

anomaly, or whether anomalies can still predict returns even when insiders are trading in the

opposite direction, while fully controlling for any return predictability attributable to the level of

insider trading activity itself. We consider this test both in time series and in cross section. This

approach has not been considered in past studies. To implement our approach for a given

anomaly, we compare the direction of insider trading to the predicted direction of the anomaly,

and tag each stock in each month based on the agreement or disagreement between the two. For

instance, a stock may exhibit a low book-to-market ratio. In this case, the value anomaly on

average predicts low future stock returns. Based on observed insider trading, we then classify

this stock into one of three separate groups, 1) insiders agree with the anomaly if insiders are

selling, 2) insiders disagree with the anomaly if insiders are buying, and 3) insiders are neutral if

they are not trading. If instead a stock exhibits a high book-to-market ratio, then the first two

groups are reversed as insider agreement would be revealed if they are buying rather than selling.

Hence, as insider trading is stochastic, the insider agreement variables load both on insider

purchases and insider sales at different times. By using insider agreement minus disagreement

5

instead of raw insider trading, we thus further reduce the likelihood that these measures can

proxy for risk.

The testable implications are as follows. Under efficient-markets (the risk hypothesis) the

predictive ability of anomalies should not on average be affected by the presence of insider

agreement or disagreement. Investors should demand higher compensation for higher risk at all

times, regardless of the direction of insider trading. Thus, we would expect large firms and low

book-to-market stocks to exhibit lower future returns and small firms and high book-to-market

stocks to exhibit higher future returns regardless of whether insiders agree, disagree or are

neutral. Raw insider trading controls should maintain their predictive power if insiders

additionally have access to private information, and their inclusion should not significantly affect

the anomaly coefficients.

Under the market inefficiency hypothesis, we would expect the predictive ability of

anomalies to be strongest in time periods when insiders agree with the anomaly, and to be absent

or even reversed when insiders disagree with the anomaly. Suppose that a given stock

experiences substantial decline in price relative to its book value of equity, thus resulting in a

high book-to-market ratio. Also suppose that insiders are selling this stock. In this case, there is

a disagreement between anomaly and insider trading, and the inefficiency hypothesis would

predict no value premium. If instead, insiders are buying, then we predict a strong value

premium that is likely due to misvaluation even controlling for the expected price impact of the

level of insider trading itself.

Our evidence indicates that, conditional on insider agreement, both lagged insider trading

and the 13 asset pricing anomalies we consider retain their predictive ability and also become

economically large. This finding is consistent with the interpretation that both insider trading

and anomalies represent noisy measures of underlying mispricing, which are not completely

subsumed by the other variable. Consequently, they both survive in a horse race.

However, conditional on insider disagreement, the predictive ability of anomalies

remarkably disappears for all 13 anomalies, even while the predictive ability of lagged insider

trading levels survives. When there is disagreement, insider trading correctly identifies the

future direction of stock returns while the anomalies do not. The fact that anomalies lose their

predictive power when insiders disagree with the anomaly suggests that there would be no

compensation for risk at these times. This interpretation does not square well with the efficient-

6

markets risk-based hypothesis, since investors should continue to demand higher compensation

for risk. Evidence also shows that, for some anomalies including Ohlsons-O score and

composite equity issuance, the direction of future stock returns is reversed and is opposite of that

predicted by the anomaly when insiders disagree. A risk-based story would then have to explain

why the risk-premium would have one sign when insiders agree, and another when insiders

disagree, which seems difficult to do. Overall, while we cannot rule out the risk hypothesis, our

evidence indicates that market mispricing likely plays an important role in all 13 anomalies we

test. We find that a dominant portion of the information content of anomalies comes from

periods when there is agreement with insider trading signals. When there is disagreement, the

predictive ability of the anomalies disappears.

Our contributions are three-fold: (1) we provide a general testing framework for assessing

whether any anomaly (including those not yet discovered) is likely due to risk factors or potential

market mispricing, (2) we provide evidence that 13 well-established anomalies in the literature

lose their predictive ability when confronted with insider disagreements, and (3) we provide new

information to potential investors who invest in anomalies. Sorting on any given anomaly alone

might be inadequate to capture all available mispricing. Investors can jointly consider insider

trading and anomaly signals, and might time anomaly investments to episodes when they will

deliver superior returns.

Our paper is organized as follows: Section 2 reviews the literature on asset pricing

anomalies for each of the 13 anomalies analyzed in this paper. We also review the literature

regarding the sources of the underlying predictive ability of the anomalies. Section 3 discusses

our data and construction of insider scores. Section 4 presents our findings and robustness

checks. Section 5 concludes.

2. Anomalies and Related Literature

Standard models of risk such as Sharpe (1964) and Lintner (1965) have difficulty in

explaining the return variations associated with common anomalies. Subsequent extensions of

the standard models by adding book-to-market, size and momentum factors also fail to explain

7

most of the anomalous predictions.9 In this paper, we reexamine returns for 11 anomalies

identified in Stambaugh, Yu, and Yuan (2012, 2014). To this list, we add the post-earnings

stock price drift, book-to-market and firm size. Also, we use a single measure of bankruptcy

probability, Ohlsons-O measure.10 As a result, we consider 13 anomalies.

For all anomalies other than book-to-market, size, and momentum, we control for the 3-

factor Fama and French (1993) model. For firm size and book-to-market, we delete the anomaly

under investigation from this asset pricing model, but keep the remaining two factors. The

anomalies investigated in this paper are:

1) Post-earnings announcement drift, where stock prices drift in the same direction of the

earnings surprise (actual earnings minus expected earnings) over a period of several weeks or

months.11

If the earnings surprise is positive, then the future stock price drifts upward. If the

earnings surprise is negative, then the future stock price drifts downward. Drift is robust to

measuring expected earnings using both times-series models as well as analyst forecasts. The

market inefficiency interpretation of the post-earnings drift is that investors underreact to the

current earnings surprise information. Over time, as prices incorporate more of the fundamental

information, stock prices drift in the same direction as the earnings surprise. The risk-based

explanation states that higher returns proxy for higher risk.

2) Net operating assets, scaled by total assets, which predict negative long-run stock

returns. Higher net operating assets are associated with lower future profitability and lower

future stock returns.12

Hirshleifer, Hou, Teoh and Zhang (2004) suggest that investors overreact

to current accounting profitability relative to cash profitability, even though higher operating

earnings relative to cash earnings also indicates lack of sustainability of recent earnings

performance. Under this hypothesis, lower future returns suggest that overreaction is corrected

when lower future earnings performance is revealed.

9 See Fama and French (1993) and Carhart (1997). Fama and French (1992, 1996) show that other anomalies such

as leverage, dividend-yield, and earnings-to-price rations are explained by market, size and book-to-market factors. 10

We obtain similar results using instead the Merton distance-to-default measure or the Campbell, Hilscher, Szilagyi

(2008) default probability measure. 11

See Ball and Brown (1968), Jones and Litzenberger (1970), Latane, Joy and Jones (1970), Latane and Jones

(1979), Rendlemen, Jones, and Latane (1982), Foster, Olsen and Shevlin (1984), Bernard and Thomas (1989,

1990), Affleck-Graves and Mendenhall (1992), Abarbanell and Bernard (1992), Bartov, Radhakrisnan and Krinsky

(2000), Collins and Hribar (2000), Narayanamoorthy (2003), Liang (2003), Livnat (2003), and Mendenhall (2004),

and Livnat and Mendelhall (2006). 12

Sloan (1996), Barton and Simko (2002), Hirshleifer, Hou, Teoh, Zhang (2004).

8

3) Gross profitability (defined as revenues minus cost of goods sold, divided by assets),

which is associated with higher future stock returns.13

Novy-Marx (2012) finds that sorting on

gross profit-to-assets creates abnormal benchmark-adjusted returns, with more profitable firms

having higher returns than less profitable firms and argues that gross profits scaled by assets is

the cleanest accounting measure of true economic profitability. The farther down the income

statement one goes, the more polluted profitability measures become, and the less related they

are to true economic profitability. The market inefficiency interpretation suggests that investors

underreact to higher gross profitability, which indicates more productive assets. Over time, as

the under-reaction is corrected, future returns are higher. The risk-based hypothesis states that

higher profitability proxies for higher risk.

4) Return on assets, which predicts higher future stock returns.14

Fama and French

(2006) show that more profitable firms have higher expected returns than less profitable firms.

Chen, Novy-Marx, and Zhang (2010) show that firms with higher past return on assets earn

abnormally higher subsequent returns. Wang and Yu (2010) find that the anomaly exists

primarily among firms with high arbitrage costs and high information uncertainty, suggesting

that mispricing is likely at work. The market inefficiency interpretation is similar to gross

profitability. The risk-based hypothesis states that higher profitability itself proxies for higher

risk.

5) Higher investment in assets (annual change in gross property, plant, and equipment

plus the annual change in inventories scaled by the lagged book value of assets) is associated

with lower future stock returns.15

Titman, Wei and Xie (2004) interpret the predictive ability of

investments in assets as consistent with the empire building hypothesis. Under this hypothesis,

lower future returns suggest that investors typically underreact to empire building implications.

6) Higher asset growth (one year percentage change in total assets) is negatively related

to future stock returns. 16

Higher asset growth is similar to higher investments in assets. Cooper,

Gulen, and Schill (2008) find that firms that grow their total assets more earn lower subsequent

returns. They interpret their findings as consistent with an investors initial overreaction to

positive changes in future business prospects implied by asset expansions.

13

Novy-Marx (2012, 2013). 14

Haugen and Baker (1996), Cohen, Gompers, Vuolteenaho (2002), Fama and French (2006. 2008). 15

Fairfield, Whisenant and Yohn (2003), Titman, Wei and Xie (2004). 16

Cooper, Gulen and Schill (2008).

9

7) Higher book-to-market is associated with higher future stock returns.17

The market

inefficiency interpretation is that variations in book-to-market ratio capture underreaction to

fundamental information. As the underreaction is corrected over time, higher book-to-market

firms generate higher future stock returns, while lower book-to-market ratio firms generate lower

future stock returns. The risk-based hypothesis states that book-to-market proxies for distress

risk.

8) Net stock issuance is negatively related to future stock returns.18

Ritter (1991) and

Loughran and Ritter (1995) find that equity issuers under-perform matching non-issuers with

similar characteristics in subsequent years. The market inefficiency interpretation of this finding

is that firms issue equity when it is overvalued and retire equity when it is undervalued.

Furthermore, investors underreact to net stock issuance decisions at the time of announcement.

9) Higher bankruptcy prediction scores are associated with not higher, but lower future

stock returns.19

The market inefficiency interpretation suggests that investors underreact to

income statement and balance sheet information for distressed firms. As the true condition of the

firm is revealed in the future, stock prices decline. We use Ohlson (1980) O-score as the distress

measure. The Ohlsons O-score is calculated as the probability of bankruptcy in a static model

using accounting variables, such as net income divided by assets, working capital divided by

market assets, and current liability divided by current assets.20

10) Higher accruals are associated with lower future stock returns.21

Total accruals are

calculated as changes in noncash working capital minus depreciation expense scaled by average

total assets for the previous two fiscal years. Sloan (1996) shows that firms with high accruals

earn abnormal lower returns in the future relative to low accrual firms. Sloan (1996) suggests

that investors overestimate the persistence of the accrual component of earnings when forming

earnings expectations. Over time, as more fundamental information is revealed, these pricing

errors are corrected.

17

Rosenberg, Reid and Lanstein (1985), Chan, Hamao and Lakonishok (1991), Fama and French (1992, 1993). 18

Ikenberry, Lakonishok and Vermaelen (1995), Loughran and Ritter (1995), Daniel and Titman (2006) and Pontiff

and Woodgate (2008). 19

Altman (1968), Ohlson (1980), Zmijewski (1984), Campbell, Hilscher, and Szilagyi (2008) and Jackson and

Wood (2013). 20

The appendix provides the details of Ohlsons-O measure. 21

See Sloan (1996), Dechow, Khimich and Sloan (2011).

10

11) Composite equity issuance is associated with lower future stock returns.22

Daniel and

Titman (2006) study an equity issuance measure, composite equity issuance, defined as the

amount of equity a firm issues or retires in exchange for cash or services. Under this measure,

seasoned issues and share-based acquisitions increase the issuance measure while repurchases,

dividends, and other actions that take cash out of the firm reduce this issuance measure. Daniel

and Titman (2006) find that issuers underperform non-issuers in the subsequent years. The

market inefficiency interpretation is similar to net stock issuance, in that managers engage in net

equity issuance when stock prices are high and purchase net equity when stock prices are low.

12) Small firms have higher average future returns than large firms.23

The market

inefficiency hypothesis suggests that investors discount the cash flows of small firms at a higher

rate than would be justified by systematic risk alone. Also, if stock prices fall too much, firm

size gets small. Subsequently, as pricing errors are corrected, small firms tend to have higher

positive future stock returns. The risk-based hypothesis states small firm size itself proxies for

higher systematic risk.

13) Momentum shows that firms with lower returns over the past 6 to 12 monthstend to

have lower stock returns in the next few months, and firms with higher returns over the past 6 to

12 tend to have higher returns in the next few months.24

Jegadeesh and Titman (1993) show that

high past recent returns forecast high future returns. Antoniou, Doukas, and Subrahmanyam

(2011) find that the momentum effect is stronger when sentiment is high, and they suggest this

result is consistent with the slow spread of bad news during high-sentiment periods.25

In addition

to market momentum, industry momentum also has predictive value which suggests it is also

potentially due to delayed information transmission and thus long-term valuation corrections.26

Despite decades of research, there is still little consensus as to why these anomalies

predict future returns. A likely reason is that the sorting variables, whether it is stock price,

profitability, asset productivity, average stock returns, recent stock returns, or firm size are

difficult to uniquely link to either mispricing or risk.

22

Ikenberry, Lakonishok and Vermaelen (1995), Loughran and Ritter (1995), Daniel and Titman (2006) and Pontiff

and Woodgate (2008). 23

Banz (1981), Fama and French (1993). 24

Jegadeesh and Titman (1993) and Novy-Marx (2012). 25

Hong and Stein (1999), Huberman and Regev (2001), Menzly and Ozbas (2006), Cohen and Frazzini (2008). 26

Moskowitz and Grinblatt (1999) and Hoberg and Phillips (2012). In the appendix, we provide additional

information on each anomaly and a detailed description of how these anomalies are constructed.

11

Fama and French (1993) argue that small firm size and the value factor (namely high

book-to-market) proxy for a risk-based distress factor. Daniel and Titman (1997), however,

demonstrate that future returns are related to size and value characteristics but not the small-

minus-big (SMB) and high-minus-low (HML) factor loadings, which argues against the risk-

based interpretation. Davis, Fama and French (2000) then extend the sample of Daniel and

Titman and provide new evidence supporting the risk-based explanation. Going further,

Campbell, Hilscher, Szilagyi (2008) find that while distressed firms have high loadings on the

SMB and HML factors, they also generate lower returns, which once again argues against the

risk-based interpretation. In a review of the literature, Subrahmanyam (2010) states: Predictive

variables used emanate from informal arguments, alternative tests of risk-return models,

behavioral biases, and frictions. More than fifty variables have been used to predict returns. The

overall picture, however, remains murky, because more needs to be done to consider the

correlational structure amongst the variables, use a comprehensive set of controls, and discern

whether the results survive simple variations in methodology.

Other more recent studies also come to conflicting conclusions: Avramov, Chordia,

Jostova and Philipov (2013) find that, with the exception of the accruals anomaly, the

profitability of the price momentum, earnings momentum, credit risk, dispersion, idiosyncratic

volatility, and capital investments anomalies derive exclusively from periods of financial

distress. None of these strategies is profitable when periods surrounding credit rating

downgrades are excluded from the sample. This finding is consistent with the risk-based story.

In contrast, Hanson and Sunderam (2014) estimate the amount of capital devoted to equity

arbitrage strategies and find that the amount of capital devoted to exploiting the value and

momentum strategies has increased since the 1980s, thereby reducing the returns from these

strategies. This finding is consistent with mispricing argument.

Stambaugh, Yu and Yuan (2012, 2014) also conclude that the predictive ability of

anomalies arises from investor sentiment and limits to arbitrage. They show that anomaly

returns are stronger following high levels of sentiment, and that the short leg of the anomalies

accounts for most of the high performance. Similarly, using short interest data to proxy for short

arbitrage, Hwang and Liu (2014) provide evidence that anomalies with high average returns, low

risk, and diversification benefits are more popular among short arbitrageurs. Edelen, Ince and

Kadlec (2015) find that institutional investors contribute to the mispricing. They examine

12

institutional demand around stock return anomalies. They find that institutions buy stocks with

anomaly characteristics that predict low returns. McLean and Pontiff (2013) estimate that the

post-publication decay in average returns from anomalies is about 35%. They also find that

stocks in characteristic portfolios experience higher volume, variance, short interest, and higher

correlations with portfolios that are based on published characteristics. Overall, they conclude

that mispricing is an important part of predictability. In contrast, however, Israel and Moskowitz

(2013) conclude that shorting is not important in generating profits for the value and momentum

strategies using a long sample from 1926 to 2011 for U.S firms and from 1972 to 2011 for

international firms.

Our paper contributes to this literature using a novel approach. Our key innovation is that

we use insider trading as a measure of mispricing. Furthermore, we construct these measures

such that they are unlikely to sort on risk. Following the recent literature,27

we examine

anomalies in a unified framework by comparing the predictive ability of each anomaly against

anomaly-specific insider trading variables. The unified framework allows us to focus on the

commonalities between anomalies and helps us explore a parsimonious explanation across all

anomalies. Second, we use a long time-series (almost 40 years) and universal cross-sectional

coverage during our sample. Third, we have a single all-inclusive proxy for mispricing based on

insider trading, which is relatively independent of variations in risk. We describe our data and

the construction of our insider trading variables next.

3. Data 3.1. Sample, Anomalies and Insider Score

We obtain stock price information from Center for Research in Security Prices (CRSP)

and financial statement data from the COMPUSTAT industrial files. The insider trading data

come from the union of the Thomson Reuters Insider Filing Data Feed (1996 to 2013) and

backward extensions using archived annual purchases from the National Archives (1975 to

1995). We henceforth refer to this database as the ``Back-Extended Thomson Reuters Database

or simply the combined insider trading database. Our sample includes U.S. common stocks

27

See Fama and French (1996, 2008), Stambaugh, Yu, and Yuan (2012), Hou, Xue and Zhang (2015), Avramov,

Chordia, Jostova and Philipov (2013), and Edelen, Ince and Kadlec (2015).

13

(CRSP share codes of 10 or 11) that are covered by all three databases. The time period is from

January 1975 through December 2013. We restrict attention to this interval due to the

availability of insider trading data, which first becomes available in January of 1975. We

include firm month observations beginning only from the time when the firms first appear in the

combined insider trading database. We exclude utilities, financials, and stocks priced under $5.

Following Shumway (1997), we adjust monthly stock returns for delistings using the CRSP

monthly delisting file. Our final dataset has over 20,000 unique CUSIPs and over 1,000,000

firm month observations.

The combined Insider Filing Database includes all trades reported to the Securities and

Exchange Commission (SEC) - Ownership Reporting System. The data contains all open market

purchases and sales by officers, directors, and beneficial owners (direct or indirect owners of

more than 10% of any equity class of securities) of publicly traded firms.28

Shares acquired

through exercise of options, stock awards, and trades with corporations are excluded. The final

sample is limited to firms for which stock return data are available in CRSP. Finally, in order to

deal with potential misreports and incorrect outliers, three filters are used. On the insider

transaction date, (1) the insider transaction price must be less than twice the closing price of the

stock; (2) the number of shares of the insider transactions will be less than the daily volume of

trade of the stock; and (3) the number of shares of the insider transaction will be less than the

outstanding number of shares for the stock.29

The combined insider trading database provides two dates associated with an insider

transaction. The transaction date is the date of the actual transaction, when an insider sells or

purchases shares of their own company. The report date is the date when an insider transaction

is made public by the Securities and Exchange Commission. Although our main focus in this

paper is on the information content of insider trades (favors trade dates), we also consider the

28

For most of the sample period analyzed here (prior to August 29, 2002), Section 16(a) of the Securities and

Exchange Act requires that insider transactions be disclosed within the first 10 days of the month following the

month of the trade. Section 16(b) prohibits insiders from profiting from short-term price movements defined as

profitable offsetting pairs of transactions within 6 months of each other, while Section 16(c) prohibits profiting from

short-sales. Sarbanes- Oxley Act of 2002 (effective August 29, 2002) has modified insider trading regulations in

many significant ways. First, the new reporting requirement states that insider transactions must be reported

electronically by the end of the second business day following the day on which the transaction is executed both

through EDGAR and corporate public websites. Sarbanes-Oxley also prohibits purchase and sale of securities during

black-out periods. 29

Qualitative results do not change if these filters were not enforced. Results are available upon request.

14

tradability by outside investors of the information contained in these transactions (favors report

dates). We thus carry out the analyses using both trade date and report date separately.

We create a standardized insider trading measure by normalizing total insider purchases

and sales in a given month. We use a standardized measure to account for the fact that, on

average, insiders tend to sell more shares than they buy. There is also cross-sectional variation in

this tendency to sell. Insiders in smaller firms are much less likely to sell compared to insiders in

larger firms. This tendency to sell has also been increasing over time due to changes in the

structure of executive compensation and the growing portion of compensation provided in the

form of stock options.30

For robustness, we repeat the analyses using actual shares traded without

standardization. We obtain qualitatively similar results, which are reported in Table 8, Panel B.

We create a standardized insider trading measure (Insider score) by normalizing the net

number of shares bought or sold by insiders in a given month. We first compute net insider

trades (NIT) for firm i in month t as the total number of shares insiders bought minus the total

number of shares insiders sold divided by the total number of shares outstanding at the end of

month t:

, = , ,

, (1)

If a firm has no observed insider trades in given month, it is assumed to have experienced zero

insider trading in that month and we assign an NIT value of zero to that firm. Next, we

standardize the NIT values for each firm and each month, using the average and standard

deviation of NIT values computed over 36 months. In computing the standardized insider trades,

the NIT average and standard deviation values are lagged by 12 months to ensure the

standardization itself is not what is informative. The standardized Insider score is computed as:

, = , ,12:48

,12:48 (2)

30

Seyhun (1998).

15

We define insiders as buyers if the insider score is above 0.426, as sellers if the insider score

is below 0.426, and as neutral otherwise. The values 0.426 and -0.426 divide a standard

normal distribution into three equal probability groups. On average, insiders are classified as

buyers 8% of the time and as sellers 10% of the time in our sample. This is because a

substantial number of smaller firms have zero insider trades in some months.

Since we use anomaly characteristics in cross-sectional regressions, we winsorize all

variables except Size and Momentum at their 1st and 99

th percentile values. Table 1 provides

summary statistics for the anomaly characteristics and the insider score that we use in the

analyses. Table 2 shows the correlation between these variables. On average, the anomaly

characteristics are not highly correlated with each other. This observation is consistent with

Stambaugh, Yu and Yuan (2012) and Green, Hand and Zhang (2013) who find that the portfolio

returns of a large set of anomalies are also not highly correlated. The correlation of the insider

score with the anomalies is also not very high.31

This low correlation provides greater cross-

sectional variation when we examine the profitability of anomalies when insiders trade against or

consistent with a given anomaly.

Since we are trying to explain the time series variation in the returns of anomalies based

on insider trading, it is important for us to observe variation in insider trades when anomalies

predict both high and low returns. For instance, if insiders always buy high book-to-market

stocks, and always sell low book-to-market stocks, we would not have observations when

insiders are trading against the direction of returns implied by the book-to-market characteristic.

To examine this further, we report average insider scores for five groups of stocks sorted each

month based on anomaly characteristics. The averages are reported in the left panel of Table 3.

Stocks are sorted based on anomalies such that the high group is associated with higher future

returns. Consistent with the low correlations, we find that there is significant dispersion in

insider trading scores within each group. For most anomalies, insiders are contrarians. For

instance, insiders tend to sell when the stock price has gone up and momentum is high, and buy

when the stock price has gone down and momentum is low. The right panel of Table 3 reports

the average anomaly characteristics for three insider trading subsamples (buy, sell, and neutral)

31

The correlation does increase when we use discrete categorical values (-1, 0, 1) corresponding to buy, sell, neutral instead of the continuous insider score.

16

created based on the insider scores as described in the previous section. We do observe some

differences in average anomalies across the groups but they are not economically significant.

3.2. Categorizing Insider Trading Strategies

Our goal in this paper is to test whether stock anomalies are driven by mispricing due to

informational inefficiencies. We do this by examining the profitability of stock anomalies

conditional on insiders agreeing with the direction of returns implied by these anomalies. If a

stock has an anomaly characteristic such that it predicts high returns in the future (high

momentum for instance), we examine the difference in future returns when insiders buy and thus

agree with the anomaly, and when insiders sell and thus disagree with the anomaly. Insiders are

well-known to trade in an opportunistic fashion exploiting mispricing of their own firms stock.

If insider trades systematically predict the direction of returns due to anomalies, then it is likely

that anomaly returns are due at least in part to mispricing. In other words, if the premium of a

given anomaly strategy disappears when insiders are trading counter to the direction of the

anomaly, then it is likely that the premium is due to potential informational inefficiency, and it is

less likely due to an equilibrium risk-reward tradeoff.

It is also possible that insider trades may be profitable because they capture idiosyncratic

inside information about the firm that is not related to any of the underlying anomaly

characteristics of the firm.32

Company returns could, in that case, be driven by both an exposure

to a risk factor, as well as inside information shocks. However, if that was the case, there would

be no reason for returns to be related to the anomaly-specific insider trading variables we create,

as we separately control for the level of insider trading activity in our regressions.

Although it is not possible to completely rule out a risk based explanation, any anomaly

that is only significant in asset pricing data when insiders are trading in a way that could benefit

from the anomaly strongly suggests that the returns are linked to informational inefficiency.

Risk-based explanations predict no link to insider trading, as the equilibrium conditions of risk

32

Insiders can earn a premium by trading against outside investors who may mis-evaluate publicly available

information about the firm. Insiders can also earn a premium by trading based on specific information about their

firm unknown to the public.

17

models require that all investors (both insiders and outside investors) are aware of the risks

associated with stock characteristics that predict high returns.

With this goal in mind, we categorize insider trading based on its consistency with

various anomaly strategies. First, we determine whether insider are buyers, neutral, or

sellers using the insider score described above. Second, we sort stocks by a given anomaly

and place them into two groups one associated with high returns and one associated with low

returns. We categorize a firm as insiders AGREE with an anomaly if i) insiders are buyers of

the stock and the stock is in the anomaly group predicting high returns, or ii) insiders are

sellers of the stock and the stock is in the anomaly group predicting low returns. Similarly, we

categorize a firm as having insiders DISAGREE with an anomaly, if i) insiders are sellers of

the stock and the stock is in the anomaly group predicting high returns, or ii) insiders are

buyers of the stock and the stock is in the anomaly group predicting low returns. If insiders are

not buyers or sellers, then we categorize the firm as having insiders who are NEUTRAL.

We form portfolios for both time-series and cross-sectional analyses based on insider

agreement and disagreement on a monthly basis. In constructing anomaly variables, we use

accounting information lagged by at least 6 months and market information lagged by one

month. For instance, if we are forming portfolios at the end of June of year t, we use stock

characteristics observed either in calendar year-end t-1 or the fiscal year-end in year t-1. These

characteristics are updated annually. The two exceptions to this procedure are the momentum

and PEAD anomalies. For momentum, we rank stocks each month based on cumulative returns

from t-12 to t-2. For the PEAD anomaly we rank stocks based on standardized unexpected

earnings for the most recent quarterly earnings announced as of month t (not including

announcements in month t).

To determine insider agreement, we use the insider score observed in month t-1. As

mentioned earlier, we compute the insider score on two different dates the trade date and the

report date. The trade date is the date when an insider completes a transaction, and the report

date is when it is reported to the SEC. The differences in dates become insignificant after the

introduction of the Sarbanes-Oxley Act of 2002, which requires insiders to report their trades

within 2 business days. For robustness, we computed returns before and after this change came

18

into effect. We discuss the results in Section 4.4. We compute both equal and value weighted

returns. Value weighted returns are more likely to provide economically meaningful results as

equal-weighted portfolios are more expensive to trade. However, the prior literature has shown

that insider trades contain greater information about future returns for smaller firms.33

Hence we

view it as important to display both results.

4. Results

4.1. Anomaly Returns

We begin our analyses by first examining the baseline profitability of anomaly returns in

our sample of firms covered by the back-extended insider trading database (without considering

the insider trading data yet). We carry out both time-series and cross-sectional analyses. For the

time-series analyses, we form 5 portfolios each month by ranking stocks based on anomaly

characteristics. We report benchmark-adjusted returns for the long leg defined as the higher

performing 5th

quintile portfolio, and for the short leg defined as the lowest performing 1st

quintile portfolio, as well as the long-short portfolio that goes long the highest performing

quintile portfolio and short the lowest performing quintile portfolio. We compute benchmark-

adjusted returns using the Fama and French (1993) market, size, and value factors. We exclude

the size and value factors when computing the benchmark-adjusted returns for the size and book-

to-market anomalies, respectively. We compute both value weighted and equal-weighted

returns.

For the cross-sectional analyses, we run the following Fama-MacBeth regression of

returns on each of the anomaly characteristics controlling for size, book-to-market and

momentum:

, = ,1 + 1,1 +

2--,1 + 3,1 + , (3)

Here , is the return of stock i in month t. As with the time-series regressions, we exclude the

size and book-to-market in computing coefficients on the size and book-to-market anomaly

33

Seyhun (1986, 1998).

19

characteristics respectively. We also compute value weighted Fama-MacBeth coefficients by

running a weighted least squares regression in each month and then computing the usual time-

series average of the estimated coefficients.

Table 4 reports the results. The first panel on the left contains the value weighted long,

short and long minus short 3-factor alphas, as well as the coefficient on the anomaly

characteristic, , from the Fama-MacBeth regressions. The second panel on the right shows the

equal-weighted results. The average monthly benchmark-adjusted return across all 13 long-short

strategies is 0.44% for value weighted returns and 0.58% for equal-weighted returns. All the

anomalies are priced significantly in the Fama-MacBeth regressions. These results are consistent

with findings in the prior literature and confirm that the anomalies we consider are priced in the

cross-section of stocks covered by the back extended insider trading database.

4.2. Cross-sectional Analyses

As mentioned earlier, our goal is to test whether stock anomalies are driven by mispricing

due to informational inefficiencies. In particular, we test to see if insider trades systematically

predict future anomaly returns. To do this, we split the sample of stocks into three groups based

on insider agreement as described in the previous section. We put stocks into the Insiders

Agree group if either of the following two conditions hold: (1) The insider score is above the

threshold value (0.46) such that insiders are categorized as buyers and the stock is in the long

anomaly group predicting high returns; or, (2) the insider score is below the threshold value (-

0.46) such that insiders are categorized as sellers and the stock is in the short anomaly group

predicting low returns. We put stocks in the Insiders Disagree group if the opposite conditions

apply. If insiders are not trading or their trades do not cross the threshold, then we put those

stocks in the Insiders Neutral group.

We then run Fama-MacBeth regressions to see how the anomalies are priced in the cross-

section of stocks in each of the three groups. If we find that an anomaly is significantly priced

when insiders agree, but is not priced when insiders disagree, then the anomaly is likely

20

explained at least in part by mispricing resulting from informational inefficiencies. For example,

if the value premium is present only when insiders are employing strategies that are consistent

with value investing, then our results would support the conclusion that the value premium is

likely due to mispricing and it is less likely due to an equilibrium risk/reward trade-off. In

particular, equilibrium risk-based explanations predict no link to insider trades, since insiders

(just like outside investors) are required by assumption to be aware of the risk associated with

each asset.

In the Fama-MacBeth regressions, we control for the insider score to make sure that the

results are robust to holding the level of insider trading constant in each of the three samples. In

particular, we run the following Fama-MacBeth regression separately for each anomaly:

, = ,1 + 1 ,1 +

2,1 + 3--,1

+ 4,1 + , (4)

Table 5 reports the results. For brevity, we only report the coefficients on the anomaly

characteristics () and insider score () for the three samples. As before, we report both equal-

weighted and value weighted results separately under the corresponding headers, Equal-

Weighted and Value Weighted respectively. Panel A shows the coefficient estimates where

insider trades are categorized based on their values observed on the trade date. Panel B reports

the results where insider trades are categorized based on their values observed on the report date.

Results based on the trade date are most theoretically motivated regarding tests of market

inefficiency, and results based on the report date are relevant for constructing tradable portfolios.

We find that anomalies are priced significantly in the cross-section when insiders agree.

For the insiders agree group, both the economic and the statistical significance of anomalies are

larger when compared to samples where insiders are neutral.34

In addition, comparing the

predictive ability of unconditional anomalies from Table 4 with the predictive ability of the

anomaly conditioned on insider agreement in Table 5, we also find that the predictive ability of

every anomaly increases when insiders agree with it. This finding is consistent across all

34

The only exceptions are PEAD under equal weighting and net stock issuance under value-weighting. Asset

growth has a marginally more significant t-statistic (-2.20 versus -2.17) for insiders neutral than for insiders agree

under value-weighting.

21

anomalies, including size, book-to-market, and momentum. The fact that anomalies get stronger

when insiders agree suggests that anomalies generate predictable returns when mispricing is

likely, and that insider trading likely captures sources of mispricing that relate to anomalies.

Similarly, the insider trading control variable also in general retains its predictive ability when

included with the anomaly variables. For equally weighted results, insider trading is significant

in all cases. For value weighted results, insider trading retains its predictive ability in all cases,

with the exception of the book-to-market ratio and size anomalies.

When there is no insider trading (or when insiders are neutral), the anomaly variables

retain their sign and significance in predicting future abnormal returns. Comparing the

predictive ability of unconditional anomalies from Table 4 with the predictive ability of the

anomalies conditioned on neutral insiders in Table 5, we find that the predictive ability of every

anomaly remains about the same. Hence, in every case, there is little or no difference between

unconditional predictive ability and predictive ability conditional on the insiders-neutral

category. In these cases, the lack of insider trading does not allow us to distinguish between

potentially overvalued and undervalued firms.

When insiders disagree and trade counter to the anomalies, the anomalies become

insignificant in every case. Moreover, two of the thirteen anomalies switch signs and are

statistically significant with the opposite sign to the one reported in the literature. This finding

holds for both equal-weighted and value-weighted tests. For equal-weighted tests, high distress

risk proxied by the O-score is normally associated with lower future returns. When insiders

agree, we find the O-score to indeed be priced negatively in the cross-section of returns

consistent with the prior literature. When insiders disagree, however, the O-score is priced

positively. This means high distress firms are associated with higher future abnormal returns

when controlling for insider disagreements. Similarly, in the case of composite equity issuance,

the anomaly also switches sign from negative to positive when insiders disagree. This outcome

is theoretically linked to the adverse selection model of Myers and Majluf (1984), as issuance is

likely to be particularly sensitive to insider information relating to the quality of both assets in

place and the new investment opportunity being funded. Due to this strong theoretical basis for

issuance, it is interesting and perhaps expected that insider disagreement is stronger for this

anomaly.

22

Overall, the predictive ability of the anomalies completely disappears when insiders

disagree with the anomaly signals. We find this evidence to be inconsistent with the risk-based

story and consistent with mispricing story. Under the static risk-based story, we would have

expected the anomalies to retain their predictive ability since investors would demand higher

compensation for high risk at all times. In order for a more general time-varying version of the

risk hypothesis to hold, it would further require undiversified insiders to buy more shares when

equilibrium risk premia are higher. Because they are undiversified, they should be more risk

averse to additional purchases, making this explanation unlikely. In all, our results substantially

raise the bar for risk-based explanations of the 13 anomalies.

Consistent with the mispricing story, our evidence indicates that insiders can use their

private information to better interpret public anomaly signals and draw their own conclusions

about possible mispricing. At times, these interpretations go opposite the public anomaly

signals. Subsequent revelation of information indicates that the insiders indeed have better

anomaly-relevant signals than is contained in the raw anomaly variables themselves. The

willingness of insiders to trade at these times and not at other times is difficult to square with a

risk interpretation.

As expected, the results are somewhat weaker when we value-weight the observations in

the cross-sectional regressions. However, this is expected since insider trading is generally less

informative for large firms.35

Nevertheless, even using value-weighting, conditional on insider

disagreements, anomalies completely lose their predictive ability. Overall these results provide

further empirical support for the mispricing hypothesis for all the anomalies, including size,

book-to-market, and momentum.

Panel B of Table 5 shows the predictive ability of anomalies and insider trading when we

use the report date instead on trade date for insider trading. Until Sarbanes-Oxley Act of 2002,36

insiders had until the 10th

day of the next month in which a trade took place to report their trade

to the Securities and Exchange Commission. Publication delays could add another month in

disseminating insider trading information. As a result of Sarbanes-Oxley, after August 29, 2002,

35

Seyhun (1986). 36

We expect a very high degree of accuracy in insider trading information. Violations of Sarbanes-Oxley carry a

fine up to $1 million and ten years in prison, even if done mistakenly. If Sarbanes-Oxley is violated purposely, the

fine can be up to $5 million and prison sentence up to twenty years.

23

insiders are required to file their open market transactions electronically within two business

days. After June 30, 2003, the filing requirement was reduced to one business day. Hence, for

any future out of sample tests using our framework, the insider trading variables should be ideal

both in terms of power and tradability.

For equal-weighted results, Panel B of Table 5 shows that insider trading retains its

predictive ability regardless of whether it agrees or disagrees with anomalies. In contrast,

anomalies get stronger when they agree with insider trading, and they completely lose their

predictive ability when they disagree with insider trading. Accruals and Ohlsons-O flip signs

and are also significant when confronted with insider disagreements. These findings are based

on reported information that was available to the public at the time portfolios could have been

formed. They suggest that there is useful public available information in insider trading that

investors can use to improve anomaly trading profits. For value weighted results based on the

report date, insider trading typically loses its individual predictive ability. These results suggest

that much of the price adjustment in insider trading for large firms takes place between the trade

date and report date. The price adjustment of small firms appears to be slower since insider

trading is still informative even after the report date.

4.3. Time-series Analyses

In this section, we examine the mispricing hypothesis in time series. As with the cross-

sectional analysis, we split our sample into groups based on insider agreement. First, we sort

stocks based on each anomaly and put them into five groups. For the companies in the long leg

corresponding to the highest return quintile and for the companies in the short leg corresponding

to the lowest return quintile, we further split them into three groups based on insider trading

agreement and disagreement. This provides us with 6 portfolios: 1) long and insiders buying, 2)

long and insiders neutral, 3) long and insiders selling, 4) short and insiders buying, 5) short and

insiders neutral, 6) short and insiders selling. We then create zero cost long-short portfolios for

each anomaly when insiders are in agreement, neutral, or in disagreement. Long-Short Insiders

Agree is a portfolio computed as: long and insiders buying minus short and insiders selling.

Similarly, Long-Short Insiders Disagree is a portfolio computed as: long and insiders selling

24

minus short and insiders buying. If insiders are neither buying nor selling, then we compute the

Long-Short Insiders Neutral portfolio as: long and insiders neutral minus short and insiders

neutral.

For each of the portfolios we compute value weighted returns using the trade date to

determine whether insiders are buying, selling or are neutral.37

Based on these returns we then

compute Fama-French 3-factor alphas. We exclude the size and value factors in the computing

the alphas for the size and book-to-market anomalies respectively. Table 6 reports the 3-factor

alphas and their corresponding t-statistics for the portfolios described above.

The time-series results are consistent with the cross-sectional results we reported earlier.

For the long leg of the anomalies, we see that the 3-factor alphas are significantly higher when

insiders agree and trade in the same direction as the anomalies. When insiders are buying, the

long leg of the anomaly portfolios has significantly higher returns. When insiders sell, the long

leg alphas either become negative or insignificant. We see a similar pattern for the short-leg-of-

the-anomaly portfolios. When insiders sell, the 3-factor alphas become significantly more

negative.

Looking at the long-short portfolio alphas in Table 6, we see that the anomaly

performance is strongest when insiders agree and trade consistent with the anomalies. Hence,

the results are strongest when insiders buy in the long portfolio and when they sell in the short

portfolio. When insiders disagree and trade counter to the anomalies, the benchmark adjusted

performance largely disappears and in some cases turns negative. When insiders sell in the long

portfolio, only firm size retains its predictive ability. All other variables including momentum

and book-to-market lose their predictive ability. Investment in net assets and Ohlsons-O flip

signs. When insiders buy short portfolios, no anomaly is significant, while firm size flips signs.

In the next to last column, we present the difference in the long-short portfolio alphas when

insiders agree and when insiders disagree. Looking at long-short columns when insiders

disagree, none of these portfolios provide positive, statistically significant returns as would have

been predicted by the anomaly variables. For all the anomalies we find significant differences in

alphas. These differences are also economically significant. Across all anomalies, the difference

37

In Table 7, we report the results using equal weights and insider trades determined using the report date.

25

in the long-short performance when insiders agree and when insiders disagree is 1.02% a month.

The last column shows the abnormal returns when insiders agree minus those when insiders

disagree. These findings indicate that the predictive ability of anomalies strongly depends on the

insider trading signal. Without exception, the predictive ability of the anomaly improves when

insiders agree with it.

In Table 7, we report the time-series results for equal-weighted and value weighted

portfolios as well as portfolios formed based on insider trading determined using both the insider

trade date and the report date. For brevity, we only report the results for the long-short portfolios

when insiders agree, disagree, are neutral, and for the difference between the alphas of the

insiders agree and the insiders disagree portfolios (the last four columns in Table 6). The first

panel of Table 7 reports the 3-factor alphas for each anomaly using equal-weighted returns and

insider trades determined based on the trade date. The second panel reports the results using

value weighted returns and the report date, and the final panel reports results using equal-

weighted returns and the insider report date. The equal-weighted results are always significant

regardless of whether we use the trade date or the report date. The value weighted results lose

some significance when we use the report date. The economic significance decreases by about

30%, and for some anomalies, the difference between the performance of the agree and the

disagree portfolios become insignificant. Nevertheless, publicly available insider trading after

the report date significantly improves the predictive ability of PEAD, net operating profits, gross

profitability, accruals, composite equity issuance, firm size and momentum for large firms. For

equal-weighted portfolios the economic significance goes down only slightly when we use the

report date to categorize insider agreement. Without exception, insider trading improves the

predictive ability of anomalies in small firms. These differences are consistent with the prior

literature that has shown that insider trades tend to be more informative for smaller firms and that

even after the reporting date, insider trading information is useful.38

In contrast, much of the

stock price reaction to insider trading information in large firms takes place between trade and

report dates. Overall, the time-series results are consistent with the findings from the previous

section and lend further support to the mispricing hypothesis.

38

Seyhun (1986, 1998).

26

4.4. Robustness Checks

In this section we consider a number of robustness checks for our main results. First, we

examine the impact of categorizing insider trades using different methods. Second, we examine

the impact of using trade and report dates before and after the introduction of the Sarbanes-Oxley

Act of 2002. Finally, we examine the impact of using a one-factor market model to compute

alphas. The results are reported in Table 8. For brevity we only report the 3-factor alphas for the

Agree-Disagree portfolios described in the previous section. The headers describe the sample

used for the results reported in each of the columns. The weight header specifies whether the

returns are computed using equal or value weighting. The portfolio formation header specifies

whether we are using the report date or the trade date to categorize insider trades. The

sample/method header specifies the particular sample, time-period or method we are using to

compute the returns.

We begin by examining the differences in results using the report date and the trade date

to categorize insider trades. As mentioned earlier, the introduction of the Sarbanes-Oxley Act of

2002 should have eliminated most of differences between trade date and report date, as the law

requires insiders to report their trades within 2 business days. Panel A of Table 8 reports the

results for the time period prior to the passage of Sarbanes-Oxley Act (SOX) on September 1,

2002, and after. We do find that the differences between the trade date and the report date

become smaller after the passage of SOX. For value weighted returns, across all anomalies, the

difference between the trade date and the report date alphas is 0.55%. This difference decreases

to 0.11% after the passage of SOX.

Next, we examine the impact of categorizing insider trades using different methods. In

our analyses thus far we have used a standardized measure to take into account the fact that

insiders tend to be net sellers and insiders in larger firms tend to sell more than they purchase

compared to insiders in smaller firms. For robustness, we use actual shares traded without

standardization to categorize insider trades. Each month, we compute the net shares traded for

each firm as the total number of shares bought by insiders minus the total number of shares sold.

If the net shares traded are positive, then we categorize insiders as buying. If the net shares

traded are negative, then we categorize insiders as selling. If there are no transactions in that

27

month, then insiders are categorized to be neutral. We then form agree minus disagree

portfolio returns based on insider trading and the expected direction of anomaly returns

described in the previous section. Three-factor alphas from using actual shares traded without

standardization to categorize insider trades are reported in Panel B of Table 8. We report results

using both equal-weighted and value weighted returns. The results are consistent with those

obtained using standardized insider trades, but they do become slightly weaker when we use

value weighted returns.

Insiders do not trade frequently. This leads insiders trades to be categorized as neutral

a significant part of the time in the dataset. For robustness, we use a longer window of 6 months

to categorize insider trades. Using a longer window reduces the information contained in insider

trades but increases the percentage of time insiders are classified as buyers or sellers. To

categorize insider trades over a longer window, we first categorize insiders as buyers, sellers or

neutral based on standardized insider scores. We then count the number of times the insiders

have been categorized as buyers over the past 6 months and subtract the number of times they

have been categorized as sellers over the same 6 months. If the difference is positive, we

classify insiders as buyers. If the difference is negative, we classify insiders as sellers. If the

difference is zero, insiders are classified as being neutral. We then compute agree and disagree

portfolios as described earlier. Using this longer window, insiders are classified as sellers 19%

of the time and as buyers 17% of the time. Panel B of Table 8 reports the 3-factor alphas. As

expected, a longer window reduces the information contained in insider trades. We find weaker

results when use value weighted returns, and some anomalies lose their statistical significance.

The results continue to remain significant when we consider equal-weighted returns.

Finally, we examine the impact of using the Fama-French size and value factors to

compute benchmark-adjusted returns. Our results from the previous section show that the size

and value anomalies generate significantly higher returns when insiders agree, suggesting that

the size and value premia could be due to mispricing. Some of the anomalies we consider load

negatively on the size and value factors, improving their benchmark-adjusted performance. For

instance, stocks with high distress risk tend to load more heavily on the size and value factors.

Since high distress predicts low returns, benchmark adjustment makes the anomaly even larger in

magnitude. For robustness, we compute the alphas using the one factor CAPM model. The

28

results are reported in the last two columns of Panel B of Table 8. The differences in alphas

compared to those in Table 7 are small. Controlling for only the market factor provides

qualitatively similar results.

5. Conclusion

In this paper we revisit 13 well-established anomalies and interact the predictive ability

of the anomalies with insider trading. We find that, conditional on insider agreement, both

insider trading and the anomalies retain their predictive ability. The economic magnitude of

anomalies also becomes large. This finding is consistent with the interpretation that both insider

trading and anomaly variables represent noisy measures of underlying mispricing with neither

being completely subsumed by the other variable. Consequently, they both survive in a horse

race.

However, conditional on insider disagreements, the predictive ability of anomaly

variables disappears fully in every case, while the predictive ability of the insider trading

variable survives. This finding indicates that the information content of insider trading

dominates the anomaly. When insiders have opposite information which disagrees in direction

with the anomaly variables regarding underlying mispricing, insider trading correctly identifies

the future direction of stock returns while the anomaly does not. If anomalies were driven by

risk, the fact that anomalies lose predictive power completely when insiders disagree suggests

that there is no compensation for risk when insiders disagree. This interpretation is difficult to

square with the efficient-markets, risk-based story, which predicts that risk premia should not be

related to insider trading.

Although we cannot rule out the risk-based hypothesis, our evidence indicates that

mispricing plays an important role in the predictive ability of the anomalies. A dominant portion

of the information content of anomalies comes from agreement with insider trading signals.

When there is disagreement with insider trading, the predictive ability of the anomalies

disappears.

29

Our contributions are three-fold: (1) we provide a general framework for assessing

whether any anomaly which may be discovered in the future is likely due to risk based theories

or potential market mispricing, (2) we provide evidence that 13 of the most common anomalies

discussed in the literature lose their predictive ability when confronted with insider

disagreements, and (3) we provide guidance to potential investors investing in anomalies: they

should jointly consider insider trading signals along with anomaly signals. This would allow

investors to time the anomaly to more efficiently arbitrage economically large returns that are

most likely due to mispricing.

30

References

Abarbanell, J.S., Bernard, V.L., 1992. Tests of analysts overreaction/underreaction to earnings information as an explanation for anomalous stock price behavior. Journal of Finance 47, 1181-

1207.

Affleck-Graves, J., Mendenhall, R.R., 1992. The relationship between the value line enigma and

post-earnings-announcement drift. Journal of Financial Economics 31, 75-96.

Altman, E., 1968. Financial ratios, discriminant analysis and the prediction of corporate

bankruptcy. Journal of Finance 23, 589-609.

Anderson, C., Garcia-Feijoo, L., 2006. Empirical evidence on capital investment, growth

options, and security returns. Journal of Finance 61, 171-194.

Antoniou, C., Doukas, J.A., Subrahmanyam, A., 2011. Sentiment and momentum. Unpublished

working paper.

Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2010. Anomalies and financial distress.

Unpublished working paper, Hebrew University.

Ball, R. 1978. Anomalies in relationships between securities yields and yield-surrogates. Journal of Financial Economics 6, 103-126.

Ball, R., Brown, P., 1968. An empirical evaluation of accounting income numbers. Journal of

Accounting Research 1968 (autumn), 159-178.

Baker, M., Wurgler, J., 2007. Investor sentiment in the stock market. Journal of Economic

Perspectives 21, 129-152.

Banz, R. 1981. The Relationship between return and market value of common stock. Journal of

Financial Economics 9, 3-18.

Barberis, N., Huang, M., 2001. Mental accounting, loss aversion and individual stock returns.

Journal of Finance 56, 1247-1292.

Barberis, N., Shleifer, A., Vishny, R.W., 1998. A model of investor sentiment. Journal of

Financial Economics 49, 307-343.

Barberis, N., Thaler, R., 2003. A survey of behavioral finance. In Constantinides, G., Harris, M.,

Stulz, R. eds. Handbook of the Economics of Finance. North-Holland, Oxford, UK.

Baron, J., Simko, P.J., 2002. The balance sheet as an earnings management constraint.

Accounting Review 77 (Supplement: Quality of Earnings Conference), 1-27.

31

Bartov, E., Radhakrishnan, S., Krinsky, I., 2000. Investor sophistication and patterns in stock

returns after earnings announcements. Accounting Review 75, 43-63.

Basu, S. 1977. Investment performance of common stocks in relation to their price-earnings

ratio: A test of the efficient market hypothesis. Journal of Finance 32, 663-682.

Berk, J., 1995. A critique of size related anomalies. Review of Financial Studies 8, 275-286.

Berk, J., Green, R., Naik, V., 1999. Optimal investment, growth options and security returns,

Journal of Finance 54, 1153-1608.

Bernard, V.L., Thomas, J., 1989. Post-earnings announcement drift: Delayed price response or

risk premium. Journal of Accounting Research 27, 1-48.

Bernard, V.L., Thomas, J., 1990. Evidence that stock prices do not fully reflect the implications

of current earnings for future earnings. Journal of Accounting and Economics 13, 305-340.

Bettis, C., Coles, J., Lemmon, M., 2000. Corporate policies restricting trading by insiders.

Journal of Financial Economics 57, 191220.

Bettis, C., Vickrey, D., Vickrey, D.W., 1997. Mimickers of corporate insiders who make large-

volume trades. Financial Analysts Journal 53, 5766.

Blume, M. Stambaugh, R., 1983. Biases in computed returns: An application to the size effect.

Journal of Financial Economics 12, 387-404.

Brav, A., Heaton J.B., Li, S., 2010. The limits of the limits of arbitrage. Review of Finance 14,

157-187.

Brennan, M.J., 1970. Taxes, market valuation, and corporate financial policy. National Tax

Journal 23, 417-427.

Brennan, M.J., Chordia T., Subrhmanyam, A., 1998. Alternative actor specifications security

characteristic, and the cross section of stock returns. Journal of Financial Economics 49, 345-

373.

Campbell, J.Y., Hilscher, J., Szilagyi, J., 2008. In search of distress risk. Journal of Finance 63,

2899-2939.

Carhart, M.M., 1997. On the persistence in mutual fund performance. Journal of Finance 52, 57-

82.

Chan, L., Hamao, Y., Lakonishok, J., 1991. Fundamentals and stock returns in Japan. Journal of

Finance, 46, 1739-1764.

32

Chen, L., Novy-Marx, R., Zhang, L., 2010. An alternative three-factor model. Unpublished

working paper, University of Rochester.

Clarke, J., Dunbar, C., Kahle, K.M., 2001. Long-run performance and insider trading in

completed and canceled seasoned equity offerings. Journal of Financial and Quantitative

Analysis 36, 415430.

Cohen, L., Frazzini, A., 2008. Economic links and predicable returns. Journal of Finance 63,

1977-2011.

Cohen, L., Malloy, C., Pomorski, L., 2012. Decoding inside information. Journal of Finance 67,

1009-1043.

Cohen, R.B., Gompers, P.A., Vuolteenaho, T., 2002. Who underreacts to cash-flow news?

Evidence from trading between individuals and institutions. Journal of Financial Economics 66,

409-462.

Collins, D.W., Hribar, P., 2000. Earning-based and accrual-based anomalies: One effect or two.

Journal of Accounting and Economics 29, 101-123.

Cooper, M., Gulen H, Schill, M., 2008. Asset growth and the cross-section of stock returns.

Journal of Finance 63, 1609-1652.

Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market

under- and over-reactions. Journal of Finance 53, 1839-1885.

Daniel, K., Hirshleifer, D., Teoh, S.H., 2002. Investor psychology in capital markets: Evidence

and policy implications. Journal of Monetary Economics 49, 139-209.

Daniel, K., Grinblatt, M., Titman, S., Wermers, R., 1997. Measuring mutual fund performance

with characteristic-based benchmarks. Journal of Finance 52, 10351058.

Daniel, K., Titman, S., 1997. Evidence of the characteristics of cross sectional variation in stock

returns. Journal of Finance, 52, 1-33.

Daniel, K., Titman, S., 1998. Characteristics or covariances. Journal of Portfolio Management

24, 2433.

Daniel, K, Titman, S, 2006. Market reactions to tangible and intangible information. Journal of

Finance 61, 1605-1643.

Davis, J., Fama, E., French, K., 2000. Characteristics, covariances, and average returns: 1929-

1997. Journal of Finance 55, 389-406.

De Bondt, W. Thaler, R., 1985. Does the stock market overreact. Journal of Finance 40, 793-805.

33

De Bondt, W. Thaler, R., 1987. Further evidence on investor overreactions and stock market

seasonality. Journal of Finance 42, 557-581.

De Long, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990. Noise trader risk in financial

markets. Journal of Political Economy 98, 703-738.

Dechow, P.M., Khimich, N.V., Sloan, R.G., 2011. The accrual anomaly. Unpublished working

paper, University of California, Berkeley.

Della Vigna, S., Pollet, J., 2003. Attention, demographics, and the stock market. Unpublished

working paper, National Bureau of Economic Research.

Edelen, R.M., Ince, O.S., Kadlec, G.B., 2015. Institutional investors and stock return anomalies.

Journal of Financial Economics, forthcoming.

Elliot, J., Morse, D., Richardson, G., 1984. The association between insider trading and

information announcements. Rand Journal of Economics 15, 521536.

Fairfield, P., Whisenant, S., Yohn, T., 2003. Accrued earnings and growth: Implications for

future profitability and market mispricing. Accounting Review 78, 353-371.

Fama, E. 1976. Foundations of Finance.Basic Books, New York, NY.

Fama, E. French., K., 1992. The cross section of expected stock returns. Journal of Finance 47,

427-466.

Fama, E. French., K., 1993. Common risk factors in the returns of stocks and bonds. Journal of

Financial Economics 33, 3-56.

Fama, E., French, K., 1996. Multifactor explanation of asset pricing anomalies. Journal of

Finance 51, 55-84.

Fama, F., French, K., 2006. Profitability, investment, and average returns. Journal of Financial

Economics 82, 491-518.

Fama, E., French, K., 2008. Dissecting Anomalies. Journal of Finance 63, 1653-1678.

Fama, E., MacBeth, J., 1973. Risk, return and equilibrium: Empirical tests. Journal of Political

Economy 71, 607-636.

Fernandes, N., Ferreira, M., 2009. Insider trading laws and stock price informativeness. Review

of Financial Studies 22, 18451887.

Fidrmuc, J., Goergen, M., Renneboog, L., 2006. Insider trading, news releases and ownership

concentration. Journal of Finance 61, 29312973.

34

Finnerty, J.E., 1976. Insiders and market efficiency. Journal of Finance 31, 1141-1148.

Foster, G., Olsen, C., Shevlin T., 1984. Earnings releases, anomalies and the behavior of security

returns. Accounting Review 59, 574-603.

Fraz