Do Short-sellers Trade on Inside Information?

41
Do short-sellers trade on inside information? M.Sc. Finance 2013-14 Submitted by: Arjun Chhabra Supervisor: Student ID: Dr. Vikas Raman 1357903 Module Title: Research Methodology & Dissertation Module Code: IB93F0 Date Submitted: 01/09/2014

description

This study documents the behaviour of short-sellers on and around insider-selling events, the reporting date for mandatory SEC filings and the insider sales around news announcement dates in particular. The results are obtained primarily by running event-studies around the insider-selling events in general and those near news announcement dates, and observing trends in short-selling activity in our selected event window. Our findings corroborate the widely-held view that short-selling activity sees a surge in the days immediately before the insider sales. Then, we classify the insider selling events as large and small sales and run similar event studies to find out any differential behaviour based on the size of the insider sale in the specified event window. We conclude that large insider sales are preceded by a significant spike in short-selling activity whereas small insider sales don’t get front-run in a significant manner. Our news announcement analysis leads us to infer that when insider sales are executed around news announcements about a firm, they are front-run by short-sellers even more significantly than usual. We complement all our event studies with corresponding regressions to test whether short sales are higher indeed around our designated events after controlling for usual suspects namely, returns, intra-day volatility and volume. We reach the conclusion that short-sellers have access to private information that they use to front-run insiders systematically.

Transcript of Do Short-sellers Trade on Inside Information?

Page 1: Do Short-sellers Trade on Inside Information?

Do short-sellers trade on inside information?

M.Sc. Finance 2013-14

Submitted by: Arjun Chhabra

Supervisor:

Student ID:

Dr. Vikas Raman

1357903

Module Title: Research Methodology & Dissertation

Module Code: IB93F0

Date Submitted: 01/09/2014

“All the work contained within is my own unaided effort and conforms with the University’s

guidelines on plagiarism.”

“No substantial part(s) of the work submitted here has also been submitted by me in other

assessments for accredited courses of study, and I acknowledge that if this has been done an

appropriate reduction in the mark I might otherwise have received will be made.”

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ii Do short-sellers trade on inside information?

Abstract

This study documents the behaviour of short-sellers on and around insider-selling events, the

reporting date for mandatory SEC filings and the insider sales around news announcement

dates in particular. The results are obtained primarily by running event-studies around the

insider-selling events in general and those near news announcement dates, and observing

trends in short-selling activity in our selected event window. Our findings corroborate the

widely-held view that short-selling activity sees a surge in the days immediately before the

insider sales. Then, we classify the insider selling events as large and small sales and run

similar event studies to find out any differential behaviour based on the size of the insider

sale in the specified event window. We conclude that large insider sales are preceded by a

significant spike in short-selling activity whereas small insider sales don’t get front-run in a

significant manner. Our news announcement analysis leads us to infer that when insider sales

are executed around news announcements about a firm, they are front-run by short-sellers

even more significantly than usual. We complement all our event studies with corresponding

regressions to test whether short sales are higher indeed around our designated events after

controlling for usual suspects namely, returns, intra-day volatility and volume. We reach the

conclusion that short-sellers have access to private information that they use to front-run

insiders systematically.

Keywords: short-selling, insider sales, event study, news announcements, private

information, front-running, form-filing.

Acknowledgements: I would like to thank my supervisor, Dr. Vikas Raman for his continued

guidance and support throughout my research and report preparation. I am grateful to him

for his valuable feedback and encouragement during the past 3 months. I am also thankful to

my parents for enabling me to study this programme at Warwick Business School and

providing important advice as required. Lastly, I would like to thank my friends at the

university and back home for all their care and generous help throughout my dissertation.

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Contents

1. Introduction ........................................................................................................................... 1

2. Literature Review ................................................................................................................... 5

3. Data Sources & Sample Selection .......................................................................................... 8

4. Methodology ........................................................................................................................ 10

4.1. Short-Sales around Insider Sale Date ............................................................................ 10

4.2. Short sales around form-filing date .............................................................................. 12

4.3. Short-selling around large and small insider sales ........................................................ 12

4.4. Short-sales around news announcement-led insider sales .......................................... 12

4.5. Regression Studies-the alternate method .................................................................... 13

5. Results .................................................................................................................................. 16

5.1. Short-selling and Insider Sales ...................................................................................... 16

5.2. Short-selling and Form 4 filing ...................................................................................... 17

5.3. Short-sales and Insider Sale Size ................................................................................... 17

5.4. Short selling and news announcements ....................................................................... 18

6. Conclusion ............................................................................................................................ 20

References ............................................................................................................................... 22

Appendix .................................................................................................................................. 25

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1. Introduction

hort-selling of securities is quite common worldwide and accounts for approximately

24% of NYSE and 31% of NASDAQ daily share volume (Diether et al. 2008). Apart

from a few aberrations in the past relating to short-selling bans (most of which have been

widely debated), short-selling has largely existed simultaneously with normal buying and

selling of securities and helped price-discovery in the markets. Of late, especially in the light

of the 2008 financial crisis, there has been a tremendous interest, by academicians and

regulators alike, in the role of short-sellers and their so-called “superior” ability to process

available information about the market and predict future returns significantly. Short-sellers

have been shown to be sophisticated market participants who are more informed than the

general market and possess the ability to beat the market consistently, earning abnormal

returns over time. However, very little research has been done on the actual source of

information of short-sellers and the manner in which this information is obtained. Most of

these studies confine themselves to analysing the returns generated by short-sellers, stock-

picking ability and timing, attributing their sophistication to one factor or the other by way of

indirect implications, without addressing directly the question of where the information is

sourced from or how it is acquired, probably due to the paucity of short-interest data.

Another class of investors who are privy to the most profit-generating information is the

corporate insiders. In fact, insiders (CEO, directors, key employees, etc.) are the most

informed of all market participants since they possess all the private information about a

company that an outsider is unexpected to be privy to. The trading activity of insiders is

closely monitored by all market-watchers for it provides a very strong signal about a firm’s

fundamentals. In the absence of news announcements and analyst recommendations, insider

trading activity often lends credibility to or disproves rumours about a firm. Regulation

requires all insiders to disclose their trades to the SEC on Form 4 within two business days of

their execution, which is then made public in real time. Thus, the market tracks these trades

upon their disclosure by the SEC and exploits the information content in these trades. Insiders

are forbidden to share any confidential or private information about the firm with outsiders in

an illegal manner that may deem unfair to other market participants who may not possess this

information. Large insider trades have often been leaked in the process of execution by

S

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several unscrupulous elements for profiteering and the relative benefits of getting away with

it as compared to the costs, which are the same for small trades. Although the regulatory

bodies have unearthed and successfully prosecuted some high-profile black sheep, insider

trading goes largely uncaught.

Despite the extant extolment of short-sellers by academicians and the widespread

acknowledgement of their role in market efficiency and price discovery, some quarters in the

popular press as well as the academic community have cast doubts on the means of acquiring

information by short-sellers. Several instances in the past including the sweeping

investigation by the SEC in 2007 into the conduct of some Merrill Lynch brokers have hinted

at what might be the tip of an iceberg; specifically, that short-sellers, because of their market

stature (most short-selling activity is undertaken by hedge funds), have access to private

information which they unethically use to front-run the corporate insiders. Our results

provide proof of short-sellers trading ahead of insiders for the firms in our sample and this

phenomenon is even more pronounced for large insider sales and when such sales are based

on news about a firm.

Legislation around the world makes it a fiduciary duty of the insiders “to protect the interest

of the company’s shareholders while they are in possession of any material non-public

information about the company’s security” (U.S. Securities & Exchange Commission,

Investor Information). This is evident in the fact that insiders can’t trade immediately before

a piece of information is made public as it skews the market in their favour, and any such

activity is regarded as fraudulent. However, it’s a common knowledge that the trading

volume goes up substantially before any announcement that affects the company’s immediate

price and its future prospects. The earnings announcements by a company are a fairly good

indicator of its short-term prospects as well as the immediate price effect. Therefore, it is

worthwhile to see any trends in short-selling around the earnings announcement dates,

considering that short-sellers account for a good share of the trading volume around events

that are associated with heightened volatility. We also include earning revisions and mergers

& acquisitions announcements in our news announcement analysis as these events also

generate significant volatility around them. The possibility that, some insiders due to their

inability and legal liabilities are tipping off the short-sellers before these announcements,

looms large. Whether these insiders gain anything if at all from this tipping off or not, is out

of scope of this study. Another theory, which has been argued about recently is that, brokers

in the process of execution, leak out private information to their favoured clients in return for

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handsome commissions. This study does not chase the trail of events that lead to the passing

on of information to short-sellers. However, we do present firm evidence in favour of our

hypothesis of the front-running phenomenon. And it doesn’t paint a very good picture of the

short sellers as a community.

Front-running refers to the practice of trading in advance of a client, an insider or a broker

with superior information leading to, or in anticipation of a favourable price movement. For

example, some investors might be tipped off in advance about a large impending insider sale

and these investors then sell before the insider expecting to gain from it. Front-running is not

illegal per se as long as the information is acquired in a legitimate way; however, lines do get

blurred a lot of times and law fails to catch up with it, leading to information asymmetry and

bias against other market participants. Front-running was alleged to be one of the prominent

reasons for the collapse of the LTCM (Long-Term Capital Management) in 1998. Some

researchers have in the recent past come out with firm evidence to back up the front-running

theory in the case of LTCM. Fang Cai (2008) finds empirical evidence of market makers’

engagement in front-running against customer orders from “a particular clearing firm (coded

“PI7”) that closely match various features of LTCM’s trades through Bear Stearns”, by using

a trail of audit transactions. Thus, we set out to find similar evidence, if any, of front-running

of insider sales in particular by short-sellers in the US markets using a mix of event studies

and regressions. It is important to mention that this study has been made possible due to the

high-frequency intra-day short-interest data which has been made available by the SEC

recently for the pilot years 2005-2007 under the Regulation SHO.

In this paper, our approach is centred on, firstly, relating short-selling activity to insider-

selling activity, and next, finding out if this relationship is supported by evidence of any

significant surge in short-selling prior to insider selling events around our selected news

announcements. The study isolates only earnings announcements, earnings revisions and

mergers & acquisitions in particular for news announcement analysis since the data on these

announcement dates are readily available for all the firms in our sample and it stands the

same scrutiny in terms of explanatory power as the rest of the event studies. We begin by

running an event study around the insider-selling date to observe any trends in the short-

selling activity before and after the insider sales. We find evidence of abnormal short-sales

peaking significantly just before the designated event, and this is even more striking when we

designate the form-filing day as our event. Then, we segregate these insider sales into large

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and small as a proportion of the company’s shares outstanding as of that date, and run similar

event studies. Our results clearly show that large insider sales are more often preceded by an

abnormal increase in short-selling than small insider sales and this result holds even when we

designate form-filing date as our event. We complement the abnormal short-selling figures

with important statistics on volatility around the insider sale event and the results point in the

same direction; the variance jumps around the event date as there is increased activity around

the insider selling date and this is more pronounced for large sales as compared to small ones.

Our next step is to run a regression explaining the short sales in the estimation window as a

function of contemporaneous and previous day returns, intra-day volatility, daily volume and

whether the short-sale in question is part of the pre-event window or not. Once the evidence

on abnormal short-sales around insider selling date is out, we observe trends in abnormal

short-selling activity around the insider sales before and after news announcement dates and

likewise, run regressions to explain any abnormal behaviour. The collective evidence from all

our empirical work makes our case stronger in suggesting that short-sellers manage to gather

private information about a company and exploit it to front-run insider sales in a systematic

manner.

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2. Literature Review

Short-sellers as a community have been the recipient of a great deal of attention and a subject

of debate amongst academicians, regulators, corporations, etc. and extant literature exists on

their ability to generate abnormal returns due to their superior information processing skills.

Researchers are almost unanimous in their praise of short-sellers for their role in correcting

price deviations in the market by making use of contrarian techniques in response to market

overvaluations (Miller, 1977; Harrison and Kreps, 1978; Diamond and Verrecchia, 1987;

Scheinkman and Xiong, 2003, Dechow et al., 2001). While politicians and regulators have a

history of imposing bans on short-selling during periods of crises in the belief of limiting

volatility, they have been slammed across the board and these bans have been regretted in

hindsight.

Empirical research points to the ability of short-selling activity to predict future returns.

Diether et al. (2008) observes that a 10% increase in short-selling, measured as a proportion

of daily volume, is followed by a future decline in returns by 0.94% per month on NYSE.

Short-sellers are considered as informed traders who contribute to market efficiency in a

substantial way. In fact, some theorists have found evidence of prices diverging from

fundamental values when short-selling is constrained (Miller, 1977, Duffie et al., 2002, Hong

et al., 2006). Other researchers have argued that predictability is at best conditional and varies

with stock characteristics. Brent et al. (1990) and Lamont and Stein (2004) find that short

interest is positively related to past returns but does not predict future returns in cross-section

or time-series. Asquith et al. (2005) find return predictability only in the smallest stocks and

report that the effect is stronger in low institutional ownership stocks. As mentioned in the

previous section, while there is near unanimity about the role of short-sellers in contributing

to enhanced market efficiency, consensus still eludes on the subject of source of short-sellers’

information.

Likewise, substantial literature exists on the trading patterns and the information advantage of

corporate insiders. Most of this literature is in concordance with the general belief on the

market that insider activity is the most informed and is a fairly reliable indicator of a firm’s

health and profitability. This class of investors is very often accorded a status higher than the

“sophisticated” short-sellers in terms of forecasting a company’s financial fundamentals.

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Jaffe (1974) and Sehyun (1986) document the ability of insider activity to forecast returns as

far as several years in to the future. Ross (1978) and Zhang (2001) conclude that corporate

insiders, through their trades, confirm or contradict the information in public corporate

announcements, which are then viewed by investors as signals who act accordingly in

response. Betzer and Theissen (2005) find that even uninformed outsiders can earn abnormal

profits by mimicking insider transactions. Of late, there has also been a discussion on the

kind of trading activity these corporate insiders engage in. Rozeff & Zaman (1998) study the

trading activity of insiders and infer that it resembles those of contrarians. However, they also

report through their findings that their trading choices are more sophisticated than those of

other contrarian investors. However, insiders are also bound by a variety of corporate laws

and company rules in respect of their trading behaviour and market participation. Insiders are

forbidden from trading on or passing on any confidential material information that has the

potential to skew the market in their favour. Insider trading cases of gigantic proportions have

come to light in the past decade which has in turn brought the debate back to the alleged

nexus between insiders and some class of investors.

There have, in the past, been suggestions linking short-selling to insider sales. Massa et al

(2013) argue that insiders compete with short-sellers in the trading of private information and

that the presence of such competition accelerates the rate at which private information is

disclosed to the market. Chakrabarty and Shkilko (2012) provide empirical evidence on

information leakage in financial markets and examine insider purchases and sales for any

front-running by short-sellers. Similarly, Khan and Liu (2013) also extend their front-running

argument on exact lines, while excluding all insider sales that are in response to news

announcements, and find that short-sellers trade in advance of insiders. However, both these

studies stop short of claiming that short-sellers are trading on private information and confine

their scope to a mere lead-lag relation between short-sales and insider-sales. There is in

general very scarce discussion on the source of this information that facilitates this kind of

front-running.

Several quarters of the academic community have linked this “sophisticated” trading

behaviour of short-sellers to their ability to predict news announcements and decode the

information contained in them faster than everybody else. While there is substantial evidence

of short-sales preceding bad news announcements, there has hardly been an effort to study

short-selling before insider sales that precede news announcements about a firm. Daske et al

(2005) focus on a range of securities traded on NYSE from 2004 to 2005 and find no

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evidence of short-sale transactions concentrating prior to only bad news announcements,

ruling out private information as the source of short sale transactions in aggregate. Another

spectrum of studies have stressed on a more focused approach by way of examining changes

in monthly short interest around significant corporate announcements. For example, Dechow,

Sloan, and Sweeney (1996), Griffin (2003), Efendi, Kinney, and Swanson (2005) and Desai,

et al. (2005) find a significant increase in monthly short interest prior to events such as SEC

actions, class action lawsuits, and earnings restatements.

Evidence linking short-sales (particularly those by hedge funds) to subsequent news

announcements is quite extensive. Chague et al. (2013) propose what are called the reaction

and anticipation hypotheses. According to the former, short-sellers possess superior

information processing skills in relation to other market participants which help them trade

ahead of others. On the other hand, the anticipation hypothesis postulates that short-sellers

have access to some kind of superior information, private or otherwise, beforehand and they

exploit this information to make abnormal returns. While both hypotheses are exhaustive in

terms of suggesting the plausible explanations for the scope of short-sellers’ information,

there is scarce discussion on the actual source of information in specific terms. Engelberg et

al. (2012) test these hypotheses using all the corporate news released to the public and find

evidence in favour of the reaction hypothesis but remain inconclusive about the anticipation

hypothesis. Our study relating short sales to insider sales motivated by these news

announcements fits the anticipation hypothesis more than the reaction hypothesis. However,

the study’s main contribution is that it examines short sales around those insider sales which

appear to be motivated by these news announcements rather than around such announcements

themselves.

This study contributes to the existing literature by adding to the insider sales events those

transactions which are not in the conventional form of stocks held by insiders; more

specifically we widen our analysis by including derivative transactions by insiders in our

sample, as companies around the world have significantly begun granting options to their

employees as part of their compensation. Next, as pointed out earlier, we observe trends in

short-selling activity around announcement-related insider sales events using intra-day short

interest data which has been made available by SEC under Regulation SHO for the pilot

years 2005-07.

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3. Data Sources & Sample Selection

We restrict our sample period from 2005-06 to test for the hypothesis that short-sellers front-

run insider sales. The data on daily short-sales are obtained from the NYSE TAQ database

from January 2005 to December 2006 which has been made available under the Regulation

SHO. We restrict our sample to 2 years due to constraints of space and convenience since

intra-day data is quite voluminous and the relative benefits of extending our sample fall short

of the relative costs in terms of data cleaning. Data on prices, returns, number of shares

outstanding and volume are taken from the CRSP for the same period in daily frequency. The

earnings and earnings revisions announcement data are sourced from the Thomson Reuters

I/B/E/S database for annual and quarterly periodicities, using ticker codes obtained from our

short-sales and CRSP files. For data on mergers & acquisitions on our sample firms, we use

the Thomson One Banker database. Lastly, we obtain data on insider sales from Thomson

Reuters Insiders Database. For stock transactions, we use the Insiders Data Table 1 data and

for derivative transactions (more specifically, call and put option transactions), we use

Insiders Data Table 2. The data on SEC disclosure dates are likewise obtained from the same

sources respectively.

The TAQ database yields a total of 168,706,872 observations for intra-day frequency. We

begin by deleting all the records which are marked “E” for short-type. Such sales are

excluded from our sample as they represent the short-sales undertaken by market-makers in

the course of their inventory-management function and are marked “exempt”. While previous

studies have included market-makers’ short-sales in their sample, we exclude them to avoid

the criticism that market makers are obliged to short-sell in advance in line with their market-

making function as clients dump their shares around the insider-selling event. Although

market-makers too have been frequently accused of front-running their clients, there is no

clear evidence as to whether market-makers are the ones solely responsible for it, although

they do contribute significantly to the heightened volatility. Therefore, we only keep non-

exempt market trades as part of our study although this might be a possible limitation of our

model. We then proceed by aggregating intra-day sales for a company into daily short-sales

so that the data-matching requirements are met. The organized dataset is then merged with

the daily data obtained from CRSP by symbol and calendar time and we are left with a total

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of 409,477 observations. For data on insider sales, the stock-based insider sales transaction

data are first combined with derivative-based transaction data (call and put options, where the

former are disposed of and the latter are acquired) and then refined to keep only such

observations as deemed the most informative about a firm’s fundamentals. Thus we only

keep the CEO’s sales as part of our insider sales and discard all others. Next, we eliminate

any insider sales events that are less than 10 days apart due to overlapping event windows

and where an insider sale is executed over multiple days, we regard only the first day as a

distinct insider-selling event. The same procedures are adopted when we simulate these event

studies for the form-filing event or for the news announcement related insider sales. Finally,

we merge the two datasets respectively using ticker symbols and dates so that our final

datasets are left with 4,313 distinct insider sale events, 4,141 SEC form-filing events, 2,485

news announcement-related insider sale events and 1,268 insider sale events solely preceded

by news announcements.

Table 1 shows our descriptive statistics for the insider sale events in general and the

respective categories. As shown in panel A, the mean average daily short sales in the

estimation window of [-60,-11] before our insider sale event is 0.16% of the number of shares

outstanding whereas the mean event date short sales is 0.18%. Therefore, the average daily

short sale is higher on the event date by about 0.2 percentage points. Similarly, the standard

deviation jumps from 0.15% in the estimation window to 0.23% in the event window,

suggesting increased volatility around the insider-selling event. For insider sales, our sample

has a mean of 0.21% when calculated as a proportion of number of shares outstanding

(approximately the same as demonstrated in Khan and Liu, 2013), whereas it is about 40% as

a proportion of daily trading volume.

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4. Methodology

We begin by running the event studies to document the behaviour of short-sales on and

around our insider sale event and form-filing date, as described in Khan and Liu (2011) and

then proceed to conducting regressions to strengthen evidence of front-running. We also

demonstrate this phenomenon by computing and analysing variances for the estimation, pre-

event and post-event windows separately for both execution date and form-filing date.

Finally, we build on our hypothesis by conducting an event study around insider sale events

near news announcements in particular and checking for trends in abnormal short sales. This

event study is also complimented by regressing short-sales on our control variables and the

dummy variable suggesting any heightened abnormal behaviour around the news

announcement-related insider sale events in particular.

4.1. Short-Sales around Insider Sale Date

After finalising our dataset, we designate our insider sale event for a firm, i.e. the day it is

executed, as 0 and test for abnormal short-sales in the [-10, +10] event window. The normal

short-sales are estimated for each distinct firm-event as an average of short sales in the [-60, -

11] window, as demonstrated in Khan and Liu (2011), using the following formula:

E(Sn)=

Sj,n

Where,

E(Sn)= Average daily short-sales in the estimation window [-60, -11],

J= Number of days in the estimation window, i.e. 50,

Sj,n =Short-sales on day j in the event window for the firm-event n, where we define short-

sale as the number of shares shorted as a percentage of number of shares outstanding.

Thus, every insider sale event has now been assigned a normal level of short-sales that occur

prior to the days in the event window. Our next step is to calculate, based on the estimated

expected daily short sales, abnormal short sales in our event window [-10, +10]. This is

calculated as follows:

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ei,n=Si,n –E(Sn)

Where,

e= Abnormal short sales

n= Index for firm-event

i=Index for day, i ϵ [-10, +10]

To estimate volatility in the estimation window [-60,-11], we employ the following formula:

σ2E

=

Sj,n-E(Sn)]

2

This statistic is compared against the volatility that results around the insider-selling event

separately for days prior to and days after the event, computed as follows:

σ2B

=

Si,n-E(Sn)]2

Where, i ϵ [-10, 0]

And,

σ2A

=

Si,n-E(Sn)]2

Where, i ϵ [+1, +10]

After these statistics have been calculated, we aggregate them over firm-events to look for

any trends that provide evidence in favour of our hypothesis. The statistic for abnormal sales

in the event window is aggregated across all firm-events for each day separately as follows

and then the results are tabulated:

EN (ei) =

ei,n

Where, n= Index of firm event

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12 Do short-sellers trade on inside information?

Figure 1 sketches the event study graph, and as expected shows a peaking of abnormal short

sales around our event. Look at Table 2 for the abnormal short-sales statistics in our insider

sale event window.

4.2. Short sales around form-filing date

We run an identical event study as above and calculate the same statistics of abnormal sales,

pre-event and post-event volatilities. The form-filing date is designated as day 0 and the event

window is [-10, +10]. By form-filing date, we mean the day the insider reports the sale to the

SEC using Form 4 which is made public in real time.

Figure 3 shows the abnormal short-selling activity in our event window. Statistics on

abnormal short-sales in the form-filing event window are presented in table 4 for reference.

4.3. Short-selling around large and small insider sales

To differentiate the behaviour of short-selling on the basis of size of the insider sale, we

segregate our insider sales sample into large and small where large insider sales correspond to

the top 30 percentile of all insider sales as a proportion of number of shares outstanding and

small insider sales correspond to the bottom 30 percentile. Then, we run separate event

studies for each of them in the same event window of [-10, +10]. Our results are depicted in

figure 2 for both large and small insider sales. For abnormal short-sales figures for our large

and small samples, refer to table 3.

Likewise, we segregate our insider sales on the basis of size as above for the Form 4 filing

date and tabulate our results in Table 5.

4.4. Short-sales around news announcement-led insider sales

Short-sales peaking before the insider sales may be a result of beforehand tip-off about any

significant news announcements which would, upon release, lead to increased trading in the

security in question. Thus, our methodology goes a step further to analyse the behaviour of

short-sales around those insider sales in particular which surround significant news

announcements. We filter our insider sale events, retaining only such events that are either

preceded or followed by our class of news announcements in the 10 day window.

Specifically, first we designate the news announcement related insider sale execution date as

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the event day, i.e. day 0, and run a similar event-study in the event window [-10, +10]. Later,

we run a similar event study only around those insider sales which are preceded by these

news announcements in the 10 day window. The results are summarised in tables 6 and 7

respectively.

4.5. Regression Studies-the alternate method

Our alternate approach is centred around regressing the short-sales on a day on some factors

namely, the contemporaneous and previous day returns, intra-day volatility and volume

respectively for our entire sample of 2 years. To single out the effect of impending insider

sale, we add a dummy variable DAY which is 1 in 10 days prior to any insider-selling event,

[-10, 0] and 0 for all the other days. The regression model is specified as follows:

Si,n = αn + β1RETi,n + β2RETi-1,n + β3VOLi,n + β4VOLi-1,n + β5VARi,n +

β6VARi-1,n + β7DAYi + ϵi,n

Where,

Si,n= Short-sales on day i for the firm-event n,

RETi,n=Contemporaneous return on day i,

RETi-1,n= Previous day return,

VOLi,n= Volume of shares traded on day i,

VOLi-1,n= Volume of shares traded the previous day,

VARi,n= Intra-day price-volatility on day i,

VARi-1,n= Intra-day price-volatility on the previous day,

DAYi= 0 if i ϵ [-∞, -11] and 1 if i ϵ [-10, 0],

We report the results of this regression in panel A of table 8.

For the sake of precision and robustness to changes in the number of days in the pre-event

window, we now run an identical regression, except that the dummy variable DAY is now

assigned the value of 1 in 5 days prior to the insider-selling event, [-5, 0] and 0 for all other

days.

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Si,n = αn +β1RETi,n + β2RETi-1,n + β3VOLi,n + β4VOLi-1,n + β5VARi,n +

β6VARi-1,n + β7DAYi + ϵi,n

Where,

Si,n= Short-sales on day i for the firm-event n,

RETi,n=Contemporaneous return on day i,

RETi-1,n= Previous day return,

VOLi,n= Volume of shares traded on day i,

VOLi-1,n= Volume of shares traded the previous day,

VARi,n= Intra-day price-volatility on day i,

VARi-1,n= Intra-day price-volatility on the previous day,

DAYi= 0 if i ϵ [-∞, -6] and 1 if i ϵ [-5, 0]

Panel B of table 8 reports the results derived from this regression.

Our next regression involves adding an interaction term dummy variable SIZE*DAY where

the variable SIZE takes the value of 1 if the impending insider sale is large and 0 if it is small.

In this manner, we are able to distinguish large sales from small ones on the basis of whether

large insider sales get front-run more often than small ones in the before-event window. We

only run this regression for those days for which the impending insider-sale is either

classified as small or large and discard all others. Again, we run two separate regressions here

as above for the [-10, 0] and [-5, 0] windows for our dummy variable, DAY.

Si,n = αn +β1RETi,n + β2RETi-1,n + β3VOLi,n + β4VOLi-1,n + β5VARi,n +

β6VARi-1,n + β7SIZEi,n*DAYi + ϵi,n

Si,n= Short-sales on day i for the firm-event n,

RETi,n=Contemporaneous return on day i,

RETi-1,n= Previous day return,

VOLi,n= Volume of shares traded on day i,

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VOLi-1,n= Volume of shares traded the previous day,

VARi,n= Intra-day price-volatility on day i,

VARi-1,n= Intra-day price-volatility on the previous day,

DAYi= 0 if i ϵ [-∞, -11 or -6] and 1 if i ϵ [-10 or -5, 0],

SIZEi,n=0 if the impending insider sale is classified as small and 1 if large.

Table 9 shows the results of these regressions.

Our final regression for news announcement-led insider sale events is concerned with

regressing the short-sales on a day on the control variables as mentioned earlier. In addition,

we also introduce the interaction term ANN*DAY, where ANN is defined as 1 for all short-

sales around news announcement-led insider sales (i.e., within the 10 day event window) and

0 otherwise; DAY is 1 in the window [-10, 0], i.e. 10 days prior to and on the insider-selling

date and 0 otherwise. We repeat this procedure by redefining DAY as 1 for days in the

window [-5, 0], i.e. 5 days prior to and on the insider-selling date, and 0 otherwise (table 10).

Si,n = αn +β1RETi,n + β2RETi-1,n + β3VOLi,n + β4VOLi-1,n + β5VARi,n +

β6VARi-1,n + β7ANNn*DAYi + ϵi,n

Where,

Si,n= Short-sales on day i for the firm-event n,

RETi,n=Contemporaneous return on day i,

RETi-1,n= Previous day return,

VOLi,n= Volume of shares traded on day i,

VOLi-1,n= Volume of shares traded the previous day,

VARi,n= Intra-day price-volatility on day i,

VARi-1,n= Intra-day price-volatility on the previous day,

DAYi,= 0 if i ϵ [-∞, -11 or -6] and 1 if i ϵ [-10 or -5, 0]

ANNn= 1 if the insider sale event is led by news announcement, 0 otherwise.

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5. Results

5.1. Short-selling and Insider Sales

The results from our event study around the insider-selling date are indicative of what we

hypothesized earlier; short-sales do peak significantly before an insider sale in our sample. As

reported in table 2 and evident from figure 1, we can easily infer that short-sellers on average

front-run insiders in a significant manner. The t-stats reported for abnormal sales in our event

window are significant at 99% confidence level for days -4, -3, -2, -1, 0 and +1. Thus, short-

sales rise abnormally 4 days prior to the insider-selling event and peak on days -2 and -1,

suggesting prior knowledge of impending insider sale. As reported, abnormal short-sales in

the after-event window record consistently negative values except on day 1, implying that

short-sellers later close out their positions to profit from front-running.

The volatilities calculated for the estimation, pre-event and post-event windows also show the

same trend. The volatility of short-sales jumps from 0.02329 in the estimation window of [-

60, -11] to 0.03263 in the pre-event window of [-10, 0] and 0.02817 in the after-event

window of [+1, +10], implying heightened short-selling activity in the event window, which

is even more pronounced for days immediately before the insider-selling event. These values

were found to be significantly different from the estimation window volatility after

conducting the relevant F-tests.

We also report the results from the regression where daily short-sales are regressed on some

control variables and the dummy variable DAY, which differentiates the short-selling in the

pre-event window from that on any other normal day. As shown in table 8, the coefficient for

the DAY variable is found to be positive and significant at the 99% confidence level. Thus,

short-selling is found to be significantly higher in the pre-event window of [-10, 0] compared

to other days in our sample. The regression also returns significant coefficients for our

control variables, validating our methodology.

When we restrict the pre-event window to [-5, 0] and redefine the DAY variable as 1 for only

5 days prior to the insider-selling event, we find even more striking evidence of higher

abnormal short-selling preceding the insider sale. The coefficient of 0.0067953 for DAY[-5, 0]

is significantly higher than the coefficient of 0.0045877 for DAY[-10, 0].

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5.2. Short-selling and Form 4 filing

On conducting the event study designating the form filing date as our event, we observe

concurrent trends as in our insider sales event study. The SEC makes it mandatory for

corporate insiders to file their trades with it within two days of execution which are made

public in real time. Our event study around the SEC filing date shows that abnormal short-

selling surges significantly in the pre-event window, specifically on day -6 and peaks on days

-4 and -2 before the form-filing date. The t-statistics are significant at the 99% confidence

level for all these days. The -2 day before the form-filing day is effectively the day these

insider sales are executed. Therefore, this abnormal peaking of short-sales fits perfectly into

our hypothesis of insiders being systematically front-run by short-sellers in our sample.

We continue our analysis for the form-filing date by reporting the volatilities for estimation,

pre-event and post-event windows as above. The volatility of short-sales jumps from 0.02373

in the corresponding estimation window to 0.03244 in the pre-event window of [-10, 0], and

0.02851 in the post-event window of [+1, +10], making our hypothesis of front-running

stronger. While the pre-event window volatility is found to be significantly different from the

estimation window volatility, the post-event window volatility is not significantly different.

5.3. Short-sales and Insider Sale Size

We hypothesized that, large insider sales, due to their information content and potential

profitability relative to the costs of detection, are more likely to get front-run than small

insider sales. On segregating large sales from small ones and running an event study around

the insider selling event, we find that short sales peak significantly before a large insider sale

as seen in figure 2. The t-statistics support our findings empirically for days -3, -2 and -1 at

the 99% confidence level and for the day -4 at 90% level. For small insider sales, t-statistics

are found insignificant, as expected and even the significant ones show much less abnormal

short-selling when compared to large insider sales sample, as depicted in figure 2. Thus, our

results for the entire sample, which show a clear peaking in short-selling activity before the

event, are led by the large-insider sales being front-run than the small ones.

For the SEC filing date, we run similar event studies for large and small insider sales

separately and find parallel evidence. While abnormal short-sales peak significantly 2 days

prior to the form-filing date for the large insider sales, no such trends are observed for our

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corresponding small insider sales sample. The t-stats show significance for abnormal short-

sales before large insider sales at the 99% confidence level whereas abnormal short-sales

before small insider sales are found insignificant even at the 90% confidence level.

Therefore, we can conclude that large insider sales, being more profitable potentially, are

more likely to be preceded by heightened short-selling than small insider sales.

To avoid any criticism of methodology bias, we repeat our analysis by running a regression to

find out whether the size of the impending insider sale size affects short-selling behaviour

before the insider selling event. The interaction term SIZE*DAY is of interest here which

embodies the differential impact of large insider sale on the short-sales in our pre-event

window. As depicted in table 9, the SIZE*DAY variable has a positive coefficient which is

significant at the 99% confidence level and therefore, proves our hypothesis mentioned

above.

Similarly, when the pre-event window is confined to [-5, 0], we find a significantly positive

coefficient for our SIZE*DAY variable which is higher than the one found for the [-10, 0]

window. Short-selling in the days immediately before the insider sale date is higher indeed

when the impending insider sale is large as a proportion of the firm’s value. The higher

profitability argument is based on the fact that a large insider sale is ensued by a larger

favourable price movement and a higher volatility after the event as compared to a smaller

insider sale.

5.4. Short selling and news announcements

The results of our event study and regression for new announcement-led insider sales confirm

our earlier findings of significant front-running by short-sellers and also, importantly, ascribe

a motive for this phenomenon. When we designate the news announcement-related insider

sale execution date as the event date for the firms in our sample, we observe that abnormal

short-sales start peaking 2 days in advance of the event and the t-statistics computed show

significance at the 95% confidence level. In the post-event window, abnormal short-sales

decline and the t-statistics also return insignificant values.

For the event study around only those insider sales that precede our subset of news

announcements, we observe that short-sales spike abnormally 4 days before the execution

date and remain high until the post-event window. The t-statistics are significant at the 99%

confidence level.

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To complete our analysis, we regress short sales on a day on the same control variables as

above and the ANN*DAY variable where ANN is defined as 1 for days preceding news

announcement-led insider sales and 0 elsewhere. As shown in table 10, the coefficient for the

ANN*DAY variable is positive and significant at the 99% confidence level, suggesting that

short-sales jump much before the news announcements and short-sellers trade ahead of

insiders even though the information is still not public. When we confine the DAY variable to

1 in the pre-event window of only [-5, 0], our results are boosted by an even higher

coefficient of 0.0172143 which is significantly higher than the coefficient of 0.0095028 for

the [-10, 0] event window.

The news announcement analysis, together with our insider sale and form-filing analyses,

leads us to infer that short-sellers systematically trade ahead of insiders for profit motives and

this phenomenon exacerbates when the impending insider sale is relatively large as a

proportion of the firm’s market value. The heightened short-selling in advance of news

announcement-led insider sales in particular bridges the gap in our understanding of the

reasons for this behaviour of short-sellers. Thus, while insiders may be restricted from selling

before any significant news is announced, there is always the chance of their being front-run

by short-sellers. To what extent this front-running is facilitated by insiders themselves is a

matter of dispute; however, their legal liabilities and simultaneous privileged access to

private information puts them first in the list of possible sources of short-sellers’ information.

Also, our pre-announcement analysis for insider sales before news announcements rules out

the possibility of short-sellers acting on public information faster than insiders, since this

information is not public yet. Thus, short-sellers trading ahead of insiders before any news

announcements have access to inside information which manifests later in the form of public

announcement after the insider sale event.

This study does not take a position on as to how short-sellers obtain this information which is

strictly private since it’s clearly the domain of the regulatory authorities. However, we can

confidently make the claim that short-sellers are trading well in advance of the corporate

insiders who are touted as the most informed market participants generally. And, the reasons

they are doing it for, do not appear very encouraging. (c)

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6. Conclusion

The study attempts to shed light on the source of short-sellers’ information using event

studies and regressions. We find astounding evidence of insider sales being preceded by

abnormal short sales which peak 2 days before the insider sale is even executed, let alone

being made public. When we treat the SEC form filing day as our event, i.e. the day these

sales are made public, we get evidence on similar lines; short-sales start peaking 4 days

before the form-filing event, implying that short-sellers have access to inside information on

average that they use to front-run insiders systematically. We also prove that large insider

sales (top 30% percentile as a proportion of number of shares outstanding), are more likely to

be front-run than small insider sales (bottom 30% as a proportion of number of shares

outstanding). Large insider sales possess a greater degree of information content and their

front-running has the potential to translate into even greater profitable trades after short-

sellers have bet their money on them. Our results hold when we analyse the behaviour of

short-sales around the form-filing date for large and small sales separately. The regression

studies undertaken for our purpose strengthen our argument of front-running further as the

dependent variable of short-sales is higher on pre-event days of the execution and form-filing

dates. The insider sale size argument also proves credible when we segregate our insider sales

into small and large and run another regression.

The results from the news announcement analysis prove a shot in the arm for our study. As

shown earlier, short-sales peak before the insider sales which are motivated by news

announcements for the firms in our sample. When these short-sales are regressed on the

interaction dummy representing news announcement-motivated insider sales, after

controlling for usual explanatory variables, namely daily returns, intra-day volatility and

volume, we observe that short-sales are significantly higher in the days preceding the news

announcement-led insider sales. The results are robust and in fact, more damning when we

confine our pre-event window to just 5 days before the execution dates. When the news

announcement analysis is confined to only those insider sales that are later followed by our

subset of news announcements, we get clear evidence of similar front-running, thereby

eliminating the alternative explanation of short-sellers reacting faster than insiders to recent

public announcements, since the information they are acting on is not yet public. The news

announcement analysis, in conjunction with the execution date and SEC form-filing date

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analyses provides empirical strength to our hypothesis of short-sellers exploiting private

information for profit motives to front-run insiders in a significant manner, with or without

the knowledge of these insiders. Since insiders are banned from trading on private

information before any major news announcement on their own, our evidence on short-

selling peaking before these announcements points towards only one direction; short-sellers

obtain inside information from these very corporate insiders in an illegal manner that is unfair

towards other market participants. Several studies have hinted at insiders timing their trades

and manipulating earnings announcement dates for their advantage, often in collusion with

other market participants (Yong-Chul Shin and Weimin Wang, 2011). This study is limited in

its scope of providing any definitive proof about the mechanics of how the information is

exchanged between short-sellers and insiders; this is the realm of the regulatory authorities.

However, it does add significantly to the nascent literature on the source of short-sellers’

information. As a solution to this problem, the financial regulatory bodies such as the SEC

should monitor these trades carefully and make disclosures about insider trades more

frequently so that price discovery is fair as well as transparent.

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Appendix

Table 1: Descriptive Statistics

Panel A reports descriptive statistics for the full sample of 4,331 insider sales events; Panel B

reports descriptive statistics on insider sales events which are classified as small, specifically

the bottom 30 percentile of our full insider sales sample; Panel C reports descriptive statistics

on insider sales events which are classified as large, specifically the top 30 percentile of our

full insider sales sample; Panel D reports summary statistics on those insider sales events

which are surrounded by news announcements within the 10-day window on either side;

Panel E reports summary statistics on those insider sales events only which precede news

announcements within the 10-day window. Event Date Short Sales refers to the proportion of

short sales as a percentage of shares outstanding on the insider sale execution day; insider

sales/shares outstanding is the number of shares sold by insiders as a percentage of number of

shares outstanding; insider sales/trading volume is the number of shares sold by insiders as a

percentage of daily trading volume; shares outstanding is the number of shares outstanding in

thousands; average daily short sales is the mean short sales as a proportion of number of

shares outstanding in the estimation window of [-60, -11] before the insider sale event.

Panel A: Descriptive Statistics for Insider Sale Events

Mean Q1 Median Q3 StdDev

Event Date Short Sales (%) 0.18 0.058 0.11 0.21 0.23

Insider Sales/Shares Outstanding (%) 0.21 0.003 0.024 0.102 1.71

Insider Sales/Trading Volume (%) 39.59 0.46 3.73 15.21 343.2

Shares Outstanding (‘000) 268,288 39,687 76,383 194,302 776,390

Average Daily Short Sales (%) 0.16 0.07 0.11 0.20 0.15

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Panel B: Descriptive Statistics for Small Insider Sale Events

Mean Q1 Median Q3 StdDev

Event Date Short Sales (%) 0.14 0.051 0.09 0.17 0.16

Insider Sales/Shares Outstanding (%) 0.0013 0.000099 0.0007 0.002 0.0015

Insider Sales/Trading Volume (%) 0.3 0.02 0.116 0.37 0.56

Shares Outstanding (‘000) 506,021 57,433 119,262 194,302 1,263,017

Average Daily Short Sales (%) 0.14 0.06 0.1 0.16 0.14

Panel C: Descriptive Statistics for Large Insider Sale Events

Mean Q1 Median Q3 StdDev

Event Date Short Sales (%) 0.22 0.07 0.13 0.25 0.28

Insider Sales/Shares Outstanding (%) 0.65 0.12 0.20 0.45 3.08

Insider Sales/Trading Volume (%) 123.3 14.68 28.3 64.57 618.8

Shares Outstanding (‘000) 101,121 27,513 50,799 104,599 200,473

Average Daily Short Sales (%) 0.19 0.08 0.13 0.24 0.17

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Panel D: Descriptive Statistics for News Announcement-related Insider Sale Events

Mean Q1 Median Q3 StdDev

Event Date Short Sales (%) 0.19 0.06 0.11 0.22 0.26

Insider Sales/Shares Outstanding (%) 0.24 0.004 0.027 0.11 2.17

Insider Sales/Trading Volume (%) 42.27 0.61 3.98 16.25 408.1

Shares Outstanding (‘000) 277,708 39,687 78,530 204,948 777,829

Average Daily Short Sales (%) 0.16 0.07 0.11 0.20 0.15

Panel E: Descriptive Statistics for Insider Sale Events preceding News Announcements

Mean Q1 Median Q3 StdDev

Event Date Short Sales (%) 0.18 0.06 0.105 0.21 0.25

Insider Sales/Shares Outstanding (%) 0.26 0.003 0.026 0.12 1.95

Insider Sales/Trading Volume (%) 20.28 12.16 18.14 25.75 11.48

Shares Outstanding (‘000) 274,004 37,479 72,697 202,315 778,955

Average Daily Short Sales (%) 0.15 0.07 0.11 0.18 0.14

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Table 2: Abnormal Short Sales around all insider sales

Table 2 reports the results of our event study around all our insider-selling events in the [-10,

+10] event window where the execution date is designated as day 0. Abnormal SS refers to

the short sales over and above the average short sales in the estimation window of [-60, -11].

*, **, *** denotes two-tailed statistical significance for our abnormal short sales from 0 at

10%, 5% and 1%, respectively. Figure 1 shows these results graphically.

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 0.0016% 0.64 1 0.0078% 3.02***

-9 0.0008% 0.34 2 0.0025% 1.01

-8 0.0021% 0.84 3 -0.0003% -0.123

-7 0.0010% 0.36 4 -0.0034% -1.41

-6 -0.0016% -0.65 5 -0.0064% -2.74***

-5 0.0020% 0.75 6 -0.0017% -0.62

-4 0.0075% 2.78*** 7 -0.0043% -1.71*

-3 0.0102% 3.43*** 8 -0.0043% -1.63

-2 0.0192% 5.78*** 9 -0.0065% -2.52**

-1 0.0185% 6.39*** 10 -0.0070% -2.69***

0 0.0150% 5.25***

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Table 3: Abnormal Short Sales and Insider Sales Size

Panel A (panel B) reports the results of our event study around small (large) insider-selling

events, i.e. the bottom (top) 30 percentile as a proportion of number of shares outstanding, in

the [-10, +10] event window where the execution date is designated as day 0. Abnormal SS

refers to the short sales over and above the average short sales in the estimation window of [-

60, -11]. *, **, *** denotes two-tailed statistical significance for our abnormal short sales

from 0 at 10%, 5% and 1%, respectively. Figure 2 shows these results graphically.

Panel A: Abnormal Short Sales for Small Insider Sales Sample

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 -0.0059% -1.75* 1 -0.0042% -1.21

-9 -0.0035% -0.87 2 -0.0018% -0.43

-8 -0.0019% -0.52 3 -0.0064% -1.57

-7 -0.0010% 0.28 4 -0.0038% -1.02

-6 0.0022% 0.56 5 -0.0041% -1.08

-5 0.0026% 0.65 6 0.0025% 0.45

-4 0.0001% 0.035 7 -0.0027% -0.67

-3 -0.0005% -0.09 8 -0.0058% -1.46

-2 0.0036% 0.88 9 -0.0110% -2.86***

-1 0.0080% 2.04** 10 -0.0116% -3.09***

0 0.0024% 0.69

Panel B: Abnormal Short Sales for Large Insider Sales Sample

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 0.0040% 0.81 1 0.0130% 2.4**

-9 0.0101% 0.19 2 -0.0001% -0.019

-8 0.0052% 0.91 3 0.0022% 0.42

-7 0.0005% 0.11 4 -0.0051% -1.06

-6 -0.0031% -0.64 5 -0.0122% -2.7***

-5 -0.0023% -0.45 6 -0.0121% -2.48**

-4 0.0096% 1.82* 7 -0.0045% -0.81

-3 0.0190% 3.32*** 8 -0.0040% -0.68

-2 0.0374% 5.06*** 9 -0.0055% -1.05

-1 0.0374% 5.34*** 10 -0.0063% -1.19

0 0.0266% 4.31***

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Figure 2:

Table 4: Abnormal Short Sales for SEC form-filing date

Table 4 reports the results of our event study around all our SEC form-filing events in the [-

10, +10] event window where the form-filing date (also, the date the insider sale is made

public) is designated as day 0. Abnormal SS refers to the short sales over and above the

average short sales in the estimation window of [-60, -11]. *, **, *** denotes two-tailed

statistical significance for our abnormal short sales from 0 at 10%, 5% and 1%, respectively.

Figure 3 shows these results graphically.

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 -0.0008% -0.31 1 0.0019% 0.71

-9 -0.0012% -0.503 2 -0.0049% -1.93*

-8 -0.0012% -0.43 3 -0.0038% -1.18

-7 0.0012% 0.45 4 -0.0091% -3.69***

-6 0.0060% 2.1** 5 -0.0062% -2.47**

-5 0.0122% 3.91*** 6 -0.0023% -0.798

-4 0.0159% 5.06*** 7 -0.0039% -1.38

-3 0.0138% 5.09*** 8 -0.0072% -2.61***

-2 0.0158% 5.18*** 9 -0.0037% -1.42

-1 0.0128% 4.5*** 10 -0.0082% -3.37***

0 0.0061% 2.33**

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Figure 3:

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Table 5: Abnormal Short Sales and Insider Sales Size for form-filing events

Panel A (panel B) reports the results of our event study around form-filing days for small

(large) insider-selling events, i.e. the bottom (top) 30 percentile as a proportion of number of

shares outstanding, in the [-10, +10] event window where the form-filing date is designated

as day 0. Abnormal SS refers to the short sales over and above the average short sales in the

estimation window of [-60, -11]. *, **, *** denotes two-tailed statistical significance for our

abnormal short sales from 0 at 10%, 5% and 1%, respectively. Figure 4 shows these results

graphically.

Panel A: Abnormal Short Sales for Small Insider Sales Sample (SEC Form-filing date)

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 -0.0060% -1.6 1 -0.0064% -1.61

-9 -0.0014% -0.39 2 -0.0069% -1.74*

-8 0.0015% -0.39 3 -0.0048% -1.24

-7 0.0019% 0.52 4 -0.0071% -1.7*

-6 0.0005% 0.12 5 -0.0061% -1.51

-5 0.0080% 1.45 6 0.0006% 0.14

-4 0.0001% 0.03 7 -0.0017% -0.4

-3 0.0051% 1.24 8 -0.0106% -2.21**

-2 0.0044% 1.16 9 0.0015% 0.3

-1 -0.0009% -0.24 10 -0.0007% -0.16

0 -0.0021% -0.57

Panel B: Abnormal Short Sales for Large Insider Sales Sample (SEC Form-filing date)

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 0.0042% 0.68 1 0.0032% 0.57

-9 0.0021% 0.4 2 -0.0062% -1.27

-8 -0.0018% -0.36 3 0.0003% 0.05

-7 -0.0023% 0.44 4 -0.0174% -3.87***

-6 0.0020% 0.39 5 -0.0134% -2.95***

-5 0.0152% 2.55** 6 -0.0063% -1.04

-4 0.0368% 4.78*** 7 -0.0043% -0.7

-3 0.0227% 3.97*** 8 -0.0073% -1.31

-2 0.0290% 4.21*** 9 -0.0019% -0.38

-1 0.0231% 3.56*** 10 -0.0148% -2.94***

0 0.0076% 1.48

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Table 6: Abnormal Short Sales around News Announcement-related Insider Sales

Table 6 reports the results of our event study around those insider-selling events which are

surrounded by news announcements in the [-10, +10] event window where the execution date

is designated as day 0. Abnormal SS refers to the short sales over and above the average short

sales in the estimation window of [-60, -11]. *, **, *** denotes two-tailed statistical

significance for our abnormal short sales from 0 at 10%, 5% and 1%, respectively. Figure 5

shows these results graphically.

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 0.0032% 0.89 1 0.0173% 4.48***

-9 0.0057% 1.63 2 0.0065% 2.07**

-8 0.0085% 2.34*** 3 0.0046% 1.33

-7 0.0063% 1.56 4 0.0018% 0.54

-6 0.0062% 1.73* 5 -0.0040% -1.25

-5 0.0104% 2.58*** 6 0.0005% 0.13

-4 0.0204% 5.11*** 7 -0.0035% -1.08

-3 0.0269% 6.52*** 8 -0.0018% -0.51

-2 0.0451% 8.55*** 9 -0.0062% -1.8*

-1 0.0384% 8.7*** 10 -0.0044% -1.22

0 0.0280% 6.67***

Figure 5:

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Table 7: Abnormal Short Sales around Insider Sales preceding News Announcement

Table 7 reports the results of our event study around those insider-selling events which

precede news announcements, in the [-10, +10] event window where the execution date is

designated as day 0. Abnormal SS refers to the short sales over and above the average short

sales in the estimation window of [-60, -11]. *, **, *** denotes two-tailed statistical

significance for our abnormal short sales from 0 at 10%, 5% and 1%, respectively. Figure 6

shows these results graphically.

Event Day, i Abnomal SS t-stat Event Day, i Abnomal SS t-stat

-10 0.0068% 1.48 1 0.0245% 5.13***

-9 0.0067% 1.39 2 0.0201% 4.38***

-8 0.0026% 0.65 3 0.0175% 3.74***

-7 0.0003% 0.08 4 0.0159% 3.16***

-6 -0.0029% -0.73 5 0.0118% 2.62***

-5 -0.0024% -0.59 6 0.0161% 2.98***

-4 0.0082% 1.81* 7 0.0127% 2.94***

-3 0.0051% 1.28 8 0.0133% 2.77***

-2 0.0015% 3.01*** 9 0.0085% 1.83*

-1 0.0247% 5.21*** 10 0.0095% 1.86*

0 0.0291% 5.07***

Figure 6:

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Table 8: Regression results for Short sales and Insider Sales

Panel A (Panel B) shows the regression statistics when daily short sales are regressed on the

dummy variable DAY (DAY1), where DAY (DAY1) is 1 for the pre-event window of [-10,

0] ([-5,0]) and 0 elsewhere, and the other control variables. “Retx” refers to the daily return;

“retx1” refers to the lagged return; “lnvol” is the log of daily volume traded; “lnvol1” is the

log of volume traded a day before; “sd2” is the intra-day price volatility of the security on

that day; “sd12” refers to the previous day intra-day price volatility; “day” is a dummy

variable which is 1 in the pre-event window of [-10,0] and 0 otherwise; “day1” is a dummy

variable which is 1 in the pre-event window of [-5, 0] and 0 otherwise; “_cons” refers to the

intercept of the regression equation.

Panel A:

Panel B:

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Table 9: Regression results for Short Sales and Insider Sale Size

Panel A (Panel B) shows the regression statistics when daily short sales are regressed on the

interaction dummy variable SIZE*DAY (SIZE*DAY1), where DAY (DAY1) is 1 for the

pre-event window of [-10, 0] ([-5,0]) and 0 elsewhere, and the other control variables. “Retx”

refers to the daily return; “retx1” refers to the lagged return; “lnvol” is the log of daily

volume traded; “lnvol1” is the log of volume traded a day before; “sd2” is the intra-day price

volatility of the security on that day; “sd12” refers to the previous day intra-day price

volatility; “day” is a dummy variable which is 1 in the pre-event window of [-10,0] and 0

otherwise; “day1” is a dummy variable which is 1 in the pre-event window of [-5, 0] and 0

otherwise; “_cons” refers to the intercept of the regression equation; “size” is the other

dummy variable which is 1 for days preceding large insider sales and 0 for days preceding

small insider sales.

Panel A:

Panel B:

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Table 10: Regression results for Insider Sales around News Announcements

Panel A (Panel B) shows the regression statistics when daily short sales are regressed on the

interaction dummy variable ANN*DAY (ANN*DAY1), where DAY (DAY1) is 1 for the

pre-event window of [-10, 0] ([-5,0]) and 0 elsewhere, and the other control variables. “Retx”

refers to the daily return; “retx1” refers to the lagged return; “lnvol” is the log of daily

volume traded; “lnvol1” is the log of volume traded a day before; “sd2” is the intra-day price

volatility of the security on that day; “sd21” refers to the previous day intra-day price

volatility; “day” is a dummy variable which is 1 in the pre-event window of [-10,0] and 0

otherwise; “day1” is a dummy variable which is 1 in the pre-event window of [-5, 0] and 0

otherwise; “_cons” refers to the intercept of the regression equation; “ann” is the other

dummy variable which is 1 for those insider sales which are surrounded by news

announcements on the either side in the event window of [-10, +10], and 0 otherwise.

Panel A:

Panel B:

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