Short-selling before data breachThis version: July 15th, 2019
Abstract
This study examines whether short sellers detect firm-level data breaches. Using proprietary daily lending data and unique data-breach data, we investigate whether shortselling anticipates corporate data breaches, and whether the short-selling ex-ante would
benefit investors by improving market liquidity and price efficiency or cost investors
by extracting rent from investors as they undertake the price. First, we documentthat the level of short-selling constraints when data breaches publicly revealed. Bothborrowing costs and utilization fee jump. In addition, short interest increases. The
results are robust using propensity score matching (PSM) techniques. Second, on across-sectional basis, our results show that the level of short-selling activities predictthe cumulative abnormal returns (CARs) around data breaches. The results of event
studies show the evidence that cumulative abnormal returns (CARs) are significant
around firm-level data breaches. The CARs are also consistent across different typesof data breach. Further, we test the market impact of the short selling. We find thatshort-selling activities improve market liquidity and market efficiency. The evidence
is robust using different measures for market liquidity and price efficiency. Overall,our study provides strong evidence that short sellers exploit prior knowledge of data
breaches and help improve overall market efficiency.

Keywords: informed trading, short-selling, data breach, cumulative abnormal returns, market liquidity and price efficiency

Preliminary and incomplete draft: Please do not cite or circulate.

1.

Introduction
In this study, we examine public-company announcements of data breaches and analyze

how short sellers detect and respond to the events. We find that short sellers/arbitrageurs
anticipate data breaches based on prior information advantage. In addition, the short selling predicts the cumulative abnormal returns around data breaches. While short sellers
trade ahead of public investors, their trading activities actually facilitate price discovery andimprove market efficiency. Using proprietary daily lending data and a novel dataset of corporate data breaches involving publicly traded companies, we find strong empirical evidenceof significant market abnormalities in the equity lending market. The level of short sellingloan fee, loan utilization rate, and short interest all increase around the event date. The
contribution of our study is three-fold. First, our study contributes to a growing literaturein finance and accounting studies on data breaches and cyberattacks. Second, our study
contributes to a literature on informed trading in securities lending markets. Making use of
proprietary daily lending data assists us to identify trading abnormalities days and weeks
leading up to data breaches. The unique focus allows us to investigate the underlying market effects. Finally, our study facilities the discussion whether short selling facilities pricediscovery and improve market efficiency. We provide evidence to support the proposition in
the setting of data breaches.
Our analysis proceeds as follows. Section 2 discusses related literature and develops
testing hypotheses. Section 3 describes the sample, data sources and descriptive statistics.
Section 4 presents main empirical results. Section 5 conducts robust tests and sensitivity
analyses. Section 6 concludes.

1

2.

literature review and hypotheses development

2.1.

Literature Review

Our study is related to three major strands of academic literature. The first strand
is literature on data breaches and cyberattacks in finance and accounting. So far, vast
majority of existing studies in data breaches are focus on legal and technical aspects. For
example, from legal perspective, frequently asked research questions include the harms andconsequences of data breaches, mitigation of the cases, and improvement of data breachlaws. In addition, technical aspect of data breaches tends to center on the information
system risk and incident prevention. Surprisingly however, few studies have attempted to
examine the market activity around data breaches, particularly how market participantsinteract with each other in the event of data breaches. Our study attempts to fill the void byinvestigating the role of short sellers. Consensus tends to agree that, unlike other investors,
short sellers are usually sophisticated and well informed. The data breach events provide
an ideal setting for us to examine how short sellers interact with the events. For example,
Bianchi and Tosun (2019) studies the short-term and long-term market effect from the firsttime data breaches. They find that the daily excess returns drop, trading volume increases,
and liquidity deteriorates upon data breaches within short event windows. In the long term,
data breaches affect firms polices including dividend payouts, R&D expenses, and CEO pay.
In the same vein, Akey, Lewellen, and Liskovich (2018) finds that negative effect of data
breaches in both short-term and long-term. Interestingly, those firms suffering from data
breaches in fact increase investment in corporate social responsibilities (CSR) investments
in respond to a negative reputation shock. By the same token, Amir, Levi, and Livne (2018)studies firms incentive on cyber-attacks disclosure. They find that withheld cyber-attacks
are associated with a decline of approximately 3.6% in equity values in the month the attack
is discovered, and disclosed attacks with a substantially lower decline of 0.7%. They provide
evidence that managers tend to withhold information on the more severe attacks. Lin, Sapp,

2

Ulmer, and Parsa (2018) studies insider trading within the data breach firms. They findstatistically significant evidence of opportunistic insider trading, with insiders save the cost
due to their timely selling in the three months prior to the announcement of a cybersecurity
breach. Interestingly, they estimate that the bulk of this opportunistic trading tends to occur
55 to 72 days before the public announcement. One of the closest studies is from Mitts andTalley (2018). Their study finds evidence that arbitrageurs can and do obtain early notice ofimpending breach disclosures, and that they are able to profit from such information. They
employ at the money (ATM) equity put options on the common stock of the data-breachfirms in the main tests. They study, however, does not directly test the market liquidity andprice efficiency after the data breach events. Our study complements their study and sheds
addition light on the role short sellers plays in overall market efficiency.
The second strand of literature that our study relates to is short selling surrounding
corporate events. In microstructure models (Kyle, 1985; Glosten and Milgrom, 1985), informed traders are privately informed about the securitys intrinsic value and act on theprior knowledge before information is reveal publicly. We posit that, without violating any
securities laws and insider regulations, in the event of data breach, informed traders are able
to gain information advantage and trade more efficient than public. A large literature has
investigated short-selling activities surrounding a spectrum of major corporate events. For
example, Berkman, McKenzie, and Verwijmeren (2016) find that pre-announcement shortsellers are able to predict announcement day returns for firms private placement deals. The
effects are especially strong when hedge funds are involved and when the number of buyers
is high. Liu and Wu (2014) and Mitchell, Pulvino, and Stafford (2004) both examine theshort-selling activity around firm mergers. They find similar evidence of arbitrage shortselling around merger announcement. Massoud, Nandy, Saunders, and Song (2011) findevidence that short-selling among hedge fund borrowers prior to public announcements ofboth loan originations and loan amendments. Christophe, Ferri, and Hsieh (2010) find that
abnormal levels of short-selling within the three days before analyst downgrades are publicly3

announced and that the pre-announcement abnormal short-selling is significantly related tothe subsequent share price reaction to the downgrades. Henry and Koski (2010) show that,around seasoned equity offerings (SEOs) issue dates, higher levels of pre-issue short-sellingare significantly related to larger issue discounts for non-shelf-registered offerings. KARPOFF and LOU (2010) find that abnormal short interest increases steadily in the 19 months
before the misrepresentation is publicly revealed, particularly when the misconduct is severe. Their study indicates that short sellers anticipate the eventual discovery and severityof financial misconduct. LASSER, WANG, and ZHANG (2010) and Christophe, Ferri, andAngel (2004) both find evidence of abnormal short-selling in pre-earnings announcement.
Data breach cases are similar yet different from many of those corporate events. On one
hand, data breaches are firm-level events. Based on market efficient hypotheses, market
participants are able to access the information at the same time while the cases are revealed
to the market. On the other hand, however, data breaches do not come from firm decisions.
In addition, the relative rarity makes these cases more independent and idiosyncratic. The
theoretical foundation and empirical results from other studies have motivated us to develop
our hypothesis. To our best knowledge, we are among the first to examine the short-sellingaround data breach. Fortunately, the proprietary daily lending data allows us to conduct
powerful tests in this particular setting.
Our study also relates to the studies on price discovery and market efficiency. Boehmer
and Wu (2013) find that stock prices are more accurate when short sellers are more active.
Intraday informational efficiency of prices improves with greater shorting flow.

2.2.

Main hypotheses

H1: Short-selling activity increases around data breaches.
H2: Short-selling activity predicts cumulative abnormal returns around data breaches.
H3: short-selling facilitates price discovery and improves market liquidity and market
efficiency.
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3.

Data and results

3.1.

Data breaches data

We obtain the data of announced corporate data breaches from Privacy Rights Clearing
house (PRC) over the period 2005 to 2018. PRC provides the data-breach announcement
date, the name of the firm, the type of data breach and the description of events. The typesof data breach include CARD (Payment Card Fraud), HACK (Hacking or Malware), INSD
(Insider), PHYS (Physical Loss), PORT (Portable Device), STAT (Stationary Device), DISC
(Unintended Disclosure) and UNKN (Unknown). PRC reported 8804 data breaches madepublic since 2005. After ruling out private firms and matching with daily short-selling data,
we finally have 704 data-breach events and 486 unique firm.

3.2.

Daily short-selling data

We use a proprietary dataset of equity lending supply and loans from Markit Securities
Finance Buy Side Analytics Data Feed. Markit collects lending information from many ofthe largest custodians (125 of them) and prime brokers (32 of them) in the securities lending
industry. The data cover more than 85% of the securities lending market. The complete
daily security-level data is available starting from July 2006. The equity lending databaseincludes several variables from the equity loan market. The primary interest are sharesborrowed (Short Interest), the active quantity of shares available to be borrowed (LoanSupply), the active utilization rate (Utilization), the weighted average loan fee across all
shares currently on loan (Loan Fee).

3.3.

Firm level data

We obtain daily stock prices and volumes from CRSP, annual fundamentals data fromCOMPUSTAT, quarterly institutional ownership data from Thomson Reuters Institutional(13f). The fundamental measures include Return on Asset (ROA), total debt divided by total
5asset (Leverage), firm size (Size), book-to-market ratio (BM). The daily trading measureis stock trading volume (VOL) and a dummy variable for NASDAQ stocks (NASDAQ).
Ownership is the total number of quarterly shares held by institutions.

3.4.

Measure of market liquidity and price efficiency

We use various measures to examine the market liquidity and price efficiency. The firstmeasure is firm-level liquidity using two proxies. The second measure is based on shortselling patterns lending supply and loan fee. The third measure relies on option trading
dynamics.

3.5.

Descriptive statistics

Table 1 reports the detailed descriptive statistics of some key variables from the study.
The statistics are based on the (-30, -1) event window. ABSS measures the abnormal shortselling. The mean and median of ABSS are 0.08 and -0.13, respectively. The opposite signof mean and median value for ABSS is a little surprising, the distribution of which seems
to be quite skewed. ABVOL measures the abnormal trading volume. Not surprisingly, themean and median are 0.09 and 0.04, respectively. DCBS is Daily Cost of Borrow Score - a
relative measure of borrow cost constructed by Markit. It ranges from 1 (least expensive toborrow) to 10 (most expensive to borrow). The mean and median is 1.24 and 1, respectively.
Utilization is the value of assets on loan from beneficial owners (beneficial owner value onloan) divided by the total lendable assets (beneficial owner inventory value), expressed as apercentage. Utilization measures the relationship between demand and supply and provides
an insight into how the interplay of demand and supply affects stock price movements. The
calculation is short value divided by short supply. The mean of utilization is 14.66% and6.11%, respectively. Loan fee is the annualized borrowing fee. Rebate is the annualized
rebate fee. The mean and median for short interest are 0.03 and 0.04, respectively. The
reminder of the table is based on firm-level characteristics. ROA measures the return on
6assets indicating the profitability of the firms. The mean and median is 0.05 and 0.04,
respectively. We use natural log of market value for size measure. Leverage measures the
sample firms’ debt level. BM reports book-to-market ratio. Nasdaq is a dummy variable.
Nasdaq equals to one if the firm is listed on Nasdaq exchange and zero otherwise. VOL
measures the average trading volume. Ownership measures the institutional ownership.
Table 2 reports the Pearson pairwise correlation among short selling related variables. We
can observe a few interesting patterns in the table. As expected, there is a strong correlation
between the DCBS and loan fees, rebate (negative sign), short interest, utilization (all P
value ¡.0001). As mentioned, Daily Cost of Borrow Score (DCBS) is a relative measure ofborrow cost constructed by Markit. It ranges from 1 (least expensive to borrow) to 10 (mostexpensive to borrow). In addition, short interest is positively associated with utilization andabnormal short volume (both P value <.0001). Also, while abnormal short selling does notappear to strongly associate with DCBS, fee, and rebate, it is positively associated withutilization and abnormal short volume (both P value <.0001). Overall, our statistics match
the patterns previously documented in the literature.

4.

Main empirical results
Methodologically, we implement an event study, a set of multivariate regressions analyses,

as well as a set of Difference-in-Difference (DiD) analyses. Using event study, we present
the significant negative cumulative abnormal returns (CARs) around the data breach events.
The negative CARs tend to be consistent across various types of data breaches. In addition,
the results from multivariate regressions provide direct evidence that short sellers can and do
predict the event day negative returns based on their prior knowledge of potential incidents.
Further, the DiD approach identifies targeted firms and control firms and allows us to drill
down to the trading activities for data breach firms. It assists us further disentangle theinteraction between short sellers and data breach firms.

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4.1.

Characteristics of short selling surrounding data breaches

Figure 1 presents the short selling activities around data breach event dates. Figure
1a plots the LendableValue and ValueOnLoan amount. To provide the dynamics of short
supply for the firms, we rely on the field of LendableValue from Markit, which measures thevalue of stock inventory, available to lend. This lendable supply captures the shares madeavailable for lending by investors with long positions in the stock. To estimate short value,
we rely on the field of ValueOnLoan from Markit, which measures the total value of stock on
loan/borrowed. While, in reality, equity loans are borrowed for different purposes including
short-selling, convertible bond arbitrage and dividend-tax arbitrage strategies (see Aggarwal
et al. 2015), vast majority of the stock on loans are undertaken as a tool of shorting. Thus,
equity value on loan is a good measure of short-selling activity.
Figure 1b plots the short selling Utilization rate. Taken together, loan utilization rate,
daily borrowing costs (DCBS) and loan fee provide insight to the short sale lending. Utilization measures the value of assets on loan from lenders divided by the total lendable value. In
other words, utilization captures the percentage of lendable shares that are actually on loan.
Utilization measures the relationship between demand and supply and provides an insight
into how the interplay of demand and supply affects stock price movements. It is calculated
as: Utilization = LenderValueOnLoan / LendableValue.
Figure 1c plots the Indicative Fee and Indicative Rebate, respectively. Loan fee/Indicative
Fee provides a direct measure of the expensiveness of borrowing shares. Markit defines loan
fee as expected borrow cost, in fee terms, for a hedge fund on a given day. The calculation
uses both borrow costs between agent lenders and prime brokers as well as rates from hedge
funds to produce an indication of the current market rate. Loan fee is not necessarily theactual rate but rather an indication of the estimated standard market cost. Loan fees are set
in two different ways, depending on the collateral type. Since cash collateral is most dominant
form of collateral in the U.S. Market, the loan fee is defined as the difference between therisk-free interest rate and rebate rate. The rebate rate is the portion of interest rate on the8collateral the borrower receives. Rebate rate received by the borrower = general collateralrate lending/loan fee). Under typical circumstances, loan fee is inversely associated rebate
rate.
Figure 1d plots DCBS score. DCBS is assigned for each firm on a daily basis based on
the lending fees from the previous seven days.
We further examine the short selling activities during several windows including (-5, -1),(-1, 1), (1, 5), (-10, -1), (-20, -1), (1, 10), and (1, 20). Table 3 summarizes the results. PanelA reports the mean value and Panel B reports the median value. Following Henry and Koski(2010), ABSS(t) = SSVOL(t) / AVESS - 1, and ABVOL(t) = VOL(t) / AVEVOL - 1. The
benchmark period is from day -70 to day -30 relative to the data-breach date. ShortInterestis the number of shares short from Markit divided by shares outstanding from the CRSP
(Muravyev, Pearson, and Pollet, 2018). What seems to be puzzling to us is that, while
DCBS, utilization, loan fee, and rebate all tend to be much higher days leading to the data
breach, two relative measures ABSS and ABVOL tend to be much higher post event date.
In order to investigate what drives the results, we further decompose the data breachcases into 8 different types. Table 4 reports the results. The categories of data breaches
include CARD (Payment Card Fraud), HACK (Hacking or Malware), INSD (Insider), PHYS
(Physical Loss), PORT (Portable Device), STAT (Stationary Device), DISC (Unintended
Disclosure) and UNKN (Unknown).
To make more sense of short-selling activities, we employ propensity-score matching(PSM) method to ensure that treatment and control firms are as similar as possible. Following Mitts and Talley (2018), we employ PSM method to select control firms by one-to-one
matching on 1) 4-digit SIC industry code (i.e. an indicator for each), 2) log of market capitalization, 3)log of total assets, 4) log of net income, and 5) log of total liability. ATTACK
is equal to 1 for attacked firms and equal to 0 for control firms; PRE is equal to one prior tocorporate data-breach announcements and equal to zero after the announcement dates. The
daily short-selling measures are from pre-30 day to post-30 day relative to the announcement

9

dates. Table 5 shows the results of Difference-in-Difference (DiD) regression results. Panel A
shows the results of ValueOnLoan, LendableValue, DCBS, Fee, and Rebate. Panel B shows
the results of Utilization, ShortInterest, ABSS, and ABVOL.

4.2.

Is short-selling predictive of cumulative abnormal returns?

We conduct a host of event studies to examine the Cumulative Abnormal returns (CAR)
surrounding a data breach case. Table 6 reports the CARs based on the CRSP value-weighted
returns. N-P Ratio is the number of negative CARs versus the number of positive CARs.
We conduct multivariate regression analysis. Table 7 reports the results. Dependent
variable is the CARs of window (0,+1d), The measures of short selling activity include
ABSS, ABVOL, DCBS, Utilisation, Fee, Rebate and ShortInterest under the window (-10d,1d). The control variables are return on asset (ROA), firm size (Size), total debt to total
asset (Leverage), book-to-market ratio (BM), trading volume (VOL), institutional ownership(Ownership) and the dummy variable for NASDAQ stocks (NASDAQ).

4.3.

Market activities, liquidity and price efficiency

To measure stock price efficiency after data breach, we use three measures. The firstmeasure is firm-level liquidity using two proxies. The second measure is based on shortselling patterns lending supply and loan fee. The third measure relies on option trading
dynamics. Similar to Gormley, Kaplan, and Verma (2019), we use two proxies for firm-levelliquidity including the average ratio of the daily absolute return to the dollar trading volume
on that day (commonly known as the Amihud (2002) illiquidity measure) and closing bid-ask
spreads scaled by share price. Consistent with Saffi and Sigurdsson (2011), lending supply
has a significant impact on efficiency and on the distribution of returns. Stocks with limited
lending supply and high loan fees are associated with low price efficiency. We find that,after data breach, there is an increase in lending supply leads to both an increase in priceefficiency and decrease in skewness (daily return distribution and standard deviation) and a10lower frequency of extreme negative returns. Our pricing efficiency measures adopted fromSaffi and Sigurdsson (2011) are based on general stock return characteristics, such as return
standard deviation (Stdev), skewness (Skew), and kurtosis (Kurt).

5.

Robust tests

5.1.

Additional market efficiency measures

We use additional measures for market liquidity. We follow Buti et al. (2011) and
Huszr and Prado (2019). These measures include daily average high and low price spread(HLspread), bid-ask spread (BAspread), and the natural logarithm of the high and low pricespread and the bid-ask spread (e.g., LogBAspread and LogHLspread).

5.2.

Interactions between public news, firm events, and short sellers return
predictability

Following Boehmer, Duong, and Huszr (2018), using news data from Thomson Reuters
News Analytics, we find that short sellers are skilled at processing public information contained in news, consistent with Engelberg, Reed, and Ringgenberg (2012). However, theyalso trade on return-predictive private information that goes beyond public information.

6.

Conclusion
This study examines whether short sellers detect firm-level data breaches. Using propri-

etary daily lending data and unique data breach data, we investigate whether short-selling
anticipates corporate data breaches, and whether the short-selling ex-ante would benefit investors by improving market liquidity and price efficiency or cost investors by extracting rent
from investors as they undertake the price. First, we document that the level of short-selling
constraints when data breaches publicly revealed. Both borrowing costs and utilization fee
11

jump. In addition, short interest increases. Second, on a cross-sectional basis, our results
show that the level of short-selling activities predict the cumulative abnormal returns arounddata breaches. The results of event studies show the evidence that cumulative abnormal returns (CARs) are significant around firm-level data breaches. The CARs are also consistentacross different types of data breach. Further, we test the market impact of the short selling. We find that short-selling activities improves market liquidity and price efficiency. The
evidence is robust using different measures for market liquidity and price efficiency. Overall,our study provides strong evidence that short sellers exploit prior knowledge of data breaches
and help improve market efficiency. Our study is subject to a few caveats. One caveat is that
we cannot directly identify the channels that short sellers obtain the private information ofsubsequent data breaches. Further research needs to discuss the informed-trading channels
and mechanisms. The other caveat is that we control for the size and types of data breachcases, we cannot quantify the severity of the case, especially non-monetary and reputational
consequences. Its a difficult task given the uniqueness of each case and firm characteristics.

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Table 1: Summary Statistics. ABSS is abnormal short selling; ABVOL is abnormal stock
trading volume; DCBS is daily cost of borrow score; Utilisation is marked value of assets onloan from beneficial owners divided by the total lendable assets, expressed as a percentage;
Fee is annualized loan fee; Rebate is annualized rebate fee; and ShortInterest is the average
short supply value . ABSS, ABVOL, DCBS, Utilisation, Fee, Rebate and ShortInterest areunder the window (-30d,-1d). Other relevant firm-level variables include ROA (return on
asset), firm size (Size), total liabilities to total assets (Leverage), book-to-market ratio (BM),a dummy variable (NASDAQ), stock trading volume (VOL), and institutional ownership(Ownership).
Variable
ABSS
ABVOL
DCBS
Utilisation
Fee
RebateShortInterest
ROA
Size
Leverage
BM
NASDAQ
VOL
OwnershipN

Mean

440 0.08
437 0.09
440 1.24
438 14.66
440 0.01
440 0.03
437 0.03
440 0.05
440 9.66
436 1.91
402 0.48
440 0.32
440 13.13
421 789.96

S.D.

Min

0.25

Mdn

0.75

Max

0.88
-0.94 -0.36
0.42-0.86 -0.1
1.05
1
1
19.1
0
2.14
0.05
0
0
0.06
-0.59 0.05
0.04
0
0
0.08
-0.63 0.01
2.39
2.01 7.95
1.39
0.94 1.16
0.35
0.01 0.22
0.47
0
0
50.56
0
0.89
1311.33 0.03 62.86

-0.13
0.04
1
6.11
0
0.05
0.01
0.04
9.56
1.57
0.4
0
2.65
240.51

0.21
0.17
1
19.81
0
0.05
0.04
0.08
11.31
1.98
0.63
1
8.6
735.35

6.1
1.93
10
93.07
0.59
0.05
0.26
0.87
14.75
16.13
2.42
1
638.716411.73

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Table 2: Correlation. This table presents the Pearson Correlation among the measures ofshort selling activities. DCBS is daily cost of borrow score; Fee is annualized loan fee; Rebate
is annualized rebate fee; ABSS is abnormal short selling; ShortInterest is the average short
supply value; Utilisation is marked value of assets on loan from beneficial owners divided by
the total lendable assets, expressed as a percentage; and ABVOL is abnormal stock trading
volume.
DCBS
DCBS

Fee

RebateShortInterest

ABSS

Utilisation ABVOL

1

Fee

0.79961(<.0001)
Rebate-0.7340-0.8875
1(<.0001) (<.0001)
ABSS-0.0068-0.0049-0.0042(0.2241) (0.3788) (0.4481)
ShortInterest
0.22200.1566-0.1423(<.0001) (<.0001) (<.0001)
Utilisation
0.52790.3658-0.3110(<.0001) (<.0001) (<.0001)
ABVOL-0.0016-0.0063-0.0655(0.7783) (0.2707) (<.0001)

10.0356(<.0001)
0.0166(0.0031)0.2551(<.0001)1410.7019(<.0001)
0.0331(<.0001)1
0.0095(0.0968)1

15

Fig. 1. Characteristics of short sellings. The short-selling activities include the marked value of assets on loan from beneficial
owners (ValueOnLoan) from a, the total lendable assets (LendableValue) from a, Utilisation from b, the loan fee (IndicativeFee)
from c, the rebate fee (IndicativeRebate) from c, and daily cost of borrow score (DCBS) from d.

Table 3: Short selling activity around data-breach events. This table summarize measuresof short selling activity for variance windows around the data-breach event date. Following
OLt
OLt− 1, ABV OLt = AVVEV− 1. The benchmark
Henry and Koski (2010), ABSSt = SSV
AV ESS
OL
period is from day -70 to day -30 relative to the data-breach date. ShortInterest is the number of shares short from Markit divided by shares outstanding from the CRSP (Muravyev,
Pearson and Pollet, 2018).
Panel A: Mean(-5,-1)DCBS

1.279***(24.96)
Utilisation
15.842***(17.62)Fee
1.62%***(4.06)
Rebate2.99%***(6.99)
ShortInterest 3.03%***(14.47)ABSS
0.094(1.51)
ABVOL
0.203***(7.31)(-1,1)
1.302***(29.92)14.453***(20.17)
1.61%***(5.17)
1.91%***(5.67)
3.06%***(19.64)
1.674***(3.03)
0.698***(7.34)(1,5)(-10,-1)

1.192*** 1.246***(32.91)(27.00)12.230*** 16.253***(15.19)(18.17)
0.77%*** 1.30%***(8.46)(4.92)
0.52%*** 3.29%***(4.22)(11.11)
3.60%*** 3.03%***(15.08)(17.24)4.759***
0.104*(2.93)(1.77)
1.642*** 0.253***(6.10)(8.77)(-20,-1)(1,10)(1,20)

1.218*** 1.184*** 1.182***(28.27)(33.67)(34.07)16.761*** 12.271*** 12.433***(18.37)(15.39)(15.87)
1.11%*** 0.75%*** 0.76%***(5.58)(8.62)(8.65)
3.45%*** 0.53%*** 0.50%***(14.92)(4.41)(4.24)
3.10%*** 3.60%*** 3.58%***(18.20)(15.25)(15.57)0.168*** 4.770*** 4.599***(2.91)(2.92)(2.98)
0.170*** 1.656*** 1.590***(7.70)(5.94)(6.19)

Panel B: Median(-5,-1)DCBS

1.276***(24.65)
Utilisation
15.548***(17.34)Fee
1.66%***(3.93)
Rebate2.96%***(6.60)
ShortInterest 2.99%***(14.36)ABSS
0.076(1.23)
ABVOL
0.118***(4.98)(-1,1)
1.319***(23.93)14.760***(17.82)
1.79%***(4.00)
2.79%***(5.87)
2.73%***(16.97)0.228**(2.33)
0.143***(4.04)(1,5)(-10,-1)

1.194*** 1.236***(31.98)(25.66)12.135*** 15.343***(14.99)(17.34)
0.77%*** 1.23%***(8.37)(4.71)
0.52%*** 3.39%***(4.16)(11.69)3.59%*** 2.82%***(14.99)(16.99)4.753***
0.059(2.94)(1.03)
1.510***
0.045**(5.56)(2.37)

16(-20,-1)(1,10)(1,20)

1.205*** 1.186*** 1.185***(27.71)(32.97)(32.65)15.695*** 11.990*** 12.032***(17.00)(14.87)(14.94)
0.94%*** 0.76%*** 0.76%***(5.77)(8.37)(8.32)
3.68%*** 0.53%*** 0.53%***(18.89)(4.32)(4.30)
2.86%*** 3.59%*** 3.61%***(16.73)(15.05)(15.22)0.047
4.800*** 4.711***(0.85)(2.92)(3.02)-0.022
1.413*** 1.332***(-1.39)(5.20)(5.28)

17(-5,-1)1.000.
7.826***(3.01)
0.42%***(44.48)
4.83%***(509.08)
1.65%***(4.30)
0.051(0.36)
0.360**(2.38)
20

1.000.
Utilisation
13.396***(3.54)
Fee
0.41%***(45.33)
Rebate3.70%***(8.00)
ShortInterest 2.19%***(6.04)
ABSS
0.290(1.35)
ABVOL
0.369(1.44)
N
23

DCBS

N

ABVOL

ABSS

ShortInterest

RebateFee

Utilisation

DCBS(-5,-1)1.130***(18.25)15.726***(4.63)
0.64%***(4.94)
2.47%***(7.66)
2.31%***(6.67)
0.333*(1.89)
0.037(0.41)
23(-1,+1)PHYS

1.067***(20.57)9.813***(3.64)
0.49%***(6.08)
3.16%***(26.57)
2.20%***(4.71)
1.683**(2.12)
1.819(1.55)
20(-1,+1)CARD

1.227***(8.40)
15.149***(3.58).
0.81%**(2.48)
0.24%(0.48)
2.73%***(4.00)
0.722(1.60)
0.209(1.23)
23(+1,+5)

1.175***(8.89)
13.501***(3.10)
0.65%***(2.90)-0.24%-(0.69)3.36%***(3.36)
4.832**(2.06)
4.826*(1.72)
20(+1,+5)1.059***(28.01)
15.118***(7.69)
0.49%***(8.98)
4.74%***(76.33)
3.01%***(8.50)
0.099(0.93)
0.271***(4.29)
86(-5,-1)1.270***(10.39)14.035***(6.64)
1.94%(1.56)
2.60%**(1.98)
2.00%***(7.20)
0.063(0.43)
0.119***(2.63)
88(-5,-1)(+1,+5)1.081***(28.63)13.174***(8.75)
0.55%***(7.86)
3.89%***(30.66)
3.27%***(9.30)
0.379**(2.39)
0.425***(2.93)
86(-1,+1)PORT

1.044***(25.60)10.888***(7.40)
0.48%***(5.40)
2.26%***(8.06)
3.45%***(6.90)
0.835**(2.26)
0.629***(4.76)
86(+1,+5)1.345*** 1.213***(11.21)(16.10)13.149*** 11.054***(7.48)(5.12)
1.91%** 0.73%***(2.24)(5.69)
1.50%*
0.34%(1.64)(1.60)
2.30%*** 2.93%***(7.94)(5.31)
2.752
8.161(1.52)(1.47)
0.557*** 1.208***(3.96)(3.82)
88
88(-1,+1)

DISC

HACK(+1,+5)(-1,+1)STAT

1.531***(3.08)
25.484***(3.17)
1.57%(1.48)
0.92%(0.92)
7.60%***(2.78)
0.930(1.16)
1.898*(1.69)
8(+1,+5)

1.540*** 1.310***(14.65)(14.03)
16.554*** 13.546***(12.57)(8.87)
2.96%*** 1.12%***(3.49)(4.30)
0.01%-0.30%(0.02)(-1.06)
3.28%*** 3.84%***(12.27)(9.38)
0.824*** 2.291***(3.84)(4.15)
0.580*** 1.432***(4.55)(3.05)
160
160(-1,+1)1.813**
1.750**(2.23)(2.49)
25.640*** 23.872***(3.45)(3.48)
2.64%
2.28%(1.20)(1.25)
2.61%
2.08%(1.19)(1.21)
3.88%** 5.09%***(2.41)(2.91)-0.230**0.151(-2.18)(0.46)
0.315*
1.996(1.80)(1.53)
88(-5,-1)1.513***(11.87)18.911***(10.83)
2.96%***(2.83)
1.10%(0.99)
3.51%***(7.99)
0.003(0.04)
0.198***(4.04)
160(-5,-1)1.370***(9.00)
17.076***(5.42)
1.13%***(3.76)
3.62%***(10.15)
3.84%***(3.79)
0.152*(1.64)
0.313***(3.19)
52(-5,-1)

1.065***(22.70)13.015***(5.91)
0.53%***(5.47)
4.50%***(27.16)
3.27%***(5.49)
0.276(0.84)
0.042(0.67)
69(-5,-1)1.244***(12.95)15.414***(5.84)
0.89%***(4.49)
2.74%***(11.54)
3.23%***(5.70)
4.390(1.27)
1.049***(2.80)
52(-1,+1)UNKN

1.092***(26.86)
11.939***(6.80)
0.55%***(7.36)
3.14%***(21.35)
3.35%***(5.91)
2.502(1.22)
0.968***(2.95)
69(-1,+1)INSD

1.038***(42.98)11.644***(4.42)
0.39%***(10.04)
0.98%***(8.82)
4.07%***(4.56)
12.793(1.30)
3.490**(2.53)
52(+1,+5)

1.149***(17.36)
9.978***(5.70)
0.59%***(6.15)
0.36%(1.53)
3.52%***(4.71)
6.831(1.14)
2.026**(2.51)
69(+1,+5)

Table 4: Short selling activity for different types of breach. The categories of data breaches include CARD (Payment Card
Fraud), HACK (Hacking or Malware), INSD (Insider), PHYS (Physical Loss), PORT (Portable Device), STAT (Stationary
Device), DISC (Unintended Disclosure) and UNKN (Unknown).

18

Year
Industry
Adj R-sqr
Observations

ConstantBM

Leverage

SIZE

ROA

ATTACK*PREPRE

ATTACKYear
Industry
Adj R-sqr
Observations

ConstantBM

Leverage

SIZE

ROA

ATTACK*PREPRE

ATTACKt-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Panel B

Panel A

0.199***(15.77)-0.0833***(-6.493)0.0636***(3.711)
0.338***(3.479)0.142***(61.93)0.0673***(14.16)-0.0662***(-4.368)-0.799***(-22.49)
Yes
Yes
13.00%
39,196(2)

1.839***(41.28)Yes
Yes
11.90%
43,397-0.591***(-27.19)0.583***(26.46)
0.0486(1.629)(11)-0.450***(-20.81)0.645***(28.87)-0.0396(-1.339)-5.038***(-30.07)-0.302***(-75.73)-0.0131(-1.608)-0.157***(-5.905)
4.933***(80.63)
Yes
Yes
23.50%
38,405(12)

Utilisation

0.562***(22.82)
Yes
Yes
3.50%
44,2440.216***(17.81)-0.0897***(-7.402)0.0926***(5.609)(1)

ValueOnLoan

7.292***(43.07)-3.432***(-19.94)-3.571***(-15.56)54.96***(42.04)3.990***(130.9)1.774***(27.83)
2.191***(10.84)-37.43***(-78.71)
Yes
Yes
39.30%
39,506(4)0.0376***(31.84)
Yes
Yes
2.67%
41,5568.26e-05(0.140)-0.000282(-0.478)-0.00380***(-4.751)(13)0.00412***(6.910)-0.000504(-0.832)-0.00434***(-5.398)-0.0866***(-19.25)-0.00805***(-73.74)-0.00112***(-5.034)-0.000359(-0.504)
0.118***(71.04)
Yes
Yes
18.90%
36,842(14)

ShortInsterest

6.012***(15.43)
Yes
Yes
8.90%
44,6207.802***(40.75)-3.549***(-18.56)-3.098***(-11.91)(3)

LendableValue

1.324***(6.350)
Yes
Yes
7.21%
44,244(16)
1.029***(9.293)-2.976***(-26.40)-0.757***(-5.029)-11.63***(-13.63)-0.330***(-16.42)0.576***(13.81)-2.104***(-15.80)
4.462***(14.30)
Yes
Yes
9.15%
39,196ABSS

0.778***(7.590)-2.945***(-28.72)-0.568***(-4.063)(15)

1.033***(57.48)
Yes
Yes
1.12%
44,186(6)-0.0398***(-4.217)0.0289***(3.005)0.0679***(5.298)0.0989(1.361)-0.0688***(-40.24)0.0474***(13.36)
0.0712***(6.242)
1.516***(57.09)
Yes
Yes
6.66%
39,138DCBS-0.0707***(-7.989)0.0124(1.401)0.0754***(6.259)(5)

1.551***(16.46)
Yes
Yes
6.49%
41,815(8)-1.176***(-24.20)-2.411***(-49.04)
1.363***(20.87)-5.578***(-15.26)0.201***(22.76)0.204***(11.35)0.580***(10.05)-0.453***(-3.358)
Yes
Yes
6.22%
37,067(18)-0.000970***(-2.787)
0.000869**(2.452)0.00270***(5.716)-0.0139***(-5.181)-0.00158***(-25.00)
0.000968***(7.395)-0.00149***(-3.535)
0.0179***(18.25)
Yes
Yes
2.97%
39,138Fee

ABVOL-1.099***(-23.30)-2.436***(-51.78)
1.368***(21.51)(17)0.00449***(7.085)
Yes
Yes
0.61%
44,186-0.00163***(-5.227)0.000564*(1.808)0.00272***(6.403)(7)0.0302***(39.54)
Yes
Yes
38.40%
44,186(10)0.00146***(3.538)0.0328***(78.04)-0.00299***(-5.332)
0.0302***(9.514)0.00178***(23.78)-0.00268***(-17.25)0.00134***(2.675)0.0170***(14.68)
Yes
Yes
33.60%
39,138

Rebate0.00250***(6.648)0.0333***(88.48)-0.00352***(-6.868)(9)

Table 5: Difference-in-Difference Regressions. Following Mitts and Talley (2018), we employ PSM method to select control
firms by one-to-one matching on 1) 4-digit SIC industry code (i.e. an indicator for each), 2) log of market capitalization, 3)
log of total assets, 4) log of net income, and 5) log of total liability. ATTACK is equal to 1 for attacked firms and equal to 0
for control firms; PRE is equal to one prior to corporate data-breach announcements and equal to zero after the announcement
dates. The daily short-selling measures are from pre-30 day to post-30 day relative to the announcement dates.

19-0.35%***(-2.59)Mean-0.40%**(-2.31)Mean-1.34%(-1.60)0.12%(0.21)-0.43%**(-1.96)0.25%(1.19)-0.75%*(-1.86)-0.39%**(-2.11)-1.32%(-1.51)-0.80%**(-2.09)

UNKN

STAT

PORT

PHYS

INSD

HACK

DISC

CARD

-1.45%(-1.49)-0.12%(-0.2)-0.46%(-1.48)-0.04%(-0.09)-0.64%(-0.98)-0.21%(-0.83)-0.79%(-0.64)-0.96%**(-2.03)

Panel D: Type of BreachOverall

Panel C: CARs

UNKN

STAT

PORT

PHYS

INSD

HACK

DISC

CARD

Panel B: Type of BreachOverall

Panel A: CARs

-0.36%-0.12%-0.26%-0.47%-0.05%-0.51%-0.30%-0.63%-0.32%Median

N

60

14

129

26

70

161

96

18

574

N

60

13

129

26

70

161

96

18

573

(-2,+1)-0.66%-0.86%-0.16%-0.31%

0.15%-0.11%-0.08%-0.44%-0.18%Median(0,+1)

1.31

1.00

1.30

1.17

1.06

1.48

1.23

1.57

1.30

N-P Ratio

1.73

1.60

1.26

1.60

0.94

1.12

1.18

1.25

1.22

N-P Ratio-0.06%(-0.07)-0.10%(-0.17)-0.37%(-0.83)0.35%(0.82)-0.98%*(-1.74)-0.14%(-0.58)-1.41%(-0.92)-0.67%(-1.54)-0.26%(-1.38)

Mean

0.06%
(0.05)
0.09%(0.15)-0.67%(-1.33)0.06%(0.13)-0.29%(-0.44)-0.84%**(-2.22)-1.75%(-1.41)-0.55%(-1.08)-0.46%**(-2.15)

Mean

60

13

129

26

70

161

96

18

573

N

-0.68%-0.54%-0.21%-0.45%

0.85%-0.04%-0.32%-0.60%-0.22%Median

60

14

129

26

70

161

96

18

574

N

(-1,+2)-0.38%-1.34%-0.50%-0.20%

0.18%-0.03%-0.49%

0.53%-0.21%Median(0,+5)

1.73

2.50

1.19

1.89

0.75

1.04

1.23

1.25

1.17

N-P Ratio

1.14

1.60

1.58

1.36

0.79

1.06

1.18

0.50

1.15

N-P Ratio

0.41%(0.29)-0.09%(-0.14)-0.52%(-1.10)0.29%(0.63)-1.00%(-1.51)-0.21%(-0.74)-0.99%(-0.56)-0.75%(-1.39)-0.31%(-1.48)Mean-0.95%(-0.97)-0.24%(-0.40)-0.46%*(-1.72)0.29%(0.93)-1.02%**(-2.37)-0.15%(-0.75)-0.70%(-0.55)-0.50%(-1.21)-0.31%**(-2.06)

Mean

60

14

129

26

70

161

96

18

574

N

-0.93%-0.38%-0.29%-0.83%

0.72%-0.11%-0.21%-0.37%-0.23%Median

60

14

129

26

70

161

96

18

574

N

(-1,+3)-0.34%-0.68%-0.16%-0.86%

0.12%-0.34%-0.41%-0.17%-0.30%Median(-1,+1)

1.40

1.80

1.15

1.60

0.75

1.09

1.09

1.57

1.13

N-P Ratio

1.40

1.80

1.24

2.25

0.94

1.27

1.53

1.57

1.32

N-P Ratio

Table 6: Cumulative Abnormal returns (CAR) following a data breach under the windows, (0,+1d), (0,+5d) and (-1,+1d).
This table reports the CARs based on the CRSP value-weighted average. N-P Ratio is the number of negative CARs versus thenumber of positive CARs. The categories of data breaches include CARD (Payment Card Fraud), HACK (Hacking or Malware),
INSD (Insider), PHYS (Physical Loss), PORT (Portable Device), STAT (Stationary Device), DISC (Unintended Disclosure)and UNKN (Unknown).

20

t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Year
Industry
Observations
Adj R-sqr

Constant

NASDAQ

Ownership

VOL

BM

Leverage

Size

ROA

ABVOL

Utilisation

ShortInterest

ABSS

Rebate

Fee

DCBS

(2)

-0.00511(-0.660)
Yes
Yes
431
2.9%

-0.00330(-1.242)0.00765(0.400)0.000212(0.192)-0.000527(-0.438)-0.00638(-1.250)-2.12e-05(-0.263)-5.87e-07(-0.287)
0.00322(0.897)-0.00420(-0.303)
Yes
Yes
370
5.8%

-0.000673 -0.00243*(-0.553)(-1.686)(1)(4)-0.00551(-0.725)
Yes
Yes
431
3.8%

-0.00318(-1.226)0.00159(0.0836)
7.78e-05(0.0716)-0.000606(-0.510)-0.00762(-1.505)-1.99e-05(-0.250)-4.66e-07(-0.231)0.00216(0.602)-0.00388(-0.289)
Yes
Yes
370
7.7%

-0.0417** -0.0732***(-2.014)(-3.161)(3)-0.00761(-0.997)
Yes
Yes
431
3.9%

0.0414**(2.188)(5)-0.00316(-1.218)-9.07e-05(-0.00476)
0.000117(0.108)-0.000388(-0.326)-0.00757(-1.498)-1.28e-05(-0.161)-6.51e-07(-0.323)
0.00233(0.654)-0.00800(-0.602)
Yes
Yes
370
7.9%

0.0692***(3.256)(6)-0.000441(-0.281)(8)-0.00403(-1.516)0.0134(0.709)0.000528(0.483)-0.000651(-0.539)-0.00515(-1.011)-3.54e-05(-0.440)-7.26e-07(-0.353)
0.00347(0.962)-0.00611 -0.0101(-0.800) (-0.747)
Yes
Yes
Yes
Yes
431
370
3.0%
5.0%

-0.00108(-0.788)(7)0.0730*(1.873)(10)-0.00420(-1.614)0.0172(0.910)0.000854(0.775)-0.000634(-0.529)-0.00579(-1.144)-3.58e-05(-0.447)-5.98e-07(-0.293)0.00391(1.090)-0.00688 -0.0155(-0.895) (-1.130)
Yes
Yes
Yes
Yes
428
370
2.9%
6.0%

0.0326(0.917)(9)-0.00591(-0.771)
Yes
Yes
428
2.7%

-2.24e-06(-0.0302)(11)0.000127(1.359)-0.00485*(-1.824)0.0271(1.205)0.000767(0.660)-0.000659(-0.546)-0.00448(-0.877)-4.73e-05(-0.582)-5.16e-07(-0.251)
0.00324(0.894)-0.0147(-1.038)
Yes
Yes
368
5.6%

(12)

Table 7: Regression Analysis. Dependent variable is the CARs of window (0,+1d), The measures of short selling activity
include ABSS, ABVOL, DCBS, Utilisation, Fee, Rebate and ShortInterest under the window (-10d,-1d). The control variables
are return on asset (ROA), firm size (Size), total debt to total asset (Leverage), book-to-market ratio (BM), trading volume(VOL), institutional ownership (Ownership) and the dummy variable for NASDAQ stocks (NASDAQ).

Appendix A.

Definitions

Trading and Lending
characteristics

Definition

SourceCumulative Abnormal Market-adjusted cumulative abnormal CRSP
Return (CAR)
return (CAR) in various windows
around data breach event date
ABSS
ABVOL

Utilization

DCBS

Loan Fee
Short Interest

Abnormal short-selling in various windows around data breach event date
Abnormal trading volume in various
windows around data breach event
dateMarked value of assets on loan from
beneficial owners (beneficial ownervalue on loan) divided by the total
lendable assets (beneficial owner inventory value), expressed as a percentage.
Daily Cost of Borrow Score - a relative
measure of borrow cost constructed byMarkit. It ranges from 1 (least expensive to borrow) to 10 (most expensive
to borrow)
Annualized stock loan fee. This variable reflects the expected borrow cost.

Markit and CRSP

Average short supply value

Markit and CRSP

Return on assets, calculated as net income divided by total assets
Financial leverage, calculated as total
debt divided by total assets
An indicator = 1 if the firm is listed on
NasdaqMarket value standardized by naturallog
Book to market ratio
Daily Trading volume
The quarterly data on institutional
ownerships (before lockup expiration)

Compustat

CRSP

MarkitMarkitMarkitTrading and Lending
characteristics
ROA
Leverage
NasdaqFirm size
BM
VOL
Ownership

21

Compustat
CRSP
Compustat
Compustat
CRSP
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24