The Dark Side of Asset Redeployability: Future Stock Price Crashes
Yangyang Chen
School of Accounting and Finance
The Hong Kong Polytechnic University
yangyang.chen@polyu.edu.hk
Jeffrey Ng
School of Accounting and Finance
The Hong Kong Polytechnic University
tee-yong-jeffrey.ng@polyu.edu.hk
Jeffrey A. Pittman
Faculty of Business Administration
Memorial University of Newfoundland
jpittman@mun.ca
Walid Saffar
School of Accounting and Finance
The Hong Kong Polytechnic University
walid.saffar@polyu.edu.hk
Abstract
There is tension underlying whether asset redeployability, which refers to the salability of
the corporate capital assets of the firm, shapes crash risk. On one hand, greater asset
redeployability engenders liquidity benefits that should enhance financial stability, thereby
mitigating future stock price crash risk. On the other hand, asset redeployability enables
managers to opportunistically exploit asset sales to engage in upward real earnings
management in order to hide bad news, which, in turn, increases future stock crash risk.
We find that, on average, asset redeployability is positively associated with stock price crashrisk, suggesting that relying on redeployable assets to orchestrate upward real earnings
undermines shareholders’ interests. Reinforcing that real earnings management explains
the positive association between asset redeployability and stock price crash risk, we find that
this association is stronger for firms experiencing greater internal and external pressure to
manage earnings. In additional evidence supporting the real earnings management
channel, we find that asset redeployability is associated with a higher likelihood of justmeeting or beating analyst forecasts, and recording of gains from asset sales. We
contribute to extant research by providing evidence implying that asset redeployability has adark side stemming from managers’ incentives to suppress bad news, particularly wheninternal and external forces motivate them to manage real earnings upward.
September 2017
JEL Classification: G12, G14, G31, M41
Keywords: Asset redeployability; Stock price crash risk; Bad news hoarding; Asset sales* We gratefully acknowledge the helpful comments on an earlier version of this paper from Xiongbing
Chen, Jun Huang, Koren Jo, Qingyuan Li, Xinshu Mao, Chang Meng, Grace Pownall, Ghon Rhee,
Joshua Ronen, Madhu Veeraraghavan, Xiongyuan Wang, Nan Yang, and Huan Zou. Our paper has
also benefited from comments from seminar participants at Beijing Technology and Business
University, Hong Kong Polytechnic University, Peking University, Shanghai University of Financeand Economics, Wuhan University, and Zhongnan University of Economics and Law.

The Dark Side of Asset Redeployability: Future Stock Price Crashes

1.

Introduction
In this paper, we analyze whether firm-specific stock price crash risk is sensitive toasset redeployability. Secondary markets for corporate capital assets are important for firms
to be able to sell and buy corporate assets in response to their shifting liquidity needs,
investment opportunities, and business strategies (Ramey and Shapiro, 2001; Schlingemannet al., 2002; Gavazza, 2010). Asset redeployability reflects the extent to which its capital
assets can be liquidated at reasonable prices (Kim and Kung, 2017). Although the impact ofa firm’s asset structure on its financial stability remains an important question (Miller andOrr, 1966; Williamson, 1986; Anand and Singh, 1997; Wagner, 2007), empirical evidence on
whether a core aspect of asset structure, asset redeployability, shapes financial stability
remains scarce. Indeed, despite its importance, asset redeployability has attracted little
attention outside the economics and finance literatures. In particular, extant accounting
research seldom examines economic outcomes stemming from asset redeployability,
including its effects on firms’ financial stability. Accordingly, we extend prior research byexamining the link between asset redeployability and the risk of future stock price crashes.1
To the best of our knowledge, this relation has not been analyzed in previous literature,
likely because firm-level data on asset redeployability have only become available recently(Kim and Kung, 2017).
Stock price crash risk refers to the tendency for firm-specific stock prices to drop

The choice of stock price crashes as a proxy for financial stability stems from extant
research documenting that firms that face liquidity problems or hide bad news experience anincreasing likelihood of extreme negative stock price returns in the future (Clark and Weinstein, 1983;
Hutton et al., 2009). In this paper, we argue that asset redeployability could be associated withextreme negative outcomes because of the link between asset redeployability and liquidity and thelink between asset redeployability and the suppression of bad news.
1

1

significantly in a short period of time. Recent studies imply that the accumulation of bad
news is the key factor that triggers stock price crash (e.g., Jin and Myers, 2006; Bleck andLiu 2007). Underlying this view is that managers possess more private information about
the firm than outside investors. Due to various incentives such as compensation and careerconcerns (Kothari et al., 2009; Kim et al., 2011a), managers have a tendency to hoard badnews for an extended period of time, anticipating that it will ultimately disappear or be
offset by subsequent good news.

However, the amount of bad news a manager is willingor able to withhold is limited (Jin and Myers, 2006). When the accumulation of bad news
eventually reaches a tipping point at which managers can no longer suppress thefirm-specific negative information, it is suddenly revealed to the market at once, resulting ina stock price crash.
Extant theory and evidence provides competing predictions on the relation between
asset redeployability and crash risk.

In one direction, a major upside stemming from

redeployable assets is that the sale of these assets can generate liquidity when the firm isexperiencing resource constraints. It is important that the firm has sufficient liquidity tocater to its operational needs, as well as to meet its financial obligations (Opler et al., 1999;
Dittmar et al., 2003; Bates et al., 2009).

Consequently, from the perspective of liquidity

demand, redeployability facilitates raising cash when liquidity needs arise, which, in turn,enhances the stability of the firm and reduces the likelihood of negative events occurring(Williamson, 1988; Shleifer and Vishny, 1992; Kim and Kung, 2017).

It follows that theliquidity channel predicts a negative association between asset redeployability and stock
price crash risk.
In the other direction, prior research implies that managers rely on selective
corporate asset sales in orchestrating real earnings management designed to conceal bad
news (e.g., Bartov, 1993; Black et al., 1998; Hermann et al., 2003). In particular, managers
2can choose to sell assets with unrealized gains to book an accounting profit.

Graham et al.

(2005) provide survey evidence suggesting that firms are eager to engage in real earnings
management in striving to meet the market’s earnings expectations, even if this practiceundermines shareholder value.

In fact, assets that can lead to a greater boost to theearnings number are more likely to be the firm’s more productive assets that can be sold toother firms at a higher price.

Real earnings management, compared to accruals-based

earnings management, is likely to be not only costlier, but also more difficult to curtail(Cohen et al., 2008).

To the extent that investors later become aware that the firm waswithholding bad news and/or that firm value has fallen due to the loss in productive
capacity, its stock may become more vulnerable to a crash (Jin and Myers, 2006; Hutton et al.,2009). Consequently, the real earnings management channel predicts a positive association
between asset redeployability and stock price crash risk.
In a nutshell, there are two conflicting channels, liquidity and real earnings management,that makes the relation between asset redeployability and stock price crash risk an empiricalquestion.

Our primary purpose in this paper is to empirically clarify which channel

dominates in shaping crash risk.
To examine the relation between asset redeployability and stock price crash risk, werely on a new measure on asset redeployability developed by Kim and Kung (2017).

Briefly,

this firm-level measure is the value-weighted average of the industry-level redeployability
index across business segments in which the firm operates; industry-level redeployability isevaluated using data from the Bureau of Economic Analysis (BEA) capital flow table.
In analyzing a large sample of U.S. firms covering the period from 1984 to 2015, wefind that firms with higher asset redeployability are more likely to experience a future stock
price crash.

Reflecting its first-order economic materiality according to our coefficientestimates, crash risk increases by 5.5 percentage points with a one standard-deviation
3

increase across the asset redeployability distribution.
alternative

asset

redeployability

measures,Our results are robust to specifying

addingvariouscontrols,

estimating

random-effect panel regressions, and excluding financial crisis periods from the analysis.
Our core evidence continues to hold in an instrumental variable framework that further
dispels the endogeneity threat to reliable inference.

Collectively, our evidence lends

support to the intuition that the real earnings management channel, which involves
managers selectively exploiting corporate asset sales to manage real earnings upward inorder to suppress bad news, dominates the liquidity channel in shaping crash risk.
To shed further light on the real earnings management direction, we examine
whether the relation between asset redeployability and crash risk varies in the cross-section.
This analysis not only provides insights on the channel through which the documented
relationship operates, but also strengthens identification given that this link is unlikely toarise if our measure of asset redeployability simply reflects unobserved economic forces; i.e.,
it would be hard to attribute this pattern of evidence to a competing explanation. We
expect to observe that the positive impact of asset redeployability on stock price crash risk
will be more pronounced in the presence of factors that exacerbate firms’ real earning
management. More specifically, we conduct a series of cross-sectional tests to examine
whether the impact of asset redeployability on stock price crash risk varies systematicallywith internal and external performance pressure.

Our evidence implies that theimportance of asset redeployability to stock price crash risk rises for firms with high-power
CEO equity incentives, firms with CEOs in the early and later in their tenure, and firms withoverconfident CEOs.

These results suggest that firms are more likely to use asset

transactions to hide bad news when their CEOs face greater internal performance pressure.
Additionally, we find that the impact of asset redeployability on stock price crash risk isconcentrated in firms that have a large fraction of their shares held by transient institutional4

investors, firms that enjoy greater analyst coverage, and firms that exhibit relatively higher
earnings response coefficients.

These results are consistent with the narrative thatmanagers with greater external short-term performance pressure from the stock market are
more eager to suppress bad news using asset transactions, translating into a stronger link
between asset redeployability and firm-specific crash risk.

Importantly, this analysis

reinforces the real earnings management view given that it would be difficult to attribute
this pattern of evidence to an alternative explanation.
In additional analyses, we follow Hutton et al. (2009) by narrowing our focus to thepost-Sarbanes-Oxley Act (SOX) period. In contrast to Hutton et al. (2009) who report thatthe positive association between financial reporting opacity crash vanishes after SOX, wefind that the positive association between asset redeployability and crash persists afterward.
This evidence reconciles with Cohen et al. (2008) in that real earnings management is notconstrained and might even be exacerbated by SOX, which mainly tackles accruals-based
earnings management.2
Next, we conduct validity tests on the intuition that higher asset redeployability is
associated with upward real earnings management.

To the extent that managers areindeed resorting to liquidating corporate assets in attempting to conceal bad news, weshould observe patterns that support that firms with greater asset redeployability actively
manage earnings upward.

We initially examine the likelihood of a firm just meeting or

beating analyst expectations (Bartov et al., 2002).

Consistent with asset redeployability

facilitating the use of asset liquidation to avoid disappointing the markets’ expectations, wefind that asset redeployability is associated with a higher likelihood of just meeting or
It is important to stress that the financial reporting opacity measure under study in Hutton
et al. (2009) essentially captures accruals-based earnings management to hide bad news. Ourevidence that the positive relation between asset redeployability and crash remains after SOX helps
reinforce that: (i) we are documenting a relation different from Hutton et al. (2009); and (ii) realearnings management to hide bad news plausibly explains the positive relation.
2

5

beating analyst expectations.

We also find that asset redeployability increases thelikelihood of selling assets that result in the recording of an accounting gain. In otherwords, our evidence suggests that firms do dispose of their assets to avoid reporting losses.
Our analysis contributes to extant research in several ways.

First, we extend priorevidence on outcomes stemming from corporate asset sales and purchases.

For example,

Warusawitharana (2008) shows that increases (decreases) in profitability raise (reduce) the
likelihood of asset purchases, and lower levels of liquid assets reduce the likelihood of asset
sales. This literature focuses on the economic rationale (e.g., investment opportunities andagency problems) behind firms engaging in corporate asset transactions.

Grounded in thehistorical-cost nature of accounting for corporate assets, we argue that earnings
management motivations can persuade firms to undertake asset sales, which, in turn, canaffect the stability of the firm.

It is important to stress that ex ante asset redeployability

appears to benefit firms by ensuring that they retain the flexibility to dispose of the assets
when they are experiencing liquidity problems, thereby potentially enhancing firm stability.
However, in responding to calls for evidence on the implications of firms manipulating realbusiness activities (e.g., Xu et al., 2007; He and Tian, 2013), our analysis suggests that a darkside may accompany asset redeployability given that the sale of corporate assets may beundertaken to conceal bad news to the detriment of outside shareholders. Importantly, ouranalysis provides a more comprehensive picture of how a combination of businessfundamentals, accounting rules, and managerial incentives can shape corporate outcomes.
Second, we contribute to research on real earnings management by considering
whether firms resort to selling long-term assets in attempting to manage earnings upward(e.g., Bartov, 1993; Black et al., 1998; Hermann et al., 2003).

Rather than focusing on firms’

actual liquidation of assets, we examine the underlying nature of their asset structures in theform of asset redeployability.

This ex ante approach facilitates analyzing the role that asset
6

structure plays in the future stability of the firm evident in its stock price crash risk.

Ourevidence reconciles with theory demonstrating that real earnings management consumes
economic resources and makes it difficult for investors to gauge underlying firm value fromobserving reported earnings (Wang, 2006).3
Third, we extend research on the determinants of stock price crash risk to include
asset redeployability. Set against extensive prior evidence focusing on the importance of
firm-level characteristics to stock price crash risk, we initiate research on the link between
firms’ asset structure and their crash risk.

Investors naturally consider issues that affectfuture extreme returns (i.e., higher moment effects) to be highly relevant to their interests(e.g., Pan, 2002; Xing et al., 2010; Yan, 2011). After reporting evidence that firms are more
likely to suffer a future stock price crash when asset redeployability is greater, we analyze
whether this relation intensifies when internal and external forces exert more pressure on
firms to manage real earnings upward.
Fourth, we extend emerging research on the consequences of asset redeployability.
There is extensive economics and finance evidence on the role that asset redeployability
plays in capital structure outcomes such as debt maturity (e.g., Benmelech, 2009), financing
costs (e.g., Benmelech and Bergman, 2009; Ortíz-Molina and Phillips, 2014), and leverage(e.g., Campello and Giambona, 2013), or asset reallocation through mergers and trading insecondary markets (Almeida et al., 2011).

In contrast, hardly any accounting research

examines the economic implications stemming from asset redeployability.

3

Taking

It is important to stress at the outset that we focus on the implications of asset
redeployability on stock price crash risk. Asset redeployability is not simply a proxy forearnings management as ex ante there are valid reasons to expect that it could be linked tovarious corporate actions/channels such as the sale of assets to generate cash when needed(an efficient use of asset redeployability that increases a firm’s stability and lowers its crashrisk), or the sale of assets to manage earnings upwards (an opportunistic use of asset
redeployability to hide bad news, which later translates into higher crash risk).
7

advantage of firm-level data on asset redeployability that has only recently become available(Kim and Kung, 2017), we help close this gap by analyzing the impact of asset
redeployability, which facilitates real earnings management, on future stock price crash risk.
Cross-sectional analyses showing that this link intensifies when managers face more
performance pressure reconciles with this earnings management explanation.
Finally, research on the role that asset redeployability plays in firm financial stability
would naturally interest investors, policymakers, regulators, and managers. For example,
our evidence that stock price crash risk rises with asset redeployability is relevant toinvestors eager to protect their interests.

Firms exploiting asset redeployability not only

facilitates the hoarding of bad news, but also it may undermine shareholder welfare byleading to lower future cash flows stemming from the loss of productive capacity.4

Giventhat our evidence suggests that asset redeployability is partly responsible for the information
asymmetry that engenders stock price crashes, our research implies that policymakers andregulators should closely monitor firms that exhibit higher asset redeployability, which
provides insiders with wider scope to orchestrate real earnings management in order toconceal negative firm-specific information.
The rest of this paper is organized as follows.

Section 2 reviews prior analytical andempirical research in developing the motivation for our predictions. Section 3 describes thedata, variables, and summary statistics.

Section 4 reports the main findings and Section 5

presents results from our cross-sectional analyses.

Section 6 covers some additional

analyses and Section 7 concludes.
2.

Hypotheses Development

4 As stressed earlier, the assets whose sales can most help in boosting earnings numbers arelikely to be those that are more productive and command a higher price in secondary markets forcorporate assets.

8

Asset redeployability refers to the salability of the capital assets of the firm.

Priorresearch highlights two important features of the market for such assets: (i) there is an active
secondary market for many corporate assets; and (ii) there is ample variation in asset
redeployability within and across industries (Warusawitharana, 2008; Kim and Kung, 2017).
Kim and Kung (2017) find that after an increase in uncertainty, firms having more
redeployable capital assets reduce investment less, consistent with theory linking asset
redeployability to investment irreversibility (i.e., the wedge between purchase andliquidation values of the assets).

Additionally, they document that more redeployable

assets exhibit higher recovery rates and are traded more actively in secondary markets.
From an economic standpoint, an important consequence of asset redeployability is
the liquidity that the assets bring, especially in times when a firm requires cash for servicing
its financial obligations. Prior studies analyzing the impact of the liquidity of a firm’s
assets in terms of financial stability have typically focused on its current assets.

In hisseminal research on the predictors of bankruptcy, Altman (1968) shows that the liquidity ofshort-term assets, measured as working capital scaled by total assets, is a major determinantof bankruptcy.

Subsequent evidence corroborates the stability benefits stemming fromhaving more liquid current assets (e.g., Ohlson, 1980; Campbell et al., 2008; Campello et al.,2011).
Asset redeployability relates to the liquidity of non-current (i.e., capital) assets.
Williamson (1988) argues that given that redeployable assets have high liquidation values,
they can facilitate arranging debt financing. Also, in the event that the firm cannot service
its debts, creditors can seize the assets and redeploy them, making creditors less likely to
pursue adverse actions when the firm begins to exhibit financial distress.

A number ofstudies focus on the expected stability of firms attributable to the liquidity of redeployable
assets when examining the capital structure outcomes of asset redeployability.
9Forexample, Benmelech (2009) finds that firms’ asset redeployability affects both the amountand the maturity structure of their debt.

Reinforcing its liquidity benefits, Campello andGiambona (2013) find that greater asset redeployability facilitates borrowing for firms thatare more likely to face credit frictions, especially during periods when credit is tight.
Ortíz-Molina and Phillips (2014) report that firms with more redeployable assets enjoy
cheaper financing costs.

Overall, redeployable assets may help stabilize firm business

operations, potentially lowering the incidence of negative events (e.g., project failure due tohigh cost of capital, or financial distress due to inadequate cash to repay debt) and hence
future stock price crash risk.
From an accounting perspective, assets that are redeployable provide opportunities
for managers to engage in real earnings management via asset sales that can facilitate thehoarding of negative information. Prior research documents that firms engage in various
forms of real earnings management ranging from cutting expenditures (e.g., Roychowdhury,2006) to selling securities and capital assets to recognize unrealized gains (e.g., Ellul et al.,2015; Bartov, 1993; Hermann et al., 2003).
is most relevant for our purposes.

Real earnings management involving asset sales

Bartov (1993) finds that income from corporate asset

sales is significantly higher for firms with decreasing earnings before income from asset
sales and are clustered in the fourth fiscal quarter, consistent with managers choosing thetiming of asset sales to manage earnings upward.

Hermann et al. (2003) report thatmanagers of Japanese firms generate income from sales of corporate assets and marketablesecurities in order to meet their earnings guidance.
Similarly, anecdotal evidence reinforces that firms exploit asset sales in their realearnings management activities.

For example, a February 16, 2002 article in The Globe andMail recounts that IBM used the $300 million in proceeds from the disposal of one of its
operating units to add about 8 cents per share to fourth quarter earnings, resulting in IBM
10beating analyst forecasts by 1 cent (Scott, 2014: 185).

The same article mentioned that IBM’s

share price fell by 4% due to the revelation of IBM’s actions. A follow-up article in thesame newspaper criticized IBM for its creative accounting.5 The Securities and Exchange
Commission (SEC) later opened a preliminary inquiry into IBM’s accounting practices andnoted that IBM had claimed that the reason for higher operating earnings was due to tightcost controls, as opposed to proceeds from the sale of its assets. Although the inquiry waslater suspended, the SEC issued a bulletin reminding firms to report gains or losses on asset
sales separately in their financial statements.
Assets whose sale can have a large impact on earnings are more likely to be the moreproductive assets for which other firms are willing to pay a higher price.6 It follows that realearnings management via asset sales can be very costly to shareholders, especially relative toaccruals-based earnings management (Cohen et al., 2008). For example, in striving to meetshort-term earnings expectations, managers could sell assets valuable for the long-term
future of the firm. In a survey of U.S. chief financial officers (CFOs), Graham et al. (2005)
report that a majority of CFOs indicate that they are willing to sacrifice long-term value tomeet market expectations of short-term earnings targets due to their own wealth, career, andexternal reputation concerns. Accordingly, to the extent that greater asset redeployability
enables managers to undertake asset sales to help suppress bad news, asset redeployability
may be positively associated with stock price crash risk.
In short, there are reasons to expect that we will observe a positive (negative)
5 http://www.theglobeandmail.com/report-on-business/ibm-critics-expect-reckoning-on-fir

ms-creative-accounting/article25292004/
For example, assume three machineries each with a book value of $2 million. Further
assume that the market values of the machineries are $1 million, $3 million, and $5 million because ofdifference in productivity. To manage earnings upwards, selling the least productive machinery will
be counterproductive because it will result in a loss of $1 million. Selling the most productive
machinery will have the largest impact on earnings because it will result in a gain of $3 million,
although this disposal will also negatively affect the firm’s productive capacity to its longer-term
detriment.
6

11

relation between asset redeployability and stock price crash risk under the real earnings
management (liquidity) channel.

Although there is tension underlying this researchquestion, we predict, on balance, that firms with more redeployable assets tend to
experience more stock price crash risk (all hypotheses are stated in the alternative):
H1: Asset redeployability is positively related to stock price crash risk.
Prior research implies that performance pressure is a major determinant of earnings
management.

This pressure can arise from either internal forces, particularly incentives

facing top management, and external forces given the emphasis that stakeholders place onfirm earnings. We expect to observe that, to the extent that real earnings management
indeed explains the positive association between asset redeployability and stock price crashrisk, this association would be stronger under conditions where there is greater pressure to
manage earnings upward.
In initially focusing on the role of internal pressure that the CEO experiences, weconsider three drivers of this pressure, namely, CEO equity compensation, CEO tenure, andCEO overconfidence. Extensive prior research suggests that managers have incentives tomanage earnings upward to increase their equity compensation.

Cheng and Warfield (2005)

find that managers with greater equity incentives are more likely to report earnings thatmeet or just beat analysts’ forecasts in order to increase the value of their shares.
Bergstresser and Phillippon (2006) find that firms with CEOs whose overall compensation ismore sensitive to company share prices exhibit more earnings management. Kim et al.
(2011a) document that executive equity compensation incentives are positively associatedwith firm future stock price crash risk.
Concerning CEO tenure, Dechow and Sloan (1991) show that CEOs in their final
years in office slash R&D spending, consistent with incentives to increase reported earnings
to maximize compensation given their short employment horizon. Kalyta (2009) finds that12

CEOs tend to engage in income-increasing earnings management in the pre-retirement
period when their pension plans are based on firm performance. Further, Ali and Zhang(2015) show that CEOs tend to overstate firm earnings more in their early years of tenure tofavorably influence the market’s perception of their ability.
Moreover, Malmendier and Tate (2005; 2008) suggest that overconfident CEOs tend
to overestimate the future cash flows of investment projects and their ability to control theperformance of these projects.

To avoid intervention by “impatient” investors,

overconfident CEOs are reluctant to disclose privately observed negative feedback about the
projects and even manipulate accounting disclosure to convey their optimistic beliefs about
the projects’ prospects (Kim et al., 2016).

Schrand and Zechman (2012) document that

overconfident executives are more likely to exhibit an optimistic bias, leaving them more
susceptible to start on a slippery slope toward financial misreporting. In particular, theyfind evidence supporting that optimistic bias leading to an increased likelihood that the
manager intentionally exaggerates earnings in later years, culminating in the firm becoming
subject to a SEC Accounting and Auditing Enforcement Release (AAER). This discussion
motivates our next prediction:
H2a: The positive relation between asset redeployability and stock price crash risk is greater whenmanagers experience more internal pressure to hide bad news.
Next, we consider the moderating role that pressure applied by outside
stakeholders plays.

Specifically, we focus on three drivers of this pressure, namely,transient institutional investors, financial analysts, and earnings-related stock price pressure.
First, prior research implies that different types of institutional investors can exert differentpressure on managers to engage in earnings management. Transient institutional investors
are institutions with high portfolio turnover, highly diversified portfolio holdings, andstrong interests in short-term trading profits (Bushee, 1998).
13

Porter (1992) argues that

transient institutions favor short-term price appreciation and exit in response to poor
earnings.

Bushee (1998; 2001) documents that transient institutions prefer firms withgreater expected short-term earnings, which makes managers overly short-term focused.
Matsumoto (2002) finds that management’s incentives to avoid negative earnings surprises
is greater for firms with higher transient institutional ownership, as revelation of negative
earnings surprises may lead to their large-scale exit.
Second, financial analysts are perceived by managers to be one of the most importantgroups that affects stock price of their firms (Graham et al., 2005). Analyst forecasts are a
primary earnings target that managers strive to meet given that failure to do so may lead tosignificant declines in stock prices (Degeorge et al., 1999). As a key aspect of the job ofanalysts is to forecast near-term earnings and make corresponding stock recommendations,they might impose excessive performance incentives on managers to focus intently on the
short-run. For example, Burgstahler and Eames (2006) find that firms manage earnings
upwards to avoid reporting earnings lower than analysts’ expectations. He and Tian (2013)attribute their evidence that firms covered by more analysts are less innovative to analysts
overly pressuring managers toward achieving short-term goals at the expense of focusing on
long-term innovative projects.

Huang et al. (2017) report a positive relation between

analyst coverage and whether a firm meets or beats analyst forecasts, suggesting that greater
analyst coverage raises the pressure on managers to meet short-term performance targets.
Finally, we examine whether the role that asset redeployability plays in crash risk
hinges on earnings-related stock price pressure.

An adverse consequence of missing

market expectations is the tendency for firms to experience large declines in their stock
prices (e.g., Bartov et al., 2002; Skinner and Sloan, 2002). Managers naturally prefer toavoid stock price declines given its negative implications evident in managerial
compensation (Bushman and Indjejikian, 1993), managerial turnover (Warner et al., 1988),
14and lawsuits by shareholders (Kellogg, 1984; Lev and Villiers, 1994). Moreover, the adverse
impacts could extend to contracts involving various stakeholders (e.g., creditors, customers,
suppliers, and employees) who are concerned about the financial condition of the firm
(Opler and Titman, 1994).7 When the market is highly responsive to earnings surprises,managers have stronger incentives to inflate earnings because any negative unexpected
earnings will lead to a large drop in stock prices.

Accordingly, to the extent that stock

prices are more sensitive to earnings news, we expect that stock price pressure on the
managers to engage in earnings management to be greater, leading to our final prediction:
H2b: The positive relation between asset redeployability and stock price crash risk is greater whenmanagers experience more external pressure to hide bad news.
3.

Data and Variables

3.1. Sample
Our initial sample is comprised of firms at the intersection of the asset
redeployability data from Kim and Kung (2017), financial data from Compustat, and stock
return data from the Center for Research in Security Prices (CRSP).

The analysis starts in1984 because this is the first year for which asset redeployability data is available. We
follow Kim et al. (2011a; 2011b) by excluding observations with negative book value ofequity, with year-end stock prices less than $1, or with fewer than 26 weeks of stock returndata. Similarly, we exclude observations with insufficient information for constructing thecrash risk measures, and those with missing values for other regression variables. We
winsorize all variables (except for dummy variables) at both the 1st and 99th percentiles tomitigate the impact of outliers and database coding errors. After imposing these screens,
we are left with a final sample consisting of 99,968 firm-year observations for 12,110 unique
For example, Merton’s (1974) model uses the market value of a firm’s equity in calculatinga firm’s default risk and captures the notion that market prices contain forward-looking information
suited for determining likelihood of default. See also Hillegeist et al. (2004) and Vassalou and Xing
(2004).
715

firms during the period 1984-2015.
3.2. Stock Price Crash Risk Measure
After recent research (e.g., Chen et al., 2001; Hutton et al., 2009; Chang et al., 2017),
we proxy for stock price crash risk using two measures: the crash dummy (CRASH) andnegative skewness (NSKEW).

Both measures are based on firm-specific weekly returns

estimated by the residual return from the following expanded market model:

ri ,t  0  1rmkt ,t 2  2 rmkt ,t 1  3rmkt ,t  4 rmkt ,t 1  5rmkt ,t 2   i ,t(1)

where ri,t is the return on stock i in week t, rmkt,t is the return on the CRSP value-weighted
market index, and εi,t is the error term. We include the lead and lag market index returns toaccount for non-synchronous trading (Dimson, 1979).

Following prior research (e.g., Chenet al., 2001; Hutton et al., 2009), we estimate the firm-specific weekly return, Wi,t, as thenatural logarithm of one plus the regression residual (i.e., Wi,t =ln(1+εi,t)).
Our first measure of crash risk, the crash dummy, is an indicator variable that equals
one for a firm that experiences one or more crash weeks during a fiscal year, and zerootherwise.

We follow Hutton et al. (2009) by defining crash weeks as those for which afirm experiences firm-specific weekly returns that are 3.09 standard deviations below themean firm-specific weekly returns over the fiscal year.

The number 3.09 is chosen to

generate a 0.1% frequency in the normal distribution.
Our second measure of crash risk, negative skewness, is calculated by taking thenegative value of the third moment of firm-specific weekly returns during the fiscal year,
scaled by the standard deviation of firm-specific weekly returns raised to the third power.
Specifically, for each firm i in year t, we calculate negative skewness as:

NSKEWi ,t  [n(n  1)3/ 2 Wi.3t ]/[(n 1)(n  2)(Wi ,2t )3/ 2 ](2)where n is the number of observations of firm-specific weekly returns during fiscal year t.
16

Higher values of negative skewness indicate a more left-skewed distribution of stock returns,
reflecting a higher likelihood of stock price crash.
3.3. Asset Redeployability Measure
We employ the asset redeployability data from Kim and Kung (2017). 8 They
construct the measures using the 1997 BEA capital flow table, which breaks down
expenditures on new equipment, software, and structures by 180 assets for 123 industries in
the BEA table. Kim and Kung (2017) derive firm-level asset redeployability measures foreach firm in each year using a three-step procedure.
In the first step, Kim and Kung (2017) specify an asset’s redeployability score as thesum of weights of industries that use the asset among the 123 BEA industries.

Industryweight is calculated using market capitalization of Compustat firms in each BEA industry.
In the second step, the authors take the value-weighted average of the asset-level
redeployability scores across the 180 assets in the BEA table to generate an industry-level
asset redeployability index.

The weight is each industry’s expenditure on a particular asset

divided by its total capital expenditure from the BEA table. In the last step, Kim and Kung
(2017) construct the firm-level asset redeployability measure as the value-weighted averageof the industry-level redeployability index across business segments in which the firm
operates.

The weight is the proportion of each business segment’s sales over the firm’s

total sales. Business segment sales data is obtained from Compustat segment files. If
segment data is missing for a firm-year, Kim and Kung (2017) impute the firm-level asset
redeployability measure from industry-level measures based on the firm’s industry
classification in Compustat.
3.4. Control Variables
8
The
data
is
available
fromHyunseob
Kim’s
http://blogs.cornell.edu/hyunseobkim/,
andHowardKung'shttps://sites.google.com/site/howardpkung1/

17

websitewebsiteat
at

In the estimations, we include several control variables to account for other
determinants of stock price crash risk. Return volatility (SIGMA) is the standard deviationof firm-specific returns, as firms with more volatile stock returns are likely to be more crashprone.

Stock return (RET) is the average weekly return during the year. We include past

returns because Chen et al. (2001) document that past returns have predictive power for
future crash risk.

Stock turnover (DTURN) is the de-trended average monthly turnoverrate of the stock, which proxies for differences of opinion among investors and has beenshown to be positively related to future crash risk (Chen et al., 2001).

To control for the sizeand growth effects on future crash likelihood, we include firm size (SIZE), calculated as thenatural log of market capitalization, and market-to-book (MB), calculated as the market
value of equity divided by the book value of equity.
long-term debt divided by total assets.

Leverage ratio (LEV) is calculated as

We include leverage ratio given its potential

negative association with future crash risk (Kim et al., 2011a). Return on assets (ROA)
reflects past firm performance, measured as income before extraordinary items scaled bylagged total assets. Additionally, we control for abnormal accruals (ABN_ACC), measured
by the absolute value of discretionary accruals calculated following Dechow et al. (1995).
We also control for abnormal expenses (ABN_EXP), measured by abnormal product costs
minus abnormal discretionary expenses, both of which are calculated following
Roychowdhury (2006) and Cohen and Zarowin (2010).

Finally, we control for lagged

negative skewness since Chen et al. (2001) find that stock return skewness is persistent overtime.
3.5. Descriptive Statistics
Table 1 presents the summary statistics for the regression variables.

The tableshows that the mean value of the crash dummy is 0.185, suggesting that 18.5% of the sample
firms experience one or more crash weeks during the fiscal year.
18

The mean value ofnegative skewness is -0.057, indicating a slightly positively skewed firm-specific return
distribution in our sample.

The statistics for the stock price crash risk measures fairly

closely resemble those reported in prior research (e.g., Hutton et al., 2009; Kim et al., 2011a;
2011b; Chang et al., 2017).
(0.409).

Finally, the mean (median) value of asset redeployability is 0.399

The summary statistics for the control variables are largely consistent with priorstudies as well.
[Insert Table 1 here]
Table 2 presents the Pearson correlations between the variables in the regression
analysis. This includes that the two cash risk measures, the crash dummy and negativeskewness, are highly correlated (0.607), suggesting that the two measures capture similar
underlying constructs.

Further, asset redeployability is positively correlated with bothstock price crash risk measures (0.013 for the crash dummy and 0.017 for negative skewness).
Although this provides univariate evidence implying a positive association between asset
redeployability and stock price crash risk, we evaluate in the next section whether this result
holds in a multivariate framework.
[Insert Table 2 here]
4.

Empirical Results

4.1. Baseline Regression
In this section, we perform multivariate regression analysis on the relation between
asset redeployability and stock price crash risk. We follow prior research in estimatingthese models (e.g., Hutton et al., 2009; Kim et al., 2011a; 2011b; Chang et al., 2017):

CRASH i ,t  0  1 ARi ,t 1   2 NSKEWi ,t 1  3 SIGMAi ,t 1   4 RETi ,t 1
 5 DTURNi ,t 1  6 SIZEi ,t 1  7 MBi ,t 1  8 LEVi ,t 1  9 ROAi ,t 1
 10 ABN _ ACCi ,t 1  11 ABN _ EXPi ,t 1  Yrt   i ,t

19

,

(3)

NSKEWi ,t  0  1 ARi ,t 1   2 NSKEWi ,t 1  3 SIGMAi ,t 1   4 RETi ,t 1
 5 DTURNi ,t 1  6 SIZEi ,t 1  7 MBi ,t 1  8 LEVi ,t 1  9 ROAi ,t 1,(4)

 10 ABN _ ACCi ,t 1  11 ABN _ EXPi ,t 1  Yrt   i ,twhere i denotes the firm, t denotes the year, Yrt denotes the year fixed-effects, and εi,t is theerror term. We estimate equation (3) using logit and equation (4) using ordinary leastsquares (OLS).

The z- or t-statistics (in parentheses) are computed using standard errors

robust to heteroskedasticity and clustering at both the firm and industry levels.
the industry classification in the BEA table in clustering standard errors.

We adopt

Since all of the

independent variables are lagged by one year, the sample size for the tests is reduced to
84,964 firm-year observations.
The baseline regression results are reported in Table 3. In Column (1) where wefocus on the crash dummy, the coefficient on asset redeployability is positive and highly
statisticallysignificant(z-statistic=2.729),

implying

that

firms

with

higherasset

redeployability are more likely to experience a future stock price crash. The marginal effectof asset redeployability on the crash dummy (evaluated at the mean values of the
independent variables) is 0.096, suggesting that a one-standard-deviation increase in asset
redeployability (0.105) leads to an increase in crash probability of 0.096*0.105=1.01%. This
is 5.5 percentage points compared to the sample mean of the crash dummy (18.5%),
reflecting that the impact of asset redeployability on stock price crash risk is also
economically material.
In Column (2), we tabulate the evidence after specifying negative skewness as thedependent variable. Reinforcing the crash dummy-based results, the coefficient on asset
redeployability loads highly positively (t-statistic=2.715). In terms of economic significance,
a one-standard-deviation increase in asset redeployability (0.105) raises negative skewness
by 0.112*0.105=0.012.

Overall, we provide strong, consistent evidence supporting the

hypothesis that high asset redeployability enables managers to hide bad news through asset
20

transactions, which leads to the stockpiling of bad news and subsequent stock price crash.
The results for the control variables are largely consistent with recent research (e.g.,
Hutton et al., 2009; Kim et al., 2011a; 2011b; Chang et al., 2017). More specifically, stock
price crash risk is positively associated with return volatility, stock return, stock turnover,firm size, market-to-book, return on assets, and accruals earnings management, while
negatively associated with the leverage ratio. Untabulated results show that the largest
variance inflation factor (VIF) is 1.476, which is below 5, dispelling concerns surrounding
multi-collinearity in our setting (O’Brien, 2007).
[Insert Table 3 here]
4.2. Robustness Checks
In this section, we conduct several robustness checks to evaluate whether ourbaseline results are materially sensitive to model re-specification. For the sake of brevity,
we only report the coefficient on asset redeployability in the analysis in Table 4.
First, we re-estimate the regressions after replacing the asset redeployability
measures.

In the baseline analysis, we employ the asset redeployability measure calculated

based on an asset-level redeployability score that uses market capitalization of Compustat
firms in each BEA industry-year as the weight. In this section, we use the modified versionof the measure which incorporates correlation of outputs among firms within industries.
We also adopt the asset redeployability measure calculated based on the asset-level
redeployability score that uses the equal weight for each BEA industry-year.

The results

are presented in Panel A of Table 4, which show that the coefficient on asset redeployability
remains positive and statistically significant (smallest z- or t-statistic=2.347).

This evidence

suggests that our core findings persist under alternative asset redeployability measures.
Second, we consider alternative firm-specific thresholds in defining crash weeks. In
21the baseline regression, we define crash weeks as those weeks during which a firm
experiences firm-specific weekly returns that are 3.09 standard deviations below the mean
firm-specific weekly returns. In this section, we alter the threshold to 4 standard deviations
below the mean firm-specific weekly returns in defining the crash dummy.

We also use ageneral instead of a firm-specific threshold to identify crash weeks, as a firm-specificthreshold might be subject to the concern that it is not economically significant enough to bea crash for stocks with low volatility.

For this analysis, we specify crash weeks as those

weeks during which a firm experiences firm-specific weekly returns that are below -15%.
We report these results in Panel B of Table 4, which include that the coefficient on asset
redeployability remains positive and statistically significant (smallest z- statistic=2.876).
This evidence collectively implies that our core results hold under alternative firm-specificthresholds in defining crash weeks.9
Finally, we construct stock price crash measures using firm-specific returns so thatthe crashes are largely firm-specific events. However, it is still likely that market-wide
economic shocks affect both individual firms and industry asset redeployability, which
results in the association between asset redeployability and stock price crash. Although
including year fixed effects in the regressions alleviates this concern, we also analyze
whether our evidence is sensitive to confronting this issue by excluding the financial crisis
periods (i.e., this involves removing observations from 1987, 2000-2002, and 2007-2008 in
successive regressions). Reassuringly, the results reported in Panel C of Table 4 include
that the coefficient on asset redeployability remains positive and statistically significant(smallest z- or t-statistic=2.226) when we narrow our focus to the non-crisis years. These
results suggest that our findings are not driven by market-wide economic shocks.

9 Similarly, the results continue to hold when we use 3.5 standard deviation, 4.5 standard
deviation, -10%, and -20% as the threshold in defining crash weeks.

22

[Insert Table 4 here]
4.3. Endogeneity Tests
Since asset redeployability is measured at the industry level, it is less likely thatfirm-specific stock price crash has a material impact on asset redeployability in the wholeindustry, implying that reverse causality is less of a threat to reliable inference in our setting.
However, there is still some concern that asset redeployability and stock price crash risk arecorrelated with variables omitted from the regressions, spuriously driving our findings. In
this section, we conduct three tests to tackle this concern.
First, we add several controls that reflect determinants of stock price crash risk
according to prior research.

These variables include tax avoidance (Kim et al., 2011a),managerial equity incentives (Kim et al., 2011b), religious adherence (Callen and Fang, 2015),
accounting conservatism (Kim and Zhang, 2016), CEO overconfidence (Kim et al., 2016), andstock liquidity (Chang et al., 2017). We further control for the financial constraints index
proposed by Hadlock and Pierce (2010), and firms’ information environment proxied byanalyst forecast dispersion (Johnson, 2004) and auditor industry specialization (Dunn andMayhew, 2004).

Although adding these controls to the regressions is responsible forserious data attrition (i.e., data constraints lead to the sample size falling steeply to 12,269
firm-year observations), we continue to find that asset redeployability enters positively (atthe 5% level) in both regressions in Panel A of Table 5 despite the loss in power.
Second, we estimate random-effects regression models to further mitigate theomitted variable concerns.

This method assumes that the variation across firms is random

and uncorrelated with the independent variables in the regression model. An advantage ofrandom-effects regression is that it allows for time-invariant variables in the regression.
Since stock price crash is not a common event, the dependent variables in our setting arelargely time-invariant. Accordingly, random-effects estimation better suits the data than23

would a fixed-effects regression model. The results are presented in Panel B of Table 5.
The regression in Column (1) is performed by random-effect Logit model and the regressionin Column (2) is performed by random-effect panel regression. In both regressions, thecoefficient on asset redeployability remains positive and statistically significant at the 1%
level, lending additional support that potential omitted variable bias is not behind our coreevidence.
Last, we further address the endogeneity issue using an instrumental variableapproach despite the standard difficulty in identifying strong, valid instruments.

In thespirit of recent research involving selecting an instrumental variable (e.g., Laeven andLevine, 2009; Lin et al., 2013; Boubaker et al., 2017), the instrumental variable we specify is
the historical industry asset structure.10

First, we identify the first year each firm appears inour sample. Then, we calculate the historical industry asset redeployability as the averageasset redeployability of other firms in the same primary industry during that year. The
initial average asset redeployability in a firm’s industry is a suitable instrument for thefirm’s asset redeployability given that an individual firm’s asset structure is correlated with
its industry average but it is unlikely that an individual firm’s stock price crash risk isdirectly driven by the historical industry average asset structure other than through its effecton the firm’s own asset structure.

The regression results are presented in Panel C of Table 5.

In the panel, we report in Column (1) the results of the first-stage regression in which asset
redeployability is the dependent variable.

The results show that the coefficient on

historical industry asset redeployability is positive and statistically significant (t-statistics
10.474).

Partial F-statistic (untabulated) suggests that historical industry asset

redeployability explains a significant portion of variation in asset redeployability in our
These studies rely on the initial industry average ownership structure to instrument for thefirm’s ownership structure, while we use the initial industry average asset structure to instrument for
the firm’s asset structure.
10

24

sample.

Further, historical industry asset redeployability is unlikely to have a direct impact

on firm-specific crash risk. It follows that historical industry asset redeployability is a valid
instrument in our setting (Larcker and Rusticus, 2010).
We adopt the standard two-stage least squares (2SLS) regression for negativeskewness. However, we are unable to use the same approach for the crash dummy because
the standard 2SLS regression will yield biased estimates if the dependent variable is adummy variable (Wooldridge, 2002). Instead, we follow Wooldridge (2002) by using thepredicted value of asset redeployability from Column (1) as an instrumental variable in astandard instrumental regression. The results are presented in Columns (2) and (3) of thepanel. In both cases, the coefficient on instrumented asset redeployability is positive andstatistically significant (smaller z- or t-statistics 3.069), helping to mitigate the endogeneity
threat.
[Insert Table 5 here]
5.

Cross-Sectional Tests

5.1. The Role of Internal Performance Pressure
We deepen our analysis by examining whether the impact of asset redeployability on
stock price crash risk varies systematically with internal performance pressure.

We focuson the role of three internal performance pressure measures, including CEO equity
incentives, CEO tenure, and CEO overconfidence.

As stressed earlier in Section 2, we

expect to observe that the importance of asset redeployability to stock price risk to be larger
for firms with high CEO equity-based compensation, firms with CEO in the early and later
years of their tenure, and firms with overconfident CEO.
After Kim et al. (2011a), we calculate CEO equity incentives (CEO_INCT) as the ratioof the CEO’s equity compensation pay-performance-sensitivity over the sum of equity
25compensation pay-performance-sensitivity, salary and bonus in each year.

CEO tenure(CEO_TNR) is defined as a dummy variable equal to one if the CEO is in the first three years
or the last year of his/her tenure, and zero otherwise. Further, we follow Malmendier andTate (2005; 2008) by measuring CEO overconfidence (CEO_CONFI) as a dummy variableequal to one for all years after a CEO holds options that are at least 67% in the money, andzero otherwise. Data used to calculate the three measures are obtained from Compustat’s
ExecuComp database.
To test the impact of internal performance pressure on the relation between asset
redeployability and stock price crash risk, we interact asset redeployability with eachmeasure and integrate the interaction term into the regression specifications in equations (3)and (4).

The results are presented in Table 6.

Columns (1) and (2) show that thecoefficient on the interaction term between asset redeployability and CEO equity incentives
is positive and statistically significant (smaller z- or t-statistics 2.27), suggesting that theeffect of asset redeployability on stock price crash is stronger when the CEO has a highproportion of equity incentives.

In Columns (3) to (6), we report the results for CEO tenure

and CEO overconfidence, which implies that the impact asset redeployability on stock pricecrash is stronger when the CEO is in their early years or last year of tenure, or when the CEO
is overconfident.

These results are consistent with the intuition that managers are more

likely to exploit asset transactions to conceal bad news when they face greater internal
performance pressure (i.e., their motivation for hoarding bad news through asset
transactions is higher).
[Insert Table 6 here]
5.2. The Role of External Performance Pressure
Similar to internal performance pressure, external performance pressure from thestock market may also affect managers’ incentives to hide bad news through asset
26

transactions.

We employ three measures of stock market performance pressure, namely,transient institutional ownership, analyst coverage, and the earnings response coefficient.
Transient institutional investors are institutions with high portfolio turnover, highly
diversified portfolio holdings, and strong interests in short-term trading profits (Bushee,1998). Bushee (1998) finds that firms with more transient institutional investors myopically
cut R&D investment to hide earnings declines. Cutting R&D investment is a form of upward
real earnings management (Roychowdhury, 2006). We calculate transient institutional
ownership (TRAIO) as the number of shares held by transient institutions divided by total
number of shares outstanding. We obtain institutional ownership data from Thomason
Financial and institutional investor classification data from Brian Bushee’s website.11
He and Tian (2013) and Huang et al. (2017) provide an excellent discussion of theprior literature on the possible impact of analysts on earnings management.

They stressthat the presence of more analysts can have opposing effects on earning management:
increase external performance pressure on firms to manage earnings while at the same timeincrease monitoring that could constrain earnings management.

Empirically, they find thatanalysts exert too much pressure on managers to meet short-term earnings goals, which
motivates firms to engage in upward earnings management.12 We calculate the number ofanalysts following the firm (NUMANA) as the average number of analysts that make annual
earnings-per-share forecasts in each month during the year.

We obtain the analyst

Bushee (1998) classifies institutional investors into transient, quasi-index and dedicatedbased on portfolio turnover and diversification by institutional investors. The classification data isavailable in the following website: http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html
11

Prior research has also shown that analysts can play a monitoring role that reduces
earnings management. For example, Yu (2008) finds that analyst coverage reduces accruals-based
earnings management. As stressed earlier, it is possible that managers substitute real earnings
management for accruals-based earnings management when accruals-earnings management is moreconstrained (Cohen et al., 2008). In the context of our study, if we find that more analyst coverageincreases the positive association between asset redeployability and crash risk, the results will beconsistent with an external performance pressure interpretation, similar to He and Tian (2013) and
Huang et al. (2017).
12

27

following data from I/B/E/S.
The earnings response coefficient measures the extent of abnormal stock returns in
response to the unexpected component of the firm’s reported earnings. High values of theearnings response coefficient indicates a strong stock market reaction to firm earnings
surprises.

We specify the earnings response coefficient (ERC) as the regression coefficient

by regressing market abnormal return against unexpected earnings for earnings
announcements during the preceding three years. We follow Truong and Corrado (2014) incomputing the market abnormal return during a three-day window around earnings
announcements and measure unexpected earnings as actual earnings minus the median of
analysts’ forecasts, then scaled by stock price. We expect managers of firms whose stock
prices are more sensitive to earnings, i.e., firms with higher earnings response coefficient, toface greater pressure to manage earnings in order to hide bad news.
In successive regressions, we interact asset redeployability with transientinstitutional ownership, analyst following, and the earnings response coefficient, and add
the interaction terms to the analysis. The results are presented in Table 7.

In Columns (1)and (2), we find that the coefficient on the interaction between asset redeployability andtransient institutional ownership is positive and statistically significant in both the crashdummy and the negative skewness regressions (smaller z- or t-statistics 1.703).

The

evidence for the interactions involving analyst coverage and the earnings response
coefficient appear in Columns (3) to (6).

These results imply that the role that asset

redeployability plays in stock price crash risk rises when there are more analysts following
the firm and when the market reaction to unexpected earnings is high. Collectively, thisevidence supports the narrative that managers with greater short-term performancepressure from the stock market are more likely to hide bad news using asset transactions,which results in a stronger effect of asset redeployability on crash risk.
28

[Insert Table 7 here]
6.

Additional Analysis

6.1. The Effect of SOXThe Sarbanes-Oxley Act of 2002 (SOX) substantially increased the penalties forearnings manipulation, which led to a sharp decline in accruals earnings management after
its enactment (Cohen et al., 2008; Graham et al., 2005). Hutton et al. (2009) documents anoverall positive relation between discretionary accruals and stock price crash, although theyalso find that the relation disappears after the passage of SOX, suggesting that managers rely
less heavily on hiding bad news through accruals earnings management in the post-SOX era.
In this section, we examine whether the passage of SOX affects the relation between asset
redeployability and stock price crash.

Since managers can typically justify asset sales under

the pretext that these transactions are undertaken in shareholders’ best interests, they
provide convenient cover for insiders to conceal bad news. Given that bad news hoarding
activities are difficult to detect, managers may continue exploit asset sales after SOX.

In

fact, managers may resort to practicing even more real earnings management in this period
since SOX constrains accruals-based earnings management.
To provide some insight on whether there is structural shift in the link between asset
redeployability and stock price crash in the post-SOX era, we construct a post-SOX dummy(POST_SOX), which is equal to one for the post-SOX period (i.e., years after 2002), and zerootherwise.

We interact asset redeployability with the post-SOX dummy and include theinteraction term in the regression specification in equations (3) and (4).

The results arepresented in Table 8, which include that the coefficient on asset redeployability is positiveand statistically significant (smaller z- or t-statistics 2.272), while the coefficient of theinteraction term between asset redeployability and the post-SOX dummy has no perceptible
impact.

This suggests that the passage of SOX fails to deter managers from hiding bad
29

news through asset transactions, likely reflecting that this legislation does not constrain realearnings management activities.
[Insert Table 8 here]
6.2. Asset Redeployability and Meet or Beat Analyst Forecasts
So far, we use stock price crash risk as an outcome measure to capture managerial
bad news hoarding. Given that asset redeployability enables managers to hide bad news
through asset transactions, we would expect asset redeployability to be associated withother measures of earnings manipulation. Accordingly, we provide additional evidence onmanagerial bad news hoarding by examining the impact of asset redeployability on thelikelihood of beating analyst forecasts.

Specifically, we define beat analyst forecasts(BEAT_1C/BEAT_2C/ BEAT_3C) as a dummy variable equal to one if the firm’s actual
earnings beat analyst forecast consensus by one cent/two cents/three cents, and zerootherwise.
We run the same regression as equation (3), with the three beat analyst forecast
measures (instead of crash dummy) as the dependent variable, respectively.

In Table 9, we

report that the coefficient on asset redeployability is positive and statistically significant inall three regressions (smallest z-statistics 1.823).

Overall, this evidence is consistent with

our expectation that managers are able to exploit asset transactions to beat analyst forecasts
when asset redeployability is high.

This facilitates the withholding of bad news andsubsequent stock price crash.
[Insert Table 9 here]
6.3. Asset Redeployability and Asset Sale Gain or Loss
Our hypothesis is that asset redeployability enables managers to manipulateearnings through asset transactions so that that they can hide bad news to inflate short-term
30

stock prices.

In this section, we directly analyze whether there is any relation between asset

redeployability and firms’ gain or loss in asset sales.
sale gains or losses.

We employ three measures of asset

The first measure is the asset sale gain dummy (AGAIN), which is adummy variable equal to one if the firm realizes gains in aggregate in assets sales during the
fiscal year, and zero otherwise.

The second measure is asset sale gain ratio (R_AGAIN),

defined as the ratio of asset gains or losses over total assets.

The third measure is abnormal

asset sale gain ratio (ABR_AGAIN) derived after Gunny (2010) under this regression
specification.

GainAi ,tATi ,t 1

 0  1

INTi ,tASalesi ,tISalesi ,t1
  2Qt  3
 4
 5
  i ,tATi ,t 1
ATi ,t 1
ATi ,t 1
ATi ,t 1(5)where GainA is -1 times income from asset sales, AT is total assets, INT is internal funds (thesum of income before extraordinary expenses, depreciation and amortization, and R&D
expenditure), ASales is long-lived asset sales, and ISales is long-lived investment sales. We
estimate equation (5) by year and industry and take the residual as the abnormal asset salegain ratio.
We run the same regression as equation (3), with the dependent variable specified as
the asset sale gain dummy.

We also run the same model as equation (4), with thedependent variable specified as the asset sale gain ratio and the abnormal asset sale gain
ratio in successive regressions. The results from these estimations reported in Table 10.

In

all three estimations, the coefficient on asset redeployability is positive and statisticallysignificant (smallest z- or t-statistics 2.004).

This evidence implies that firms realize more

asset sale gains than losses when asset redeployability is high. This helps empiricallyvalidate our argument that when asset redeployability is high, managers are in a better
position to inflate earnings in an attempt to hide bad news through selling assets with bookgains.
31

[Insert Table 10 here]
7.

Conclusion
Prior research finds that firms’ asset structure is associated with their financial

stability (Miller and Orr, 1966; Williamson, 1986; Anand and Singh, 1997; Wagner, 2007).
Asset redeployability, which captures the salability of corporate capital assets, has long been
investigated about its role in firm’s asset structure.

In this paper, we extend extant

evidence by analyzing whether asset redeployability shapes stock price crash risk. We findthat firms with higher asset redeployability are more likely to have a future stock price crash.
This evidence lends empirical support to the real earnings management channel in which
managers dispose of corporate assets to manipulate earnings upward in order to hide badnews.

Our results are robust to specifying alternative asset redeployability measures,

estimating different model specifications, including additional control variables in theanalysis, confronting endogeneity in an instrumental variables framework, and focusing on
different sample periods.
In cross-sectional analysis, we find that the impact of asset redeployability on crashrisk is stronger when CEOs are short-term oriented, e.g., when CEO equity incentives are
high, CEOs are in the early or later years of their tenure, or CEOs are overconfident.
Moreover, we find that the importance of asset redeployability to crash risk is concentrated
in firms that are experiencing more pressure from external forces.

Specifically, asset

redeployability plays a larger role when: transient institutional investors hold large equity
stakes in the firm, analyst coverage is relatively high, or firms have high earnings response
coefficients.
We also conduct a series of additional analyses.

We find that the positive

association between asset redeployability and crash remains after SOX, potentially reflecting
32

that this legislation does not focus on constraining real earnings management.

Further, ourevidence implies that asset redeployability is correlated with a higher likelihood of meetingor beating analyst targets.

We also find that asset redeployability increases the likelihoodof selling profitable assets.

Collectively, our analysis suggests that asset redeployability

increases the likelihood of a firm stock price crash given that firms exploit corporate asset
sales to manipulate earnings when attempting to suppress negative information.
Our paper contributes to the literature on corporate asset sales and purchases.
Against the backdrop of extensive prior research focusing on the positive consequences ofasset redeployability, we provide evidence that asset redeployability has a dark side in theform of facilitating the withholding of bad news due to the historical cost nature ofaccounting for capital assets.

Our analysis implies that investors can benefit from having abetter understanding of the implications of financial reporting for capital assets.
Specifically, in facing agency problems stemming from managerial incentives, investors
need to protect their interests by closely monitoring firms against exploiting asset
redeployability to conceal bad news that can lead to a stock price crash.

33

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39

APPENDIX. Variable Definitions
Variable
Crash dummy(CRASH)
Negative skewness(NSKEW)
Asset redeployability(AR)
Return volatility(SIGMA)
Stock return(RET)
Stock turnover(DTURN)
Firm size(SIZE)
Market-to-book(MB)
Leverage ratio(LEV)
Return on assets(ROA)
Abnormal accruals(ABN_ACC)
Abnormal expenses(ABN_EXP)

CEO equity incentives(CEO_INCT)

CEO tenure(CEO_TNR)
CEO overconfidence(CEO_CONFI)
Transient institutional ownership(TRAIO)
Analyst following(NUMANA)
Earnings response coefficient(ERC)
Post-SOX dummy(POST_SOX)

Definition
Dummy variable equal to one for one or more weekly returns
falling 3.09 standard deviations below the mean weekly returns
over the fiscal year, and zero otherwise.
Ratio of the third moment of firm-specific weekly returns over the
standard deviation of firm-specific weekly returns raised to thethird power, and then multiplied by -1.
Asset redeployability measure constructed following Kim andKung (2017).
Standard deviation of firm-specific weekly returns over the fiscal
year.
100 times the mean of firm-specific weekly returns over the fiscal
year.
Average monthly stock turnovers over the current fiscal year minus
those over the previous fiscal year. Monthly stock turnover iscalculated as the ratio of monthly trading volume over the numberof shares outstanding.
Log value of the market value of equity. Market value of equity is
the product of stock price (PRCC_F) and the number of shares
outstanding (CSHPRI).
Ratio of the market value of equity over the book value of equity(CEQ). Market value of equity is the product of stock price(PRCC_F) and the number of shares outstanding (CSHPRI).
Ratio of long-term debt (DLTT) over the book value of total assets(AT).
Ratio of income before extraordinary items (IB) over book value of
total assets (AT).
Absolute value of discretionary accruals, calculated using themodified Jones model of Dechow, Sloan, and Sweeney (1995).
Abnormal production costs minus abnormal discretionary
expenses, calculated following Roychowdhury (2006) and Cohen
and Zarowin (2010).
Ratio
of
the
CEO’s
equitycompensation
pay-performance-sensitivity over the sum of equity compensation
pay-performance-sensitivity, salary and bonus.
CEO equity
compensation pay-performance-sensitivity is the dollar change inthe value of the CEO’s stock and option holdings resulting from a
1% increase in the firm’s stock price.
Dummy variable equal to one if the CEO is in the first three years
and last year of tenure, and zero otherwise.
Dummy variable equal to one for all years after a CEO holds
options that are at least 67% in the money, and zero otherwise.
Proportion of the firm's shares held by transient institutional
investors. The classification of transient institutional investors
follows Bushee (1998).
Average number of analysts that make annual earnings-per-share
forecasts in each month during the year.
Earnings response coefficient, calculated as the regression
coefficient by regressing market abnormal return against
unexpected earnings for earnings announcements during thepreceding three years.
Dummy variable equal to one for the post-SOX period (i.e., years
after 2002), and zero for the pre-SOX period (i.e., years before 2002).

Beat analyst forecast dummy(BEAT_1C/BEAT_2C/ BEAT_3C)
Asset sale gain dummy(AGAIN)
Asset sale gain ratio(R_AGAIN)
Abnormal asset sale gain ratio(ABR_AGAIN)

Dummy variable equal to one if the firm's actual earnings beatanalyst forecasts by one cent/two cents/three cents, and zerootherwise.
Dummy variable equal to one if asset gains/losses (SPPIV) is
positive, and zero otherwise.
Ratio of asset gains/losses (SPPIV) over total assets (AT).
Abnormal asset sale gain ratio calculated following Gunny (2010).

Variable names in parentheses in the right column refer to the names of the data items in the merged
Compustat/CRSP database.

41

TABLE 1.

CRASH
NSKEW
AR
SIGMA
RET
DTURN
SIZE
MB
LEV
ROA
ABN_ACC
ABN_EXPObs.

Summary Statistics
Mean
0.185
-0.0570.399
0.063-0.254
0.001
5.629
3.336
0.160
0.006
0.179
0.044

S.D.
0.388
0.812
0.105
0.034
0.309
0.103
2.114
5.514
0.168
0.199
0.216
0.465

25%
0.000
-0.4950.351
0.039-0.309-0.023
4.037
1.196
0.004
-0.0180.052-0.15099,968Median0.000-0.0770.409
0.055-0.151
0.000
5.485
1.961
0.116
0.041
0.116
0.071

The table presents the summary statistics of the variables in the analysis.
definitions are shown in the Appendix.

42

75%
0.000
0.339
0.461
0.079
-0.073
0.023
7.056
3.387
0.265
0.090
0.225
0.298

VariableTABLE 2.

CRASH
NSKEW
AR
SIGMA
RET
DTURN
SIZE
MB
LEV
ROA
ABN_ACC
ABN_EXP

Correlation Matrix
CRASH
1.000
0.607
0.013
0.051
-0.0290.027
0.039-0.006-0.0130.002-0.010
0.013

NSKEW

AR

SIGMA

RET

DTURN

SIZE

MB

LEV

ROA

ABN_ACC

ABN_EXP

1.000
0.017
-0.015
0.052
0.021
0.135
-0.002-0.0100.024-0.011
0.013

1.000
0.003
-0.012-0.014-0.0400.070-0.110
0.024
0.004
0.021

1.000
-0.9490.107-0.4770.080-0.094-0.4060.151-0.0971.000-0.1170.389-0.0920.084
0.398-0.133
0.079

1.000
0.045
0.057
0.012
0.029
0.054
-0.049

1.000
0.123
0.106
0.249
-0.052
0.051

1.000
0.038
-0.1290.071-0.1661.000-0.008
0.071
0.125

1.000
-0.1160.097

1.000-0.0341.000

The table presents the correlation matrix of the variables in the analysis. Variable definitions are shown in the Appendix.

43TABLE 3.

Asset Redeployability and Crash Risk: Main Results

Dependent Variable:
ARt-1
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1
MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo/Adjusted R2

CRASHt(1)
0.514(2.729)***0.070(4.576)***4.370(2.457)**0.665(3.747)***0.424(4.419)***0.037(2.503)**0.010(2.500)**-0.291(-3.649)***0.557(3.920)***0.142(3.568)***0.012(0.425)84,9640.021

NSKEWt(2)0.112(2.715)***0.031(6.209)***4.823(13.017)***0.478(13.870)***0.127(3.613)***0.062(17.862)***0.004(4.135)***-0.135(-5.121)***0.240(5.228)***0.062(5.089)***0.006(0.727)84,964
0.051

The table presents regression results for the relation between asset redeployability
and crash risk. Constant and year fixed effects are included in all the columns.
The regression in column (1) is performed by Logit model and the regression incolumn (2) is performed by ordinary least squares (OLS). The z- or t-statistics (inparentheses) are computed using standard errors robust to heteroskedasticity andclustering at both the firm and industry levels. ***, ** and * denote statisticalsignificance at the 1%, 5% and 10% levels, respectively. Variable definitions areshown in the Appendix.

44TABLE 4.

Asset Redeployability and Crash Risk: Robustness Checks

Panel A: Alternative asset redeployability measures(1) Redeployability measure incorporating correlation of outputs within industries
CRASHt
NSKEWtDependent variable:
(1)
(2)
ARt-1
0.996
0.249
(2.550)**(2.751)***(2) Equally weighted measure of redeployability
CRASHt
NSKEWtDependent variable:
(1)
(2)
ARt-1
0.655
0.125
(2.629)***(2.347)**
Panel B: Alternative crash dummy measures(1) Define crash dummy by on 4 standard deviations below the mean
CRASHtDependent variable:
(1)
ARt-1
0.481
(2.876)***(2) Define crash dummy based on -15% cut-off
Dependent variable:

CRASHt
(1)

ARt-1

0.758
(3.014)***
Panel C: Excluding financial crisis periods (1987, 2000-2002, and 2007-2008)
Dependent variable:
CRASHtNCSKEWt
(1)
(2)
ARt-1
0.575
0.115
(2.973)***(2.226)**

The table presents the regression results for various robustness checks. Control
variables, constant and year fixed effects are included in all the columns. For thesake of brevity, the table does not report the coefficient of control variables. The
regression in column (1) is performed by Logit model and the regression in column(2) is performed by ordinary least squares (OLS). The z- or t-statistics (inparentheses) are computed using standard errors robust to heteroskedasticity andclustering at both the firm and industry levels. ***, ** and * denote statisticalsignificance at the 1%, 5% and 10% levels, respectively. Variable definitions areshown in the Appendix.

45

TABLE 5.

Asset Redeployability and Crash Risk: Endogeneity Tests

Panel A: Additional control variables
Dependent variable:
CRASHt
(1)
ARt-1
0.309

NCSKEWt(2)
0.084(2.067)**(2.138)**Panel B: Random-effect panel regression
Dependent variable:
CRASHt
(1)
ARt-1
0.512
(3.425)***
Panel C: Instrumental variable approach
First-Stage
Regression
Dependent Variable:
ARt-1
(1)
Historical Industry AR
0.753(10.474)***
Instrumented ARt-1

NCSKEWt(2)
0.090(2.926)***
Second-Stage
Regression
CRASHt
NSKEWt
(2)
(3)

0.556
(7.390)***0.132(3.069)***The table presents results for endogeneity tests. Control variables, constant andyear fixed effects are included in all the columns. For the sake of brevity, the table
does not report the coefficient of control variables. In Panels A, the regression incolumn (1) is performed by Logit model and the regression in column (2) is
performed by ordinary least squares (OLS). In Panels B, the regression in column (1)is performed by random-effect Logit model and the regression in column (2) is
performed by random-effect panel regression. In Panel C, we use the Wooldridge(2002) method for crash dummy and two-stage least squares (2SLS) method fornegative skewness. The z- or t-statistics (in parentheses) are computed using
standard errors robust to heteroskedasticity and clustering at both the firm andindustry levels. ***, ** and * denote statistical significance at the 1%, 5% and 10%
levels, respectively. Variable definitions are shown in the Appendix.

46

TABLE 6.

Asset Redeployability and Crash Risk: The Effect of Internal Performance Pressure

Dependent Variable:
ARt-1
ARt-1*CEO_INCTt-1
CEO_INCTt-1

CRASHt(1)
0.351(1.004)1.245(2.382)**-0.223(-0.682)

NSKEWt(2)-0.006(-0.061)0.594(2.270)**-0.170(-1.593)

ARt-1*CEO_TNR-1
CEO_TNR-1

CRASHt(3)
0.376(0.947)

NSKEWt(4)0.185(1.720)*0.580(2.164)**0.271(1.089)0.009(1.279)0.006(0.136)

ARt-1*CEO_CONFIt-1
CEO_CONFIt-1
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1

0.080(3.589)***6.891(2.615)***0.827(2.724)***0.251(1.221)-0.041(-2.023)**

0.009(1.031)
5.933(6.765)***0.592(5.591)***
0.051(0.829)0.017(2.699)***0.075(3.555)***8.063(3.290)***0.998(3.442)***0.184(0.931)-0.030(-1.868)*

47

0.013
(1.576)5.846(8.199)***0.605(6.608)***0.046(0.821)
0.024(4.673)***

CRASHt(5)0.480(1.589)NSKEWt(6)0.123(1.840)*0.630(1.968)**-0.081(-0.621)0.084(3.940)***
7.393(3.035)***0.878(3.156)***0.226(1.162)-0.031(-2.086)**

0.100(1.985)**0.042(1.041)0.017(2.077)**5.420(7.606)***0.550(5.805)***0.059(1.072)0.022(4.514)***

MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo/Adjusted R2

0.009(2.200)**-0.426(-2.995)***1.005(5.950)***0.239(2.896)***-0.059(-1.315)
19,3500.015

0.002(1.623)-0.157(-3.819)***0.535(10.740)***0.057(1.848)*0.009(0.650)19,3500.025

0.011(3.071)***-0.394(-2.934)***0.981(5.639)***0.229(3.010)***-0.033(-0.868)
23,6600.016

0.003(2.421)**-0.152(-3.896)***0.486(11.035)***0.083(3.006)***0.006(0.471)
23,6600.025

0.010(2.666)***-0.404(-3.109)***0.846(4.992)***0.157(1.985)**0.062(1.498)23,7840.014

0.002(1.810)*-0.152(-3.830)***0.423(9.315)***0.051(1.809)*0.044(1.748)*23,784
0.028

The table presents regression results for the effect of internal performance pressure on the relation between asset redeployability and crashrisk. Constant and year fixed effects are included in all the columns. The regression in columns (1), (3), and (5) is performed by Logit
model and the regression in columns (2), (4), and (6) is performed by ordinary least squares (OLS). The z- or t-statistics (in parentheses) arecomputed using standard errors robust to heteroskedasticity and clustering at both the firm and industry levels. ***, ** and * denotestatistical significance at the 1%, 5% and 10% levels, respectively. Variable definitions are shown in the Appendix.

48

TABLE 7.

Asset Redeployability and Crash Risk: The Effect of External Performance Pressure

Dependent Variable:
ARt-1
ARt-1*TRAIOt-1
TRAIOt-1

CRASHt(1)
0.365(1.962)**
1.501(1.703)*0.982(2.036)**NSKEWt(2)
0.055(1.279)0.694(2.158)**0.362(2.512)**

ARt-1*NUMANAt-1
NUMANAt-1

CRASHt(3)
0.247(1.793)*

NSKEWt(4)
0.054(1.854)*0.217(2.243)**0.002(0.030)0.053(1.846)*0.012(0.979)ARt-1*ERC-1
ERC-1
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1

0.061(3.940)***1.862(1.154)0.418(2.595)***0.321(3.368)***0.014(1.095)0.027(5.238)***4.019(11.698)***0.402(12.516)***0.091(2.589)***0.054(19.761)***

0.062(4.046)***
3.792(2.238)**0.611(3.608)***0.445(4.632)***0.009(0.701)49

0.028(5.474)***4.617(12.900)***0.460(13.809)***0.135(3.890)***0.052(15.823)***

CRASHt(5)0.743(2.303)**

NSKEWt(6)0.105(2.242)**0.010(2.753)***-0.015(-1.399)
0.054(2.690)***8.357(3.817)***0.898(3.424)***0.413(2.617)***0.015(1.090)0.002(1.824)*-0.001(-0.448)0.015(2.168)**5.988(11.052)***0.580(10.525)***0.135(2.846)***0.045(11.232)***

MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo/Adjusted R2

0.010(3.020)***-0.286(-3.788)***0.500(3.780)***0.145(3.685)***0.024(0.833)84,9640.024

0.004(5.311)***-0.132(-5.212)***
0.218(4.940)***0.063(5.226)***0.010(1.314)84,9640.056

0.011(3.060)***-0.292(-3.804)***0.561(3.995)***0.163(4.077)***0.020(0.697)84,9640.021

0.005(4.933)***-0.135(-5.384)***0.239(5.265)***0.070(5.745)***0.009(1.116)
84,9640.053

0.013(3.337)***-0.261(-2.607)***0.823(4.782)***0.124(1.603)0.044(1.174)35,5370.018

0.006(4.461)***-0.165(-5.113)***0.392(6.350)***0.051(1.808)*0.035(1.865)*35,537
0.036

The table presents regression results for the effect of external performance pressure on the relation between asset redeployability and crashrisk. Constant and year fixed effects are included in all the columns. The regression in columns (1), (3), and (5) is performed by Logit
model and the regression in columns (2), (4), and (6) is performed by ordinary least squares (OLS). The z- or t-statistics (in parentheses) arecomputed using standard errors robust to heteroskedasticity and clustering at both the firm and industry levels. ***, ** and * denotestatistical significance at the 1%, 5% and 10% levels, respectively. Variable definitions are shown in the Appendix.

50

TABLE 8.

Asset Redeployability and Crash Risk: The Effect of SOX

Dependent Variable:
ARt-1
ARt-1*POST_SOX
POST_SOX
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1
MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo/Adjusted R2

CRASHt(1)
0.501(2.341)**
0.056(0.295)0.322(3.009)***0.071(4.563)***4.061(2.332)**0.647(3.760)***0.387(4.049)***0.033(2.216)**0.010(2.580)***-0.302(-3.760)***0.539(3.815)***
0.124(3.125)***0.008(0.289)81,9750.302

NSKEWt(2)
0.092(2.272)**0.044(0.753)0.167(2.873)***0.031(6.212)***4.829(13.035)***0.479(13.875)***0.126(3.613)***0.062(17.892)***0.004(4.069)***-0.135(-5.090)***0.240(5.231)***
0.063(5.123)***0.006(0.716)
81,975
0.051

The table presents regression results for the effect of SOX on the relation between
asset redeployability and crash risk. Constant and year fixed effects are included in
all the columns. The regression in column (1) is performed by Logit model and theregression in column (2) is performed by ordinary least squares (OLS). The z- or
t-statistics (in parentheses) are computed using standard errors robust to
heteroskedasticity and clustering at both the firm and industry levels. ***, ** and *denote statistical significance at the 1%, 5% and 10% levels, respectively. Variable
definitions are shown in the Appendix.

51

TABLE 9.

Asset Redeployability and Meet/Beat Analyst Forecasts

Dependent Variable:
AR t-1
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1
MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo R2

BEAT_1Ct(1)0.959(2.274)**-0.018(-0.844)
4.460(1.946)*0.745(2.968)***-0.314(-2.328)**-0.066(-4.911)***
0.023(5.813)***-0.679(-5.660)***1.338(10.101)***-0.067(-0.880)
0.009(0.180)41,9200.029

BEAT_2Ct(2)
0.937(2.200)**-0.028(-1.506)
3.373(1.824)*0.572(3.146)***-0.395(-3.689)***-0.020(-1.706)*
0.023(5.759)***-0.901(-8.054)***
1.556(10.261)***-0.050(-0.750)-0.043(-0.926)
41,9200.039

BEAT_3Ct(3)0.817(1.823)*-0.034(-1.860)*
2.744(1.576)0.430(2.387)**-0.459(-4.487)***-0.004(-0.286)
0.025(5.688)***-0.967(-8.668)***1.767(10.801)***-0.052(-0.815)-0.042(-0.938)
41,920
0.044

The table presents regression results for the relation between asset redeployability
and meet/beat analyst forecasts. Constant and year fixed effects are included in all
the columns. The regression is performed by Logit model. The z-statistics (inparentheses) are computed using standard errors robust to heteroskedasticity andclustering at both the firm and industry levels. ***, ** and * denote statisticalsignificance at the 1%, 5% and 10% levels, respectively. Variable definitions areshown in the Appendix.

52

TABLE 10. Asset Redeployability and Asset Sale Gain/Loss
Dependent Variable:
ARt-1
NSKEWt-1
SIGMAt-1
RETt-1DTURNt-1
SIZEt-1
MBt-1
LEVt-1
ROAt-1
ABN_ACCt-1
ABN_EXPt-1
Obs.
Pseudo/Adjusted R2

AGAINt(1)
0.962(2.004)**0.044(3.182)***15.661(8.036)***1.224(6.620)***-0.451(-5.009)***-0.064(-4.169)***0.015(4.408)***-0.834(-5.825)***0.158(1.145)-0.022(-0.269)-0.254(-5.570)***41,684
0.031

R_AGAINt(2)
0.006(2.807)***0.000(2.721)***0.019(1.717)*0.003(2.830)***-0.000(-0.489)-0.000(-0.014)-0.000(-1.041)-0.001(-1.211)0.003(2.642)***-0.000(-0.821)-0.001(-3.000)***79,1970.041

ABR_AGAINt
(3)
0.002
(3.380)***0.000(0.627)0.024(2.948)***0.002(2.947)***-0.002(-2.092)**-0.000(-1.479)
0.000(1.041)-0.000(-0.525)
0.002(2.426)**-0.000(-0.320)-0.000(-2.523)**
59,867
0.054

The table presents regression results for the relation between asset redeployability
and assets sale gain/loss. Constant and year fixed effects are included in all thecolumns. The regression in column (1) is performed by Logit model and theregression in columns (2) and (3) is performed by ordinary least squares (OLS). The
z- or t-statistics (in parentheses) are computed using standard errors robust to
heteroskedasticity and clustering at both the firm and industry levels. ***, ** and *denote statistical significance at the 1%, 5% and 10% levels, respectively. Variable
definitions are shown in the Appendix.

53