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Evidence from impending bankrupt firms that longhorizon institutional investors are informedabout future firm value
Santhosh Ramalingegowda
� Springer Science+Business Media New York 2014
Abstract A key assumption in many accounting and finance studies is that long
horizon institutional investors are informed shareholders. Yet past empirical research
finds no evidence that these institutions anticipate major corporate events, including
earnings-based events. I find that long horizon institutions are better informed in that
they sell more shares of impending bankrupt firms than of matched distress firms at
least one quarter ahead of bankruptcy. Share sales are greater in impending bankrupt
firms whose shareholders ultimately lose all of their equity. In additional analyses, I
document greater share sales by long horizon institutions with supposedly superior
information processing abilities and/or access to corporate management. Share sales
are significantly less in the post Regulation FD era. Overall, my findings support the
validity of the common assumption that long horizon institutions are informed.
Regulation FD appears to mitigate (but not eliminate) their information advantage.
Keywords Institutional investors � Bankruptcy � Private information � Long
horizon
JEL Classification G11 � G20 � G33
1 Introduction
This study examines whether institutional investors with long trading horizons
possess private information on future firm performance.1 Long horizon institutions
S. Ramalingegowda (&)
J.M. Tull School of Accounting, University of Georgia, 220 Brooks Hall, Athens, GA 30602, USA
e-mail: [email protected]
1 Private information includes non-public information and/or the superior processing of public
information.
123
Rev Account Stud
DOI 10.1007/s11142-013-9271-6
are characterized by low portfolio turnover, investing mainly for longer-term
dividend income or longer-term capital appreciation (Porter 1992; Bushee 1998).2
The role of long horizon institutions is controversial. A significant stream of
research in accounting, finance, and management posits that active long horizon
institutions influence corporate managerial behavior to enhance the long-term firm
value (e.g., Jensen 1989; Porter 1992; Froot et al. 1992a, b; Monks and Minow
1995; Bushee 1998). A key assumption in these studies is that, because of their large
long-term holdings, active long horizon institutions are better informed (also see
Bushee and Noe 2000; Callen et al. 2006). However, prior empirical studies that test
whether active long horizon institutions are informed find no evidence that they
anticipate major corporate events, such as break in a string of consecutive earnings
increases, earnings restatements, and long-term earnings growth revisions made up
to 2 years in the future (e.g., Ke and Petroni 2004; Ke et al. 2006; Yan and Zhang
2009; Hribar et al. 2009). For example, Ke and Petroni (2004) find no evidence that
active long horizon institutions sell ahead of a break in a string of consecutive
earnings increases, even when the break is followed by two or more quarters of
large earnings declines. The lack of evidence is puzzling given the vast literature
that argues that active long-term institutions are better informed.3 In addition, as a
result of this lack of evidence, recent research has started to question the
informedness of active long horizon institutions (e.g., Yan and Zhang 2009, p. 922).
This study provides new counter-evidence that long horizon institutions do trade
on private information about forthcoming events. Impending corporate bankruptcy
filings provide a focused setting in which to examine whether long horizon
institutions’ trade (sell) based on private information. Because long horizon
institutions focus more on long-term performance than short-term performance
(explained in hypotheses section), the events that prior studies examine may not
affect the long-term value these institutions place on the stock if the institutions’
trading horizon exceeds that examined in prior studies (e.g., exceeds 2 years). As
the length of long-term institutions’ trading horizon is unclear ex ante, one solution
to this problem is to examine these institutions’ trades in advance of an event that
results in a large persistent loss for current shareholders. A bankruptcy filing is such
an event because pre-bankruptcy shareholders usually lose most, if not all, of their
equity to creditors (e.g., Gilson 1990). This implies that, if long horizon institutions
anticipate bankruptcy filings, they should sell their stakes before the filing because
its effects will last beyond their trading horizon.
Despite the favorable qualities of the bankruptcy setting, it is plausible that I fail
to detect long horizon institutions’ informed trades there. Because active long
horizon institutions usually hold large stakes and thus have access to corporate
management (e.g., Carleton et al. 1998), any significant share sales prior to
bankruptcy filings (based on inside information or not) by these institutions may
2 Active long horizon institutions are typically private pension funds, endowments, foundations, and
banks. Examples include Berkshire Hathaway, Yale University Endowment, Bank of New York Asset
Management, etc.3 The lack of evidence of informed trading raises the question whether long horizon institutions merely
provide financial intermediary services, rather than invest based on private information as is commonly
presumed.
S. Ramalingegowda
123
encounter legal scrutiny. Therefore it is an empirical question whether long horizon
institutions sell their stakes in advance of bankruptcy filings.
Following prior research (e.g., Ke and Petroni 2004; Hribar et al. 2009), I use
Bushee’s (2001) ‘‘dedicated’’ institutions as a proxy for long horizon institutions.
Also following prior literature (e.g., Froot et al. 1992b; Ke and Petroni 2004; Hribar
et al. 2009), I take institutional investors’ trading horizons as given and focus on the
consequences of those horizons. As I cannot directly observe institutional investors’
informed trades, I use a research design similar to prior studies to capture private
information-based trades (e.g., Ke et al. 2003; Ke and Petroni 2004; Hribar et al.
2009). I use a matched pair design to control for distress factors that impending
bankrupt firms experience.
The results indicate that dedicated institutions sell more shares of bankrupt firms
than of matched firms at least one quarter ahead of the bankruptcy quarter. This
selling pattern is driven by bankrupt firms that are ultimately liquidated or
reorganized with zero equity to pre-bankruptcy shareholders than for bankrupt firms
that are ultimately liquidated or reorganized with positive equity to pre-bankruptcy
shareholders. These results are consistent with dedicated institutions having and
trading on private information on impending bankruptcies.
In an additional analysis, I provide evidence on how dedicated institutions obtain
private information on impending bankruptcies. I find that dedicated institutions that
have longer-term positions and/or larger stakes in the impending bankrupt firms sell
more shares than other dedicated institutions. This suggests that dedicated
institutions obtain private information through their better ability to process public
information and/or access to corporate management.
Regulation FD prohibits firms from privately disclosing information to selective
audiences (e.g., institutional investors). The regulation was intended to level the
playing field across investors. To the extent that dedicated institutions sell impending
bankrupt firms based on private information partially obtained from corporate
management, it is interesting to examine whether Reg FD limited the ability of
dedicated institutions to anticipate and sell impending bankrupt firms. My results
indicate that dedicated institutions sell impending bankrupt firms in periods both
before and after Reg FD. However, I find that dedicated institutions sell significantly
fewer shares in the periods after Reg FD than before. These results are consistent with
Reg FD mitigating dedicated institutions’ information advantage. These results also
suggest that dedicated institutions’ sales of impending bankrupt firms in the pre-FD
era are based on private information partially obtained from corporate management.
This study improves our understanding of the informational role of long horizon
institutional investors. As mentioned earlier, a significant stream of research argues
that long horizon (or dedicated) institutions are informed shareholders, but
empirical research has found that these institutions do not appear to anticipate
major corporate events. This lack of evidence has caused recent research to question
the informedness of dedicated institutions. My study adds to the literature in two
ways. First, it is the first to provide evidence that dedicated institutions trade in
anticipation of a major forthcoming corporate event, consistent with dedicated
institutions being informed shareholders. In addition to being of interest on its own
right, this finding is also important because it supports the validity of the common
Evidence from impending bankrupt firms
123
assumption that dedicated institutions are informed. Second, my study is the first to
provide evidence of the impact of Regulation FD on the trading behavior of
dedicated investors, who presumably have close access to corporate management.
My findings suggest that Regulation FD has been effective, at least partially, in
preventing corporations from privately disclosing information to shareholders with
close access to corporate management, thus partially mitigating these shareholders’
information advantage.
Section 2 of this study develops testable hypotheses. Section 3 discusses research
design, sample selection, and the data. Section 4 describes empirical results.
Section 5 reports additional analyses. Finally, Sect. 6 concludes.
2 Hypothesis development
Dedicated institutions likely have deep knowledge of the firms they hold on account of
their long-term positions and large stakes in the firms. Long-term position leads to better
knowledge of the firm and its managers and better ability to process public information
(e.g., Porter 1992). For example, long-term position in a firm enhances the ability to
process corporate managers’ qualitative verbal and nonverbal cues, which are
documented to provide information on future performance (Bushee et al. 2011; Mayew
and Venkatachalam 2011). Large stakes increase the net benefit of gathering and
analyzing information. In addition, prior research indicates that institutions with large
stakes in a firm enjoy greater access to corporate managers and to inside information
(e.g., Carleton et al. 1998). Lastly, dedicated institutions claim investment advisory
expenses in the millions of dollars every year, indicating that these institutions expend
huge resources to obtain private information on the stocks they hold.
Dedicated institutions are more likely to focus on trading on information that
impacts long-term value than short term value. Dedicated institutions have low
turnover because they are geared toward longer-term dividend income or longer-
term capital appreciation (Bushee 2001).4 Dedicated institutions may not sell on
forthcoming events with short-term price drops because of two reasons. First, such
trading behavior may not go over well with corporate management if it believes that
those trades will lead to an excessive price decrease. Graham et al. (2005) present
survey evidence that suggests managers are concerned about adverse price reactions
to short-term earnings decreases. Dedicated institutions have an incentive to
maintain good relations with corporate management because they aspire to be
involved in strategic decision-making.5 Second, to the extent trading large blocks of
4 ‘‘We measure our success by the long-term progress of the companies rather than by the month-to-
month movements of their stocks’’ (Warren Buffett in An Owner’s Manual 1999, Berkshire Hathaway,
Inc.).5 ‘‘Blum Capital takes a substantial position in order to establish a productive ‘seat at the table.’ We
generally strive to be the largest shareholder and, on a friendly basis, also will seek Board representation,
if we feel that it will enhance our ability to create value…. Blum Capital is as disciplined when exiting an
investment as we are when making one. Our sell decisions are based on a long-term perspective, rather
than quarterly performance or short-term price fluctuations.’’ (Statement on Strategy, Blum Capital,
http://www.blumcapital.com/strategy/index.html).
S. Ramalingegowda
123
shares exerts price pressure, trading on forthcoming events with short-term price
changes may not be profitable for dedicated institutions. Overall, dedicated
institutions are more likely to focus on collecting and trading on information that
impacts firm value in the long term.
The length of dedicated institutions’ long trading horizon is unclear ex-ante.
One solution to this problem is to examine dedicated institutions’ trades in
advance of an event that results in a large persistent loss of value. A bankruptcy
filing is such an event.6 Once a firm enters bankruptcy, it is liquidated, adjudicated
bankrupt, acquired, or reorganized in some form. In any case, pre-petition
shareholders usually lose most, if not all, of their equity to creditors. Gilson
(1990) analyzes the reorganization plans of bankrupt firms and finds that pre-
petition shareholders retain only 12 (20 %) ownership of common stock in the
median (mean) reorganized entity (also see LoPucki and Whitford 1993). These
findings imply that, if dedicated institutions anticipate future bankruptcy filings,
they should sell their stakes before the filing because its effects will last beyond
their trading horizon.
Overall, I expect dedicated institutions to sell shares of impending bankrupt
firms. More importantly, I expect dedicated institutions to sell shares of impending
bankrupt firms more than that of matched distressed firms.
H1 Dedicated institutions sell shares of impending bankrupt firms more than that
of matched distress firms.
The above expectation may not hold for two reasons. First, it is not obvious
whether investors, including dedicated institutions, anticipate impending bankrupt-
cies on average. Anecdotal evidence suggests that many institutional investors held
large amounts of technology and airline stocks in the quarters before their
bankruptcy filings (e.g., Fitzpatrick 2004). Second, even if dedicated institutions
anticipate bankruptcy filings, it is not obvious they would sell their stakes in
advance of bankruptcy filings. Under Section 10(b) of the Securities Exchange Act,
shareholders with more than 10 % holdings are prohibited from short swing profits.
Moreover, any shareholder who trades on inside information faces sanctions.
Because dedicated institutions hold large stakes and thus have access to corporate
management (e.g., Carleton et al. 1998), any share sales prior to bankruptcy filings
(based on inside information or not) by these institutions may encounter legal
scrutiny.7 Overall, it is an empirical question whether long horizon institutions sell
their stakes in advance of bankruptcy filings.
6 Lang and Stulz (1992) report an average CAR of -22 % for 2 days ending on the filing date (also see
Clark and Weinstein 1983). Formal announcements of bankruptcy filings are commonly seen first in press
releases (http://www.sec.gov/investor/pubs/bankrupt.htm). Once the company declares bankruptcy, it
must file form 8-K with the SEC within 4 days stating whether the bankruptcy petition is under Chapter 7
or 11 of the bankruptcy code.7 When Icahn Capital sold shares of Blockbuster Inc. prior to its bankruptcy filing, creditors filed a
lawsuit alleging insider trading (http://www.insidermonkey.com/blog/2010/12/24/carl-icahn-accused-of-
illegal-insider-trading).
Evidence from impending bankrupt firms
123
Some firms emerge from bankruptcy as reorganized firms with pre-petition
shareholders retaining a significant positive equity position.8 Because dedicated
institutions focus on long-term performance, they are more likely to be concerned
about firms in which they anticipate their ultimate equity position to be zero than in
bankrupt firms in which they anticipate their ultimate equity position to be positive.
Therefore, if dedicated institutions have private information, I expect them to
differentiate among future bankrupt firms based on the ultimate equity position of
pre-petition shareholders. I expect dedicated institutions to sell more shares of
impending bankrupt firms that ultimately are liquidated or reorganized with zero
equity to pre-petition shareholders than that of impending bankrupt firms that
ultimately are liquidated or reorganized with positive equity to pre-petition
shareholders. This leads to the following hypothesis:
H2 Relative to share sales in matched distressed firms, dedicated institutions’
share sales in impending bankrupt firms that ultimately are liquidated or reorganized
with zero equity to pre-petition shareholders are greater than dedicated institutions’
share sales in impending bankrupt firms that ultimately are liquidated or reorganized
with positive equity to pre-petition shareholders.
3 Research design, sample selection, and data sources
3.1 Proxy for long horizon institutional investors
I identify long horizon institutional investors based on the classification scheme
used in Bushee (2001). This scheme, or a variation of it, is used by a number of
other studies (e.g., Bushee 1998; Ke and Petroni 2004; Hribar et al. 2009).
According to this classification scheme, past portfolio turnover is used as a proxy for
a future trading horizon. To classify the trading orientation, Bushee (2001) collects
six variables that capture the past investment behavior of each institutional investor
in terms of both portfolio diversification and turnover. He then uses factor analysis
to produce one factor that captures the average size of an institution’s stake in its
portfolio firms and another factor that captures the degree of portfolio turnover and
cluster analysis to group similar institutions into one of three clusters: transient,
quasi-indexing, or dedicated institutions. Transient institutions are characterized by
high turnover and high diversification. Quasi-indexing institutions have low
turnover and high diversification. And dedicated institutions have low turnover
and low diversification. Transient institutions also have short trading horizons and
actively trade for short-term profits. Quasi-indexers are considered passive investors
because they tend to make buy-and-hold investments in a broad set of companies.
8 One main reason for this is that some firms file for bankruptcy protection even though they are
economically viable to achieve objectives that are unrealized outside the bankruptcy arena. Reasons
include unilateral abrogation of contractual obligations. It is alleged that Texaco filed for chapter 11
protection to avoid paying litigation damages of $10.53 billion to Pennzoil, even though Texaco’ equity
was estimated to be $13 billion and liquidation value of up to $26 billion (Delaney 1992).
S. Ramalingegowda
123
Dedicated institutions follow a long-term buy-and-hold strategy, investing large
stakes in a few companies.9 I use dedicated institutions as a proxy for long horizon
institutions. Although Bushee performs the trading classification annually, each
institution’s trading classification is highly stable over time. The classifications have
a year-to-year correlation of greater than 0.80. Therefore, following prior studies
(e.g., Ke and Petroni 2004; Ke and Ramalingegowda 2005), I assign each institution
to the type most frequently seen over the maximum available sample period. This
method results in 170 dedicated institutions.
3.2 Matched sample
Bankrupt firms experience distress conditions many periods before bankruptcy. Any
test of investor trading in anticipation of bankruptcies should also account for
potential trading in response to distress conditions. To test whether dedicated
institutions anticipate bankruptcy filings, I use a matched pair design. Seyhun and
Bradley (1997) find that bankrupt firms experience a price drop beginning 2 years
before the filing month. Therefore, for each bankrupt firm, I choose a matching firm
with similar distress conditions 2 years before the bankruptcy filing quarter, when it
is unlikely that bankruptcy could have been predicted with confidence. However, all
results are robust when I choose matching firms 1 year before the bankruptcy filing
quarter. I choose the matching firm based on the following criteria: (1) the matching
firm has at least 16 consecutive quarters of required data prior to the bankruptcy
quarter to estimate the regression equation (Sect. 3.4 discusses in detail why I
require 16 consecutive quarters); (2) the matching firm did not file for bankruptcy in
any of the years up to 5 years following the filing of the corresponding bankrupt
firm (Loderer and Sheehan (1989) adopt a similar approach); and (3) the ex ante
probability of bankruptcy (BKPROB) of the matching firm is closest to that of the
bankrupt firm. The last condition ensures that both matching and bankrupt firms
have similar distress conditions at that time. Based on Shumway (2001),
BKPROB = ea/(1 ? ea), where, a = -13.303 - 1.982 (net income/total
assets) ? 3.593 (total liabilities/total assets) - 0.467*log (market value/total
market value on CRSP) -1.809 (market adjusted returns) ? 5.791 (SD of market
model residuals). According to this model, higher BKPROB indicates higher
probability of bankruptcy. I use the bankruptcy model developed by Shumway
(2001) because this model incorporates returns-based factors that institutions are
likely to consider in evaluating stocks. However, my results are robust to using
Ohlson’s (1980) model (untabulated). ‘‘Appendix’’ illustrates the matching process
for a specific bankrupt firm.10
9 Consistent with Bushee’s classifications, the average percentage of dedicated institutions in my sample
that have held their existing stocks in their portfolios for more than 1 (2) year(s) is 74 (56 %).10 I do not require that the matching firm be in the same industry as the bankrupt firm because with that
restriction, I do not find matching firms that have BKPROB similar to the bankrupt firms, on average.
However, untabulated analysis indicates that my results are robust when I incorporate this restriction.
Evidence from impending bankrupt firms
123
3.3 Empirical model
To examine the relationship between dedicated institutions’ trades and impending
bankruptcy filings, I conduct an event study analysis in which firm quarters are
arranged in event time according to the length of the period by which they precede
the bankruptcy filing quarter. I estimate the following fixed-effects regression model,
where the matched-pair difference in the change in aggregate percentage ownership
(i.e., change in aggregate percentage ownership in the bankrupt firm—change in
aggregate percentage ownership in the corresponding matched firm) by dedicated
institutions is regressed on the pair-wise difference in explanatory variables.
dDDEDICATEDit ¼ ap þ b1QTR1þ b2QTR2þX14
3
bdCONTROLSþ eit ð1Þ
The prefix ‘‘d’’ in front of a variable indicates the difference between the
bankrupt firm and its matched firm variable. The description and computation of the
variables are as follows:
DDEDICATEDit DEDICATEDit - DEDICATEDit-1, where DEDICATEDit is the
aggregate percentage ownership of firm i at the end of calendar
quarter t by institutional investors classified as ‘‘dedicated’’
institutions by Bushee (2001)
ap Fixed effects dummy for each pair of bankrupt and matched
firms;
QTR1 One if dedicated institutional ownership change measurement
quarter is the first quarter prior to the bankruptcy filing quarter,
zero otherwise;
QTR2 One if dedicated institutional ownership change measurement
quarter is the second quarter prior to the bankruptcy filing
quarter, zero otherwise;
CONTROLS11:
BKPROBit-1 Probability of bankruptcy (in percentage, per Shumway 2001
Table VI Panel b) of firm i at the end of quarter t-1;
MOMit Buy-and-hold raw return of firm i over quarter t;
MOMit-1 Buy-and-hold raw return of firm i over quarter t-1;
MOMit-4,t-2 Buy-and-hold raw return of firm i over -quarters t-4 to t-2;
TVOLit-1 Average of monthly trading volume divided by shares
outstanding of firm i in quarter t-1;
PRICEit Price per share of firm i at the end of quarter t;
MVit-1 Market capitalization of the common stock of firm i at the end of
quarter t-1;
BMit-1 Ratio of common book equity to total market capitalization of
firm i at the end of quarter t-1;
11 Control variables that rely on Compustat information are measured at t-1 because quarter t-1
information (and not quarter t information) is publicly known in period t.
S. Ramalingegowda
123
UEit-1 Earnings surprise based on seasonal random walk divided by
total assets of firm i for the earnings of quarter t-1;
SPRANKit-1 S&P common stock ranking (7 = A?���0 = if stock is not rated or
is in reorganization or liquidation) of firm i at the end of quarter t-1;
YIELDit-1 Dividend of firm i for the past year divided by MVit-1;
DEDICATEDit-1 Aggregate percentage ownership of firm i at the end of calendar
quarter t-1 by institutional investors classified as ‘‘dedicated’’
institutions
The main variables of interest are QTR1 and QTR2. QTR1 measures the change in
aggregate percentage ownership by dedicated institutions in bankrupt firms relative to
that in matched firms in the first quarter preceding bankruptcy, compared to this relative
change in the baseline quarters. Similarly, QTR2 captures the relative change in the
second quarter preceding bankruptcy as compared to the relative change in the baseline
quarters. Because it is not clear how many quarters in advance dedicated institutions may
anticipate bankruptcies, I rely on prior literature and examine up to two quarters prior to
the filing. Seyhun and Bradley (1997) examine the returns to bankrupt firms in the
5 years before the filing month. They show that the price dropped 17.44 % in the second
year before the bankruptcy filing month, 47.82 % in the first year before the filing month,
37.20 % in the six months before the filing month, and 28.09 % in the filing month.
Because most of the percentage price decrease occurs in the window starting 6 months
before the filing month, examining institutional trading response in the two quarters
before the bankruptcy quarter is a powerful test, ex-ante.12 I also report results of
analysis that includes additional variables representing quarters three and four, and year
two before the bankruptcy quarter. To support Hypothesis 1, I expect dedicated
institutions to sell more shares of bankrupt firms than matched distress firms before the
bankruptcy quarter. Specifically, I expect a negative coefficient on the QTR variables. I
acknowledge that this research design does not allow for verification of whether a
negative coefficient on the QTR variables results from the share sales of bankrupt firms
or share purchases of matched firms. Thus I also present analysis wherein I estimate the
regression on my sample of bankrupt firms and matched firms separately.
BKPROBit-1 controls for stock sales based on the probability of bankruptcy that is
publicly known, ex-ante. MOMit, MOMit-1, and MOMit-4,t-2 control for dedicated
institutions’ contrarian strategy of investing in stocks with poor current and past
returns (Ke and Petroni 2004). MVit-1 captures dedicated institutions’ preference for
larger firms (Ke and Petroni 2004). DEDICATEDit-1 controls for dedicated
institutions’ beginning-of-period ownership, which is known to negatively affect
their current period investment (Ke and Petroni 2004). TVOLit-1 and PRICEit proxy
for stock liquidity. PRICEit also controls for stock sales based on potential delisting
news.13 BMit-1 and UEit-1 capture some institutions’ preference for value versus
12 My sample of bankrupt firms experiences a price drop of around 21 % in the second year before the
filing quarter, 60.1 % in the first year before the filing quarter, and 42.8 % in the 6 months before the
filing quarter.13 My results are robust when I include an indicator variable equal to one if the price is less than $5, zero
otherwise. I do not control for stock delistings because all 128 sample firms are delisted in or after the
bankruptcy filing quarter. Current quarter returns control for any public information on potential delistings.
Evidence from impending bankrupt firms
123
growth firms and high vs. low unexpected earnings, respectively (Bushee 2001; Ke
and Petroni 2004). SPRANKit-1 and YIELDit-1 account for some institutions’
preference for stocks that are considered prudent investments (Bushee 2001).
Because prior literature finds little or mixed evidence on how dedicated institutions
respond to TVOLit-1, PRICEit, BMit-1, UEit-1, SPRANKit-1, and YIELDit-1, I do not
have predictions on these variables (Bushee 2001; Ke and Petroni 2004).14
To test whether dedicated institutions sell more shares of bankrupt firms that
ultimately are liquidated or reorganized with zero equity to pre-petition shareholders
than that of bankrupt firms that ultimately are liquidated or reorganized with
positive equity to pre-petition shareholders (Hypothesis 2), I estimate the following
fixed effects regression model:
dDDEDICATEDit ¼ ap þ b1QTR1þ b2QTR2þ ðb1aQTR1þ b2aQTR2Þ
� ZEROEQi þX14
3
bdCONTROLSþ eit ð2Þ
ZEROEQi 1 for bankrupt firms that ultimately are liquidated or reorganized with
zero equity to pre-petition shareholders and 0 for bankrupt firms that
ultimately are liquidated or reorganized with positive equity to pre-
petition shareholders;
The remaining variables are as defined under Model 1. To support Hypothesis 2, I
expect a negative coefficient on the QTR 9 ZEROEQi variables.15
3.4 Sample selection and data sources
Table 1 Panel A reports my sample selection process. My initial bankruptcy sample
consists of 2,176 US public firms in CRSP that filed bankruptcy petitions under
Chapter 11 or Chapter 7 from years 1983 to 2010.16 In the case of multiple filings by a
firm, I choose the earliest filing only. I then delete bankruptcies prior to 1987. The
previous two steps ensure that a firm filing for bankruptcy in or after 1987 has not filed
for bankruptcy more than once in the previous 4 years. I use 4 years because I require a
minimum of 16 consecutive quarters of data prior to the bankruptcy quarter to estimate
the regression model (explained below). Next, I delete bankruptcies after year 2005.
14 I do not control for analyst forecast revisions because only 19 % of my sample of 128 bankrupt firms
has analyst earnings forecast revision data in all 16 quarters prior to the bankruptcy quarter. Nevertheless,
as a robustness check, I include analyst earnings forecast revisions as an additional control variable after
assuming zero for any missing revision (Ke et al. 2008). My results are robust. Similarly, I add analyst
recommendations as another control variable. My results are robust. Finally, my results are robust when
controlling for going concern opinions issued by auditors.15 Equation 2 does not include ZEROEQ as a standalone variable because of high collinearity between
ZEROEQ and the matched-pair/firm dummies. However, my results are robust to including ZEROEQ and
excluding the matched-pair/firm dummies in the regressions. Also, my results are robust to an alternate
model, in which dCONTROLS are also interacted with ZEROEQ.16 Data samples in prior research also range from 1980 to 2000s (e.g., Hillegeist et al. 2004), consistent
with bankruptcy rules remaining largely unchanged.
S. Ramalingegowda
123
This ensures that a matching firm did not file for bankruptcy in any of the years up to
5 years following the filing of the corresponding bankrupt firm (Loderer and Sheehan
(1989) adopt a similar approach). The above steps result in 1,509 bankrupt firms over
the years 1987–2005. Deleting firms that are not in institutional ownership database
and Compustat results in 1,268 bankrupt firms. Next, I require a minimum of 16
consecutive quarters of data to estimate the regression model. For my main analyses,
as discussed in Sect. 3.3, I rely on prior literature and examine trading in up to two
quarters prior to the bankruptcy filing quarter. However, in additional tests, I examine
trading in the periods up to 2 years prior to the filing quarter. Thus, to have the same
firms in the main and additional tests and to have at least eight quarters in the baseline, I
require a minimum of 16 consecutive quarters of data prior to the bankruptcy quarter.
The requirement of at least eight quarters in the baseline ensures that dedicated
institutions own both the bankrupt firm and the matched firm for at least eight quarters
prior to the point of matching. Requiring a minimum of 16 consecutive quarters of
necessary data reduces my sample to 128 bankrupt firms.17 Most of the reduction (from
1,268 to 307) is because of missing financial statement data in the four quarters prior to
the bankruptcy quarter. The large loss of firms in the years prior to bankruptcy is
because companies generally stop reporting financial statements to Compustat two or
more years prior to bankruptcy (Gentry et al. 1985). My final sample consists of 128
bankrupt firms with 2,803 quarterly observations over the years 1987–2005.18
Bankruptcy filing data is obtained from the SDC Platinum database and from
New Generation Research Inc. All US public companies that have $10 million or
more in assets and file for Chapter 11 bankruptcy protection are in SDC Platinum.
Data on the major US public companies that file for Chapter 11 or Chapter 7
bankruptcy protection are obtained from New Generation Research Inc. Information
on the ultimate equity position of pre-petition shareholders is manually collected
from LEXIS–NEXIS. Financial statement data are from CRSP/COMPUSTAT
Merged Industrial Quarterly. Returns data are from the CRSP monthly file.
Institutional ownership data are from CDA Spectrum.
4 Empirical Results
Table 1 Panel B reports descriptive statistics on regression variables of the 128
bankrupt and 124 matching firms19 for the quarter 2 years before the bankruptcy
quarter (which is the time matching firms are chosen). The last two columns report
the difference in the means and the test of difference in means respectively. As
reported in the last column, there are no significant differences between bankrupt
and matched firms with respect to most variables. To mitigate the effect of any
17 In untabulated analysis, I examine the robustness of my results on the initial 1,268 bankrupt firms. In
another robustness check, I require 12 consecutive quarters of data instead of 16. In both cases, the results
are similar to findings on the final sample of 128 bankrupt firms.18 Institutional ownership has increased overtime (e.g., Gompers and Metrick 2001). To account for
yearly effects, my research design uses fixed effects. Also, my results are robust to the exclusion of any
one of the following sets of years: 1987–1991, 1992–1996, 1997–2001, 2002–2005.19 Four matching firms are included in the sample twice.
Evidence from impending bankrupt firms
123
Ta
ble
1S
ample
sele
ctio
nan
ddes
crip
tive
stat
isti
cs
#F
irm
s#
Ob
s
(A)
Sam
ple
sele
ctio
n
CR
SP
firm
sth
atfi
led
for
ban
kru
ptc
yfr
om
19
83
to2
01
02
,176
Aft
erre
tain
ing
on
lyth
ein
itia
lb
ank
rup
tcy
fili
ng
of
each
firm
2,0
45
Aft
erre
stri
ctin
gto
ban
kru
ptc
yfi
lin
gs
fro
m1
98
7to
20
05
1,5
09
Aft
erd
elet
ing
firm
sn
ot
inC
om
pu
stat
1,2
74
Aft
erd
elet
ing
firm
sn
ot
inS
pec
tru
m1
,268
Aft
erdel
etin
gfi
rms
that
do
not
hav
edat
aon
regre
ssio
nvar
iable
sfo
ral
lfo
ur
quar
ters
pri
or
toth
eban
kru
ptc
yquar
ter
307
Aft
erd
elet
ing
firm
sth
atd
on
ot
hav
ed
ata
on
regre
ssio
nv
aria
ble
sfo
ral
lei
gh
tq
uar
ters
pri
or
toth
eb
ank
rup
tcy
qu
arte
r2
29
Aft
erdel
etin
gfi
rms
that
do
not
hav
edat
aon
regre
ssio
nvar
iable
sfo
ral
l12
quar
ters
pri
or
toth
eban
kru
ptc
yquar
ter
165
Aft
erd
elet
ing
firm
sth
atd
on
ot
hav
ed
ata
on
regre
ssio
nv
aria
ble
sfo
ral
l1
6q
uar
ters
pri
or
toth
eb
ank
rup
tcy
qu
arte
r1
28
5,3
27
Aft
erre
tain
ing
am
axim
um
of
24
qu
arte
rsp
rio
rto
ban
kru
ptc
yq
uar
ter,
ifav
aila
ble
12
82
,803
Ban
kru
pt
firm
sM
atch
edfi
rms
Tes
to
fd
iffe
ren
ce
Mea
nS
DM
edia
nM
ean
SD
Med
ian
Mea
nP
r[
|t|
(B)
Des
crip
tive
stat
isti
cson
128
ban
kru
pt
firm
san
dth
eir
corr
espondin
gm
atch
edfi
rms
atth
eti
me
mat
chin
gfi
rms
are
chose
n,
whic
his
two
yea
rsbef
ore
the
ban
kru
ptc
y
fili
ng
qu
arte
r
DD
ED
ICA
TE
Dit
-0
.145
2.5
57
0.0
00
-0
.028
2.7
01
0.0
02
-0
.117
0.7
17
DE
DIC
AT
ED
it-
it1
0.0
04
9.6
87
6.7
46
8.2
35
8.7
56
6.0
15
1.7
69
0.1
22
BK
PR
OB
it-
10
.145
0.4
27
0.0
35
0.1
70
0.6
06
0.0
35
-0
.025
0.1
73
MO
Mit
0.0
08
0.4
05
-0
.012
0.0
61
0.3
66
0.0
48
-0
.052
0.2
24
MO
Mit
-1
-0
.011
0.3
91
-0
.075
-0
.063
0.2
92
-0
.071
0.0
52
0.1
47
MO
Mit
-4,t
-2
-0
.077
0.4
13
-0
.168
-0
.019
0.6
11
-0
.078
-0
.059
0.2
99
TV
OL
it-
10
.377
1.2
76
0.0
88
0.0
96
0.1
83
0.0
50
0.2
82
0.0
15
PR
ICE
it1
1.3
33
11
.88
56
.938
11
.75
51
2.1
82
6.9
53
-0
.423
0.7
31
MV
it-
16
63
.25
62
,737
.195
12
0.0
98
94
8.3
86
5,3
52
.229
69
.76
6-
28
5.1
30
0.5
91
S. Ramalingegowda
123
Ta
ble
1co
nti
nu
ed
Ban
kru
pt
firm
sM
atch
edfi
rms
Tes
to
fd
iffe
ren
ce
Mea
nS
DM
edia
nM
ean
SD
Med
ian
Mea
nP
r[
|t|
BM
it-
11
.078
1.4
46
0.9
06
0.9
44
0.8
57
0.7
54
0.1
35
0.3
24
UE
it-
1-
0.0
19
0.0
79
-0
.002
0.0
15
0.2
16
0.0
00
-0
.034
0.1
26
SP
RA
NK
it-
12
.727
2.3
90
3.0
00
3.2
89
2.4
37
4.0
00
-0
.563
0.0
61
YIE
LD
it-
10
.007
0.0
22
0.0
00
0.0
19
0.0
59
0.0
00
-0
.012
0.0
25
Mea
nS
DM
edia
n
(C)
Des
crip
tive
stat
isti
cson
port
foli
och
arac
teri
stic
sof
ded
icat
edin
stit
uti
ons
SIZ
Ejt
6,0
43
.84
26
,47
1.0
28
69
.28
nF
IRM
Sjt
25
1.6
04
46
.72
10
4.6
9
AG
Eji
15
.54
16
.63
9.0
0
(B):
Su
bsc
rip
t‘‘
t-1
’’re
fers
toth
eq
uar
ter
mat
chin
gfi
rms
are
cho
sen
(i.e
.,2
yea
rsb
efo
reth
eb
ank
rup
tcy
fili
ng
qu
arte
r).D
DE
DIC
AT
ED
it=
DE
DIC
AT
ED
it-
DE
DI-
CA
TE
Dit
-1,
wher
eD
ED
ICA
TE
Dit
isth
eag
gre
gat
ep
erce
nta
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
nd
arq
uar
ter
tb
yin
stit
uti
on
alin
ves
tors
clas
sifi
edas
‘‘ded
icat
ed’’
inst
itu
tio
ns
by
Bu
shee
(20
01).
DE
DIC
AT
ED
it-1
isth
eag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
t-1
by
inst
ituti
onal
inves
tors
clas
sifi
edas
‘‘ded
icat
ed’’
inst
ituti
ons
by
Bush
ee(2
00
1).
BK
PR
OB
it-1
isth
epro
bab
ilit
yof
ban
kru
ptc
y(i
nper
centa
ge,
per
Shum
way
20
01
Tab
leV
IP
anel
b)
of
firm
iat
end
of
qu
arte
rt-
1.
MO
Mit
isth
eb
uy
-and
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt.
MO
Mit
-1is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt-
1.
MO
Mit
-4,t
-2is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rst-
4to
t-2
.T
VO
Lit
-1is
the
aver
age
of
mon
thly
trad
ing
vo
lum
ed
ivid
edb
ysh
ares
ou
tsta
nd
ing
of
firm
iin
qu
arte
rt-
1.
PR
ICE
itis
the
pri
cep
ersh
are
of
firm
iat
the
end
of
qu
arte
rt.
MV
it-1
isth
eto
tal
mar
ket
capit
aliz
atio
nof
the
com
mon
stock
of
firm
iat
the
end
of
quar
ter
t-1.
BM
it-1
isth
era
tio
of
com
mo
nb
oo
keq
uit
yto
tota
lm
arket
capit
aliz
atio
nof
firm
iat
the
end
of
quar
ter
t-1.U
Eit
-1is
the
earn
ing
ssu
rpri
seb
ased
on
ase
aso
nal
ran
do
mw
alk
div
ided
by
tota
las
sets
of
firm
ifo
rth
eea
rnin
gs
of
qu
arte
rt-
1.
SP
RA
NK
it-1
isth
eS
&P
com
mon
sto
ckra
nk
ing
(7=
A?���
0=
ifst
ock
isnot
rate
dor
isin
reorg
aniz
atio
nor
liquid
atio
n)
of
firm
iat
the
end
of
quar
ter
t-1.
YIE
LD
it-1
isth
ed
ivid
end
of
firm
ifo
rth
ep
ast
yea
rd
ivid
edb
yM
Vit
-1
(C):
Port
foli
och
arac
teri
stic
so
fth
e144
ded
icat
edin
stit
uti
ons
exam
ined
inth
isst
udy.
SIZ
Ejt
isth
ed
oll
arv
alue
(in
mil
lion
s)o
feq
uit
yp
ort
foli
oo
fd
edic
ated
inst
itu
tio
nj
at
end
of
cale
ndar
quar
ter
t.nF
IRM
Sjt
isth
enum
ber
of
firm
shel
din
the
port
foli
oof
ded
icat
edin
stit
uti
on
jat
end
of
cale
ndar
quar
ter
t.A
GE
jiis
the
nu
mb
ero
fca
len
dar
qu
arte
rsfi
rmi
ish
eld
ind
edic
ated
inst
itu
tio
nj’
sp
ort
foli
o
Evidence from impending bankrupt firms
123
differences in quarters other than the matching quarter, my regression analyses
control for differences between bankrupt and matched firms with respect to
variables that affect institutional trading.
Panel C of Table 1 provides descriptive statistics on dedicated institutions’ equity
portfolio. Although there are 170 dedicated institutions according to Bushee’s
classification (see Sect. 3.1), this table focuses on the 144 dedicated institutions that
hold shares in any of the 128 bankrupt firms examined in this study. The mean
dollar value of dedicated institutions’ equity portfolio is US $6,043.83 million. The
mean number of firms held is approximately 252. The mean number of quarters a
firm is held is 15.54 quarters.
Table 2 reports the univariate pattern of aggregate percentage ownership before
the bankruptcy quarter by dedicated institutions in bankrupt and matched firms.
Columns 1 and 2 report the pattern for the full sample of bankrupt and their matched
firms, respectively. Columns 3 and 4 pertain to 48 bankrupt firms that are liquidated
or reorganized with positive equity to pre-petition shareholders and to their matched
firms, respectively. Columns 5 and 6 relate to 58 bankrupt firms that are liquidated
or reorganized with zero equity to pre-petition shareholders, and to their matched
firms, respectively.20 The results indicate that dedicated institutions decrease their
ownership over the 12 quarters by 30 % (from 10.17 to 7.11 %) in an average
bankrupt firm and by 44 % in an average bankrupt firm with zero equity. Note that
the figures corresponding to QUARTER = -1 are the ownership level at the end of
the quarter prior to the bankruptcy quarter and not the final ownership level (prior to
the bankruptcy date), which is likely to be significantly lower. Because institutional
investor data is available only on a quarterly basis, I cannot observe the intensified
selling that occurs within the bankruptcy quarter prior to the bankruptcy date (the
median distance between the start of the bankruptcy quarter and the bankruptcy date
is 47 days). Nevertheless, the pattern in Table 2 is consistent with dedicated
institutions selling more shares of bankrupt firms than of matched firms ahead of the
bankruptcy filing quarter, especially in the two quarters prior to the bankruptcy
quarter. One possible explanation of why dedicated institutions may not sell
intensively earlier than two quarters, simply based on financial distress, is that
dedicated institutions are generally contrarian investors, investing in poorly
performing firms (Bushee 2001; Ke and Petroni 2004). Dedicated institutions are
likely to buy or hold poorly performing stocks unless they have private information
that those firms are headed for bankruptcy, in which case they are likely to sell.
Table 3 Panel A reports regression results in support of hypothesis 1.21 The
results from estimating Model 1 is reported in Column 1. My main variables of
interest are QTR1 and QTR2. The coefficient on QTR1 is significantly negative,
consistent with dedicated institutions selling more shares of impending bankrupt
20 Information on the ultimate equity position is unavailable for the remaining 22 bankrupt firms mainly
because these cases are not yet resolved or because documentation of resolution, if any, is unavailable.21 In all tables, standard errors are clustered by firm. Further, inferences made from all tables are robust
to the deletion of outliers according to the DFITS influence statistic or the R-student ratio.
S. Ramalingegowda
123
Ta
ble
2P
atte
rnof
aggre
gat
eper
centa
ge
ow
ner
ship
by
ded
icat
edin
stit
uti
ons
inim
pen
din
gban
kru
pt
firm
san
dm
atch
edfi
rms
QU
AR
TE
RD
ED
ICA
TE
Dit
DE
DIC
AT
ED
itD
ED
ICA
TE
Dit
(1)
(2)
(3)
(4)
(5)
(6)
All
ban
kru
pt
firm
s(n
=1
28
)
All
mat
ched
firm
s(n
=1
28
)
Ban
kru
pt
firm
sw
ith
po
siti
ve
equ
ity
(n=
48
)
Mat
ched
firm
sfo
rb
ank
rup
tfi
rms
wit
hp
osi
tiv
eeq
uit
y(n
=4
8)
Ban
kru
pt
firm
sw
ith
zero
equ
ity
(n=
58
)
Mat
ched
firm
sfo
rb
ank
rup
t
firm
sw
ith
zero
equ
ity
(n=
58
)
-1
7.1
18
.66
8.7
38
.14
5.8
89
.43
-2
8.2
48
.47
8.8
58
.11
8.2
18
.95
-3
8.6
98
.49
9.3
08
.20
9.0
09
.11
-4
9.2
87
.97
9.8
97
.79
9.7
38
.48
-5
9.8
38
.06
10
.54
7.8
31
0.3
28
.59
-6
9.6
08
.09
10
.00
7.9
21
0.4
68
.66
-7
9.7
17
.96
10
.02
7.6
51
0.3
78
.58
-8
9.8
68
.21
10
.40
7.2
31
0.3
49
.25
-9
10
.00
8.2
41
0.7
36
.73
9.9
89
.45
-1
09
.82
8.0
51
0.6
46
.85
9.6
48
.95
-1
11
0.1
38
.08
10
.56
6.8
31
0.3
38
.91
-1
21
0.1
77
.80
10
.52
6.6
71
0.5
08
.55
Th
eta
ble
rep
ort
sth
ep
atte
rno
fag
gre
gat
ep
erce
nta
ge
ow
ner
ship
(sh
ares
hel
das
ap
erce
nta
ge
of
shar
eso
uts
tan
din
g)
by
ded
icat
edin
stit
uti
on
sin
all
ban
kru
pt
and
thei
r
mat
ched
dis
tres
sed
firm
s,a
sub
sam
ple
of
ban
kru
pt
firm
sth
atu
ltim
atel
yar
eli
qu
idat
edo
rre
org
aniz
edw
ith
po
siti
veeq
uit
yto
pre
-pet
itio
nsh
areh
old
ers
and
thei
rm
atch
ed
dis
tres
sed
firm
s,an
dsu
bsa
mp
leo
fb
ank
rup
tfi
rms
that
ult
imat
ely
are
liq
uid
ated
or
reo
rgan
ized
wit
hze
roeq
uit
yto
pre
-pet
itio
nsh
areh
old
ers,
and
thei
rm
atch
edd
istr
esse
d
firm
s.Q
UA
RT
ER
isth
eq
uar
ter
rela
tiv
eto
the
ban
kru
ptc
yq
uar
ter.
DE
DIC
AT
ED
itis
the
aggre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
tb
y
inst
itu
tio
nal
inv
esto
rscl
assi
fied
as‘‘
ded
icat
ed’’
inst
itu
tion
sb
yB
ush
ee(2
00
1)
Evidence from impending bankrupt firms
123
firms than matched distressed firms based on private information about impending
bankruptcy filings.
Because the dependent variable in Column 1 is the difference between the change
in ownership in bankrupt firms and matched firms, one could argue that the negative
QTR coefficient might reflect share purchases of the matched firms and not
necessarily share sales of the bankrupt firms. To rule this argument out, change in
ownership in bankrupt firms is regressed on the explanatory variables of bankrupt
firms. The results are reported in Column 2. As expected, QTR1 is significantly
negative. With respect to matched firms (Column 3), there is no evidence of share
purchases or sales. Overall, the results in Columns 2 and 3 suggest that the result in
Column 1 is driven by the share sales of impending bankrupt firms and not share
purchases of matched firms.
Regarding control variables, an insignificant coefficient on BKPROBit-1 is not
surprising because many variables used to compute BKPROBit-1 are controlled for
separately in the regressions. BKPROBit-1 becomes significantly negative when I
remove MOMit, MOMit-1, MOMit-4,it-, ln(PRICE)it, ln(MV)it-1, and UEit-1 from the
regression. The coefficients on MOM are negative as expected but insignificant. The
positive coefficient on ln(PRICE)it is consistent with dedicated institutions avoiding
low-priced impending bankrupt firms. Although the coefficient on ln(MV)it-1 is
insignificant, it becomes significantly positive when I exclude ln(PRICE)it from the
regressions, consistent with the prior literature that finds a positive coefficient on
ln(MV)it-1 when ln(PRICE)it is not controlled for in the regressions (e.g., Ke and
Petroni 2004). The insignificant coefficients on many other control variables are
generally consistent with dedicated institutions not relying heavily on financial
variables, as publicly disclosed, in their trading decisions (Bushee 2001). However, in
this study, any trades based on superior processing of public information (including
that of control variables) are likely captured by the QTR variables.
In Table 3 Panel B, I examine whether dedicated institutions anticipate bankruptcies
in periods up to two years before the bankruptcy quarter. Specifically, I include
additional variables representing quarters three and quarter four and year two before the
bankruptcy quarter. In Columns 1 and 2, QTR1 is negative and statistically significant at
the 1 % level (one-tail). With respect to the other QTR variables, in Column 1, QTR2 is
significantly negative only at the 10 % level (one-tail), QTR3 is significantly negative at
the 1 % level (one-tail), and QTR4 is not statistically significant. The marginal
significance on QTR2 and the insignificant coefficient on QTR4 likely reflect the
marginal and insignificant difference in selling across bankrupt (column 2) and matched
firms (column 3). Overall, evidence on individual QTR coefficients indicates that
dedicated institutions have private information about impending bankruptcy filings at
least one quarter in advance of the bankruptcy quarter. I leave it to the reader to evaluate
the significance of selling in QTR2 to QTR4.22
In Hypothesis 2, I expect dedicated institutions to sell more shares of bankrupt firms
that ultimately are liquidated or reorganized with zero equity to pre-petition shareholders
than that of bankrupt firms that ultimately are liquidated or reorganized with positive
22 Untabulated results indicate that the coefficient on QTR1 is significantly more negative than that on
QTR2 at the 5 % level (one-tail) in Columns 1 and 2.
S. Ramalingegowda
123
Ta
ble
3D
edic
ated
inst
ituti
onal
inves
tors
’tr
adin
gbeh
avio
rin
impen
din
gban
kru
pt
firm
s
Pre
dic
ted
sig
ndD
DE
DIC
AT
ED
itP
red
icte
dsi
gn
Col
2,
3
DD
ED
ICA
TE
Dit
(1)
Ban
kru
pt
firm
s
(2)
Mat
ched
firm
s
(3)
(A)
Tra
din
gin
up
totw
oq
ua
rter
sp
rio
rto
ban
kru
ptc
yq
uar
ter
QT
R1
--
1.0
66
(2.6
04)*
**
QT
R1
-,
±-
0.8
92
(2.4
54)*
**
0.1
43
(0.6
87)
QT
R2
--
0.2
85
(1.1
02)
QT
R2
-,
±-
0.2
91
(1.2
58)
0.0
15
(0.0
87)
CO
NT
RO
LS
dln
(BK
PR
OB
) it-
1-
0.0
16
(0.3
18)
ln(B
KP
RO
B) i
t-1
-0
.062
(0.9
78)
-0
.02
3
(0.4
26)
dM
OM
it-
-0
.16
1
(0.7
69)
MO
Mit
--
0.1
35
(0.5
13)
-0
.15
1
(0.7
89)
dM
OM
it-1
--
0.0
64
(0.5
20)
MO
Mit
-1-
0.0
37
(0.2
58)
-0
.26
0
(1.5
65)*
dM
OM
it-4
,t-2
--
0.0
31
(0.3
18)
MO
Mit
-4,t
-2-
0.0
15
(0.1
34)
-0
.11
9
(1.1
76)
dln
(TV
OL
) it-
1±
-0
.13
5
(1.7
06)*
ln(T
VO
L) i
t-1
±-
0.1
68
(1.7
88)*
-0
.04
3
(0.4
96)
dln
(PR
ICE
) it
±0
.508
(2.5
37)*
*
ln(P
RIC
E) i
t±
0.5
18
(2.3
49)*
*
0.1
92
(0.9
78)
dln
(MV
) it-
1?
-0
.10
3
(0.6
51)
ln(M
V) i
t-1
?0
.071
(0.3
43)
-0
.12
9
(0.6
53)
dB
Mit
-1±
-0
.02
2
(2.6
37)*
**
BM
it-1
±-
0.0
11
(1.4
79)
-0
.24
6
(1.0
99)
Evidence from impending bankrupt firms
123
Ta
ble
3co
nti
nu
ed
Pre
dic
ted
sig
ndD
DE
DIC
AT
ED
itP
red
icte
dsi
gn
Co
l2
,3
DD
ED
ICA
TE
Dit
(1)
Ban
kru
pt
firm
s(2
)M
atch
edfi
rms
(3)
dU
Eit
-1±
-0
.216
(1.1
68)
UE
it-1
±-
0.0
23
(0.2
12)
-0
.22
8
(0.9
69)
dY
IEL
Dit
-1±
2.3
28
(3.1
36)*
**
YIE
LD
it-1
±-
1.7
85
(0.7
39)
0.6
38
(1.4
12)
dS
PR
AN
Kit
-1±
0.0
04
(0.0
82)
SP
RA
NK
it-1
±0
.030
(0.5
39)
0.0
36
(1.0
13)
dD
ED
ICA
TE
Dit
-1-
-0
.174
(11
.667
)**
*
DE
DIC
AT
ED
it-1
--
0.1
98
(11
.378
)**
*
-0
.13
6
(7.4
20)*
**
Con
stan
t0
.436
(4.0
00)*
**
Con
stan
t-
2.3
07
(0.6
77)
0.7
03
(0.2
06)
Obse
rvat
ions
2,8
03
Obse
rvat
ions
2,8
03
2,7
38
Ad
just
edR
20
.093
Ad
just
edR
20
.126
0.0
65
Fix
edef
fect
sin
clu
ded
Mat
ched
pai
rF
ixed
effe
cts
incl
ud
edF
irm
and
qu
arte
rF
irm
and
qu
arte
r
Pre
dic
ted
sig
ndD
DE
DIC
AT
ED
itD
DE
DIC
AT
ED
it
(1)
Pre
dic
ted
sig
nC
ol
2,
3B
ank
rup
tfi
rms
(2)
Mat
ched
firm
s(3
)
(B)
Tra
din
gin
up
totw
oye
ars
pri
or
tob
ank
rup
tcy
qu
arte
r
QT
R1
--
1.2
72
(2.9
95)*
**
QT
R1
-,
±-
1.2
62
(3.0
63)*
**
0.0
46
(0.1
67)
QT
R2
--
0.4
48
(1.6
21)*
QT
R2
-,
–-
0.6
19
(2.2
97)*
*
-0
.07
2
(0.2
96)
QT
R3
--
0.9
40
(3.0
66)*
**
QT
R3
-,
±-
0.6
09
(1.9
37)*
*
0.2
95
(1.2
58)
S. Ramalingegowda
123
Ta
ble
3co
nti
nu
ed
Pre
dic
ted
sign
dD
DE
DIC
AT
ED
itD
DE
DIC
AT
ED
it
(1)
Pre
dic
ted
sig
nC
ol
2,
3B
ank
rup
tfi
rms
(2)
Mat
ched
firm
s(3
)
QT
R4
--
0.3
20
(1.1
81)
QT
R4
-,
±-
0.4
85
(1.9
99)*
*
-0
.282
(1.3
16
)
QT
R5
_8
-0
.020
(0.0
96)
QT
R5
_8
-,
±-
0.1
38
(0.6
86)
-0
.187
(1.0
73
)
CO
NT
RO
LS
dln
(BK
PR
OB
) it-
1-
0.0
35
(0.6
91)
ln(B
KP
RO
B) i
t-1
-0
.072
(1.1
33)
-0
.019
(0.3
42
)
dM
OM
it-
-0
.19
4
(0.9
29)
MO
Mit
--
0.1
52
(0.5
78)
-0
.157
(0.8
23
)
dM
OM
it-1
--
0.0
43
(0.3
50)
MO
Mit
-1-
0.0
41
(0.2
83)
-0
.247
(1.4
97
)*
dM
OM
it-4
,t-2
--
0.0
00
(0.0
03)
MO
Mit
-4,t
-2-
0.0
34
(0.2
99)
-0
.113
(1.1
28
)
dln
(TV
OL
) it-
1±
-0
.12
3
(1.4
58)
ln(T
VO
L) i
t-1
±-
0.1
62
(1.7
28)*
-0
.042
(0.4
83
)
dln
(PR
ICE
) it
±0
.458
(2.2
96)*
*
ln(P
RIC
E) i
t±
0.4
88
(2.2
39)*
*
0.1
89
(0.9
69
)
dln
(MV
) it-
1?
-0
.12
4
(0.7
81)
ln(M
V) i
t-1
?0
.058
(0.2
83)
-0
.128
(0.6
53
)
dB
Mit
-1±
-0
.02
1
(2.5
55)*
*
BM
it-1
±-
0.0
11
(1.5
57)
-0
.239
(1.0
61
)
dU
Eit
-1±
-0
.17
4
(0.8
89)
UE
it-1
±-
0.0
04
(0.0
34)
-0
.206
(0.8
89
)
Evidence from impending bankrupt firms
123
Tab
le3
con
tin
ued
Pre
dic
ted
sig
ndD
DE
DIC
AT
ED
itD
DE
DIC
AT
ED
it
(1)
Pre
dic
ted
sig
nC
ol
2,
3B
ank
rup
tfi
rms
(2)
Mat
ched
firm
s(3
)
dY
IEL
Dit
-1±
2.2
97
(3.1
71)*
**
YIE
LD
it-1
±-
1.5
45
(0.6
38)
0.6
83
(1.5
36)
dS
PR
AN
Kit
-1±
0.0
03
(0.0
61)
SP
RA
NK
it-1
±0
.027
(0.4
73)
0.0
36
(1.0
16)
dD
ED
ICA
TE
Dit
-1-
-0
.174
(11
.685
)**
*
DE
DIC
AT
ED
it-1
–-
0.1
98
(11
.457
)**
*
-0
.135
(7.4
09)*
**
Co
nst
ant
0.4
90
(4.0
27)*
**
Con
stan
t-
2.5
72
(0.7
52)
0.5
05
(0.1
48)
Ob
serv
atio
ns
2,8
03
Ob
serv
atio
ns
2,8
03
2,7
38
Ad
just
edR
20
.09
5A
dju
sted
R2
0.1
27
0.0
67
Fix
edef
fect
sin
clu
ded
Mat
ched
pai
rF
ixed
effe
cts
incl
ud
edF
irm
and
qu
arte
rF
irm
and
qu
arte
r
Sta
nd
ard
erro
rsar
ecl
ust
ered
by
firm
and
abso
lute
val
ues
of
t-st
atis
tics
are
report
edbel
ow
coef
fici
ent
esti
mat
es
Th
ep
refi
x‘d
’in
fro
nt
of
av
aria
ble
ind
icat
esth
ed
iffe
ren
ceb
etw
een
the
ban
kru
pt
firm
and
the
mat
ched
firm
var
iab
le.D
DE
DIC
AT
ED
it=
DE
DIC
AT
ED
it-
DE
DI-
CA
TE
Dit
-1,
wher
eD
ED
ICA
TE
Dit
isth
eag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
tb
yin
stit
uti
onal
inves
tors
clas
sifi
edas
‘‘ded
icat
ed’’
inst
itu
tion
sb
yB
ush
ee(2
00
1).
QT
R1
equ
als
on
eif
ded
icat
edin
stit
uti
on
alo
wn
ersh
ipch
ang
em
easu
rem
ent
qu
arte
ris
the
firs
tq
uar
ter
pri
or
toth
eb
ank
rup
tcy
fili
ng
qu
arte
r,ze
roo
ther
wis
e.S
imil
arly
,Q
TR
xeq
ual
sone
ifded
icat
edin
stit
uti
onal
ow
ner
ship
chan
ge
mea
sure
men
tquar
ter
isth
ex
thq
uar
ter
pri
or
toth
eb
ank
rup
tcy
fili
ng
qu
arte
r,ze
roo
ther
wis
e.Q
TR
5_
8eq
ual
so
ne
ifd
edic
ated
inst
itu
tio
nal
ow
ner
ship
chan
ge
mea
sure
men
tq
uar
ter
isan
yq
uar
ter
fro
mq
uar
ters
fiv
eto
eig
ht
pri
or
toth
eb
ank
rup
tcy
fili
ng
qu
arte
r,ze
roo
ther
wis
e.B
KP
RO
Bit
-1is
the
exan
tepro
bab
ilit
yof
ban
kru
ptc
y(i
nper
centa
ge,
per
Shum
way
20
01
Tab
leV
IP
anel
b)
of
firm
iat
end
of
qu
arte
rt-
1.
MO
Mit
isth
eb
uy
-and
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt.
MO
Mit
-1is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt-
1.
MO
Mit
-4,t
-2is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rst-
4to
t-2
.T
VO
Lit
-1is
the
aver
age
of
mo
nth
lytr
adin
gv
olu
me
div
ided
by
shar
eso
uts
tand
ing
of
firm
iin
qu
arte
rt-
1.
PR
ICE
itis
the
pri
cep
ersh
are
of
firm
iat
the
end
of
qu
arte
rt.
MV
it-1
isth
eto
tal
mar
ket
capit
aliz
atio
nof
the
com
mon
stock
of
firm
iat
the
end
of
quar
ter
t-1.
BM
it-1
isth
era
tio
of
com
mon
bo
ok
equ
ity
toto
tal
mar
ket
capit
aliz
atio
nof
firm
iat
the
end
of
quar
ter
t-1.
UE
it-1
isth
eea
rnin
gs
surp
rise
bas
edo
na
seas
on
alra
nd
om
wal
kd
ivid
edb
yto
tal
asse
tso
ffi
rmi
for
the
earn
ing
so
fq
uar
ter
t-1
.S
PR
AN
Kit
-1is
the
S&
Pco
mm
on
sto
ckra
nk
ing
(7=
A?���
0=
ifst
ock
isn
ot
rate
do
ris
inre
org
aniz
atio
no
rli
qu
idat
ion
)o
ffi
rmi
atth
een
do
fq
uar
ter
t-1
.Y
IEL
Dit
-1is
the
div
iden
do
ffi
rmi
for
the
pas
ty
ear
div
ided
by
MV
it-1
.D
ED
ICA
TE
Dit
-1
isth
eag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
t-1
by
inst
ituti
onal
inves
tors
clas
sifi
edas
‘‘ded
icat
ed’’
inst
ituti
ons
by
Bush
ee(2
00
1)
**
*,
**
,*
1,
5,
and
10
%si
gn
ifica
nce
(on
e-ta
iled
ifsi
gn
pre
dic
ted
,tw
o-t
aile
do
ther
wis
e),
resp
ecti
vel
y
S. Ramalingegowda
123
Ta
ble
4C
ross
-sec
tio
nal
var
iati
on
ind
edic
ated
inst
itu
tio
ns’
trad
ing
beh
avio
rin
imp
endin
gb
ank
rup
tfi
rms
bas
edo
nw
het
her
ban
kru
pt
firm
sar
eu
ltim
atel
yli
qu
idat
edo
r
reo
rgan
ized
wit
hp
osi
tive
equ
ity
or
zero
equ
ity
top
re-p
etit
ion
shar
eho
lder
s
Pre
d.
sig
ndD
DE
DIC
AT
ED
itP
red
.
sign
Co
l2
,3
DD
ED
ICA
TE
Dit
(1)
Ban
kru
pt
firm
s
(2)
Mat
ched
firm
s
(3)
QT
R1
±0
.126
(0.3
13)
QT
R1
±0
.19
5
(0.6
27
)
-0
.09
9
(0.3
85)
QT
R2
±-
0.1
77
(0.4
94)
QT
R2
±-
0.3
16
(1.1
44
)
0.1
25
(0.4
09)
QT
R1
9Z
ER
OE
Qi
--
2.6
78
(3.4
93)*
**
QT
R1
9Z
ER
OE
Qi
-,
±-
2.3
40
(3.6
14
)**
*
0.5
99
(1.7
44)*
QT
R2
9Z
ER
OE
Qi
--
0.3
07
(0.4
89)
QT
R2
9Z
ER
OE
Qi
-,
±-
0.2
40
(0.5
44
)
-0
.32
6
(0.8
53)
CO
NT
RO
LS
dln
(BK
PR
OB
) it-
1-
-0
.033
(0.6
00)
ln(B
KP
RO
B) i
t-1
--
0.0
13
(0.1
80
)
-0
.04
4
(0.6
93)
dM
OM
it-
-0
.124
(0.5
14)
MO
Mit
--
0.1
89
(0.5
95
)
-0
.06
6
(0.2
78)
dM
OM
it-1
-0
.002
(0.0
13)
MO
Mit
-1-
0.1
24
(0.7
42
)
-0
.43
7
(2.0
04)*
*
dM
OM
it-4
,t-2
--
0.0
49
(0.4
79)
MO
Mit
-4,t
-2-
-0
.011
(0.0
95
)
-0
.13
8
(1.1
27)
dln
(TV
OL
) it-
1±
-0
.218
(2.3
18)*
*
ln(T
VO
L) i
t-1
±-
0.1
50
(1.3
57
)
-0
.13
4
(1.4
34)
Evidence from impending bankrupt firms
123
Ta
ble
4co
nti
nu
ed
Pre
d.
sig
ndD
DE
DIC
AT
ED
itP
red
.
sig
n
Col
2,
3
DD
ED
ICA
TE
Dit
(1)
Ban
kru
pt
firm
s
(2)
Mat
ched
firm
s
(3)
dln
(PR
ICE
) it
±0
.562
(2.6
67)*
**
ln(P
RIC
E) i
t±
0.6
44
(2.7
13)*
**
0.1
04
(0.4
52)
dln
(MV
) it-
1?
-0
.21
5
(1.2
51)
ln(M
V) i
t-1
?-
0.1
62
(0.7
34)
-0
.00
4
(0.0
17)
dB
Mit
-1±
-0
.01
8
(1.6
48)
BM
it-1
±-
0.0
12
(1.3
22)
-0
.06
2
(0.3
90)
dU
Eit
-1±
-0
.28
0
(1.4
80)
UE
it-1
±-
0.1
54
(1.5
49)
-0
.07
6
(0.3
17)
dY
IEL
Dit
-1±
2.1
08
(2.9
91)*
**
YIE
LD
it-1
±-
3.0
32
(1.4
55)
0.4
04
(0.8
92)
dS
PR
AN
Kit
-1±
0.0
25
(0.4
66)
SP
RA
NK
it-1
±0
.051
(0.9
37)
0.0
19
(0.5
15)
dD
ED
ICA
TE
Dit
-1-
-0
.16
9
(10
.957
)**
*
DE
DIC
AT
ED
it-1
--
0.1
89
(9.4
11)*
**
-0
.13
5
(6.6
29)*
**
Con
stan
t0
.677
(5.6
71)*
**
Co
nst
ant
1.1
07
(0.3
04)
-2
.15
9
(0.5
60)
Obse
rvat
ions
2,3
37
Obse
rvat
ions
2,3
37
2,2
85
Ad
just
edR
20
.098
Ad
just
edR
20
.134
0.0
60
S. Ramalingegowda
123
Ta
ble
4co
nti
nu
ed
Pre
d.
sign
dD
DE
DIC
AT
ED
itP
red
.
sign
Co
l2
,3
DD
ED
ICA
TE
Dit
(1)
Ban
kru
pt
firm
s
(2)
Mat
ched
firm
s
(3)
Fix
edef
fect
sin
cluded
Mat
ched
pai
rF
ixed
effe
cts
incl
uded
Fir
man
dquar
ter
Fir
man
dquar
ter
QT
R1
?Q
TR
19
ZE
RO
EQ
pv
alu
e(o
ne-
tail
)
--
2.5
52
(0.0
0)
QT
R1
?Q
TR
19
ZE
RO
EQ
pv
alu
e(o
ne-
tail
)
--
2.1
45
(0.0
0)
Sta
nd
ard
erro
rsar
ecl
ust
ered
by
firm
and
abso
lute
val
ues
of
t-st
atis
tics
are
rep
ort
edb
elo
wco
effi
cien
tes
tim
ates
Th
ep
refi
x‘d
’in
fron
to
fa
var
iab
lein
dic
ates
the
dif
fere
nce
bet
wee
nth
eb
ank
rup
tfi
rman
dth
em
atch
edfi
rmv
aria
ble
.D
DE
DIC
AT
ED
it=
DE
DIC
AT
ED
it-
DE
DI-
CA
TE
Dit
-1;
wher
eD
ED
ICA
TE
Dit
isth
eag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
tb
yin
stit
uti
onal
inves
tors
clas
sifi
edas
‘‘ded
icat
ed’’
inst
itu
tio
ns
by
Bu
shee
(20
01).
QT
R1
equ
als
on
eif
ded
icat
edin
stit
uti
on
alo
wn
ersh
ipch
ang
em
easu
rem
ent
qu
arte
ris
the
firs
tq
uar
ter
pri
or
toth
eb
ank
rup
tcy
fili
ng
qu
arte
r,
zero
oth
erw
ise.
QT
R2
equ
als
on
eif
ded
icat
edin
stit
uti
on
alo
wn
ersh
ipch
ang
em
easu
rem
ent
qu
arte
ris
the
seco
nd
qu
arte
rp
rio
rto
the
ban
kru
ptc
yfi
lin
gq
uar
ter,
zero
oth
erw
ise.
ZE
RO
EQ
ieq
ual
so
ne
for
ban
kru
pt
firm
sth
atu
ltim
atel
yar
eli
qu
idat
edo
rre
org
aniz
edw
ith
zero
equ
ity
top
re-p
etit
ion
shar
eho
lder
san
dze
rofo
rb
ank
rup
tfi
rms
that
ult
imat
ely
are
liq
uid
ated
or
reorg
aniz
edw
ith
po
siti
veeq
uit
yto
pre
-pet
itio
nsh
areh
old
ers.
BK
PR
OB
it-1
isth
eex
ante
pro
bab
ilit
yof
ban
kru
ptc
y(i
nper
centa
ge,
per
Sh
um
way
20
01
Tab
leV
IP
anel
b)
of
firm
iat
end
of
qu
arte
rt-
1.
MO
Mit
isth
eb
uy
-and
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt.
MO
Mit
-1is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt-
1.
MO
Mit
-4,t
-2is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rst-
4to
t-2
.T
VO
Lit
-1is
the
aver
age
of
mon
thly
trad
ing
vo
lum
ed
ivid
edb
ysh
ares
ou
tsta
nd
ing
of
firm
iin
qu
arte
rt-
1.
PR
ICE
itis
the
pri
cep
ersh
are
of
firm
iat
the
end
of
qu
arte
rt.
MV
it-1
isth
eto
tal
mar
ket
capit
aliz
atio
nof
the
com
mon
stock
of
firm
iat
the
end
of
qu
arte
rt-
1.
BM
it-1
isth
era
tio
of
com
mon
bo
ok
equ
ity
toto
tal
mar
ket
cap
ital
izat
ion
of
firm
iat
the
end
of
qu
arte
rt-
1.
UE
it-1
isth
eea
rnin
gs
surp
rise
bas
edo
na
seas
on
alra
nd
om
wal
kd
ivid
edb
yto
tal
asse
tso
ffi
rmi
for
the
earn
ings
of
qu
arte
rt-
1.
SP
RA
NK
it-1
isth
eS
&P
com
mon
stock
ran
kin
g(7
=A
?���
0=
ifst
ock
isn
ot
rate
do
r
isin
reorg
aniz
atio
no
rli
quid
atio
n)
of
firm
iat
the
end
of
quar
ter
t-1.Y
IEL
Dit
-1is
the
div
iden
do
ffi
rmi
for
the
pas
ty
ear
div
ided
by
MV
it-1
.D
ED
ICA
TE
Dit
-1is
the
agg
reg
ate
per
cen
tag
eo
wn
ersh
ipo
ffi
rmi
atth
een
do
fca
len
dar
qu
arte
rt-
1b
yin
stit
uti
on
alin
ves
tors
clas
sifi
edas
‘‘d
edic
ated
’’in
stit
uti
on
sb
yB
ush
ee(2
00
1)
***,
**,
*1
,5
,an
d10
%si
gnifi
cance
(one-
tail
edif
sign
pre
dic
ted,
two-t
aile
doth
erw
ise)
,re
spec
tivel
y
Evidence from impending bankrupt firms
123
equity to pre-petition shareholders. Table 4 Column 1 reports the regression results of
Model 2. The main variables of interest are QTR1 9 ZEROEQi and QTR2 9 ZEROEQi.
As expected, QTR1 9 ZEROEQi is significantly negative, consistent with dedicated
institutions selling more shares of bankrupt firms that result in zero equity than those that
result in positive equity to pre-petition shareholders. Table 4 Columns 2 and 3 report
regression results for bankrupt and matched firms separately. The results in Column 2 are
similar to those in Column 1, suggesting that the differential share sales observed in
Column 1 is driven by share sales of impending bankrupt firms and not by share purchases
of matched firms. Overall, the results are consistent with dedicated institutions having and
trading on private information on impending bankruptcies.23
Table 5 Hypothetical capital losses dedicated institutions avoid by selling shares in impending bankrupt
firms
Quarter x relative to
bankruptcy quarter
Mean difference between (1) abnormal buy-and-hold return from buying
shares at the beginning of Quarter x and selling shares at the end of the
quarter immediately prior to the bankruptcy announcement date and (2)
abnormal buy-and-hold return from buying shares at the beginning of Quarter
x and selling shares at the bankruptcy announcement date (cross-sectional t-
statistics in parentheses)
All bankrupt
firms n = 128
Bankrupt firms with
positive equity n = 48
Bankrupt firms with zero
equity n = 58
-12 19.25 %
(8.38)
15.57 %
(5.09)
20.80 %
(5.41)
-8 19.79 %
(9.27)
14.71 % 20.14 %
(6.42)(5.98)
-4 23.77 %
(9.75)
19.15 %
(6.12)
25.70 %
(5.98)
-3 29.13 %
(8.87)
21.51 %
(5.54)
34.40 %
(5.70)
-2 31.36 %
(9.97)
26.65 %
(4.51)
32.84 %
(8.18)
-1 35.15 %
(10.81)
28.07 %
(5.28)
38.86 %
(8.56)
For each bankrupt firm, I compute the difference between (1) abnormal buy-and-hold return from buying
shares at the beginning of Quarter x and selling shares at the end of the quarter immediately prior to the
bankruptcy announcement date, and (2) abnormal buy-and-hold return from buying shares at the
beginning of Quarter x and selling shares at the bankruptcy announcement date. A positive difference
between (1) and (2) indicates the loss dedicated institutions avoid by selling bankrupt firms at the end of
the quarter immediately prior to the bankruptcy announcement date versus at the bankruptcy
announcement date. Each cell in the table reports the cross-sectional mean difference between (1) and (2)
along with the cross-sectional t-statistics (in parenthesis). Abnormal returns are calculated relative to the
CRSP equal weighted index
23 In an untabulated analysis, I mitigate the concern that share sales by dedicated institutions may be
capturing some unknown omitted factors that drive their trading behavior. Specifically, I find that
dedicated institutions sell more shares than other less informed long horizon institutions do (i.e., quasi-
indexer institutions), consistent with dedicated institutions selling based on their private information and
not unknown omitted factors that drive trades by long horizon institutions in general.
S. Ramalingegowda
123
The documented sales, based on private information, in the quarter before
bankruptcy filing are economically significant. Because the mean dedicated
institutional ownership in bankrupt firms with zero equity at the beginning of the
quarter prior to the bankruptcy quarter is 8.21 % and the mean quarter end market
capitalization of these firms in the quarter before the bankruptcy quarter is US
$789.16 million, the -2.145 % (-2.340 ? 0.195) figure in Table 4 Column 2 is
26.13 % (2.145/8.21) of dedicated institutions’ total ownership and US $16.92
million (2.145 %*789.16) million in value on a firm basis, on average.
Table 5 reports the potential capital losses that dedicated institutions avoid by
selling impending bankrupt firms at the end of the quarter immediately prior to the
bankruptcy announcement date versus at the bankruptcy announcement date.
Specifically, for each bankrupt firm, I compute the difference between (1) an
abnormal buy-and-hold return from buying shares at the beginning of Quarter x and
selling shares at the end of the quarter immediately prior to the bankruptcy
announcement date and (2) an abnormal buy-and-hold return from buying shares at
the beginning of Quarter x and selling shares at the bankruptcy announcement
date.24 A positive difference between (1) and (2) indicates the loss that institutions
avoid by selling bankrupt firms at the end of the quarter immediately before the
bankruptcy announcement date versus at the bankruptcy announcement date. Each
cell in Table 5 reports the cross-sectional mean difference between (1) and (2) along
with the cross-sectional t-statistics (in parenthesis). Column 1 reports the pattern for
the full sample of 128 bankrupt firms. Column 2 pertains to 48 positive equity firms
and Column 3 relates to 58 zero equity firms. The evidence indicates that the
abnormal loss avoided is economically significant. For example, for purchases made
3 years before the bankruptcy quarter, the mean abnormal loss avoided by selling at
the end of the quarter immediately prior to the bankruptcy announcement date
versus at the bankruptcy announcement date is 20.80 % for an average bankrupt
firm that results in zero equity.
5 Additional analyses
5.1 Variation in dedicated institutions’ trading behavior based on institutions’
length of holdings and magnitude of stakes in impending bankrupt firms
Although it is difficult to observe how dedicated institutions obtain private
information about impending bankruptcies, this subsection sheds some light on this
issue. As detailed in Sect. 2, dedicated institutions’ information advantage might
arise from their long-term positions and large stakes in the firms they hold. To
examine whether dedicated institutions’ information advantage arises from their
long-term positions in the bankrupt firms, I split dedicated institutions into two
groups based on the length of their holdings in the bankrupt firms one year before
24 Abnormal returns are calculated relative to the CRSP equal weighted index. Specifically, an abnormal
return for each firm is the buy-and-hold raw return minus buy-and-hold CRSP equal weighted index
return.
Evidence from impending bankrupt firms
123
Ta
ble
6V
aria
tio
nin
ded
icat
edin
stit
uti
on
s’tr
adin
gb
ehav
ior
bas
edo
nth
eir
len
gth
of
ho
ldin
gs
and
mag
nit
ud
eo
fst
akes
inim
pen
din
gb
ank
rup
tfi
rms
Sig
ndD
DE
DIC
AT
ED
_
LE
NG
TH
it
dD
DE
DIC
AT
ED
_
BL
OC
Kit
(1)
(2)
QT
R1
±0
.31
1
(0.8
87
)
0.1
94
(0.5
91)
QT
R2
±0
.20
6
(0.6
22
)
0.1
16
(0.2
88)
QT
R1
9Z
ER
OE
Qi
±-
0.5
70
(1.0
73
)
-0
.77
5
(1.4
92)
QT
R2
9Z
ER
OE
Qi
±-
0.3
62
(0.6
23
)
-0
.24
5
(0.3
80)
QT
R1
9L
Ti
±-
0.4
81
(1.1
86
)
QT
R2
9L
Ti
±-
0.6
10
(1.2
83
)
QT
R1
9Z
ER
OE
Qi
9L
Ti
--
1.5
32
(2.1
30
)**
QT
R2
9Z
ER
OE
Qi
9L
Ti
-0
.46
1
(0.6
44
)
QT
R1
9T
OP
5i
±-
0.2
43
(0.6
48)
QT
R2
9T
OP
5i
±-
0.4
29
(0.8
30)
QT
R1
9Z
ER
OE
Qi
9T
OP
5i
--
1.1
20
(1.7
07)*
*
S. Ramalingegowda
123
Ta
ble
6co
nti
nu
ed
Sig
ndD
DE
DIC
AT
ED
_
LE
NG
TH
it
dD
DE
DIC
AT
ED
_
BL
OC
Kit
(1)
(2)
QT
R2
9Z
ER
OE
Qi
9T
OP
5i
-0
.230
(0.3
06)
CO
NT
RO
LS
dln
(BK
PR
OB
) it-
1-
-0
.015
(0.4
43
)
-0
.01
5
(0.4
75)
dM
OM
it-
-0
.062
(0.4
44
)
-0
.06
2
(0.4
56)
dM
OM
it-1
-0
.00
3
(0.0
31
)
0.0
03
(0.0
36)
dM
OM
it-4
,t-2
--
0.0
23
(0.3
71
)
-0
.02
3
(0.3
90)
dln
(TV
OL
) it-
1±
-0
.100
(1.9
62
)*
-0
.09
8
(1.9
43)*
dln
(PR
ICE
) it
±0
.27
3
(1.9
97
)**
0.2
71
(2.0
75)*
*
dln
(MV
) it-
1?
-0
.161
(1.2
14
)
-0
.16
1
(1.2
75)
dB
Mit
-1±
-0
.009
(1.5
67
)
-0
.00
9
(1.7
24)*
dU
Eit
-1±
-0
.137
(1.5
03
)
-0
.13
6
(1.5
87)
Evidence from impending bankrupt firms
123
Ta
ble
6co
nti
nu
ed
Sig
ndD
DE
DIC
AT
ED
_
LE
NG
TH
it
dD
DE
DIC
AT
ED
_
BL
OC
Kit
(1)
(2)
dY
IEL
Dit
-1±
1.0
93
(1.8
85
)*
1.1
00
(2.3
84)*
*
dS
PR
AN
Kit
-1±
0.0
10
(0.3
71
)
0.0
10
(0.3
71)
dD
ED
ICA
TE
D_X
it-1
(X=
LE
NG
TH
,o
rB
LO
CK
)
--
0.1
43
(12
.99
5)*
**
-0
.13
9
(12
.882
)**
*
Con
stan
t0
.30
0
(3.7
00
)**
*
0.2
94
(3.9
13)*
**
Ob
serv
atio
ns
4,6
74
4,6
74
Ad
just
edR
20
.08
30
.080
Fix
edef
fect
sin
cluded
Mat
ched
pai
rM
atch
edpai
r
Tes
to
fsu
mo
fco
effi
cien
tsQ
TR
19
LT
?Q
TR
19
ZE
RO
EQ
9L
T
QT
R1
xT
OP
5?
QT
R1
9
ZE
RO
EQ
9T
OP
5
Su
mo
fco
effi
cien
ts
pv
alu
e(o
ne-
tail
)
--
2.0
13
(0.0
0)
-1
.36
3
(0.0
2)
Tes
to
fsu
mo
fco
effi
cien
tsQ
TR
1?
QT
R1
9Z
ER
OE
Q?
QT
R1
9L
T?
QT
R1
9
ZE
RO
EQ
9L
T
QT
R1
?Q
TR
19
ZE
RO
EQ
?
QT
R1
xT
OP
5?
QT
R1
9
ZE
RO
EQ
9T
OP
5
S. Ramalingegowda
123
Ta
ble
6co
nti
nu
ed
Sig
ndD
DE
DIC
AT
ED
_
LE
NG
TH
it
dD
DE
DIC
AT
ED
_
BL
OC
Kit
(1)
(2)
Su
mo
fco
effi
cien
ts
pv
alu
e(o
ne-
tail
)
--
2.2
72
(0.0
0)
-1
.94
4
(0.0
0)
Sta
nd
ard
erro
rsar
ecl
ust
ered
by
firm
and
abso
lute
val
ues
of
t-st
atis
tics
are
rep
ort
edb
elo
wco
effi
cien
tes
tim
ates
Th
ep
refi
x‘d
’in
fro
nt
of
av
aria
ble
indic
ates
the
dif
fere
nce
bet
wee
nth
eb
ank
rup
tfi
rman
dth
em
atch
edfi
rmv
aria
ble
.D
DE
DIC
AT
ED
_L
EN
GT
Hit
isth
ech
ang
ein
agg
reg
ate
per
cen
tag
eo
wn
ersh
ipo
ffi
rmi
over
cale
ndar
quar
ter
tb
yD
ED
ICA
TE
D_L
To
rD
ED
ICA
TE
D_L
TO
TH
.D
ED
ICA
TE
D_L
Tit
isth
eag
gre
gat
ep
erce
nta
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
tb
y‘‘
ded
icat
ed’’
inst
ituti
ons
that
hold
the
ban
kru
pt
firm
for
atle
ast
ayea
ren
din
g1
yea
rbef
ore
the
ban
kru
ptc
yfi
ling
quar
ter.
DE
DI-
CA
TE
D_
LT
OT
Hit
isth
eag
gre
gat
ep
erce
nta
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
nd
arq
uar
ter
tb
y‘‘
ded
icat
ed’’
inst
itu
tio
ns
no
tcl
assi
fied
asD
ED
ICA
TE
D_L
Tit.
DD
ED
ICA
TE
D_B
LO
CK
itis
the
chan
ge
inag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iover
cale
ndar
quar
ter
tb
yD
ED
ICA
TE
D_T
OP
5o
rD
ED
ICA
TE
D_T
OP
5O
TH
.
DE
DIC
AT
ED
_T
OP
5it
isth
eag
gre
gat
eper
centa
ge
ow
ner
ship
of
firm
iat
the
end
of
cale
ndar
quar
ter
tb
y‘‘
ded
icat
ed’’
inst
ituti
ons
that
are
among
the
five
larg
est
shar
eho
lder
sin
the
ban
kru
pt
firm
1y
ear
bef
ore
the
ban
kru
ptc
yfi
lin
gq
uar
ter.
DE
DIC
AT
ED
_T
OP
5O
TH
itis
the
agg
reg
ate
per
cen
tag
eo
wn
ersh
ipo
ffi
rmi
atth
een
do
f
cale
ndar
quar
ter
tb
y‘‘
ded
icat
ed’’
inst
ituti
ons
not
clas
sifi
edas
DE
DIC
AT
ED
_T
OP
5it.Q
TR
1eq
ual
so
ne
ifo
wn
ersh
ipch
ang
em
easu
rem
ent
qu
arte
ris
the
firs
tq
uar
ter
pri
or
to
the
ban
kru
ptc
yfi
ling
quar
ter,
zero
oth
erw
ise.
QT
R2
equ
als
on
eif
ow
ner
ship
chan
ge
mea
sure
men
tq
uar
ter
isth
ese
con
dq
uar
ter
pri
or
toth
eb
ank
rup
tcy
fili
ng
qu
arte
r,ze
ro
oth
erw
ise.
ZE
RO
EQ
ieq
ual
so
ne
for
ban
kru
pt
firm
sth
atu
ltim
atel
yar
eli
qu
idat
edo
rre
org
aniz
edw
ith
zero
equ
ity
top
re-p
etit
ion
shar
eho
lder
san
dze
rofo
rb
ank
rup
tfi
rms
that
ult
imat
ely
are
liq
uid
ated
or
reorg
aniz
edw
ith
po
siti
veeq
uit
yto
pre
-pet
itio
nsh
areh
old
ers.
LT
equ
als
on
eif
DD
ED
ICA
TE
D_L
EN
GT
His
by
DE
DIC
AT
ED
_L
T,L
Teq
ual
s
zero
ifD
DE
DIC
AT
ED
_L
EN
GT
His
by
DE
DIC
AT
ED
_L
TO
TH
.T
OP
5eq
ual
so
ne
ifD
DE
DIC
AT
ED
_B
LO
CK
isb
yD
ED
ICA
TE
D_
TO
P5,
TO
P5
equ
als
zero
ifD
DE
DI-
CA
TE
D_
BL
OC
Kis
by
DE
DIC
AT
ED
_T
OP
5O
TH
.B
KP
RO
Bit
-1is
the
ex-a
nte
pro
bab
ilit
yo
fban
kru
ptc
y(i
nper
centa
ge,
per
Shum
way
20
01
Tab
leV
IP
anel
b)
of
firm
iat
end
of
qu
arte
rt-
1.
MO
Mit
isth
eb
uy
-and
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt.
MO
Mit
-1is
the
bu
y-a
nd
-ho
ldra
wre
turn
of
firm
io
ver
qu
arte
rt-
1.
MO
Mit
-4,t
-2is
the
bu
y-
and
-hold
raw
retu
rno
ffi
rmi
ov
erq
uar
ters
t-4
tot-
2.T
VO
Lit
-1is
the
aver
age
of
mo
nth
lytr
adin
gv
olu
me
div
ided
by
shar
eso
uts
tand
ing
of
firm
iin
qu
arte
rt-
1.P
RIC
Eit
isth
e
pri
cep
ersh
are
of
firm
iat
the
end
of
qu
arte
rt.
MV
it-1
isth
eto
tal
mar
ket
capit
aliz
atio
nof
the
com
mon
stock
of
firm
iat
the
end
of
quar
ter
t-1.B
Mit
-1is
the
rati
oo
fco
mm
on
bo
ok
equ
ity
toto
tal
mar
ket
cap
ital
izat
ion
of
firm
iat
the
end
of
qu
arte
rt-
1.
UE
it-1
isth
eea
rnin
gs
surp
rise
bas
edo
na
seas
on
alra
nd
om
wal
kd
ivid
edb
yto
tal
asse
tso
ffi
rmi
for
the
earn
ing
so
fq
uar
ter
t-1
.S
PR
AN
Kit
-1is
the
S&
Pco
mm
on
stock
rank
ing
(7=
A?���
0=
ifst
ock
isnot
rate
do
ris
inre
org
aniz
atio
nor
liquid
atio
n)
of
firm
iat
the
end
of
qu
arte
rt-
1.
YIE
LD
it-1
isth
ed
ivid
end
of
firm
ifo
rth
ep
ast
yea
rd
ivid
edb
yM
Vit
-1.
dD
ED
ICA
TE
D_
Xit
-1is
the
agg
reg
ate
%o
wn
ersh
ipo
ffi
rmi
atth
een
do
fca
len
dar
qu
arte
rt-
1b
yin
stit
uti
on
ssp
ecifi
edin
the
dep
enden
tv
aria
ble
**
*,
**
,*
1,
5,
and
10
%si
gnifi
cance
(one-
tail
edif
sign
pre
dic
ted,
two-t
aile
doth
erw
ise)
,re
spec
tivel
y
Evidence from impending bankrupt firms
123
Table 7 The impact of Regulation FD on dedicated institutions’ trading behavior
Pred sign dDDEDICATEDit
QTR1 ± 0.080
(0.103)
QTR2 ± 0.426
(0.850)
QTR1 9 ZEROEQi - -5.833
(3.068)***
QTR2 9 ZEROEQi - -1.319
(1.107)
QTR1 9 FD ± 0.053
(0.069)
QTR2 9 FD ± -0.501
(0.691)
QTR1 9 ZEROEQi 9 FD ? 3.964
(1.981)**
QTR2 9 ZEROEQi 9 FD ? 1.182
(0.727)
CONTROLS
dln(BKPROB)it-1 - -0.050
(0.669)
dMOMit - 0.077
(0.217)
dMOMit-1 - 0.038
(0.205)
dMOMit-4,t-2 - -0.024
(0.179)
dln(TVOL)it-1 ± -0.349
(3.070)***
dln(PRICE)it ± 0.597
(1.919)*
dln(MV)it-1 ? -0.248
(1.081)
dBMit-1 ± 0.046
(1.145)
dUEit-1 ± -0.308
(1.619)
dYIELDit-1 ± 4.216
(2.311)**
dSPRANKit-1 ± -0.072
(0.876)
S. Ramalingegowda
123
Table 7 continued
Pred sign dDDEDICATEDit
dDEDICATEDit-1 - -0.167
(9.528)***
Constant 1.010
(5.499)***
Observations 1,289
Adjusted R2 0.108
Fixed effects included Matched pair
Sales of zero equity firms pre FD
QTR1 ? QTR1 9 ZEROEQ - -5.753
p value (one-tail) (0.00)
Sales of zero equity firms post FD
QTR1 ? QTR1 9 ZEROEQ ? QTR1 9
FD ? QTR1 9 ZEROEQ 9 FD
± -1.736
p value (two-tail) (0.04)
Sales of zero equity firms post FD minus pre FD
QTR1 9 FD ? QTR1 9 ZEROEQ 9 FD
p value (one-tail)
? 4.017
(0.02)
Difference in sales of zero and positive equity firms in post FD
QTR1 9 ZEROEQ ? QTR1 9 ZEROEQ 9 FD
p value (two-tail)
± -1.869
(0.03)
Standard errors are clustered by firm and absolute values of t-statistics are reported below coefficient
estimates
The prefix ‘d’ in front of a variable indicates the difference between the bankrupt firm and the matched
firm variable. DDEDICATEDit = DEDICATEDit - DEDICATEDit-1; where DEDICATEDit is the
aggregate percentage ownership of firm i at the end of calendar quarter t by institutional investors
classified as ‘‘dedicated’’ institutions by Bushee (2001). QTR1 equals one if dedicated institutional
ownership change measurement quarter is the first quarter prior to the bankruptcy filing quarter, zero
otherwise. QTR2 equals one if dedicated institutional ownership change measurement quarter is the
second quarter prior to the bankruptcy filing quarter, zero otherwise. ZEROEQi equals one for bankrupt
firms that ultimately are liquidated or reorganized with zero equity to pre-petition shareholders and zero
for bankrupt firms that ultimately are liquidated or reorganized with positive equity to pre-petition
shareholders. FD equals one if the firm filed for bankruptcy in years 2001 to 2005. FD equals zero if the
firm filed for bankruptcy in years 1995 to 1999. BKPROBit-1 is the ex ante probability of bankruptcy (in
percentage, per Shumway 2001 Table VI Panel b) of firm i at end of quarter t-1. MOMit is the buy-and-
hold raw return of firm i over quarter t. MOMit-1 is the buy-and-hold raw return of firm i over quarter t-1.
MOMit-4,t-2 is the buy-and-hold raw return of firm i over quarters t-4 to t-2. TVOLit-1 is the average of
monthly trading volume divided by shares outstanding of firm i in quarter t-1. PRICEit is the price per
share of firm i at the end of quarter t. MVit-1 is the total market capitalization of the common stock of firm
i at the end of quarter t-1. BMit-1 is the ratio of common book equity to total market capitalization of firm i
at the end of quarter t-1. UEit-1 is the earnings surprise based on a seasonal random walk divided by total
assets of firm i for the earnings of quarter t-1. SPRANKit-1 is the S&P common stock ranking
(7 = A?���0 = if stock is not rated or is in reorganization or liquidation) of firm i at the end of quarter
t-1. YIELDit-1 is the dividend of firm i for the past year divided by MVit-1. DEDICATEDit-1 is the
aggregate percentage ownership of firm i at the end of calendar quarter t-1 by institutional investors
classified as ‘‘dedicated’’ institutions by Bushee (2001)
***, **, * 1, 5, and 10 % significance (one-tailed if sign predicted, two-tailed otherwise), respectively
Evidence from impending bankrupt firms
123
the bankruptcy filing quarter. Following Bushee and Goodman (2007), an institution
is classified as a long-term investor if the institution held the firm for at least a year.
DEDICATED_LT are dedicated institutions that hold the bankrupt firm for at least a
year ending 1 year before the bankruptcy filing quarter. DEDICATED_LTOTH are
dedicated institutions not classified as DEDICATED_LT. If dedicated institutions
sell impending bankrupt firms based on private information obtained from their
long-term positions in the bankrupt firms, I expect to find that DEDICATED_LT sell
more shares than DEDICATED_LTOTH do. To test this, I stack ownership changes
by DEDICATED_LT and DEDICATED_LTOTH and estimate the following model:
dDDEDICATED LENGTHit ¼ ap þ b1QTR1þ b2QTR2þ ðb1aQTR1þ b2aQTR2Þ
� ZEROEQi þX14
3
bdCONTROLS
þ ½b15QTR1þ b16QTR2
þ ðb15aQTR1þ b16aQTR2Þ � ZEROEQi� � LT þ eit
ð3aÞ
DEDICATED_LENGTHit is the change in aggregate percentage ownership of firm
i over quarter t by DEDICATED_LT or DEDICATED_LTOTH. QTR1, QTR2, ZE-
ROEQ, and CONTROLS are as defined earlier. LT = 1 if DDEDICATED_LENGTH
is by DEDICATED_LT, and LT = 0 if DDEDICATED_LENGTH is by DEDI-
CATED_LTOTH. Table 6 Column 1 reports the results. QTR1*ZEROEQ*LT is
significantly negative. Further, the sum of QTR1*LT and QTR1*ZEROEQ*LT is
also significantly negative. These results indicate that, in the quarter ahead of
bankruptcy filings of zero equity firms, DEDICATED_LT sell more shares than
DEDICATED_LTOTH do, consistent with dedicated institutions’ information
advantage arising from their long-term positions in the bankrupt firms.
To examine whether dedicated institutions’ information advantage arises from their
large stakes, I split dedicated institutions into two groups based on the magnitude of their
holdings in the bankrupt firms 1 year before the bankruptcy filing quarter. DEDI-
CATED_TOP5 are dedicated institutions that are among the five largest shareholders in
the bankrupt firm one year before the bankruptcy filing quarter.25 DEDI-
CATED_TOP5OTH is dedicated institutions not classified as DEDICATED_TOP5. If
dedicated institutions sell shares of impending bankrupt firms based on private
information obtained from their large stakes, I expect to find that DEDICATED_TOP5
sell more shares than DEDICATED_TOP5OTH do. To test this, I stack ownership
changes by DEDICATED_TOP5 and DEDICATED_TOP5OTH and estimate the
following model:
25 Ninety-three percent of DEDICATED_TOP5 are also DEDICATED_LT. Thirty-seven percent of
DEDICATED_LT are also DEDICATED_TOP5.
S. Ramalingegowda
123
dDDEDICATED BLOCKit ¼ ap þ b1QTR1þ b2QTR2þ ðb1aQTR1þ b2aQTR2Þ
� ZEROEQi þX14
3
bdCONTROLS
þ ½b15QTR1þ b16QTR2þ ðb15aQTR1þ b16aQTR2Þ� ZEROEQi� � TOP5þ eit
ð3bÞ
DDEDICATED_BLOCKit is the change in aggregate percentage ownership of firm
i over quarter t by DEDICATED_TOP5 or DEDICATED_TOP5OTH. QTR1, QTR2,
ZEROEQ, and CONTROLS are as defined earlier. TOP5 = 1 if DDEDI-
CATED_BLOCK is by DEDICATED_TOP5, and TOP5 = 0 if DDEDI-
CATED_BLOCK is by DEDICATED_TOP5OTH. Table 6 Column 2 reports the
results. QTR1*ZEROEQ*TOP5 is significantly negative. Further, the sum of
QTR1*TOP5 and QTR1*ZEROEQ*TOP5 is also significantly negative. These
results indicate that in the quarter ahead of bankruptcy filings of zero equity firms,
DEDICATED_TOP5 sell more shares than DEDICATED_TOP5TH do, consistent
with dedicated institutions’ information advantage arising from their large stakes in
the bankrupt firms.
Overall, the results in Table 6 are consistent with dedicated institutions selling
shares of impending bankrupt firms based on private information acquired from
their long-term positions (which potentially provide better ability to process public
information) and/or from their large stakes (which potentially provide access to firm
management) in the bankrupt firms.26
5.2 The impact of regulation FD on dedicated institutions’ trading behavior
Regulation FD, adopted in 2000, prohibits firms from privately disclosing
information to certain audiences (e.g., institutional investors). It was intended to
level the playing field across investors. Consistent with the regulation achieving its
purpose, prior literature finds that it decreased the information advantage of short-
term institutions in firms that hold conference calls (Ke et al. 2008). Therefore it is
interesting to examine whether Reg FD limited the ability of dedicated institutions
to sell impending bankruptcies. To the extent dedicated institutions obtain
information about impending bankruptcies from corporate management, I expect
dedicated institutions to sell less in the periods after Reg FD than before. I examine
this issue by adding to Model 2, an indicator variable, FD, and interactions between
FD and the QTR and QTR*ZEROEQ variables. FD equals one if the firm filed for
bankruptcy in 2001 to 2005 and equals zero if the firm filed for bankruptcy in 1995
to 1999. Following Ke et al. (2008), I omit 2000 for this test. I use 1995 to 1999 as
the pre-FD sample to have the same number of years in the pre- and post-FD
samples (Ke et al. also use 1995–1999 as their pre-FD sample). However, my results
26 Untabulated analyses indicate that these results stem from share sales in bankrupt firms and not share
purchases in matched firms. Also, the results are robust to when dCONTROLS are interacted with TOP5
and LT.
Evidence from impending bankrupt firms
123
are similar to that reported if I use similar number of bankrupt firms in the pre- and
post-FD samples.
Table 7 reports the results. QTR*ZEROEQ is -5.833 and statistically significant.
The sum of QTR and QTR*ZEROEQ is -5.753 and also significant. These results
suggest that dedicated institutions sell shares in anticipation of impending
bankruptcy of zero equity firms in the periods prior to Reg FD. The sum of
QTR1 ? QTR1*ZEROEQ ? QTR1*FD ? QTR1*ZEROEQ*FD is -1.736 and
significant, consistent with dedicated institutions selling shares in the periods after
Reg FD. However, the coefficient on QTR*ZEROEQ*FD is 3.964 and significant.
Further, the sum of QTR*FD and QTR*ZEROEQ*FD is 4.017 and significant.
These results indicate that dedicated institutions are selling significantly fewer
shares in the periods post-FD compared to pre-FD.27 Overall, the results from
Table 7 are consistent with Reg-FD mitigating dedicated institutions’ information
advantage. These results also suggest that the sales I observe in the pre-FD era are
based on private information partially obtained from management.
6 Conclusion
A key assumption in many studies in accounting, finance, and management is that
long horizon institutional investors have private information about future long-term
value of the firms they hold. Yet past empirical research finds no evidence that long
horizon institutions trade in anticipation of impending events, such as break in a
string of consecutive earnings increases, earnings restatements, earnings surprises,
and long-term earnings growth revisions. In light of this lack of evidence, recent
research has questioned the informedness of dedicated institutions. This study
provides new counter evidence that long horizon institutions do trade on private
information about forthcoming events. I find that long horizon institutions-proxied
by Bushee’s (2001) ‘‘dedicated’’ institutions-sell more of their stakes in bankrupt
firms than matched distress firms at least one quarter ahead of the bankruptcy
quarter. This selling pattern is stronger in impending bankrupt firms whose
shareholders ultimately lose all of their equity. These results are consistent with
dedicated institutions having and trading upon private information about impending
bankruptcies of firms they hold.
In further tests, I provide evidence on how dedicated institutions obtain private
information on impending bankruptcies. Consistent with dedicated institutions
obtaining private information through their better ability to process public
information and/or access to corporate management, I find that dedicated
27 In untabulated analysis, I find that Reg FD affected the trading behavior of dedicated institutions with
longer-term positions in bankrupt firms (DEDICATED_LT) and of dedicated institutions with larger
stakes in bankrupt firms (DEDICATED_TOP5). Specifically, dedicated institutions with longer-term
positions sell zero equity bankruptcies more than other dedicated institutions do, in both pre- and post-
Reg FD periods. However, incremental sales are significantly less post-Reg FD than pre-Reg FD.
Dedicated institutions with larger stakes sell zero equity bankruptcies more than other dedicated
institutions do, only in pre-Reg FD periods. There is no such evidence in post-Reg FD periods. Further,
incremental sales are significantly less post-Reg FD than pre-Reg FD periods.
S. Ramalingegowda
123
institutions with longer-term positions and/or larger stakes in the impending
bankrupt firms sell more shares than other dedicated institutions.
Finally, I examine the impact of Regulation FD on dedicated institutions’
informed trading. My tests reveal that dedicated institutions sell shares of
impending bankrupt firms in the periods both before and after Reg FD. However,
I find that dedicated institutions sell significantly fewer shares in the periods after
Reg-FD than before. These results are consistent with Reg FD mitigating (but not
eliminating) dedicated institutions’ information advantage.
My study improves our understanding of the informational role of long horizon
institutional investors. First, it is the first to provide evidence that long horizon
institutions trade in anticipation of major forthcoming corporate events, consistent
with long horizon institutions being informed shareholders. In addition to being of
interest on its own right, this finding is also important because it supports the
validity of the common assumption that long horizon institutions are informed.
Second, my study is the first to provide evidence of the impact of Reg FD on the
trading behavior of large long-horizon investors, who presumably have close access
to corporate management. My findings suggest that Reg FD, at least partially,
prevented firms from privately disclosing information to shareholders with close
access to corporate management, thus, partially mitigating these shareholders’
information advantage.
Acknowledgments I am grateful to James Ohlson (the editor) and anonymous reviewers for their
helpful suggestions. I also appreciate helpful comments from Ben Ayers, Linda Bamber, Jenny Gaver,
Joel Houston, Bin Ke, Jim McKeown, Harold Mulherin, Karl Muller, Yong Yu, and the workshop
participants at the following institutions: London Business School, Penn State University, University of
Florida, University of Georgia, and University of Minnesota, as well as Shuba V. Raghavan (senior
research associate, Yale Endowment), and Elysia Wai Kuen Tse (associate research and strategy, Lasalle
Investment Management) for informative discussions about their funds’ investment objectives and style. I
thank Brian Bushee for sharing his institutional ownership classification scheme.
Appendix: Illustration of the matching process
This appendix illustrates how a matching firm is chosen for Chyron Corp (SIC
3663) which filed for bankruptcy on September 17, 1990. First, I retain all firms that
have data on regression variables (including aggregate percentage ownership by
dedicated institutions) for at least 16 consecutive quarters prior to the quarter
Chyron filed for bankruptcy. This step provides 1,115 potential matching firms.
Next, I delete firms that filed for bankruptcy in any of the years up to 5 years
following the filing by Chyron. This step results in 1,078 potential matching firms.
In step three, out of 1,078 firms, I choose the one whose probability of bankruptcy
(BKPROB) is closest to that of Chyron, 2 years prior to Chyron’s bankruptcy filing
quarter. The matching firm with the closest BKPROB is Valpey-Fisher Corp (SIC
3679). The absolute difference in BKPROB between the two companies is 0.002.
The table below lists the pattern of aggregate percentage ownership by dedicated
institutions in Chyron and Valpey-Fisher at the end of each quarter for 3 years
before the bankruptcy quarter. At the matching quarter, dedicated institutions own
Evidence from impending bankrupt firms
123
7.83 % in Chyron and 6.19 % in Valpey-Fisher. In the subsequent quarters,
dedicated institutions decrease their ownership significantly in Chyron, with most of
the selling occurring in the two quarters prior to bankruptcy quarter. In contrast,
dedicated institutions appear to be retaining their shares in Valpey-Fisher.
QUARTER DEDICATEDit
Bankrupt firm (Chyron Corp) Matched firm (Valpey-Fisher Corp)
-1 2.90 7.07
-2 5.39 7.03
-3 6.73 7.03
-4 7.94 6.99
-5 7.94 6.79
-6 7.93 6.78
-7 7.46 6.24
-8 7.83 6.18
-9 7.83 6.19
-10 7.83 5.88
-11 7.95 5.88
-12 7.82 5.81
QUARTER refers to the quarter relative to the bankruptcy quarter. DEDICATEDit is the aggregate
percentage ownership of firm i at the end of calendar quarter t by institutional investors classified as
‘‘dedicated’’ institutions by Bushee (2001)
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