Do analysts know but not say? The case of going-

concern opinions

Version: July 17 2014

ABSTRACT

This study explores whether securities’ analysts recognize firms’ going-concern problems

and whether and how they report going-concern uncertainties to investors. We find that

analysts signal their anticipation of a going-concern opinion (GC) in two ways: (1) they

downgrade stock recommendations of GC firms more aggressively than control firms as

the event date approaches although still recommending investors to “hold” the stocks of

GC firms immediately before the GC announcement date; (2) they are more likely to cease

coverage of a GC firm than a control firm over the one-year period prior to the GC date.

We further show that analysts react to the publication of the GC audit report mainly by

stopping coverage of such firms subsequent to the event disclosure. Our results suggest

that analysts recognize firm’s going-concern uncertainties but communicate their negative

prospects using opaque language that likely cannot be understood in particular by retail

investors, who constitute the main clientele of these firms. Consistent with the SEC’s

concerns about the literal interpretation of analyst recommendations, we show that

investors cannot rely solely on analyst recommendations in this extreme bad news domain.

This is because they appear reluctant to report negatively (as signalled by “underperform”

or “sell” stock recommendations) on firms with going-concern problems highlighted in

their audit reports or even cease coverage. We further conclude that analyst relative

pessimism and coverage cessation is likely to be associated with negative expectations

about firms’ future prospects. We conclude that analysts “know” but do not “say” in the

going-concern domain where their information dissemination role is particularly salient.

Keywords: analyst behaviour, bad news announcements, stock recommendation bias,

coverage cessation

JEL classification: M41, M42, G14, G24

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Do analysts know but not say? The case of going-

concern opinions

1. Introduction

This paper explores whether securities’ analysts anticipate a going-concern audit opinion

and how they react to such an extreme bad news event. In particular, we investigate

whether these sophisticated agents signal the adverse prospects of such financially-

distressed firms, and if so, in which way. This question is important for two reasons.

First, the audit going-concern (GC) opinion provides information on the financial viability

of the firm, and has significant market impact both in the short-term (e.g., Menon and

Williams, 2010), and in the longer-term (e.g., Kausar, Taffler and Tan, 2009). Second,

Kausar et al. (2009) show retail investors are the main clientele of GC firm stocks owning,

on average, 74% of the equity of those firms at the GC announcement date. Such

unsophisticated investors are unable to process publicly available information

appropriately (e.g., Beaver, 2002; Bhattacharya, Black, Christensen and Mergenthaler,

2007; Miller, 2010; Ayers, Li and Yeung, 2011), and rely primarily on analyst stock

recommendations for investment advice in contrast to sophisticated investors (Malmendier

and Shantikumar, 2007). As such, investigating whether analysts report appropriately in

the going-concern domain is an important issue. Do these sophisticated agents actually

provide retail investors, in particular, with value-relevant information in this case?

There is an extensive literature suggesting analysts are both prone to bias in their

judgments, and reluctant to report unfavorably on firms. For instance, research shows that

the number of “buy” recommendations is systematically higher than the number of “sell”

recommendations (e.g., Womack, 1996; Barber, Lehavy, McNichols and Trueman, 2006;

Mokoaleli-Mokoteli, Taffler and Agarwal, 2009; Ertimur, Muslu and Zhang, 2011).

Analysts are also self-selective in initiating coverage of firms they view favourably, and

ceasing coverage of firms they view unfavorably (McNichols and O’Brien, 1997; Das,

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Guo and Zhang, 2006). We test whether analyst biased recommendations, and analyst self-

selection bias, are equally manifest in the case of going-concern uncertainties where the

key role played by the analyst is particularly pronounced. Investigating these issues

provide additional evidence to understand the “black box” of how analysts process

information (e.g., Ramnath, Rock and Shane, 2008; Brown, Call, Clement and Sharp,

2014).

To our knowledge, this paper is the first comprehensive account of how investment

analysts deal with GC uncertainties. It sets out explicitly to answer two main questions.

First, do securities’ analysts anticipate going-concern audit report disclosures? In

particular, (i) do they downgrade their stock recommendations for GC firms more

aggressively than for similar but non-GC firms in the 12 months prior to the GC

announcement date, and (ii) are analysts more likely to cease coverage of such firms than

of matched non-GC firms within the same time period? Second, how do securities’

analysts react to the publication of a first-time GC audit report? Specifically, (i) we

compare their stock recommendations for GC firms before and after the GC disclosure

event, and (ii) test the extent to which analyst coverage is maintained post-GC

announcement.

We find that sell-side analysts of firms that subsequently receive a GC audit report

modification do appear to anticipate firms’ going-concern uncertainties, and recognize

what this connotes for their future prospects. However, and more importantly, our results

suggest that they do not signal this unambiguously. Analysts do downgrade a proportion of

their stock recommendations for firms with forthcoming GC reports (although from “buy”

to “hold”), as the event date approaches, whereas there is no change in the average “buy”

recommendation for control firms. In addition, analysts are significantly more prone to

cease coverage of GC firms than matched non-GC firms. However, there are substantial

doubts that these signals provide investors with value-relevant information as only 11% of

analyst recommendations on GC firms immediately before the GC announcement date are

unfavorable (i.e., “underperform” or “sell”), and 4 out of 10 analysts following GC firms

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one-year before the GC date cease coverage as the event date approaches. These findings

have important implications in particular for retail investors, who are the main clientele of

these firms (Kausar et al., 2009), and whom we would expect directly to benefit from

analyst recognition of firm going-concern uncertainties. Malmendier and Shantikumar

(2007) show that retail investors follow analysts’ recommendations literally and, contrary

to sophisticated investors, do not react negatively to hold recommendations. Analysts’

reluctance to report negatively on GC firms by avoiding “underperform” or “sell”

recommendations (and even ceasing coverage of these firms) may thus serve to mislead

such investors in their assessment of the future prospects of these firms, and their

investment value.

We also find that analysts tend to react to the actual publication of a GC audit report

by ceasing coverage of 1 in 4 such firms in the quarter after the information event, and in

the case of continued coverage, do not change the distribution of their stock

recommendations across buy, hold and sell categories in any way. Again, only 10% of

cases are unfavorable (i.e., “underperform” or “sell”). That analysts maintain their average

“buy” recommendation for GC firms immediately after the publication of a GC audit

report reinforces the notion that they are not surprised by such bad news and were already

aware of the going-concern uncertainties prior to its publication. Considering that a going-

concern modified audit report provides an unambiguous signal questioning the firm’s

ability to continue in business for the foreseeable future, and GC stock prices

underperform no less than 14% over the one-year period following the GC disclosure

(Kausar et al., 2009), it is reasonable to assume that these sophisticated agents know that

the future viability of going-concern firms is jeopardized, but they are reluctant to state this

directly.

This study contributes to the academic literature, and has implications for investors

in financially distressed firms. From an academic perspective, we link two areas of the

accounting and finance literature that have been developing separately so far, and provide

evidence about how securities’ analysts deal with a major bad news accounting event. In

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particular, we augment previous studies exploring analyst ability to anticipate, and react to

major negative news events (e.g., Griffin, 2003; Barth and Hutton, 2004; Clarke, Ferris,

Jayaraman and Lee, 2006; Cotter and Young, 2007; Jones and Johnstone, 2012) by

focusing on the particular case of going-concern opinions. Our results reinforce the notion

that analysts disseminate (unfavorable) GC information through a language that, at best, is

opaque to the majority of investors in such stocks who exhibit the tendency to follow

analyst recommendations literally (e.g., Malmendier and Shantikumar, 2007). Analyst

reluctance to issue negative recommendations on, and their propensity to cease coverage

of, GC firms, could be a factor in explaining the surprise with which the market receives a

GC opinion (e.g., Fleak and Wilson, 1994; Carlson, Glezen and Benefield, 1998; Menon

and Williams, 2010). It could also provide a potential explanation for the market

undereaction to the GC opinion following such public domain disclosure (Taffler, Lu and

Kausar, 2004; Kausar, et al., 2009). As such, analyst behavior in this extreme bad news

domain augments previous studies on analyst biased judgments, highlighting that analysts

do not always mean what they say and frequently say “hold” when they mean “sell”

(Shefrin, 2002) or simply cease coverage of firms they view unfavorably (McNichols and

O’Brien, 1997).

Our study emphasizes that investors should not rely on investment analyst stock

recommendations, at least in the GC domain, and reinforces the warnings on the SEC’s

website:1

“We advise all investors to do their homework before investing. If you

purchase a security solely because an analyst said the company was one of

his or her ‘top picks’, you may be doing yourself a disservice. Especially if

the company is one you’ve never heard of (…) Above all, remember that

even the soundest recommendation from the most trust-worthy analyst may

not be a good choice for you. That’s one reason we caution investors never

1 See http://www.sec.gov/investor/pubs/analysts.htm for details.

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to rely solely on [an] analyst’s recommendation when buying or selling a

stock.”

The remainder of this paper is organized as follows: section 2 discusses our

motivation and research hypotheses. Section 3 describes our sample selection process, and

provides descriptive statistics. Section 4 and section 5 investigate analyst anticipation and

reaction to the going-concern opinion, with section 6 providing additional robustness

checks. Section 7 discusses our results and concludes.

2. Motivation and research hypotheses

The going-concern principle is one of the most important accounting assumptions in the

preparation of financial statements. This principle assumes that a firm is ordinarily viewed

as continuing in business for the foreseeable future. When this assumption is explicitly

questioned by external auditors, the market typically reacts negatively, both in the short-

term (e.g., Fleak and Wilson, 1994; Carlson et al., 1998; Menon and Williams, 2010; but

contra Blay and Geiger, 2001), and in the longer-term (Taffler et al., 2004; Kausar, et al.,

2009). The going-concern context offers a unique scenario to investigate the extent to

which analysts anticipate and report major accounting (bad news) events since: (i) going-

concern opinions tend to follow a series of unfavorable firm economic events such as sales

declines, failures to make payments on debt, dividend reductions, production problems,

and lost contracts and quarterly losses (e.g., Elliot, 1982; Menon and Williams, 2010), and

(ii) there is evidence that the GC audit opinion can be predicted to some extent using

accounting information (e.g., Mutchler, 1985; Dopuch, Holthausen and Leftwich, 1987).

Two important ideas fuel our interest in how analysts deal with the going-concern

principle. First, the marginal contribution of analysts may be greater in this domain as

managers’ incentives to disseminate unfavourable information are low (Kothari, Shu and

Wysocki, 2009). As Hong, Lim, and Stein (2000) argue, managers of firms sitting on good

news will push this out the door themselves. On the contrary, managers have few

incentives to bring investors up to date quickly in the context of unfavorable information.

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Second, the literature suggests that investors are significantly more inefficient in dealing

with bad news in comparison to good news (e.g., Bernard and Thomas, 1989; Womack,

1996; Dichev and Piotroski, 2001; Kausar et al., 2009). Importantly, this phenomenon is

emphasized in the GC domain as the market underreacts to the publication of a going-

concern audit report (bad news) but fully anticipates the withdrawal of such a report (good

news) (Kausar et al., 2009). In this paper, we explore whether the contribution of

securities' analysts in the dissemination of going-concern uncertainties helps investors to

understand the negative information content of such an event or, alternatively, if they

mislead investors in the recognition of going-concern uncertainties.

We use analyst stock recommendations and cessation of coverage decisions to test

analyst anticipation of/reaction to a going-concern opinion. Stock recommendations

represent a clear and unequivocal course of action to investors (Elton, Gruber and

Grossman, 1986) as they are reported on a simple and finite scale common to all stocks,

avoiding ambiguous interpretations of information (McNichols and O’Brien, 1997).

Analyst recommendations are the bottom line of the research report (e.g., Schipper, 1991)

and offer a unique opportunity to study analyst judgment and preferences across large

samples of stocks (Jegadeesh, Kim, Krische and Lee., 2004). Moreover, there is evidence

that analyst recommendations are economically relevant to investors (e.g., Jegadeeshet al.,

2004; Boni and Womack, 2006; Green, 2006), and influence their decision-making process

(Kelly, Low, Tan and Tan, 2012). Analyst recommendations are crucial in the recognition

of going-concern uncertainties as the large majority of GC firm stock is owned by retail

investors (Kausar et al., 2009) who rely heavily on analyst stock recommendations

(Malmendier and Shantikumar, 2007). In fact, compared with institutional investors, retail

investors seem to ignore analyst optimism bias, and follow their recommendations literally.

In particular, Malmendier and Shantikumar (2007) show that retail (large) investors tend to

buy (not react) following a buy recommendation, and not react (sell) after a hold

recommendation. Mikhail, Walther and Willis (2007) similarly reinforce the idea that retail

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investors trade more in response to recommendation upgrades and buy recommendations

when compared to professional investors.

Investigating the analyst’s decision to cease coverage of a GC firm is also important

as analyst coverage adds value to a firm (Branson, Guffey and Pagach, 1998; Mola, Rau

and Khorana, 2013), and because there is evidence that analysts are prone to drop the

coverage of firms associated with unfavorable information (e.g., McNichols and O’Brien,

1997; Shon and Young, 2011; Young and Peng, 2013). We thus assume that the decision

to stop reporting on a GC firm is likely to be associated with analyst reluctance to report

negatively. This idea is further supported by the fact that analysts usually remains at the

same brokerage firm after dropping firm coverage (Clarke et al., 2006). Such propensity to

cease coverage of bad news firms is likely to lead to the average observable

recommendation for these firms to be positively biased, as analysts are not required to

revise their stock recommendations prior to deciding to stop following firms.

Consequently, ceasing coverage (and saying nothing) is a way to avoid issuing negative

recommendations (McNichols and O’Brien, 1997). Analyst propensity to drop coverage

also occurs when firms are associated with adverse accounting fundamentals (Shon and

Young, 2011), when firms are more likely to be delisted (Mola et al., 2013), or in the case

of firms associated with accounting fraud (Young and Peng, 2013).

There is mixed evidence about the ability of analysts to anticipate bad news. On the

one hand, studies suggest that analysts fail to anticipate earnings declines associated with

high accruals (Bradshaw, Richardson, and Sloan, 2001; Teoh and Wong, 2002; Barth and

Hutton, 2004), and firm restatements and corrective disclosures (Griffin, 2003). On the

other hand, there is evidence that analysts are able to anticipate some types of accounting

fraud (e.g., Dechow, Sloan, and Sweeney, 1996; Cotter and Young, 2007), as well as

bankruptcy announcements (e.g., Moses, 1990; Clarke et al., 2006). For instance, Clarke et

al. (2006) conclude that analysts anticipate forthcoming bankruptcy announcements as

their stock recommendations for firms subsequently entering into Chapter 11 become

significantly more unfavorable compared with similar non-bankrupt firms in the one-year

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period before the event disclosure date. However, Jones and Johnstone (2012) dispute this

idea as analysts’ negative recommendation revisions do not become aggressively so until

the very last months prior to failure leaving their recommendations overly optimistic until

immediately prior to Chapter 11 filing. Cotter and Young (2007) highlight that analysts are

more likely to drop coverage of firms that commit larger frauds than revise their

recommendations downwards, and similar behavior is also documented in the bankruptcy

domain (Jones and Johnstone, 2012).

Firm going-concern problems have a major impact on their stock prices (e.g.,

Kausar et al., 2009; Menon and Williams, 2010), and thus should not be ignored by

analysts following these firms. In addition, analysts should have competitive advantage in

anticipating this acute bad news event as they are less likely to misunderstand the

implication of adverse financial information when compared to naïve investors (Ramnath

et al., 2008). We thus test if biased analyst behavior, which is particularly manifested in the

bad news domain (e.g., Das, 1998; Easterwood and Nutt, 1999; Brown, 2001, Abarbanell

and Lehavy, 2003; Mokoaleli-Mokoteli et al., 2009), affects analyst ability to anticipate a

going-concern opinion in two ways. First, we explore their stock recommendations, and

second their coverage decisions.

If analysts are able to anticipate going-concern problems, we expect that their stock

recommendations for GC firms become significantly more unfavorable than for similar

non-GC firms as the event date approaches. If this is the case, and if analysts are willing to

communicate their unfavorable view of the GC firm’s future prospects, we should observe

an average negative recommendation (i.e., “underperform or “sell”) before the GC

announcement date. Alternatively, analysts may recognize the going-concern issues the

firm is facing, and signal this by withdrawing coverage instead of reporting negatively on

the firm. In this case, the lower tail of the recommendation distribution would be censored

leading to the average observable recommendation appearing overoptimistic. Our first two

hypotheses expressed in null form are as follows:

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H1: There are no significant differences between analyst recommendations

for GC firms and similar but non-GC firms in the pre-GC event period;

H2: Analysts are not more prone to drop the coverage of GC firms than

similar but non-GC firms in the pre-GC event period.

The second part of this study explores the parallel question of how analysts react to

the disclosure of a going-concern opinion. It is reasonable to assume that, given the

negative implications of a GC audit report, analysts should react to the announcement of a

GC opinion as they do for other bad news information releases (e.g., Easterwood and Nutt,

1999; Griffin, 2003; Cotter, Tuna and Wysocki, 2006; Jones and Johnstone, 2012). This

reaction should be reflected in their stock recommendations as almost 80% of revisions

are in response to corporate events such as earnings news, financing news or new business

(Altinkiliç and Hansen, 2009). However, analyst reaction to relevant news announcements

is not unbiased. Easterwood and Nutt (1999) suggest that analysts are systematically

optimistic as they underreact (overreact) to extreme bad (good) news in the earnings

announcement domain, and Conrad, Cornell, Landsman and Rountree (2006) find that

analysts are reluctant to downgrade their stock recommendations. Moreover, studies also

show that analysts are reluctant to follow and report on firms following a negative news

event. For instance, Griffin (2003) shows that the number of analysts covering firms

associated with corrective disclosures decreases slowly over several months following the

announcement date whilst Jones and Johnstone (2012) report a similar pattern for their

sample of large bankrupt firms.

If analysts recognize the adverse impact of a going-concern audit report, and are

willing to report negatively on these GC firms, we would expect to observe stock

recommendation downgrades (at least for “strong buy”, “buy” and “hold”

recommendations) immediately following the publication of GC audit reports. In fact,

considering GC firms typically underperform in the one-year period following the GC

announcement date (Kausar et al., 2009) we should expect to observe clear

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“underperform” or “sell” recommendation signals. Again, if analysts are reluctant to report

negatively on GC firms, they may decide to withdraw coverage without directly signaling

this major bad news event to the investment community. Our hypotheses to test analyst

reaction to the publication of a going-concern audit report are defined as follows:

H3: There are no significant differences between analyst recommendations

for GC firms in the pre-event and post-event period;

H4: Analysts are not more prone to drop the coverage of GC firms than

similar but non-GC firms in the post-event period.

3. Data and descriptives

In this section, we explain how we construct our sample of going-concern cases, and

matched set of non-GC control firms. We also provide descriptive statistics.

3.1. Going-concern sample selection

The use of a clean GC sample is particularly important as identifying a first-time GC firm

is not a straightforward process (e.g., Butler, Leone, and Willenborg, 2004; Kausar et al.,

2009), and because the use of a biased sample may lead to inferences that lack validity

(e.g., Asare, 1990; Kausar et al., 2009). Our sample construction process draws heavily on

Kausar et al. (2009), and is designed to eliminate GC firm misclassifications in standard

databases (e.g., Kausar et al., 2009). We start constructing our sample of GC cases using

10-K Wizard’s EDGAR database free-text search tool, and identify firms with going-

concern opinions from 1994 to 2007. The joint combinations of keywords we use as search

strings are “raise substantial doubt” and “ability to continue as a going-concern”.

Following this initial step, we exclude cases for which firms are not found in the

CRSP/COMPUSTAT merged file, cases for which firms have insufficient accounting data

on COMPUSTAT, and cases where firm common stock is not traded on the NYSE,

AMEX or NASDAQ in the 12-month period before the GC disclosure event.

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Drawing on recent studies (e.g., Kausar et al., 2009; Menon and Williams, 2010), we

remove non-first-time GC audit report cases as a continuing GC opinion has less

information content than a first-time report (Mutchler, 1985; Mutchler, Hopwood and

McKeown, 1997).2 We also remove firms classified as “utilities”, “financials”, in a

“development stage”, or foreign-registered, or those that had already filed for Chapter 11

prior to the GC audit report publication date. This process identifies 1,257 first time GC

audit reports. From these we finally eliminate an additional 638 cases with no analyst

coverage within the two calendar years before the GC announcement date. Finally, we use

Factiva to identify the 10.7% of cases with public domain disclosures of GC news (a few

days) prior to the 10-K filing date (Menon and Williams, 2010). For these early GC

announcers, we consider the respective press release date to be the event date, while using

the 10-K filing date for other cases.

Our final example consists of 619 non-finance, non-utility industry firm-year

observations with first-time going-concern opinions published between 01.01.1994 and

12.31.2007 with stocks listed on the NYSE, AMEX or NASDAQ that have analyst

coverage before the GC announcement date. There are 44 GC cases each year, on average,

ranging between a minimum of 14 cases in 1994, and a maximum of 105 in 2001.

3.2. Control firm selection

Investigating how securities’ analysts deal with the GC audit report by solely studying GC

firm cases might introduce a selection bias since analysts cannot know ex ante which firms

will receive a GC opinion. Drawing on Clarke et al. (2006), we overcome this problem by

comparing analyst behavior across GC firms and similar but non-GC firms. An important

issue is how to match GC firms appropriately. Size and bankruptcy risk are two important

characteristics that should not be ignored in any matching process as GC firms are

typically small and, by definition, acutely financially distressed. Importantly, these two

characteristics are also likely to influence analyst behavior. Size is commonly seen as a

2 We define a GC audit report as first-time if the firm did not receive a GC opinion in the previous fiscal year.

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risk factor that has the ability to predict stock returns (Fama and French, 1992), and is

strongly correlated with analyst coverage (e.g., Bhushan, 1989; Hong et al., 2000). In

addition, bankruptcy risk is likely to influence analyst behavior as analysts are reluctant to

follow and report on firms associated with negative information (e.g., McNichols and

O’Brien, 1997; Shon and Young, 2011; Young and Peng, 2013). Thus, for our main

results, we construct our set of non-GC control firms as those with similar size and

Altman´s (1968) z-score. We also conduct robustness tests using alternative GC-firm

matching criteria as described in section 6 below, with results very similar to our main

results.

We identify 618 non-GC control firms by matching each of our sample firms with

the firm with most similar size and bankruptcy risk.3 The matching process is as follows.

First, for each sample firm, we identify all non-financial, non-utility, and non-GC firms

listed on the NYSE, AMEX and NASDAQ at the GC announcement date. We then

exclude all potential match candidates with no analyst coverage within the two calendar

years before the GC announcement date. Next, for each GC case, we identify all firms with

market value between 70% and 130% of the sample firm, and finally, from the remaining

set of match candidates, we choose the control firm with the closest z-score to that of our

sample firm. Market capitalization is measured one-year before the GC date, and firm z-

score is computed using data drawn from the last annual accounts published before the GC

date.

3.3. Descriptive statistics

Table 1 compares descriptive statistics for our 618 GC firms with their control firms;

variables are winsorized at the 1% level. As can be seen, our sample of GC firms is

typically composed of small firms with high distress risk. For instance, GC firms have low

market capitalization (mean size = $173.2m), low net sales (mean sales = $178.9m), low

total assets (mean total assets = $211.8m) and have high bankruptcy risk (mean z-score =

3 We fail to find a matching firm for one of our 619 GC firm cases.

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1.06). As to be expected, there are no significant differences in firm size and z-score

between our GC firms and that of their matched conterparts. We thus conclude that our GC

and non-GC firms share similar size and bankruptcy risk.

However, table 1 also reveals that GC firms are significantly more unprofitable than

their control firms (mean return on assets = -67% vs. -27%) and have experienced

significantly worse market performance prior to the GC event (mean monthly return [t=-11

to -1] = -5% vs. 1%). Also, GC firm ability to meet short-term debt obligations is

significantly lower than in the case of their matched counterparts (mean current ratio =

1.85 vs. 3.86), and GC firms are significantly more leveraged (total debt/total assets = 35%

vs. 25%). Finally, GC firm mean standardized unexpected earnings (SUE) = 0.37 (median

SUE = -0.43), suggesting that majority of our GC firms are associated with a decline in

their earnings compared with the previous year. Control firms, on the other hand, manifest

an increase in earnings (mean [median] SUE = 1.1[0.12]). Such significant differences in

the characteristics of our GC and control firms highlight the importance of controlling for

potential confounding factors in our multivariate analysis, and conducting appropriate

robustness tests to ensure our conclusions are not driven by omitted factors affecting

analyst behavior.

Table 1 here

3.4. Analyst data

We investigate analyst anticipation of, and reaction to, firms’ GC audit reports drawing on

their stock recommendations and firm coverage decisions. Stock recommendations are

obtained from the Institutional Broker Estimates System (I/B/E/S) database. For each stock

recommendation, we gather the following information: recommendation date, broker

identification, analyst identification, and I/B/E/S recommendation code. Next, and

following Zhang (2008), we exclude all observations with zero analyst-specific

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identification code.4 All recommendations are then sorted by date relative to the GC

announcement day (t=0), and allocated to event-quarters. Event-quarters are defined as 90

calendar day periods relative to the GC announcement date.5 We follow the I/B/E/S

recommendations ranking scheme directly; this codes analyst recommendations on a five-

point scale: (1) “strong buy”, (2) “buy”, (3) “hold”, (4) “underperform”, and (5) “sell”.

Because I/B/E/S codes “strong buy” recommendations as 1 and “sell” as 5, more positive

stock recommendations have lower numerical values. The analyst stock recommendations

we use are sourced from I/B/E/S Recommendations – Detail File. We define analyst i

recommendation for firm j in event-quarter q (RECi,j,q) as the latest recommendation issued

by the analyst within the event-quarter. For those quarters where analyst i does not issue a

recommendation or decides to drop the coverage of firm j, no recommendation is

registered.6 RECj,q is then derived as the mean (or median) across all analyst

recommendations for firm j in event-quarter q. Our final data set consists of 3,508 RECj,qs

for GC firms, and 3,433 RECj,qs for non-GC matched firms from event-quarter -8 to event-

quarter +1.

4. Analyst anticipation of a going-concern opinion

This section investigates whether analysts anticipate and signal a forthcoming GC opinion,

and formally tests hypotheses H1 and H2. In particular, we examine (i) if analysts

downgrade their stock recommendations more aggressively for GC firms than similar non-

GC firms, and (ii) whether they are more likely to downgrade and cease coverage of GC

firms compared with control firms. Analyst response to the GC announcement is

investigated in section 5.

4.1. Initial results

4 I/B/E/S assigns a zero identification code if the broker did not provide an analyst name to be associated with the

recommendation. Such information is required to observe changes in analyst recommendations over time. 5 For example, event-quarter -1 relates to the 90-day period starting with calendar day -1 and terminating with calendar day -90 relative to the GC date (day 0), and event-quarter -2 is the 90-day period inclusive of calendar day -91, and calendar day -

180 relative to the GC date, etc. 6 Cessation of stock coverage date is taken from the I/B/E/S Recommendations – Stopped Estimates File.

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This sub-section examines the changes in analyst recommendations over time for both GC

firms and matched non-GC firms over the 8 event quarters prior to the GC announcement

date. In particular, we compare stock recommendations issued by analysts across our two

groups of firms in each event quarter. We also compare the percentage of firms for which

analysts are issuing unfavorable recommendations (“underperform” and “sell”

recommendations, i.e., numerical recommendations > 3.5), and the respective proportions

of analysts that decide to cease coverage of the two groups of firms.

Table 2 summarizes our results. We find that, from event-quarter -8 to event-quarter

-5, analyst behavior is similar across GC firms and non-GC firms of similar size and

bankruptcy risk. Analysts are advising investors to buy the stocks of both GC and control

firms up to a year before the GC announcement (numerical recommendations are

approximately 2.0). The percentage of unfavorable new recommendations is very low in

both cases (typically around 5%), and the percentage of analysts dropping firm coverage

each quarter is also very similar (around 10%). Thus, our results suggest that, up to a year

before the GC event, analysts do not distinguish firms with forthcoming going-concern

uncertainties from matched non-GC firms and thus, on a face value basis, appear to share

similar expectations about the investment performance of both types of firm.

Table 2 here

However, analysis of event-quarters closer to the GC date reveals a different pattern.

Table 2 shows that, commencing from event-quarter -4, the average stock recommendation

issued for GC firms becomes significantly more unfavorable than for non-GC firms. In

general, analysts downgrade their quarter q mean and median stock recommendations for

GC firms from “buy” (1.5≤RECq<2.5) to “hold” (2.5≤RECq<3.5), while maintaining their

previous “buy” recommendations for matched non-GC firms. Importantly, from event-

quarter -3 to event-quarter -1, between group mean and median stock recommendation

differences are now highly significant. For example, in the case of event-quarter -1 GC

- 17 -

firm mean (median) new recommendation is 2.70 (3.00) (“hold”), compared with the mean

(median) new recommendation 1.90 (2.00) (“buy”) for non-GC firms, with these

differences both significant at the 0.1% level. In addition, the percentage of GC firms with

unfavorable new recommendations reaches 18% in contrast to the 4% of control firms in

the same situation (difference again significant at the 0.1% level).

Table 2 also shows that, in contrast to our non-GC control firms, analyst coverage of

GC firms decreases as the event date approaches. The percentage of analysts that stop

following GC firms increases from event-quarter -4 (12%) to event-quarter -1 (20%), but

no similar pattern is observed for non-GC firms. Table 2 also shows that in the critical

event-quarter immediately prior to the GC announcement the percentage of new analyst

stock recommendations for GC firms is less than 60% of those for control firms (275 v

472) relating to only 26% of such firms compared with 44%. This is despite this event-

quarter arguably being the one where investors are most dependent on analysts to

understand the consequences of impending going-concern uncertainties.

Overall, our initial evidence suggests that analysts are able to anticipate firm events

associated with a GC audit report in the year leading up to the GC event. Specifically, they

signal this by downgrading their stock recommendations for GC firms (from “buy” to

“hold”) compared with their maintained “buys” on matched non-GC firms, and by being

more prone to cease coverage of such firms. However, there are substantial doubts about

whether retail investors in particular, who own the majority of the equity of GC firms, are

able to associate analyst relative pessimism, and propensity to drop coverage of such firms,

with a negative view on their future prospects. We thus suggest that analysts are aware that

the future viability of firms with impending GC uncertainties is jeopardized but do not

state this clearly. Their reluctance, then, to report negatively is manifested in one of three

alternative scenarios: (i) issuing a “hold” recommendation, (ii) dropping coverage of the

firm, or (iii) not issuing a new (revised) recommendation on a timely basis.

In the year leading up to the GC event, analysts that continue to follow the firm may

upgrade, reiterate or downgrade their last recommendation issued before event quarter – 4.

- 18 -

However, analysts are not obliged to issue new recommendations on a quarterly basis.

Thus, we also employ “active recommendations” for firm i by analyst j (ACTRECi,j) as our

stock recommendations, ignoring quarters when the analyst issues no stock

recommendations on the firm. Specifically, we compute analyst i’s active recommendation

for firm j in event-quarter q (ACTRECi,j,q) in a similar way to the recommendations we

used in our previous analyses (RECi,j,q), but with an important difference. In those cases

where there is no new stock recommendation in a given event-quarter not due to the

analyst dropping coverage of the firm, we assume that the analyst’s previous event-quarter

recommendation still applies in the current event-quarter.

Table 3 presents the transition matrix of recommendation changes between event-

quarter -4 and event-quarter -1 for GC firms (1,608 active recommendations), and matched

non-GC firms (1,430 active recommendations). Panel A of table 3 shows that 58% of

analysts were issuing favorable recommendations (“strong buy” or “buy”) on GC firms

one year before the GC announcement, and only 6% of the cases were associated with

unfavorable recommendations (“underperform” or “sell”). Interestingly, 41% of analysts

ceased coverage of such firms within the one year period before the GC announcement

date. Of the remaining analysts following GC firms immediately before the GC date (948),

only 11% (101), were issuing unfavorable recommendations, 48% (450) were neutral and

42% (397) of their recommendations were favorable. Panel B of table 3 shows a similar

pattern for matched non-GC firms in event-quarter -4. However, the percentage of analysts

that drop coverage of such firms within the one year period before the GC date is lower

(28%), and the percentage of favorable recommendations remains stable. Compared to

non-GC control firms, it seems that analysts tend to avoid issuing unfavorable

recommendations on GC firms either by maintaining their positive (“buy”) rating, limiting

any downgrades to “hold”, or ceasing coverage of such firms.

Table 3 here

- 19 -

4.2. Multivariate evidence

In an attempt to ensure these univariate results are not driven by other factors that might

explain analyst stock recommendation decisions when confronted with firm going-concern

uncertainties, this sub-section conducts multivariate analysis to examine analyst behavior

in more detail.

4.2.1. Logistic regression models

We estimate two logistic regression models with binary dependent variables relating to

whether the analyst downgrades his/her stock recommendation or not in the one year

period leading up to the GC announcement, and similarly whether there is cessation of

coverage or not. Specifically, we compare all active recommendations for firm i by analyst

j in event-quarter -4 (ACTRECi,j,-4) with active recommendations for firm i by analyst j in

event-quarter -1 (ACTRECi,j,-1). Our respective binary dependent variables are then defined

as follows. DOWN(-4,-1)i=1 if analyst i downgrades his/her active stock recommendation for

firm j from event-quarter -4 to event-quarter -1, and 0 otherwise, and CEASE(-4,-1)i=1 if

analyst i ceases coverage of firm j from event-quarter -4 to event-quarter -1, and 0

otherwise.

Our logistic regression models then take the following form:

( 4, 1)Pr( 1) / (1 )i iz z

iDOWN e e (1)

( 4, 1)Pr( 1) / (1 )i iz z

iCEASE e e (2)

where

0 1 2 3 4 5 6

7 8 9 10

11 12 13 14

( / )

_ _

_ _ _ _

i i i i i i i

i i i i

i i i i i

z GCF LNSIZE RANALY B M MOM ROA

ZSCORE SUE PREV REC ANALY EXP

ANALY F EXP BROKER SIZE POST REG AUDITOR u

(3)

0 , …,14 are the parameter estimates, and

iu is a mean zero stochastic error term.

- 20 -

Independent variables are selected as potentially influential in the analyst decision to

issue a negative signal to the market, i.e., downgrade his/her stock recommendation or

cease firm coverage. Our prime variable of interest is the binary variable GCF (going-

concern firm) that equals 1 when the firm has a going-concern audit report, and 0 in the

case of the matched non-GC firm. A positive (negative) and significant coefficient on GCF

suggests that analysts are more (less) likely respectively to downgrade, or cease coverage,

of a GC firm than a control firm. The other explanatory variables are mainly associated

with firm characteristics or analyst/broker characteristics. Seven relate to firm

characteristics that are likely to impact analyst behavior in the GC domain, a further four to

analyst and broker characteristics, and the last two seek to consider the analyst reporting

environment and measure audit quality.

Market capitalization (LNSIZE) proxies for firm information environment (e.g.,

Hong et al., 2000; Jiang, Lee, and Zhang, 2005; Zhang, 2006), and is defined as the natural

log of the firm’s market value one year before the GC announcement date. Given that

analysts tend to follow larger firms (e.g., Bhushan, 1989; Hong et al., 2000), we expect

LNSIZE to have a negative relationship with the analyst decision to cease coverage of a

firm. However, we have no expectation of the direction of the relationship in the case of

the analyst decision to downgrade his/her stock recommendation. On the one hand, we

may expect analysts to be more reluctant to downgrade larger firms because of the

potential greater impact in terms of jeopardizing their relationship with the firm. On the

other hand, the analyst may be unwilling to signal his/her concerns about the firm’s future

viability by ceasing coverage in the case of larger firms and so is “forced” to continue to

follow the firm but downgrade its stock recommendation because of the reputational issues

associated with not signaling impending adverse events.

To proxy for analyst interest in the firm, and capture the influence of size on analyst

coverage, RANALY measures residual analyst coverage (Hong et al., 2000), and represents

the residual from the simple linear regression of (1 + ANALY) on LNSIZE, where ANALY is

the number of analysts following the firm one year before the GC announcement date. We

- 21 -

do not use ANALY in our regression models as analyst coverage is strong correlated with

firm size. Our expectations of the sign of the relationship between residual analyst

coverage, and both the analyst’s decision to downgrade, and cease coverage, are similar to

the sign on LNSIZE for the same reasons. The book-to-market ratio (B/M) proxies for the

market’s view on the firm’s future prospects, and is defined as the book value of equity

(computed from the last annual financial accounts reported before the GC date) divided by

market capitalization one year before the GC announcement date. We expect B/M to be

related to the analyst coverage decision given its relationship with firm stock returns and

analyst preferences. Given that analysts prefer growth stocks (e.g., Jegadeesh et al., 2004),

we expect they are more likely to cease coverage of stocks with high B/M ratios (value

stocks) than stocks with low B/M ratios (growth stocks). However, the expected sign on

the relationship between the downgrade decision (i.e,, “underperform” or “sell”) and B/M

ratio is not clear. On the one hand, analysts may be less likely to downgrade growth stocks

due to their optimism and preference for such stocks. On the other hand, analysts may be

more likely to downgrade growth stocks in order to play a fairer game with stocks of their

preference.

Following evidence that analysts are self-selective, i.e., they tend to follow firms

where they hold favorable views, and drop coverage of those with negative prospects (e.g.,

McNichols and O’Brien, 1997; Das et al., 2006), we employ two proxies of firm

performance. Momentum (MOM) proxies for pre-event stock performance, and is defined

as average monthly raw return for the prior 11-month period (t-11 to t-1) relative to the GC

announcement month (t=0). Similarly, return on assets (ROA) proxies for firm economic

performance, and is computed as the ratio of net income to the value of total assets

computed from the last annual financial accounts reported before the GC date. Since

analysts prefer firms with superior momentum, and those that are more profitable (e.g.,

Jegadeesh et al., 2004; Mokoaleli-Mokoteli et al., 2009), we conjecture they are more

likely to downgrade, and cease coverage of, firms with poorer pre-event stock

performance, and lower profitability. Following Clarke et al. (2006), we use Altman’s

- 22 -

(1968) z-score (ZSCORE) to measure bankruptcy risk, deriving it using the last annual

financial accounts reported before the GC date. We expect analysts are more likely to

downgrade and cease coverage of stocks with lower z-scores (more distressed firms)

reflecting their stock selection bias. We also consider earnings surprise as a potential

variable influencing the analyst decision processes as this may impact their reaction to the

GC opinion. We speculate a positive earnings surprise could mitigate the impact of a GC

announcement on analyst views about the firm because of the conflicting signals.

Conversely, we expect analysts to be more prone to downgrade and cease coverage of

firms with adverse (i.e. GC reinforcing) earnings surprise figures. Drawing on Foster,

Olsen and Shevlin (1994), we define standardized unexpected earnings (SUE) as the ratio

of the difference between the current quarterly and previous earnings figures to the

absolute value of the firm’s current quarter earnings.

Analyst characteristics are also believed to influence analyst behavior. For example,

accuracy is, generally, positively associated with analyst experience (e.g., Clement, 1999;

Hong et al., 2000; Drake and Myers, 2011; Shon and Young, 2011), as well as with analyst

firm-specific experience (Mikhail et al., 2003). We use ANALY_EXP and ANALY_F_EXP

to proxy for analyst experience, and analyst firm specific experience, respectively.

ANALY_EXP is computed as the number of years between the analyst’s last

recommendation issued before the GC announcement date, and the first record of the

analyst in the I/B/E/S Detail History file. Similarly, ANALY_F_EXP is derived as the

number of years between the analyst’s last recommendation issued before the GC

announcement date and the first record of the analyst for that specific firm in the I/B/E/S

Detail History file. We expect these two independent variables to have a positive

relationship with the analyst decision to downgrade a firm’s stock recommendation.

However, the relationship between analyst experience and decision to drop coverage is

unclear because, as argued above, analysts may cease following a firm to avoid reporting

negatively on it, or be “forced” to continue to cover the firm but with a downgraded

recommendation for reputational reasons. We also use BROKER_SIZE to proxy for

- 23 -

brokerage house information level, and associated analyst reputation, as analysts working

for larger brokers have more resources to draw on in their investment decisions, and are

more likely to have higher reputations as large brokers tend to hire top-rated analysts

(Clement, 1999; Hong and Kubik, 2003). Our expectations for the relationship between

BROKER_SIZE and our dependent variables are qualitatively the same as for ANALY_EXP

and ANALY_F_EXP. As in Hong and Kubik (2003), BROKER_SIZE is measured as the

number of analysts working for the broker in the year of the GC event.

It is also reasonable to assume that the nature of the analyst’s previous stock

recommendation (PREV_REC) influences his/her subsequent behavior. PREV_REC is

defined as the latest recommendation issued by the analyst before event-quarter -4. It is

likely to have a negative relationship with the decision to downgrade a stock

recommendation as analysts have more degrees of freedom to downgrade from a favorable

recommendation (lower numeric value) than from an unfavorable one (higher numeric

value). The association between PREV_REC and analyst decision to drop firm coverage is

unclear as analysts may drop the coverage of a firm with a favorable recommendation to

avoid reporting negatively on it, or may drop coverage of a firm with an already

unfavorable recommendation rather than downgrade this further (where possible).

Audit quality may also influence analyst behavior in the GC domain as higher

quality audits are likely to be associated with more timely, and less expected, GC audit

reports. For this reason, we expect that analysts are less likely to downgrade and cease

coverage of firms associated with higher quality audits. Following prior studies suggesting

that Big 4/5 auditors provide higher quality audits (DeAngelo, 1981; Francis and Krishnan,

1999), we define AUDITOR = 1 when the announcing firm is audited by a Big 4/5 auditor

in the calendar year preceding the GC announcement year, and 0 otherwise.

Finally, we employ a dummy variable (POST_REGj) to distinguish

recommendations issued before and after the enactment of new regulations affecting the

analyst’s reporting environment. In July 2002, NYSE (amendment Rule 472) and NASD

(Rule 2711) became effective aimed at providing investors with better information to

- 24 -

assess analyst research. These regulatory changes were triggered by concerns that investors

were being misled by analysts’ optimistic research reports and by several corporate

episodes. Importantly, these regulatory changes impacted analyst (e.g., Barber et al., 2006;

Mohanram and Sunder, 2006; Barniv, Hope, Myring and Thomas, 2009; Kandan,

Madureira, Wang and Zach, 2009), and auditor behavior (Li, 2009). Following Kandan et

al. (2009), POST_REG = 1 if an analyst’s stock recommendation is issued after September

1, 2002, and 0 otherwise. We expect a positive relationship between POST_REG, and the

analyst’s decision to downgrade his/her stock recommendation in the case of going-

concern uncertainties.

4.2.2. Multivariate results

Table 4 provides our logistic regression models estimating the probability of analyst stock

recommendation downgrades, and analyst cessation of firm coverage, in the one year prior

to the GC announcement date. Panel A of table 4 estimates model (1) relating to the

probability of analyst stock recommendation downgrade. The key going-concern variable

coefficient is positive and highly significant (0.420, p=0.001). This suggests that, ceteris

paribus, analysts are more likely to downgrade their stock recommendations for firms with

forthcoming GC audit reports than for control firms. These results are consistent with our

univariate ones of the previous sub-section; analysts appear able to anticipate going-

concern issues in the 12 months leading up to the GC date, on the basis they are relatively

pessimistic in the case of these firms when compared to non-GC firms sharing similar size

and financial distress characteristics.

Four other variables are also significant in explaining the analyst’s decision to

downgrade a stock recommendation: LNSIZE (coefficient = 0.168, p= 0.001), MOM (-

3.01, p=0.000), PREV_REC (-0.822, p<0.001), and B/M (-0.15, p<0.05). On this basis,

analysts appear more prone to downgrade their stock recommendations for larger rather

than smaller firms consistent with them either being “forced” to downgrade their stock

recommendations to avoid reputational consequences or, in the case of smaller (less well-

- 25 -

followed) firms, being tempted to say nothing or even dropping coverage.7 The negative

and highly significant coefficients on MOM and PREV_REC suggest that analysts are also

more likely to downgrade their stock recommendations for firms with poorer prior returns,

and those with previously more favorable recommendations (lower numeric values).

Analyst preference for firms with more positive (less negative) momentum (Jegadeesh et

al., 2004) is consistent with relatively stronger prior returns ameliorating the negative

impact of financial distress. Also, it is clearly easier to downgrade a favorable

recommendation than an unfavorable one (for obvious reasons analysts cannot downgrade

a “sell” recommendation). Finally, we find that analysts are more prone to downgrade

growth stocks (lower B/M ratios). Considering that analysts prefer growth stocks to value

stocks, we conjecture that this might be a factor in their decision to continue to report on,

although downgrade their recommendations for, such stocks, whereas coverage cessation

is more likely to be associated with the “less desirable” value stocks. Panel B of table 4

provides additional evidence on this issue.

Table 4 here

Panel B of table 4 presents model (2) estimating the probability of analyst cessation

of firm coverage. The sign and significance of our key independent variable (GCF) remain

unchanged (0.262, p<0.005), suggesting that analysts are also more prone to cease

coverage of GC firms than control firms in the year leading up to the GC event date.

However, although the same four independent variables as in Panel A remain significant,

together with the addition of ROA, interestingly, signs are reversed in the case of LNSIZE,

B/M and PREV_REC. Analyst rationale to drop GC firm coverage does not appear to be

the same as with their recommendation downgrade decisions. Specifically, the negative

coefficient on LNSIZE (-0.091, p<0.01) suggests, ceteris paribus, analysts tend to drop

7 This issue is further explored in our discussion of table 4, panel B below.

- 26 -

coverage of smaller firms. Taken together with the evidence in panel A, this is consistent

with a reputation argument. Contrary to small firms, analysts tend to continue the coverage

of large stocks but are “forced” to downgrade their stock recommendations for reputational

reasons. The B/M variable now takes a positive sign (0.097, p<0.001) indicating analysts

are also more likely to stop following value-like stocks, consistent with their preference to

continue to report on, albeit downgrade, more growth-like stocks, again as indicated in

panel A. The positive coefficient on PREV_REC (0.113, p<0.01) indicates that the poorer

the prior year stock recommendation (the higher its numerical value) the more likely the

analyst is to cease coverage. Momentum continues to be a highly significant determinant

in the case of analyst cessation of coverage (-3.569, p<0.001), and ROA is now also

significant (-0.097, <0.05). Analysts appear more likely to drop coverage in the case of

firms with lower (more negative) prior returns, and those which are less profitable (more

unprofitable).

In summary, our multivariate results reinforce our initial conclusions that analysts

are able to recognize firm going-concern uncertainties, and communicate this either by

downgrading their associated stock recommendations or ceasing coverage. We thus reject

null hypotheses H1 and H2. Analysts are more likely to issue a negative signal on GC firms

(downgrade or coverage cessation) than for similar non-GC firms. Tables 2 and 3 suggest

that in their reluctance to downgrade their previous recommendation for GC firms to

“underweight” or “sell” (rather than from “buy” to “hold”), and stopping coverage,

analysts’ signals are opaque or even potentially misleading. The implications of such value

relevant information as that conveyed by a forthcoming GC audit opinion are not being

communicated clearly to investors.

5. Analyst reaction to the going-concern opinion

In this section, we examine how analysts respond to a going-concern modified audit report,

and test directly hypotheses H3 and H4. We conduct similar analyses to the previous

section to examine if, and how, analysts react to the disclosure of a GC opinion as

- 27 -

measured by their post-GC stock recommendation, and continuing firm coverage

decisions. Specifically, we test if GC stock recommendations in the post-GC event-quarter

differ significantly from those in the pre-GC event-quarter. In addition, we examine if

analysts are more likely to downgrade, and cease coverage of firms following the

publication of a going-concern opinion compared with matched non-GC firms.

5.1. Initial results

First, we explore the change in analyst GC firm stock recommendations between event-

quarter -1 and event-quarter +1. We expect analyst reaction to the GC audit report (if any)

should occur as soon as the event becomes publicly known. We also compare the

percentage of firms with unfavorable recommendations (numerical recommendations >

3.5), and the percentage of analysts that decide to cease stock coverage within the same

period.

In the event-quarter following the publication of a GC audit report only 116 GC

firms have new stock recommendations (184 new analyst recommendations) compared

with 163 (275) in event quarter -1. Moreover, there is no significant difference in the

nature of analyst recommendations before, and after, the GC date with mean (median)

stock recommendation in event-quarter -1 = 2.73 (3.00), and 2.76 (3.00) in event-quarter

+1. Similarly, we fail to find any difference in the percentage of unfavorable

recommendations in event-quarter -1 compared with event-quarter +1 (18% vs. 19%). On

the other hand, 24% of analysts drop coverage of GC firms in event-quarter +1, compared

with 20% in event-quarter -1 (difference significance at p<0.001). This suggests that

analysts react to the publication of a GC audit report by being more likely to cease

coverage of such stocks rather than downgrading their stock recommendations for those

stocks they continue to cover. Even though analysts recognize that there are substantial

doubts about the ability of GC firms to continue in their current form in the foreseeable

future, nonetheless, they continue to recommend investors to “buy” or “hold” such GC

stocks, and are reluctant to say “underperform” or “sell”.

- 28 -

Table 5 presents the transition matrix of active recommendation changes between

event-quarter -1 and event-quarter +1 for GC firms (1,203 active recommendations) and

matched non-GC firms (1,540 active recommendations). Panel A shows the number of

downgrades of GC stock recommendations following the GC announcement date to be

quite small. For example, only 12% of GC stock recommendations rated as “strong buy”

were downgraded following the GC event, 5% of those previously with “buy”

recommendations, and only 2% of “holds”. However, analyst coverage of 19% (25%) of

stocks with previous “strong buy” and “buy” (“hold”) recommendations were dropped

following the GC event date. This suggests that analysts are reluctant to report negatively

on firms even following the announcement of such acute bad news, preferring to cease

coverage, and thereby say nothing. Panel B of table 5 reinforces this idea presenting very

similar results in terms of lack of change in matched non-GC firm stock recommendations.

However, there is now a much lower propensity to cease coverage of such firms between

the two quarters (12% v. 23%).

Table 5 here

5.2. Multivariate evidence

We employ similar logistic regression models to those in section 4.2 to investigate the

extent to which analysts are more likely to downgrade their stock recommendations for, or

cease coverage of, GC firms compared with non-GC matched firms following publication

of the going-concern audit report.

5.2.1. Logistic regression models

Two models are estimated with DOWN(-1,+1), and CEASE(-1,+1) as binary independent

variables respectively. Similar to equations (1) and (2), DOWN(-1.+1)i=1 if analyst i

downgrades his/her active stock recommendation for firm j from event-quarter -1 to event-

quarter +1, and 0 otherwise. CEASE(-1,+1)i =1 if analyst i ceases coverage of firm j between

- 29 -

event-quarter -1 to event-quarter +1, and 0 otherwise. Our regression models are of the

same form as equations (1) and (2) in section 4.1 above:

( 1, 1)Pr( 1) / (1 )i iz z

iDOWN e e (4)

( 1, 1)Pr( 1) / (1 )i iz z

iCEASE e e (5)

except that residual analyst coverage, RANALY in equation (3) is computed as the residual

from the regression of (1+ANALY) on LNSIZE, where ANALY is now the number of

analysts following the firm immediately prior to the GC announcement. We exclude all

recommendations for firms delisted within event-quarter +1 as analyst coverage will

automatically cease in these cases.

5.2.2. Multivariate results

Table 6 reports the results for models (4) and (5). Panel A estimates the probability of an

analyst stock recommendation downgrade immediately following the going-concern audit

report compared with non-GC control firms. As can be seen, our going-concern dummy

variable (GCF) is not significant at conventional levels confirming our univariate results

that analysts do not react to the publication of a GC audit report by downgrading their

stock recommendations more aggressively compared to control firms. We interpret this as

further confirmatory evidence of analysts being reluctant to report negatively on GC firms

even after the GC event itself. Similar to table 4, panel A, the estimated coefficients for

MOM and PREV_REC are negative, and significant (-4.160, p<0.005, and -1.173, p<0.001

respectively). This indicates that following the GC date analysts are also more likely to

downgrade their stock recommendations for firms with poorer past performance, and those

with previous more favorable stock recommendations. Coefficients on RANALY and

ZSCORE are positive and significant (0.279, p<0.05, and 0.239, p<0.001 respectively),

- 30 -

suggesting that analysts are also more prone to downgrade firms with higher analyst

residual coverage, and, interestingly, firms with lower bankruptcy risk. Analyst firm

experience (ANALY_F_EXP) is now positively related to the likelihood of stock

recommendation downgrade (0.121, p<0.005), i.e., more experienced analysts appear more

willing to downgrade their stock recommendations. Finally, the positive and significant

coefficient for POST_REG (0.705, p=0.001) indicates that, following the enactment of the

the NYSD and NASD regulations governing analyst behavior in July 2002, analysts were

more likely to downgrade their stock recommendations for GC firms.

Table 6 here

Panel B of table 6 reports on the probability of analyst coverage cessation

immediately following the GC announcement date. Controlling for other factors that might

impact on the analyst decision to cease stock coverage, the GCF variable is positive and

highly significant (0.469, p<0.001). This suggests that analysts react to this major

accounting event by being more prone to cease coverage of GC stocks than control firms.

The coefficient for MOM is negative (-2.145, p<0.01), and that on PREV_REC is positive

(0.119, p<0.05) confirming our previous results in panel B of table 4. Analysts are again

more likely to cease coverage of stocks with poorer past performance, and firms with more

unfavorable previous stock recommendations. Taken together with our univariate results in

table 5, we interpret these findings as analysts being willing to downgrade a favorable

stock recommendation to neutral, but unwilling to downgrade a neutral recommendation to

unfavorable. In these cases, they simply tend to drop coverage presumably to avoid having

to report negatively on such firms. Finally, we find that the estimated coefficients for

RANALY and B/M are again negative and significant (-0.169, p<0.05, and -0.145, p<0.05

respectively). The analyst’s decision to signal a negative view on the firm’s future

prospects following a GC opinion seems to depend on level of analyst coverage: analysts

tend to drop coverage of firms with lower analyst residual coverage, but downgrade firms

- 31 -

with higher residual analyst coverage. This might possibly be to avoid negative

reputational consequences. In addition, the analyst decision to cease stock coverage of

stocks is also facilitated in the case of growth stocks.

To summarize, table 6 shows that analysts are not more likely to downgrade their

recommendations on GC firms following the GC date compared to control firms, but are

more prone to cease coverage. More generally, analysts tend to downgrade favorable

recommendations, and are more likely to stop following such stocks in the case of

previously unfavorable recommendations. In aggregate, we find that securities’ analysts do

not ignore the publication of a GC audit report but react to it in a particular way.

Accordingly, we cannot reject H3, but do reject H4. We interpret our results as suggesting

analysts prefer to stop following GC firms to avoid a downgrade on their previous average

“hold” recommendation. Again, lack of analyst propensity to provide “underperform” or

“sell” recommendations even after the GC announcement, and the tendency towards

coverage cessation do not serve to provide retail investors (in particular) with value-

relevant information.

6. Additional tests

This section explores whether our results are robust to using alternative benchmarks to

identify similar but non-GC matched firms, and alternative stock recommendation

categories.

6.1. Controlling for alternative benchmarks

In theory, analyst stock recommendations for GC firms might be related to firm

characteristics other than size and zscore that also reflect analyst preferences. For example,

B/M ratio and momentum have the ability to explain cross-sectional variation in stock

returns (e.g., Fama and French, 1992; Jegadeesh and Titman, 1993, 2001), and it is known

that analysts prefer growth stocks, and stocks with positive momentum (Jegadeesh et al.,

2004). Also, our previous results highlight that prior stock returns are highly influential in

determining analyst propensity to issue a negative signal on our set of highly distressed

- 32 -

firms. Industry may also be a relevant factor as analysts tend to follow a portfolio of firms

in a specific industry (Boni and Womack, 2006).

We thus also conduct parallel analyses using different sets of firm characteristics to

test the robustness of our results. In particular, we repeat all our analyses with non-GC

control firms selected on three alternative bases: (i) matching on size and B/M ratio, (ii)

matching on size and momentum, and (iii) matching on industry, size and z-score. Our

results are substantively the same as those reported in our main analyses. Analysts are

more likely to downgrade and cease coverage of GC firms compared with portfolios of

non-GC firms compiled on all three bases before the GC date. Similarly, they are more

likely to cease coverage following the event date. We thus conclude that our results are

robust to the use of alternative non-GC firm matching criteria.

6.2. Controlling for alternative stock recommendation categories

We also test if analysts anticipate the disclosure of a GC opinion by downgrading their

stock recommendations for GC firms more aggressively than control firms using

alternative stock recommendation categories. As previously discussed, stock

recommendations obtained from the I/B/E/S database ignore analyst opinions when no

stock recommendations are issued in a specific event-quarter. We thus use analyst active

recommendations (ACTRECi,j,q), as described in section 4.1 above, and inferred

recommendations (INFRECi,j,q) as robustness tests. Analyst i’s inferred recommendation

for firm j in event-quarter q (INFRECi,j,q) is derived similarly to his/her active

recommendation (ACTRECi,j,q) but now, drawing on Clarke et al. (2006), when the analyst

ceases coverage of a firm, we infer an unfavorable recommendation for that event-quarter,

and for the subsequent two event-quarters.8

Rerunning all our analyses separately substituting both ACTRECi,j,q, and INFRECi,j,q

leads to essentially identical empirical results confirming our conclusions are robust to the

8 An “underperform” recommendation is inferred if the analyst’s last recommendation issued prior to coverage cessation is a

“strong buy” or “buy”, and a “sell” recommendation is inferred otherwise. We limit the inference of an unfavorable

recommendation to the two event-quarters following coverage cessation given the evidence that the impact of a

recommendation change may last 6-months (Womack, 1996).

- 33 -

use of stock recommendation categories drawn up on different bases. Mean and median

stock recommendations are still significantly more unfavorable for GC firms than matched

non-GC control firms in the one-year period before the GC date, and the percentage of

firms with unfavorable recommendations is higher. This reinforces our conclusion that

analysts are more likely to downgrade their stock recommendations more aggressively for

GC firms than for control firms. We also confirm that analysts prefer to drop the coverage

of GC firms following the GC date rather than downgrading their stock recommendations.

7. Discussion and conclusion

This study contributes to our understanding of whether analysts anticipate, and react to,

going-concern uncertainties, and if investors may rely on such sophisticated agents in

making investment decisions about firms in financial distress as signaled by a going-

concern audit report opinion. We show that analysts are aware of impending firm going-

concern problems, and distinguish between GC firms and matched firms as the GC

announcement date approaches, as well as immediately after the GC event. However, we

argue that analyst reluctance to issue unfavorable recommendations, and their propensity

to cease coverage of GC stocks, may mislead, in particular, retail investors.

Using a sample of 618 non-finance, non-utility industry firm-year observations with

first-time going-concern opinions published between 01.01.1994 and 12.31.2007, we find

that securities’ analysts anticipate a forthcoming GC opinion by (1) downgrading their

average stock recommendation for GC firms from “buy” to “hold” in comparison with

matched non-GC firms where recommendations remain at “buy”, and (2) by being more

likely to cease coverage of GC stocks than control firms as the event date approaches.

Post-GC announcement, we also find that analysts do not ignore the publication of such

major accounting news, but now tend to react only by being more prone to drop coverage

of GC firms compared with similar non-GC matched firms. Importantly, in the case of

maintained firm coverage, analysts do not respond to the GC event by revising down their

previous “buy” or “hold” recommendations.

- 34 -

However, despite the evidence that analysts anticipate and react to the GC audit

report, we reject the idea that analysts are reporting appropriately to investors on the

prospects of such financially-distressed firms. In fact, 42% of analysts following GC firms

still had “strong buy” or “buy” recommendations on these stocks immediately before the

GC date, 48% of recommendations were “hold”. Only 11% were unfavorable

(“underperform” or “sell”) recommendations. Brokerage firms (e.g., Credit Suisse, UBS

Warburg, Salomon Smith Barney, Morgan Stanley, Merrill Lynch, etc) define a “hold”

recommendation as one where a stock is perceived to be fairly priced whereas a favorable

(unfavorable) recommendation is associated with stocks that are expected to outperform

(underperform) the market return in the short-run. However, studies show that, following

the publication of a GC opinion, stock prices underperform no less than 14% over a one-

year period (e.g., Kausar et al., 2009). This is particularly important in our domain as retail

investors, who hold the majority of the equity of GC firms (Kausar et al., 2009), tend to

follow analyst recommendations literally and do not understand that, contrary to large

investors, a “hold” recommendation may well be associated with a negative outlook

(Malmendier and Shanthikumar, 2007). Analyst behavior in the case of GC firms is in line

with the idea that analysts “do not always mean what they say” and frequently issue “hold”

recommendations when they mean “sell” (Shefrin, 2002, pp. 258). We speculate this could

contribute to explaining why retail (naïve) investors increase their holdings of GC stocks at

the expense of institutional investors both in the one-year period preceding the GC

announcement date, and for at least 6 months post-audit report publication (Kausar, Kumar

and Taffler, 2013).

Analyst propensity to cease coverage of GC firms before, and after, the GC date,

reinforces the notion that analysts are less interested in following firms on which they hold

negative views (e.g., McNichols and O’Brien, 1997; Griffin, 2003) and, presumably, tend

to replace these firms with others where they have more favorable opinions (e.g.,

McNichols and O’Brien, 1997; Kecskés and Womack, 2007). For example, the evidence

that 41% of analysts that were following GC firms one-year before the GC date decide to

- 35 -

cease coverage as the event date approaches has important consequences in the

interpretation of the average observable “hold” recommendation for such stocks, and

explains, at least partially, why this does not fall below “hold”. Considering that analysts

do not downgrade stock recommendations when they cease coverage of firms (e.g.,

McNichols and O’Brien, 1997), the lower tail of the recommendation distribution for GC

firms becomes censored leading to the average observed recommendation being more

favorable than the true unobservable average stock recommendation.

Overall, despite the idea that the marginal contribution of securities’ analysts to

investors may be greater in the case of the bad news domain (e.g., Hong et al., 2000), we

conclude that investors cannot rely on these sophisticated agents as messengers of going-

concern uncertainties. As the SEC advises, investors should do their homework, and

understand that they cannot follow analyst stock recommendations literally as analysts may

communicate unfavorable information through more ambiguous language, as we have

shown, or not at all.

- 36 -

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- 42 -

TABLE 1

Descriptive Statistics – Sample Firms vs. Control Firms

This table compares descriptive statistics for our sample and control firms. Control firms are selected employing the control firm approach based on size and z-score as described in section 3.2. The last four columns

report the mean and median differences between the variables of each portfolio. The significance of the t-test (Wilcoxon-Mann-Whitney test) is showed in brackets on the right of the mean (median) differences. The

significance of the differences is reported in parentheses. * denotes significant at 5%, ** denotes significant at 1%, and *** denotes significant at 0.1%.

SIZE = market value of equity measured by market capitalization in $ million; SALES = sales in $ million; TA = total assets in $ million; ROA=return on assets (net income/total assets); CR = current ratio (current

assets/current liabilities); LEV=total debt/total assets; ZSCORE=financial distress measure computed as in Altman (1968). All annual accounting data used to compute these variables are taken from the last annual

financial accounts reported before the GC date. B/M= book value of equity divided by market capitalization one year before the GC announcement date; MOM = momentum, defined as the monthly average of prior 11

months (t-11 to t-1) raw returns; SUE= difference of the current quarterly earnings figure and the earnings figure reported by the firm in the previous quarter to the absolute value of the firm’s current quarter earnings.

Mean Median St. Deviation Mean Median St. Deviation

SIZE 173.2 68.0 334.0 167.2 66.4 323.6 6.0 (0.746) 1.60 (0.733)

SALES 178.9 42.0 363.2 144.7 42.9 282.5 34.2 (0.065) -0.90 (0.995)

TA 211.8 53.0 427.5 182.5 61.7 336.7 29.3 (0.180) -8.70 (0.018) *

ROA -0.67 -0.41 0.80 -0.27 -0.05 0.57 -0.40 (<0.001) *** -0.36 (<0.001) ***

CR 1.85 1.26 1.95 3.68 2.32 3.81 -1.83 (<0.001) *** -1.06 (<0.001) ***

LEV 0.35 0.29 0.30 0.25 0.17 0.24 0.10 (<0.001) *** 0.12 (<0.001) ***

ZSCORE 1.06 0.85 1.02 1.06 0.85 1.01 0.00 (0.987) 0.00 (0.966)

B/M 0.71 0.38 1.02 0.75 0.51 0.91 -0.04 (0.490) -0.13 (0.008) **

MOM -0.05 -0.05 0.07 0.01 0.00 0.07 -0.06 (<0.001) *** -0.05 (<0.001) ***

SUE 0.37 -0.43 4.19 1.11 0.12 5.59 -0.74 (0.001) ** -0.55 (<0.001) ***

p-valueMedian

Diferencep-value

( n = 618) ( n = 618) Variable

GC FIRMS CONTROL FIRMS

Mean

Diference

- 43 -

TABLE 2

Quarterly Trend in Analyst stock Recommendations – Sample Firms vs. Control Firms

This table presents the event-quarter trend in mean analyst new stock recommendations (RECq) from event-quarter -8 to event-quarter -1 for our

population of 618 non-finance, non-utility industry firm-year observations with first-time going-concern opinions published between 01.01.1994 and

12.31.2007 with stocks listed on the NYSE, AMEX or NASDAQ that have analyst coverage before the GC announcement date. Control firms are

selected employing the control firm approach based on size and z-score as described in section 3.2. Event-quarters are defined as periods of 90

calendar days relative to the GC announcement date. Recommendations are coded as 1 (strong buy), 2 (buy), 3 (hold), 4 (underperform) and 5 (sell).

The percentage of “unfavorable” recommendations is computed as the number of firms whose average numerical recommendation is higher than 3.5

divided by the total number of firms with available recommendations for that event-quarter. Coverage cessation percentage is computed as the

number of analysts ceasing the coverage of a firm divided by the total number of analysts following firms at the beginning of that event-quarter. The

two-tailed significance of the t-test (Wilcoxon-Mann-Whitney test) is reported in parentheses for the mean (median) recommendation difference,

whereas the significance of the binomial test is reported in parentheses for the difference between the percentage of “unfavorable” recommendations,

and coverage cessation. The significance of the differences is reported in parentheses. * denotes significant at 5%, ** denotes significant at 1% and

*** denotes significant at 0.1%.

GC Firms Control Firms Difference p-value GC Firms Control Firms GC Firms Control Firms

Mean 2.00 1.94 0.06 (0.400)

Median 2.00 2.00 0.00 (0.481)

% Unfavorable Rec. 0.04 0.02 0.02 (0.001) **

% Coverage Cessation 0.03 0.04 -0.01 (0.255)

Mean 2.19 1.99 0.20 (0.006) **

Median 2.00 2.00 0.00 (0.014) *

% Unfavorable Rec. 0.05 0.01 0.04 (<0.001) ***

% Coverage Cessation 0.08 0.09 -0.01 (0.316)

Mean 2.17 2.06 0.11 (0.139)

Median 2.00 2.00 0.00 (0.158)

% Unfavorable Rec. 0.05 0.04 0.01 (0.284)

% Coverage Cessation 0.11 0.09 0.02 (0.005) **

Mean 2.22 2.12 0.10 (0.169)

Median 2.00 2.00 0.00 (0.162)

% Unfavorable Rec. 0.06 0.05 0.01 (0.515)

% Coverage Cessation 0.12 0.10 0.02 (0.054)

Mean 2.34 2.10 0.14 (0.002) **

Median 2.25 2.00 0.25 (0.001) **

% Unfavorable Rec. 0.06 0.04 0.02 (0.015) *

% Coverage Cessation 0.12 0.12 0.00 (0.209)

Mean 2.60 2.14 0.46 (<0.001) ***

Median 2.80 2.00 0.80 (<0.001) ***

% Unfavorable Rec. 0.13 0.03 0.10 (<0.001) ***

% Coverage Cessation 0.15 0.10 0.05 (<0.001) ***

Mean 2.59 1.97 0.64 (<0.001) ***

Median 3.00 2.00 1.00 (<0.001) ***

% Unfavorable Rec. 0.14 0.05 0.09 (<0.001) ***

% Coverage Cessation 0.17 0.11 0.06 (<0.001) ***

Mean 2.73 2.05 0.68 (<0.001) ***

Median 3.00 2.00 1.00 (<0.001) ***

% Unfavorable Rec. 0.18 0.04 0.14 (<0.001) ***

% Coverage Cessation 0.20 0.13 0.07 (<0.001) ***

278

219

207

163

440

275

269

RECq

255

222

258

Event-Quarter

-7

233

220

Nº Firms with

Recommendations

264

235

243

245

416

476

472

-3

-2

-1

460

494-8

-6

-5

-4

270

247

Nº Recommendations

556

562

552

515

426

394

486

484

364

- 44 -

TABLE 3

Transition Matrix of Analyst Active Recommendations – Event-quarter -4 vs. Event-quarter -1

This table presents the transition matrix of changes in analyst active recommendations (ACTRECi,j) between event-quarter -4 and event-quarter -1.

Our GC firm population consists of 618 non-finance, non-utility industry firm-year observations with first-time going-concern opinions published

between 01.01.1994 and 12.31.2007 with stocks listed on the NYSE, AMEX or NASDAQ that have analyst coverage before the GC announcement

date. Control firms are selected employing the control firm approach based on size and z-score as described in section 3.2. Event-quarters are defined

as periods of 90 calendar days relative to the GC announcement date.

Panel A: Active Analyst Recommendations for GC firms

ACTRECi ,j,(q-4) 1 2 3 4 5Coverage

CessationTOTAL

152 30 71 6 6 138 403

(38%) (7%) (18%) (1%) (1%) (34%) (25%)

12 179 89 18 2 223 523

(2%) (34%) (17%) (3%) (0%) (43%) (33%)

11 12 280 20 8 260 591

(2%) (2%) (47%) (3%) (1%) (44%) (37%)

0 0 7 25 3 27 62

(0%) (0%) (11%) (40%) (5%) (44%) (4%)

0 1 3 0 13 12 29

(0%) (4%) (10%) (0%) (45%) (41%) (2%)

175 222 450 69 32 660 1608

(11%) (14%) (28%) (4%) (2%) (41%) (100%)

Panel B: Active Analyst Recommendations for matched non-GC firms

ACTRECi ,j,(q-4) 1 2 3 4 5Coverage

CessationTOTAL

274 40 39 1 2 119 475

(58%) (8%) (8%) (0%) (0%) (25%) (33%)

29 264 58 3 1 129 484

(6%) (55%) (12%) (1%) (0%) (27%) (34%)

25 16 216 9 3 145 414

(6%) (4%) (52%) (2%) (1%) (35%) (29%)

0 2 6 17 0 7 32

(0%) (6%) (19%) (53%) (0%) (22%) ((2%)

1 1 7 0 10 6 25

(4%) (4%) (28%) (0%) (40%) (24%) (2%)

329 323 326 30 16 406 1430

(23%) (23%) (23%) (2%) (1%) (28%) (100%)

4 = "Underperform"

5 = "Sell"

TOTAL

4 = "Underperform"

5 = "Sell"

3 = "Hold"

TOTAL

ACTRECi ,j,(q-1)

ACTRECi ,j,(q-1)

1 = "Strong Buy"

2 = "Buy"

3 = "Hold"

1 = "Strong Buy"

2 = "Buy"

- 45 -

TABLE 4

Logistic Regressions of Stock Recommendation Downgrade / Analyst Coverage Cessation on Predictor Variables for the (-4,-1) window

This table provides the results of running binary logistic regressions to estimate the probability of stock recommendation downgrade/analyst coverage

cessation of a firm from event-quarter -4 to event-quarter -1 using both GC and control firm stock recommendations. Our GC firm population consists

of 618 non-finance, non-utility industry firm-year observations with first-time going-concern opinions published between 01.01.1994 and 12.31.2007

with stocks listed on the NYSE, AMEX or NASDAQ that have analyst coverage before the GC announcement date. Control firms are selected

employing the control firm approach based on size and z-score as described in section 3.2. Event-quarters are defined as periods of 90 calendar days

relative to the GC announcement date. The logistic regression models are described in equations (1) and (2) in the text. The binary dependent variable

DOWN(-4,-1)i = 1 if analyst i downgrades his/her active stock recommendation on firm j between event-quarter -4, and event-quarter -1, and 0

otherwise. CEASE(-4,-1)i = 1 if analyst i ceases coverage of firm j between event-quarter -4, and event-quarter -1, and 0 otherwise.

Dummy variable GCF=1 if the firm receives a GC opinion, and 0 otherwise; LNSIZE = natural log of market capitalization measured one year before

the GC announcement date; RANALY = residual from the regression of (1+ANALY) on LNSIZE. B/M = book value of equity divided by market

capitalization one year before the GC announcement date; MOM = monthly average of prior 11 month (t-11 to t-1) raw returns; ROA = return on

assets (net income/total assets); CR = current ratio (current assets/current liabilities); ZSCORE = financial distress measure computed as in Altman

(1968). All annual accounting data used to compute these variables are taken from the last annual financial accounts reported before the GC date;

SUE = difference of the current quarterly earnings figure and the earnings figure reported by the firm in the previous quarter to the absolute value of

the firm’s current quarter earnings. PREV_REC = latest recommendation issued by the analyst in event-quarter -4; ANALY_EXP = number of years

between the last recommendation before the GC announcement date, and the first record of the analyst in the I/B/E/S Detail History file.

ANALY_F_EXP = number of years between the last recommendation before the GC announcement date and the first record of the analyst for that

specific firm in the I/B/E/S Detail History file; BROKER_SIZE = number of analysts at the respective brokerage house in the year of the GC event;

dummy variable POST_REG = 1 if the stock recommendation is issued post 8.31.2002, and 0 otherwise; dummy variable AUDITOR = 1 if the firm is

audited by a big 4/5 auditor in the calendar year preceding the GC announcement date, and 0 otherwise. The significance of the coefficients is

reported in parentheses. * denotes significant at 5%, ** denotes significant at 1% and *** denotes significant at 0.1%.

Independent variable Coefficient Coefficient

-1.869 -0.877

(<0.001) *** (0.001) **

0.420 0.262

(0.001) ** (0.004) **

0.168 -0.091

(0.001) ** (0.008) **

-0.04 0.100

(0.590) (0.066)

-0.15 0.097

(0.039) * (0.001) **

-3.01 -3.569

(0.000) ** (<0.001) ***

0.137 -0.097

(0.071) (0.029) *

0.052 -0.018

(0.289) (0.648)

0.002 0.005

(0.623) (0.208)

-0.822 0.113

(<0.001) *** (0.009) **

-0.014 -0.014

(0.272) (0.110)

0.018 0.025

(0.482) (0.156)

0.000 0.000

(0.096) (0.237)

0.206 -0.002

(0.126) (0.983)

0.369 -0.050

(0.131) (0.735)

No. of cases 3,038 3,038

Likelihood Ratio 263.68 *** 149.16 ***

Pseudo-R2

0.083 0.048

MOM

ROA

ZSCORE

SUE

PREV_REC

LNSIZE

RANALY

B/M

Dependent Variable

DOWN(-4,-1)i

Panel A

AUDITOR

Panel B

Dependent Variable

CEASE(-4,-1)i

ANALY_EXP

ANALY_F_EXP

BROKER_SIZE

REG_FD

GCF

Intercept

46

TABLE 5

Transition Matrix of Analyst Active Recommendations – Event-quarter -1 vs. Event-quarter +1

This table presents the transition matrix of changes in analyst active recommendations (ACTRECi,j) between event-quarter -1 and event-quarter +1. Our

GC firm population consists of 618 non-finance, non-utility industry firm-year observations with first-time going-concern opinions published between

01.01.1994 and 12.31.2007 with stocks listed on the NYSE, AMEX or NASDAQ that have analyst coverage before the GC announcement date. Control

firms are selected employing the control firm approach based on size and z-score as described in section 3.2. Event-quarters are defined as periods of 90

calendar days relative to the GC announcement date. Recommendations are coded as 1 (strong buy), 2 (buy), 3 (hold), 4 (underperform) and 5 (sell).

Panel A: Active Analyst Recommendations for GC firms

ACTRECi ,j,(q-1) 1 2 3 4 5Coverage

CessationTOTAL

158 6 15 1 1 43 224

(71%) (3%) (7%) (1%) (1%) (19%) (19%)

6 196 14 1 1 56 274

(2%) (72%) (5%) (0%) (0%) (20%) (23%)

5 11 408 3 3 146 576

(1%) (2%) (71%) (1%) (1%) (25%) (48%)

0 0 8 60 0 21 89

(0%) (0%) (9%) (67%) (0%) (24%) (7%)

0 0 3 1 29 7 40

(0%) (0%) (8%) (3%) (73%) (18%) (3%)

169 213 448 66 34 273 1203

(14%) (18%) (37%) (5%) (3%) (23%) (100%)

Panel B: Active Analyst Recommendations for matched non-GC firms

ACTRECi ,j,(q-1) 1 2 3 4 5Coverage

CessationTOTAL

425 25 30 1 0 56 537

(79%) (5%) (6%) (0%) (0%) (10%) (35%)

20 399 32 2 0 58 511

(4%) (78%) (6%) (0%) (0%) (11%) (33%)

9 16 336 1 1 65 428

(2%) (4%) (79%) (0%) (0%) (15%) (28%)

0 1 3 31 0 6 41

(0%) (2%) (7%) (76%) (0%) (15%) (3%)

0 0 1 0 18 4 23

(0%) (0%) (4%) (0%) (78%) (17%) (1%)

454 441 402 35 19 189 1540

(29%) (29%) (26%) (2%) (1%) (12%) (100%)

4

5

TOTAL

5

TOTAL

ACTRECi ,j,(q+1)

1

2

3

ACTRECi ,j,(q+1)

1

2

3

4

47

TABLE 6

Logistic Regressions of Ceasing Coverage / Recommendation Downgrade on Predictor Variables for the (-1,+1) window

This table presents the results of binary logistic regression models to estimate the probability of stock recommendation downgrade/analyst coverage

cessation of a firm from event-quarter -1 to event-quarter +1 using both GC and control firms stock recommendations. Our GC firm population consists of

618 non-finance, non-utility industry firm-year observations with first-time going-concern opinions published between 01.01.1994 and 12.31.2007 with

stocks listed on the NYSE, AMEX or NASDAQ that have analyst coverage before the GC announcement date. Control firms are selected employing the

control firm approach based on size and z-score as described in section 3.2. Event-quarters are defined as periods of 90 calendar days relative to the GC

announcement date. The logistic regression models are described in equations (1) and (2). The binary dependent variable DOWN(-1,+1)i = 1 if analyst i

downgrades his/her active stock recommendation on firm j between event-quarter -1 and event-quarter +1, and 0 otherwise. CEASE(-1,+1)i =1 if analyst i

ceases coverage of firm j between event-quarter -1 and event-quarter +1, and 0 otherwise.

Dummy variable GCF=1 if the firm receives a GC opinion, and 0 otherwise; LNSIZE = natural log of market capitalization measured one year before the

GC announcement date; RANALY = residuals from the regression of (1+ANALY) on LNSIZE, where ANALY=number of analysts following the firm in

event-quarter -1; B/M= book value of equity divided by market capitalization one year before the GC announcement date; MOM = monthly average of

prior 11 month (t-11 to t-1) raw returns; ROA = return on assets (net income/total assets); CR = current ratio (current assets/current liabilities); ZSCORE

= financial distress measure computed as in Altman (1968); All annual accounting data used to compute these variables are taken from the last annual

financial accounts reported before the GC date; SUE = difference of the current quarterly earnings figure and the earnings figure reported by the firm in

the previous quarter to the absolute value of the firm’s current quarter earnings. PREV_REC = latest recommendation issued by the analyst at the event-

quarter -4; ANALY_EXP = number of years between the last recommendation before the GC announcement date and the first record of the analyst in the

I/B/E/S Detail History file. ANALY_F_EXP = number of years between the last recommendation before the GC announcement date and the first record

of the analyst for that specific firm in the I/B/E/S Detail History file; BROKER_SIZE = number of analysts working for the broker at the year of the GC

event; dummy variable POST_REG=1 if the stock recommendation is issued following 8.31.2002, and 0 otherwise; dummy variable AUDITOR = 1 if the

firm is audited by a big 4/5 auditor in the calendar year preceding the GC announcement date, and 0 otherwise. The significance of the coefficients is

reported in parentheses. * denotes significant at 5%, ** denotes significant at 1% and *** denotes significant at 0.1%.

Independent variable Coefficient Coefficient

-2.911 -1.996

(<0.001) *** (<0.001) ***

-0.317 0.469

(0.176) (<0.001) ***

0.140 -0.046

(0.106) (0.304)

0.279 -0.169

(0.042) * (0.015) *

-0.113 -0.145

(0.413) (0.018) *

-4.160 -2.145

(0.003) ** (0.006) **

0.080 -0.105

(0.599) (0.113)

0.239 0.042

(<0.001) *** (0.333)

0.013 0.004

(0.088) (0.562)

-1.173 0.119

(<0.001) *** (0.031) *

-0.030 -0.001

(0.205) (0.919)

0.121 0.035

(0.002) ** (0.115)

-0.000 0.000

(0.583) (0.413)

0.705 0.005

(0.001) ** (0.966)

0.736 0.068

(0.063) (0.741)

No. of cases 2,743 2,743

Likelihood Ratio 156.79 *** 89.64 ***

Pseudo-R2

0.056 0.032

Panel B

Dependent Variable Dependent Variable

DOWN(-4,-1)i CEASE(-4,-1)i

Panel A

AUDITOR

Intercept

ANALY_EXP

ZSCORE

SUE

PREV_REC

ANALY_F_EXP

BROKER_SIZE

REG_FD

GCF

LNSIZE

RANALY

B/M

MOM

ROA