The Optional Qualitative Assessment in Impairment Tests

Dirk Black

School of Accountancy

College of Business

University of Nebraska – Lincoln

dirkblack@unl.edu

Jake Krupa

Miami Business School

University of Miami

jkrupa@bus.miami.edu

Miguel Minutti-Meza

Miami Business School

University of Miami

mminutti@bus.miami.edu

August 2019

*We acknowledge helpful comments from Michael Ettredge, Kurt Gee, Amanda Gonzales, Trevor Sorensen, Kyle Welch,

staff at the Financial Accounting Standards Board (FASB), and workshop participants at the Brigham Young University

Accounting Research Symposium, the University of Miami, and the Graduate Research Accounting Conference at Emory

University. Dirk Black acknowledges the support of the College of Business at the University of Nebraska – Lincoln.

Jake Krupa and Miguel Minutti-Meza acknowledge the support of the Miami Business School at the University of Miami.

The Optional Qualitative Assessment in Impairment Tests

ABSTRACT: We examine optional qualitative assessments in impairment tests of goodwill and

indefinite-lived intangibles. These assessments are intended to reduce impairment testing complexity,

but introduce accounting optionality. We find that firms performing qualitative assessments face

lower impairment risk and higher costs of performing quantitative impairment tests. Then, using a

difference-in-differences design, we find that qualitative assessment firms have a higher incidence of

impairments vs. firms disclosing nothing about qualitative assessments, suggesting that qualitative

analysis may make it more difficult for managers to manipulate quantitative tests to avoid

impairments. We also find that qualitative assessment firms exhibit no reduction in impairment

timeliness, and find no evidence of increased monitoring costs for auditors, regulators, and investors

surrounding the accounting standard change introducing qualitative assessments. Our findings inform

standard setters about the determinants and consequences of qualitative assessments and speak to the

broader issue of the costs and benefits of optionality in accounting.

JEL Classifications: M41; M42; M48

Data Availability: Data are available from public sources identified in the text. Hand-collected data

are available upon request.

Keywords: Goodwill; intangibles; impairment; qualitative assessment; fair value; ASC 350; SFAS

142.

1

I. INTRODUCTION

Companies face complex rules when determining the carrying value and potential impairment

of intangible assets with indefinite life, including goodwill.1 Since 2001, Intangibles – Goodwill and

Other (ASC 350, previously SFAS 142) requires companies to perform an annual impairment test for

these assets, comparing their carrying values to their estimated fair values (FASB 2001).2 Recent

Accounting Standards Updates (ASUs) issued by the Financial Accounting Standards Board (FASB)

modified the existing rules to introduce a qualitative assessment as an impairment indicator, a “Step

0” intended to reduce the complexity and costs of the quantitative two-step test in ASC 350.

ASU 2011-08 gives companies the option of beginning the goodwill impairment test by

performing a qualitative assessment at the reporting-unit level (FASB 2011). This standard introduces

a rare accounting situation where an entirely “unconditional option” is allowed in U.S. GAAP (FASB

2011, BC23, 22). This qualitative assessment considers events and circumstances informative about

the likelihood of impairment, such as economic conditions, industry and market considerations, and

financial performance. However, this assessment does not require estimating the fair value of a

reporting unit using discounted cash flows or another similar approach. If, after performing this

assessment, a company concludes that it is “more likely than not that the fair value of a reporting unit

is less than its carrying amount,” the company should perform the quantitative test. (FASB 2011, 1).

ASU 2012-02 provides the same option for indefinite-lived intangibles (FASB 2012), while ASU

2017-04 eliminates Step 2 of the impairment test and continues to allow the optional qualitative

assessment (FASB 2017), highlighting the increased prominence of the qualitative assessment.

1 Existing accounting standards make a distinction between intangible assets with finite and indefinite lives (see section

II for additional details). 2 Impairments of goodwill and other intangible assets are frequent. For instance, in the fiscal years ending between

December 15th, 2009 and December 15th, 2015, 6.8% of firms with non-missing assets included in Compustat reported

goodwill or other intangible impairments. Please refer to section II for additional details on the two-step quantitative

impairment test.

2

This study investigates the effects of the optional qualitative assessment using four research

questions. Our first research question is: What are the characteristics of firms using the qualitative

assessment option?3 This question is driven by standard setters’ intentions to reduce the cost of

impairment tests. Survey evidence from Duff and Phelps (2017) indicates that in the first three

calendar years since the adoption of ASU 2011-08 and ASU 2012-02, only 29, 43 and 54 percent of

all public firms performed a qualitative assessment. These statistics contrast with 81 percent of the

respondents expecting to implement the qualitative standard in 2011 (Duff and Phelps 2011). Among

the reasons given for bypassing this assessment were: 1) Step 1 is a “more robust analysis;” and, 2)

Step 0 is “cumbersome and/or time-consuming relative to Step 1” (Duff and Phelps 2012, 35).

In our analyses, we identify a set of firms with goodwill and intangibles on their balance sheet

from fiscal years 2009 to 2015. Next, using textual analysis and manual coding, we search annual 10-

K fillings on the SEC’s EDGAR database to identify a subsample of firms that disclose performing a

qualitative assessment or implementing ASUs 2011-08 and 2012-02.4 Using this data, we compare

three groups of firms: 1) Firms specifically disclosing that they perform a qualitative assessment; 2)

Firms mentioning the option to perform a qualitative assessment under ASUs 2011-08 and 2012-02,

discussing the change in standards, or disclosing bypassing the qualitative assessment; and, 3) Firms

remaining silent about the qualitative assessment.5 In our final sample of 1,228 firms, we classify 373

as “performing” firms, 592 as “mentioning” firms, and 263 as “silent” firms. Our study is among the

first to provide descriptive evidence on firms using the qualitative assessment option.

3 Previous research has studied determinants (Francis, Hanna, and Vincent 1996; Beatty and Weber 2006; Hayn and

Hughes 2006; Brochet and Welch 2011; Gu and Lev 2011; Ramanna and Watts 2012; Glaum, Landsman, and Wyrwa

2015; Li and Sloan 2017) and consequences of goodwill impairments (Bens, Heltzer, and Segal 2011; Li, Shroff,

Venkataraman, and Zhang 2011; Darrough, Guler, and Wang 2014). Other studies have also studied determinants of long-

lived asset impairments (Francis et al. 1996; Riedl 2004) and write-downs in general (Lawrence, Sloan, and Sun 2013). 4 We utilize financial statement disclosure to identify performers of the qualitative assessment. Per ASU 2011-08 (FASB

2011, BC24, 23), the FASB intends to have firms “make a positive assertion” about the conclusions reached when

performing a qualitative assessment. Thus, firms performing the qualitative assessment are intended disclose this action. 5 Our category classification is similar to that used in the Duff and Phelps (2014) survey. Appendix C provides examples

of the disclosures we use to classify firms into these categories.

3

In answer to our first research question, we document that firms performing the qualitative

assessment have higher past stock returns, lower book-to-market ratios, lower previous incidence of

impairment losses, and higher balances of goodwill and intangibles. These results suggest that firms

exercise the qualitative assessment option when they face comparatively lower impairment risk and

higher costs of performing the traditional quantitative impairment test.

Our second research question is: Has the incidence of impairments changed after the adoption

of the qualitative assessment standards? Agency theory and existing empirical evidence support

concerns about the impairment tests under the two-step model in ASC 350 and SFAS 142 (e.g., Beatty

and Weber 2006; Hayn and Hughes 2006; Li et al. 2011; Ramanna and Watts 2012; Li and Sloan

2017). The qualitative assessment can increase subjectivity and reduce timeliness of impairment tests.

However, there exists a tradeoff between increasing agency costs and subjectivity concerns and the

practical implementation costs of impairment testing. The benefits of impairment testing may not

always justify its costs, including fees paid to business valuation experts.6 Furthermore, a thorough

qualitative analysis may make it more difficult for managers to manipulate the inputs of the two-step

quantitative test to avoid impartments (e.g., projected cash flows and discount rates) and may allow

for signaling of the firm’s type due to the qualitative assessment’s optionality.

To answer our second research question, we examine impairments during the two and six

years (respectively) surrounding the effective date of ASU 2011-08, the earliest of the qualitative

assessment standards. We implement a difference-in-difference (DD) research design comparing

companies that disclose, mention, or are silent about the qualitative assessment. Finally, we examine

cross-sectional variation in a firm’s opportunity to manipulate impairment tests.

6 According to the Duff and Phelps (2011) survey, public companies listed the high cost of third party valuation experts

as one of the biggest challenges to performing impairment tests.

4

We find that following the effective date of ASU 2011-08, firms performing the qualitative

assessment have a higher incidence of impairment recognition relative to firms silent about the

qualitative assessment. Our results are robust to employing a model of qualitative assessment and

propensity score matching to improve covariate balance between treatment and control firms

Although the qualitative assessment requires additional judgment, the implementation of this option

results in an increase (not a decrease) in the overall incidence of impairment charges. The relative

increase in impairment incidence for a firm performing a qualitative assessment vs. a firm remaining

silent about a qualitative assessment is 9.7 percentage points for the two-year analysis and 4.9

percentage points for the six-year analysis based on the propensity-matched samples.

In cross-sectional tests examining opportunities to influence impairments, we fail to find that

the higher incidence of impairments following the standard change for firms performing qualitative

assessments varies between: 1) Firms with a high vs. low number of reporting segments; 2) Firms

with high vs. low number analyst following; and, 3) Firms with high vs. low market-to-book ratios.

Our third research question is: Has the adoption of the qualitative assessment made

impairments more difficult to predict and less timely? We compare Type I and Type II errors of an

impairment model and then examine the frequency of impairments in the first three quarters of the

fiscal year vs. the fourth quarter. Using the pre-standard-change period to train our model, we find

that the incidence of incorrectly predicting an impairment (Type I error) is lower for qualitative

assessment firms vs. other firms, while the incidence of incorrectly failing to predict an impairment

(Type II error) is higher for qualitative assessment firms vs. other firms. Using a sample of firm-years

with impairment charges, we find no difference in the incidence of early impairments between firms

using the qualitative assessment vs. other firms in the post-period. We cannot conclude that qualitative

assessments improve impairment predictability, but we conclude that qualitative assessments do not

adversely affect impairment timeliness.

5

Finally, our fourth research question is: Has the adoption of the new rules resulted in

unintended consequences reflected in audit fees, SEC enforcement, and investors’ reaction to

earnings news? The adoption of the new rules may have resulted in unintended consequences for firm

monitors as reflected in audit fees, SEC enforcement, and investors’ reaction to earnings news as

auditors, regulators, and investors adjust to the additional discretion available to management with

the qualitative assessment option. We find no evidence that qualitative assessment firms are more

likely to pay higher audit fees after the standard change or receive more SEC comment letters

pertaining to goodwill or intangibles vs. firms that are silent about the qualitative assessments. We

find no evidence that investors respond differently to earnings news from qualitative assessment firms

vs. other firms, suggesting that performing a qualitative assessment does not worsen investors’

perceptions of earnings quality. These results suggest that external monitors do not view qualitative

assessment firms as having higher financial reporting risk than other firms. Moreover, the results do

not provide consistent evidence that monitoring costs have shifted to auditors, the SEC, or investors.

Collectively, our results quantify the impact of the impairment standard change and shed light

on whether firms opportunistically use the discretion allowed by the qualitative assessment option.

Our setting is useful because the qualitative assessment option allowed under ASU 2011-08 and 2012-

02 is one of the few accounting situations where an unrestricted option is allowed to managers.

Limited evidence is available in the existing literature regarding discretionary choices in conducting

impairment tests. We provide evidence that subjectivity from the qualitative assessment option does

not decrease impairment incidence. Our findings inform the debate about the complexities and costs

of impairment tests, a topic of interest to the FASB. With the added importance of “Step 0” following

the adoption of ASU 2017-04 after December 15, 2019 and the removal of “Step 2” (FASB 2017),

this paper can help standard setters, regulators, and practitioners understand how qualitative

assessments in particular, and optionality in accounting in general, affect accounting information.

6

II. BACKGROUND, LITERATURE, AND RESEARCH QUESTIONS

The Goodwill Impairment Test

Goodwill recognized on the balance sheet represents expected future economic benefits from

intangible assets that are not identifiable and cannot be separately recognized following an

acquisition. Goodwill is typically recognized as the difference between the purchase price paid by the

acquirer and the fair value of the net assets acquired (ASC 805 Business Combinations, previously

SFAS 141, revised in 2007 (FASB 2007)). A relatively high proportion of the price paid for

acquisitions is allocated to goodwill. Shalev, Zhang, and Zhang (2013) examine a sample of 320

acquisitions by U.S. companies between 2001 and 2008. In their sample, the mean proportion

allocated to goodwill and other indefinite-lived intangible assets is 59 percent of deal value.

Impairment charges can be very large – Kraft Heinz recently recognized a $744 million goodwill

impairment loss and a $474 million intangible asset impairment charge for the six months ended June

29, 2019 (Kraft Heinz 2019; Trentmann 2019).

Starting after December 2001, ASC 350 Intangibles–Goodwill and Other (previously SFAS

142, Goodwill and other intangible assets) requires a two-step goodwill impairment test (FASB

2001). 7 ASC 350-20-35-28 requires annual goodwill impairment testing and interim goodwill

impairment testing when circumstances warrant the test. The goodwill impairment decision depends

on a two-step process involving a quantitative analysis of a company’s reporting units.8

Per ASC 350-20-35, in Step 1, the company must determine whether the fair value of the

reporting unit is less than its carrying value (including goodwill). If that is the case, the company must

7 ASC 350 (previously SFAS 142) was issued at approximately the same time as ASC 805 Business Combinations

(previously SFAS 141, Business Combinations, revised in 2007), which required the measurement of goodwill based on

the purchase method (http://www.fasb.org/summary/stsum141.shtml). 8 At the time of the acquisition, goodwill must be allocated to those reporting units of the acquiring company that are

expected to benefit from the synergies of the acquisition. Thus, goodwill is tested for impairment at the reporting unit

level. The identification of reporting units is unique to each company and begins with identifying operating segments.

7

proceed to Step 2 and determine the implied fair value of goodwill of the reporting unit by assigning

the fair value of the reporting unit used in Step 1 to all assets and liabilities of that reporting unit

assuming the reporting unit had been acquired. Then, the company must compare the implied fair

value of goodwill with the carrying amount of goodwill to determine whether goodwill is impaired.

The impairment loss is equal to the carrying value minus the implied fair value of goodwill, is

recognized in net income, and cannot be reversed in future periods.

The FASB released ASU 2011-08 Testing Goodwill for Impairment, applicable after

December 2011, introducing the option to perform a qualitative assessment of a reporting unit before

performing Step 1 of the goodwill impairment test (FASB 2011). The qualitative assessment involves

considering whether circumstances suggest that a goodwill impairment charge may need to be

recorded. ASU 2011-08 notes the following events and circumstances that may indicate the necessity

of an impairment charge: 1) Macroeconomic conditions; 2) Industry and market considerations; 3)

Cost factors; 4) Overall financial performance; 5) Other relevant entity-specific events; 6) Events

affecting a specific reporting unit; and, 7) A sustained decrease, both absolute and relative to a

company’s peers, in share price (FASB 2011).

A company must proceed with Step 1 of the quantitative goodwill impairment test only if the

qualitative assessment indicated more than a 50 percent chance that the carrying value of a reporting

unit is greater than its fair value (FASB 2011). An entity may decide to implement the qualitative

assessment or bypass it in any given period for any reporting unit (ASC 350-20-35-3B). Appendix B

shows a recommended flowchart for goodwill impairment decisions and the full list of suggested

events and circumstances that a firm should consider as part of the qualitative assessment taken from

ASU 2011-08 (FASB 2011).

Recently, the FASB released ASU 2017-04 Simplifying the Test for Goodwill Impairment,

applicable after December 2019 (FASB 2017). Per the changes in this ASU, a firm compares the

8

carrying amount of a reporting unit with its fair value (i.e., the previous Step 1 of the goodwill

impairment test). The goodwill impairment charge is equal to the excess carrying value over the fair

value of the reporting unit, up to the amount of goodwill for the reporting unit. Although ASU 2017-

04 eliminates the previous Step 2, firms are still allowed to use the optional qualitative assessment.

The Indefinite-lived Intangibles Impairment Test

The accounting standards make an important distinction between intangible assets with finite

and indefinite lives. Finite-lived intangible assets have a defined useful life. In contrast, indefinite-

lived intangible assets do not have a defined useful life. Finite-lived assets are amortized over their

useful life and subject to an impairment test similar to the one applied to other long-term assets with

finite lives, according to the rules in ASC 350 and 360. The FASB proposed the update ASU 2012-

02 Testing Indefinite-Lived Intangible Assets for Impairment, applicable after September 2012, giving

preparers the same qualitative assessment option for indefinite-lived intangible assets that ASU 2011-

08 gives for goodwill impairment testing (FASB 2012).

Research Questions

What Are the Characteristics of Firms Using the Qualitative Assessment Option?

Our first research question addresses the cost-benefit tradeoff in voluntarily implementing a

qualitative assessment: What are the characteristics of firms using the qualitative assessment option?

It is an open empirical issue as to why less than half of the firms opted to perform this assessment in

the first two years after the adoption of the new rules, despite the potential benefits of this approach

(Duff and Phelps 2017).

Per a recent practitioner article by Deloitte, due to the costs and complexity of performing

Step 1 of the impairment test, which involves a valuation exercise for each reporting unit, many firms

and auditors employ valuation experts to assist them. Several complexities arise during this analysis,

including “assignment of assets/liabilities to reporting units; supportability of forecasts from a market

9

participant perspective; discount rates and terminal value assumptions; choices of valuation multiples;

and environmental awareness with respect to financial reporting fair value estimates” (Deloitte 2011,

2). However, the Deloitte article also notes “developing appropriately detailed documentation of the

qualitative factors to support an assertion that goodwill is not impaired” could be a challenge (Deloitte

2011, 5). The article also implies that firms with poor performance might be less likely to use the

qualitative assessment option (Deloitte 2011). The Duff and Phelps (2012, 35) survey also identifies

key implementation challenges that may motivate firms to bypass the qualitative assessment,

including: 1) Step 1 is perceived as a “more robust analysis;” 2) Step 0 is more “cumbersome and/or

time-consuming relative to Step 1”; and, 3) There is “uncertainty about auditor requirements” for Step

0. Overall, we expect that small firms, high-performing firms, and firms with fewer indefinite-lived

intangibles will adopt the qualitative assessment due to the complexity of impairment tests.

Has the Incidence of Impairments Changed After the Adoption of the Qualitative Assessment

Standards?

Our second research question addresses the link between the incidence of impairments and

the adoption of the qualitative assessment rules: Has the incidence of impairments changed after the

adoption of the qualitative assessment standards? This question follows a relatively extensive prior

literature that examined the adoption of the goodwill impairment rules following SFAS 142.9 Agency

theory and existing empirical evidence support concerns about the impairment tests under the

previous two-step model in ASC 350 and SFAS 142 (e.g., Beatty and Weber 2006; Hayn and Hughes

2006; Li et al. 2011; Ramanna and Watts 2012; Li and Sloan 2017).

The qualitative assessment can increase the subjectivity and lack of timeliness of impairment

tests. In the discussion of possible costs of this qualitative option, ASU 2011-08 (FASB 2011, BC34,

9 For a detailed review of the literature on goodwill impairments, including U.S. and international standards, purchase

price allocations, determinants and consequences of goodwill impairments, and other issues, see Boennen and Glaum

(2014). In addition, see Glaum et al. (2015) for determinants of goodwill impairments under IFRS for non-U.S. firms.

10

26) explicitly mentions that: “The Board acknowledged that the amendments in this Update may

result in entities applying more judgment about when and how to perform this evaluation.” Thus, the

revised standards constitute a shock to the level of judgment required to perform impairment tests

and provide an interesting setting to examine impairment decisions. Managers have latitude in

deciding which events and circumstances should be considered, as well as in measuring and

aggregating these factors to determine whether it is “more likely than not” that the fair value of a

reporting unit is less than its carrying amount. Per ASU 2011-08 (FASB 2011, BC35, 26), “The Board

concluded that the qualitative assessment described in this Update will allow an entity to exercise

more judgment to reduce the recurring costs of calculating the fair value of a reporting unit.”

Moreover, ASUs 2011-08 and 2012-02 represent interesting challenges for firms and auditors

(Deloitte 2011). “Without a quantitative analysis of market data to justify a position, it is possible that

management could be more subjective in its interpretation of market factors to reduce the chances for

impairment, thus introducing more risk into the process of evaluating goodwill” (Deloitte 2011, 5).

The additional subjectively potentially arising from ASUs 2011-08 and 2012-02 may be exacerbated

in situations where greater opportunity is available to manipulate impairment testing, such as for firms

with many segments, low analyst following, and high market-to-book ratios.

However, implementing impairment tests annually (and more often if necessary) is a costly

endeavor. Private companies expressed concerns to the FASB over the “cost and complexity of the

first step of the two-step goodwill impairment test” (FASB 2011, 1), and public companies listed the

high cost of third party valuation experts as one of the biggest challenges to performing impairment

tests (Duff and Phelps 2011). Furthermore, the FASB’s conceptual framework states that, “Reporting

financial information imposes costs, and it is important that these costs are justified by the benefits of

reporting that information” (FASB 2010, QC35, 21). Moreover, a more principles-based qualitative

analysis may make it more difficult for managers to manipulate the inputs of the two-step test to avoid

11

impartments (e.g., projected cash flows and discount rates) and allow for better signaling of the firm’s

type due to the optionality of the qualitative assessment.

Has the Adoption of the Qualitative Assessment Made Impairments More Difficult to Predict and

Less Timely?

Our third research question, related to the incidence of impairment is: Has the adoption of the

qualitative assessment made impairments more difficult to predict and less timely? If judgment is

used to manipulate the timing of impairments, we would expect impairments to be more difficult to

predict using a statistical model based on observable firm characteristics, increasing the model’s Type

I and Type II errors. This expectation stems from a departure from past impairment test practice and

the introduction of optionality into the evaluation of goodwill and intangible assets for impairment.

Moreover, this increased subjectivity could lead to diversity of accounting practice, with may result

in less predictable impairment losses following the adoption of the standards.

Previous research argues that some managers have exploited the discretion afforded by SFAS

142 to delay goodwill impairments (Li and Sloan 2017). In addition, since the qualitative assessment

option allows management more opportunity to justify avoiding the quantitative impairment test

procedures or manipulate the timing of impairment losses, impairment tests may be performed less

frequently and/or thoroughly. If the qualitative assessment option introduces greater leeway for

delayed quantitative impairment testing and recognition, we expect more impairment losses to be

delayed to the fourth quarter after the adoption of the qualitative assessment standards.

Has the adoption of the new rules resulted in unintended consequences reflected in audit fees, SEC

enforcement, and investors’ reaction to earnings news?

With our fourth and final research question, we aim to determine whether the adoption of the

qualitative assessment standards had indirect consequences, shifting monitoring costs to auditors or

the SEC, or making earnings news less transparent to investors for firms using the qualitative

12

assessment option. Ayres, Neal, Reid, and Shipman (2018) argue that impairment tests are a difficult

task for auditors, given potential misalignment in incentives between managers and auditors. They

also document that the decision to record a goodwill impairment is associated with an increase in the

probability of auditor dismissal. The Public Company Accounting Oversight Board (PCAOB)

inspection reports reveal that goodwill impairment tests are a common audit deficiency (Hanson

2012). Moreover, a recent report by Ernst and Young (EY) examining trends in SEC comment letters

indicates that goodwill and intangibles were the sixth most common areas targeted by comment letters

in 2017 and 2018 (EY 2018, 6). Additionally, GE is facing two federal investigations of its accounting

practices – one by the SEC and one by the Justice Department – after taking a $22 billion goodwill

impairment charge in 2018 related to the 2015 acquisition of Alstrom SA (Shumsky 2018).

A potential unintended consequence of the qualitative assessment option is that auditors, SEC,

and investors will compensate for the added discretion available to managers in impairment testing

by performing more audit work and charging higher audit fees, increasing regulatory monitoring

effort and issuing more comment letters related to goodwill and intangible assets, or discounting

earnings news due to greater uncertainty related to impairment testing and loss recognition.

III. SAMPLE SELECTION AND DESCRIPTIVE STATISTICS

Sample Selection

Our sample is the intersection of the Compustat North America Annual file, CRSP, and the

panel of observations resulting from our text scraping of public companies’ 10-K filings. We begin

our sample construction with all annual observations from the Compustat North America Annual file

with non-missing, positive values of sales (SALE) and total assets (AT) with fiscal years ending

between December 15, 2009 and December 15, 2015. This yields an initial sample of 46,393 firm-

year observations, comprised of 10,671 unique firms. We then limit our sample to those firms with

13

goodwill or intangible balances (or write downs of goodwill, goodwill and other intangibles when

combined, or write downs of indefinite-lived intangibles) greater than or equal to 1 percent of total

assets, resulting in 27,306 firm-year observations. Next, we require a valid link to CRSP to calculate

the required return variables, which yields 18,542 firm-year observations. We then merge this dataset

with the panel data of our hand-collected qualitative assessment variables described in the section

below. As a result of this merge, we are left with 18,145 firm-year observations for 4,413 unique

firms. Next, we require our sample to have non-missing values of one-year lagged total assets, and

we remove firms in the Utilities industry (FF17 = 14) or firms missing an industry classification.10

This gives us a sample of 17,427 firm-year observations from 4,245 firms, which serve as the starting

point for each sample in our analyses.

We further restrict our sample to only those observations with non-missing values of our

control variables. Finally, we require firms to have three observations in both the pre- and post-

adoption periods of our sample. These restrictions result in a total of 7,368 firm-year observations for

1,228 unique firms. Table 1 details each step in our sample selection process.

Identifying Companies Adopting the Qualitative Assessment Option

Our panel of hand-collected data begins with the collection of all annual 10-K fillings on

SEC’s EDGAR database from 2010 to 2014 in an attempt to capture the first time a firm mentions

the adoption of the applicable standards (ASU 2011-08 for goodwill or ASU 2012-02 for intangibles).

Then, using regular expressions, we extract all paragraphs that mention key words (and derivations

of those words) such as “goodwill,” “intangible,” or “indefinite-lived” in conjunction with the

mention of words like “qualitative,” “step zero,” or specific mentions of the applicable standards

10 We thank Ken French for providing the Fama-French 17 industry definitions on his website at

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_17_ind_port.html.

14

(ASU 2011-08, ASU 2012-02, or Topic 350). As a result of this scraping, we identified 6,997 firm-

year disclosures meeting our search requirements from 3,046 unique firms.

Each of the extracted 10-K sections were then read by one of two people, a research assistant

and one of the co-authors, to determine how to classify the firm’s disclosure. Of the 3,046 unique

firms, we identified 76 firms that switched between performing and bypassing the qualitative

assessment. For a clean identification, we removed these 76 firms and their corresponding firm-year

observations from our sample.11 Next, we merge our hand-collected sample to our WRDS-based

sample using CIK and matching fiscal-year-end dates with DATADATE in Compustat. Based on our

search, we assign firms into one of three categories: 1) Firms specifically disclosing that they perform

a qualitative assessment; 2) Firms mentioning the option to perform a qualitative assessment under

ASUs 2011-08 and 2012-02, discussing the change in standards, or disclosing bypassing the

qualitative assessment; and, 3) Firms silent about the qualitative assessment. This approach is

consistent with the FASB’s basis for conclusions in ASU 2011-08, in that “the Board intends for an

entity to make a positive assertion about its conclusion reached and the events and circumstances

taken into consideration if it determines that the fair value of a reporting unit is not more likely than

not less than its carrying value” (FASB 2011, BC24, 23; Duff and Phelps 2014). Duff and Phelps

(2014, 4) assert, “Companies making such a positive assertion are unambiguously Step 0 Users.”

Appendix C provides examples of the disclosures that we used to classify firms into these categories.

Descriptive Statistics

Table 2, Panel A reports the descriptive statistics for the sub-samples of companies: 1)

Performing the qualitative assessment (N. Obs. = 2,238; N. Firms = 373); 2) Mentioning the standard

(N. Obs. = 3,552; N. Firms = 592); and, 3) Silent about the qualitative assessment (N. Obs. = 1,578;

11 Of these 76 firms, six are eliminated in sample screens leading up to and including merging with CRSP, and the

remaining 70 firms are eliminated after merging our hand-collected data with the Compustat/CRSP merged sample.

15

N. Firms = 263), including data from three years before and three years after the adoption of ASU

2011-08.12 The average incidences of goodwill and indefinite-lived intangible impairments or write-

downs (GW_WDi,t) are 11.3, 11.8, and 11.3 percent in the three sub-samples. The companies in the

three partitions have substantial balances of goodwill and other intangibles, ranging between 19.4 and

25.9 percent of total assets (Perc_GWi,t-1). The companies in the three partitions have total assets

ranging between 875 and 1,122 million (SIZEi,t-1). In terms of financial performance, firms

performing the qualitative assessment have the highest stock returns and the lowest book-to-market

ratios (RETi,t and BTMi,t-1).13

Table 2, Panel B, provides additional detail, partitioning the sub-sample of companies

performing the qualitative assessment (N. Obs. = 2,238) into the pre- (POSTt = 0) and post- (POSTt

= 1) adoption year. We note that that the incidence of impairments for these firms did not change

significantly surrounding ASU 2011-08 (GW_WDi,t).

To examine differences in observable variables across treatment (i.e. Performing) and control

(Mentioning and Silent) firms in the period preceding ASU 2011-08, we compare means of control

variables used in our regression analyses in Table 2, Panel C. We fail find to find that firms performing

a qualitative assessment recorded more or fewer impairment losses than firms mentioning the

qualitative assessment option.

Because some of the control variables differ between the treatment and control groups, we use

propensity score matching to improve covariate balance. Table 2, Panel D presents differences in

12 We focus our subsequent tests on years surrounding ASU 2011-08 since it is the earliest of the qualitative assessment

option standards. 13 In subsequent tables, we present sample selection information and descriptive statistics when our sample varies from

Table 2, Panel A in a particular test. For example, Table 7, Panel B presents sample selection information and descriptive

statistics for the subsample of firm-years with impairment losses.

16

means across treatment and control observations following the propensity score matching procedure,

none of which are significant.14 In subsequent tests, we control for these observable characteristics.

Table 2, Panel E provides the incidence of impairments before and after the adoption of ASU

2011-08 (POSTt = 0/1) separately for firms performing the qualitative assessment versus all other

firms (QUALi = 1/0). Within each pre/post comparison, firms are grouped into book-to-market ratio

quintiles, with quintile five containing firms with the highest book-to-market ratios. This table gives

a general sense for differences in the incidence of impairment losses for various sample partitions,

and whether that incidence changed surrounding the adoption of the new impairment standards. While

the incidence of impairments generally increases with the book-to-market ratio as expected, we find

no evidence in any comparison that the incidence of impairments significantly decreased after the

qualitative assessment option was allowed. These descriptive statistics suggest that the additional

discretion in impairment testing available after the adoption of ASU 2011-08 does not result in

significantly fewer impairment charges.

IV. RESEARCH DESIGN AND RESULTS

Characteristics of Firms Using the Qualitative Assessment Option

We examine the characteristics of firms performing a qualitative assessment, our first research

question. We estimate three determinant models, comparing firms performing the qualitative

assessment versus: 1) Firms mentioning the qualitative assessment option or silent about the

qualitative assessment; 2) Firms mentioning the qualitative assessment option; and, 3) Firms silent

about the qualitative assessment. We use only the first fiscal year following the effective date of ASU

2011-08 and estimate equation 1 using logistic regression:

14 Table 3, Panel B presents the propensity-score matching model used for the three-year pre-ASU-2011-08 period.

17

P(QUALi) = f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (1)

where QUALi is an indicator variable equal to one if firm i discloses performing a qualitative

assessment at any time in our search period of 2010 to 2014, and zero otherwise; Perc_GWi,t-1 is the

ratio of the goodwill and other intangibles balance (Compustat variables GDWL and INTANO,

respectively) to total assets for firm i in year t-1; GW_WDi,t-1 is an indicator variable equal to one if

firm i takes a write down in year t-1 (i.e., Compustat GDWLIP is less than zero), and zero otherwise.

The Controls vector includes established indicators of goodwill and intangible impairments,

plus other firm characteristics (Francis et al. 1996; Beatty and Weber 2006; Hayn and Hughes 2006;

Brochet and Welch 2011; Gu and Lev 2011; Ramanna and Watts 2012; Glaum et al. 2015; Li and

Sloan 2017). SIZEi,t-1 is the natural logarithm of total assets for firm i in year t-1; RETi,t is the annual

stock return for firm i minus the value-weighted cumulative market return during year t; RET2i,t-1 is

the two-year stock return for firm i minus the value-weighted cumulative market return from the

beginning of year t-2 to the end of year t-1; BTMi,t-1 is the ratio of the book value of equity to the

market value of equity for firm i in year t-1; BTM_INDi,t-1 is an indicator variable equal to one if the

book-to-market (BTM) ratio is greater than one for firm i in year t-1, zero otherwise; GROWTHi,t-1 is

the change in sales for firm i from year t-2 to year t-1, scaled by sales in year t-1; NASDAQAMi,t-1 is

an indicator variable equal to one if firm i’s shares are traded on the NASDAQ or AMEX exchanges

(EXCHG = 11 or 12) in year t-1, and zero otherwise; SEGSi,t-1 is the natural logarithm of the count of

business segment IDs (SID) for firm i in year t-1; HERFINDXi,t-1 is the Fama-French-17-industry-

year sum of squared sales shares for firm i in year t-1 (Sum(Share2)), where Share is lagged firm sales

divided by lagged total industry-year sales; and, INDROAi,t-1 is the average change in firm i's Fama-

French-17 industry median return-on-assets ratio over years t-5 to t-1. Fixed Effectsi,t are indicator

variables for the Fama-French 17 industries. Detailed variable definitions are in Appendix A.

18

Table 3, Panel A presents the results of estimating equation 1. Column 1 presents results

comparing firms performing the qualitative assessment vs. firms only mentioning the new standards

and firms silent about the new standards. Column 2 presents results comparing firms performing the

qualitative assessment vs. firms only mentioning the standard. Column 3 presents results comparing

firms performing the qualitative assessment vs. firms silent about the new standards. In two of three

columns, we find a positive association between past firm performance (RET2i,t-1) and the likelihood

of performing the qualitative assessment. We also find a negative association between book-to-market

ratios (BTMi,t-1) and the likelihood of performing the qualitative assessment in two of three columns.

Moreover, we find a modestly significant negative association between past recognition of

impairment losses and performing a qualitative assessment (GW_WDi,t-1) in column 3, suggesting that

firms with a low past propensity of recording goodwill impairment are more likely to exercise the

qualitative assessment option. We also find a higher likelihood of performing a qualitative assessment

when firms have more intangible assets as a percentage of total assets in columns 1 and 3 and more

segments in column 3 (Perc_GWi,t-1 and SEGSi,t-1), consistent with firms using this option when

relatively more assets and business units must be tested for impairment and more costs must be

expended to do so.

We find a negative association between sales growth and the use of the qualitative assessment

in columns 1 and 2 (GROWTHi,t-1, suggesting that slow-growing firms are more likely to perform a

qualitative assessment. We also find a negative association between firm size and the use of the

qualitative assessment in column 3 when we examine qualitative assessment firms vs. silent firms,

suggesting that smaller firms are more likely to employ the qualitative assessment option.

In Table 3, Panel B, we present estimations of equation 1 similar to Table 3, Panel A, but use

averages of the independent variables over the three-year pre-ASU-2011-08 period. We again find

some evidence of a higher likelihood of performing a qualitative assessment when firms have more

19

intangible assets as a percentage of total assets in column 2 (Perc_GWi,t-1), and some evidence that

firms currently performing well (RETi,t), but not previously performing well (RET2i,t-1), are more

likely to perform a qualitative assessment. Overall, Table 3 suggests that firms may use the qualitative

assessment option when they face comparatively lower impairment risk and higher costs of

performing the traditional quantitative impairment test.

Incidence of Impairments Pre- and Post-Adoption of the Qualitative Assessment Option

Pre/Post Analysis

We compare the incidence of impairments for the full sample and for firms performing the

initial assessment three years before and after the adoption of ASU 2011-08. We choose the adoption

date of ASU 2011-08 for our “post” measure since it preceded ASU 2012-02. We estimate the

following linear probability model of the likelihood of goodwill and indefinite-lived intangibles

impairments, controlling for several determinants of impairments suggested by prior studies:15

P(GW_WDi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (2)

All variables are as defined above with the addition of the POSTt indicator variable, which is equal

to one for the first fiscal year in which ASU 2011-08 became effective for all firms (i.e., the first

fiscal year beginning after December 15, 2011), and zero for the fiscal year immediately prior.

In Table 4, Panel A, columns 1 and 2, we estimate equation 1, controlling for determinants of

impairments suggested by prior studies. The main independent variable (POSTt) captures the average

difference in impairment less recognition surrounding ASU 2011-08. We do not find a statistically

significant coefficient for the variable POSTt for the full sample (column 1) or for firms performing

the qualitative assessment (column 2).

15 We use a linear probability model due to the interaction in the difference-in-differences model described below.

20

Difference-in-differences Analysis

We complement our pre/post analysis by implementing a difference-in-differences (DD)

research design, comparing outcomes between the categories in the six years surrounding the adoption

date of ASU 2011-08. This research design mitigates the effect of time-related trends associated with

the adoption of the new rules. We use the following linear probability model:

P(GW_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (3)

In Table 4, Panel A, columns 3-5, we present our DD analysis including three years before

and three years after the effective date of ASU 2011-08. The main independent variable is the

interaction POSTt * QUALi between the indicator POSTt, which captures the incremental effect of the

adoption of the qualitative assessment standards, and the indicator QUALi, equal to one for firms

performing a qualitative assessment at any time in our search period of 2010 to 2014 and zero for

firms only mentioning the standard and firms silent about the standard (column 3), zero for firms only

mentioned the standard (column 4), and zero for firms silent about the qualitative assessment (column

5). We find that in the post-adoption period, firms performing the qualitative assessment have an

incrementally higher likelihood of impairment loss recognition compared to firms that are silent about

the qualitative assessment (column 5).

In Table 4, Panel B, we estimate equation 3 using our propensity matched samples on the six

years (columns 1 and 2) and two years (columns 3 and 4) surrounding ASU 2011-08. Similar to Table

4, Panel A, we find that in the post-adoption period, firms performing a qualitative assessment have

a higher likelihood of impairment loss recognition compared to firms silent about the qualitative

assessment (columns 2 and 4).

21

Cross-sectional Analysis

To complement our DD analysis, we next examine whether firm managers with stronger

opportunities to avoid impairment losses use the additional discretion provided by qualitative

assessments. Specifically, we perform our DD tests allowing the coefficient on POSTt * QUALi to

vary with whether: 1) The firm has many reporting units/segments; 2) The firm has low analyst

following/external monitoring; and, 3) The firm has a high market-to-book ratio. We expect firms

with more opportunity (more reporting units), weaker external monitoring (lower analyst following),

and more valuation cushion (high market-to-book ratio) to be more likely to use the qualitative

assessment option to avoid impairment charges if managers take advantage of the greater subjectivity

allowed with the qualitative assessment option in impairment testing.

For this analysis, we stratify our sample based upon our three cross-sections of interest, the

number of reporting segments, the number of analysts issuing an EPS forecast for the firm’s fiscal

year, and the quartile ranking of the firm based on the market-to-book ratio. This stratification is

performed on the first year of adoption of the qualitative assessment (POSTt = 1) for the 1,228 firms

in our six-year pre- and post- sample, following the sample selection in Table 1. The result of the

stratification for reporting segments and analyst following is displayed in Table 5, Panel A. For the

market-to-book ratio, we sort firms into quartiles based on the value of this ratio. Since the number

of segments and analyst following are count variables, an ideal cutoff for top and bottom percentiles

cannot be formed. As such, for the reporting segments subsample, we compare firms with only one

reporting segment to firms with four or more reporting segments. For the analyst following

subsample, we compare firms with no analyst following to firms with five or more analysts following

the firm. For the market-to-book ratio subsample, we compare firms in the top quartile of the market-

to-book ratio distribution to firms in the lowest quartile of the market-to-book ratio distribution.

22

Three indicator variables are generated to identify these firms. Firms with four or more

reporting segments are assigned a value of one for High_SEGSi, and firms with one reporting unit are

assigned a value of zero for that variable. Firms with five or more analysts following are assigned a

one for High_FOLLOWi, and firms with no analyst following are assigned a zero. Firms in the top

quartile of the market-to-book ratio distribution are assigned a one for High_CUSHIONi, and firms

in the bottom quartile of the market-to-book ratio distribution are assigned a zero. With the firms

identified for each of the three subsamples, we then include six years surrounding ASU 2011-08 for

the regression estimates of equations 4-5. Highi represents High_SEGSi, High_FOLLOWi, or

High_CUSHIONi.

P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, Controlsi,t, Fixed Effectsi,t, ei,t) (4)

P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, QUALi, POSTt*QUALi, Highi*QUALi,t,

POSTt*Highi*QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (5)

Table 5, Panel B presents the results of the regression analysis for the reporting segments

subsample. Columns 1 and 2 present estimations of equation 4, while columns 3 and 4 present

estimations of equation 5. The results from the DD design in columns 3 and 4 show that the interaction

term POSTt * High_SEGSi * QUALi is not statistically significant. As such, we do not find that firms

with a high number of segments opportunistically use the qualitative assessment in a manner different

than firms with a low number of segments. Table 5, Panel C presents the results of the regression

analysis for the analyst following subsample. This results from the DD design in columns 3 and 4

show an insignificant negative coefficient for the POSTt * High_FOLLOWi * QUALi. As such, we

conclude that firms with low analyst following performing a qualitative assessment take no more

advantage of the additional discretion offered by ASUs 2011-08 and 2012-02 than firms with high

analyst following. Table 5, Panel D presents the results of the regression analysis for the market-to-

book ratio subsample. This results from the DD design in columns 3 and 4 show an insignificant

negative coefficient for the POSTt * High_CUSHIONi * QUALi. We conclude that managers of firms

23

with greater valuation cushion, as represented by high market-to-book ratios, take no more advantage

of the subjectivity available with the qualitative assessment in impairment testing than other firms.

The combined results from Tables 4 and 5 suggest that the judgment implied by the optional

qualitative assessment does not materially decrease the incidence of impairments among firms that

disclose performing an assessment. If anything, there is an increased incidence of impairment loss

recognition for firms exercising the qualitative assessment option relative to firms silent about the

qualitative assessment option, consistent with a thorough qualitative analysis making it more difficult

for managers to manipulate the inputs of the two-step quantitative test to avoid impairment losses.

The Predictability and Timeliness of Impairment Charges

We next examine whether the predictability of impairments changed with the adoption of the

revised impairment accounting standards. We use the largest possible with data available for our

control variables (N. Obs. = 12,212 from 2,811 firms). We estimate equation 2 (excluding the POSTt

variable), separately for the pre- and post-periods using logistic regression and present the results in

Table 6, Panel B. The results suggest a very modest decline in the predictability of impairment charges

in the post-period, as represented by lower Pseudo R2 and less area under the receiver operating

characteristic curve (AUC) in column 2 vs. column 1.

In Table 6, Panel C, we present results examining Type I error (incorrectly predicting an

impairment loss when a loss does not exist) versus Type II error (incorrectly failing to predict an

impairment loss when a loss exists). Using the pre-period as a hold-out sample to train our impairment

prediction model (equation 2, excluding the POSTt variable), we assign firm-year observations in the

highest 11 percent of estimated probabilities in each year of the post-period a value of one for

predicted impairment, and zero otherwise. We then perform two-sample tests of proportions for Type

I and Type II error comparing firms that perform a qualitative assessment with those that do not. We

find that the incidence of incorrectly predicting an impairment when an impairment is not recognized

24

(Type I error) is lower in the post-standard-change period for firms using the qualitative assessment

vs. other firms, while the incidence of incorrectly failing to predict an impairment when an

impairment is recognized (Type II error) is higher for firms using the qualitative assessment vs. other

firms. Given that Type I error is lower, while Type II error is higher, for firms using the qualitative

assessment after ASU 2011-08 adoption, we cannot make a definitive statement on the effect of the

accounting standard change on impairment predictability.

To examine impairment timeliness, we examine whether qualitative assessment firms are

more likely to recognize impairment charges in Q1, Q2, or Q3 than other sample firms, thus providing

evidence on whether the qualitative assessment aids or hinders early identification of impairment

losses. To do so, we estimate equation 6 using a linear probability model:

P(EARLY_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (6)

where EARLY_WDi,t is an indicator variable equal to one if an impairment charge is recorded in Q1,

Q2, or Q3, and equal to zero if an impairment charge is recorded only in Q4. In this test, we use only

firm-year observations with an impairment charge (N. Obs. = 1,416 from 897 firms). We present the

results in Table 7, Panel D. We find no difference in the incidence of early (non-fourth quarter)

impairment loss recognition between qualitative assessment firms vs. other firms (POSTt * QUALi).

While we cannot conclude that qualitative assessments improve impairment predictability, we

conclude that qualitative assessments do not appear to adversely affect impairment timeliness.

Unintended Consequences for Audit Fees, SEC Enforcement, and Investors

Audit Fees

Auditing accounting estimates is one of the largest risks faced by auditors (PCAOB 2017;

Chen, Keung, and Lin 2019). When auditing accounting estimates, auditors usually assess the

reasonableness of management’s quantitative judgments of fair value. However, with the adoption of

25

ASUs 2011-08 and 2012-02, managers can now solely rely on qualitative assessments to determine

whether an impairment exists or whether additional impairment testing is necessary. The qualitative

assessment option arguably allows more room for management bias in examining assets for possible

impairment. Auditing management's determination that an impairment does not exist based purely on

a qualitative assessment may pose more risk for auditors, since numbers (e.g., inputs to or outputs

from discounted cash flow models) that can be verified may no longer be present. If this is the case,

we expect an increase in audit effort and audit fees in response to increased audit risk for audits of

clients that perform a qualitative assessment. Conversely, if a qualitative assessment reduces the

complexity of the impairment assessment, then auditors may expend less audit effort on clients

performing a qualitative assessment. We examine whether there is a change in audit fees surrounding

the adoption of ASU 2011-08 and employ a research design similar to our main analyses as

represented by equations 7 and 8. The dependent variable is AFEEi,t, defined as the natural logarithm

of audit fees.

AFEEi,t = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (7)

AFEEi,t = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (8)

Table 9, Panel C presents the results using observations from the six years surrounding the

effective date of ASU 2011-08. New controls introduced in Table 8, Panel C include Big-4 Auditor

(BIGNi,t) and firm-specific accounting performance (ROAi,t). In our DD design in columns 3 and 4,

we find modest evidence that qualitative assessment firms experience no difference in audit fees vs.

other firms (POSTt * QUALi).

SEC Comment Letters

We examine whether firms that perform qualitative assessments face more regulatory scrutiny

in the form of SEC comment letters specifically related to goodwill or intangible assets. Increased

regulatory scrutiny may be a potential unintended consequence of performing a qualitative

26

assessment. In order to assess this possibility, we employ a research design similar to our main

analyses as represented by equations 9 and 10. The dependent variable is SEC_COMMi,t, an indicator

variable equal to one if the firm receives a comment letter related to goodwill or intangibles for fiscal

year t, zero otherwise.

P(SEC_COMMi,t) = f(POSTt, GW_WDi,t, Controlsi,t, Fixed Effectsi,t, ei,t) (9)

P(SEC_COMMi,t) = f(POSTt, QUALi, POSTt * QUALi, GW_WDi,t, Controlsi,t, Fixed Effectsi,t, ei,t)

(10)

Table 9, Panel C presents the results using observations from the six years surrounding the

effective date of ASU 2011-08. New controls introduced in Table 9, Panel C include an indicator for

merger and acquisition activity (MERGEi,t) and a loss indicator variable (LOSSi,t). In our DD design,

we find that qualitative assessment firms have a lower incidence of receiving a comment letter

compared to other firms (POSTt * QUALi).

Investor Reaction to Earnings News

Following Li et al. (2011), we explore whether investors react differently to earnings news

from qualitative assessment firms vs. other firms. If performing a qualitative assessment results in

less informative earnings, the investor reaction to earnings news is expected to be muted for

“performers” vs. other firms. We employ a DD research design similar to our main analyses as

represented by equations 11 and 12.

EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, LOSSi,t, Fixed Effectsi,t, ei,t) (11)

EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, QUALi, POSTt * QUALi, EarnSurpi,t

* QUALi,t, POSTt * EarnSurpi,t * QUALi, LOSSi,t, Fixed Effectsi,t, ei,t) (12)

The dependent variable is EarnCARi,t, defined as the three-day cumulative abnormal returns

from trading days -1 to +1 surrounding the earnings announcement date. We also construct EarnSurpi,t

defined as the difference between reported earnings per share and the last analyst forecast issued five

or more days before the earnings announcement, scaled by stock price. Table 10, Panel C displays

27

the results of the regression analysis. In our DD design in columns 3-4, we find no evidence that

investors respond differently to earnings news for qualitative assessment firms vs. other firms in the

post-adoption period. Collectively, the results from Tables 8, 9, and 10 suggest that external monitors

do not view qualitative assessment firms as having significantly higher financial reporting risk arising

from impairment testing than other firms.

VI. CONCLUSION

This study investigates the determinants and consequences of qualitative assessments in

annual impairment tests of goodwill and indefinite-lived intangibles allowed under ASUs 2011-08

and 2012-02. The qualitative assessment constitutes a preliminary test, often referred to as “Step 0,”

aimed at reducing the complexity and costs of a quantitative impairment test.

We identify firms with goodwill and intangibles on their balance sheets from 2009 and 2015.

Based on our search, we assign firms into one of three categories: 1) Firms specifically disclosing

that they perform a qualitative assessment; 2) Firms mentioning the option to perform a qualitative

assessment under ASUs 2011-08 and 2012-02, discussing the change in standards, or disclosing

bypassing the qualitative assessment; and, 3) Firms remaining silent about the qualitative assessment.

Our results suggest that firms use the qualitative assessment option when they face

comparatively lower impairment risk and higher costs of performing the quantitative impairment test.

We examine whether greater management discretion in impairment tests results in fewer recognized

impairment charges, as managers often have incentive to defer loss recognition. Perhaps surprisingly,

using a difference-in-differences analysis, we find that firms performing a qualitative assessment have

an incrementally higher likelihood of goodwill impairments compared to firms silent about the

qualitative assessment in the post-adoption period. This result does not vary with greater opportunity

to subjectively manipulate impairments tests, as represented by a high number of segments, low

28

analyst following, or a high market-to-book ratio. We also find that the availability of the qualitative

assessment option did not decrease the propensity of firms to record early impairment charges (i.e.,

in Q1-Q3 instead of Q4), suggesting that the timeliness of impairments was not harmed by the

introduction of this accounting option. Moreover, we find no evidence of increased monitoring costs

for auditors, regulators, and investors surrounding the accounting standard changes.

Our findings must be viewed within two limitations of an empirical study that relies on

publicly available data. First, we use firms’ disclosures to identify adopters of the qualitative

assessment option, though this concern is somewhat mitigated if: 1) Firms’ disclosures follow the

FASB’s statement that “the Board intends for an entity to make a positive assertion about its

conclusion reached and the events and circumstances taken into consideration if it determines that the

fair value of a reporting unit is not more likely than not less than its carrying value” (FASB 2011,

BC24, 23; Duff and Phelps 2014); and, 2) “Companies making such a positive assertion are

unambiguously Step 0 Users” (Duff and Phelps 2014, 4). Second, we cannot directly observe how

the qualitative assessment has impacted the complexity and internal costs of impairment testing.

Nevertheless, we advance the literature by providing evidence that the additional discretion available

with the optional qualitative assessment does not appear to decrease the overall incidence of

impairments. Our findings inform the standard-setting debate about the complexities and costs of

impairment tests, and whether recent changes in standards for the impairment of intangible assets

result in significant changes in accounting practice. Our findings also speak to the broader issue of

the costs and benefits of optionality in accounting.

29

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Li, K., and R. Sloan. 2017. Has goodwill accounting gone bad? Review of Accounting Studies 22 (2):

964-1003.

Public Company Accounting Oversight Board (PCAOB). 2017. Standard-setting update. Office of

the Chief Auditor, published Sep. 30, 2017.

Ramanna, K., and R. Watts. 2012. Evidence on the use of unverifiable estimates in required goodwill

impairment. Review of Accounting Studies 17 (4): 749-780.

Riedl, E. 2004. An examination of long-lived asset impairments. The Accounting Review 79 (3): 823-

852.

Shalev, R., I. Zhang, and Y. Zhang. 2013. CEO compensation and fair value accounting: Evidence

from purchase price allocation. Journal of Accounting Research 51 (4): 819-854.

Shumsky, T. 2018. GE’s $22 billion charge intensifies regulatory scrutiny. Wall Street Journal

(October 30). Available at: https://www.wsj.com/articles/ges-22-billion-charge-intensifies-

regulatory-scrutiny-1540942603.

Trentmann, N. 2019. Stock price-related impairment charges expected to rise as markets fluctuate.

Wall Street Journal (CFO Journal – August 8). Available at: https://www.wsj.com/articles/stock-

price-related-impairment-charges-at-kraft-heinz-expected-to-rise-as-markets-fluctuate-

11565302563.

31

APPENDIX A – Variable Definitions

Variable Definition Source

Dependent Variables

GW_WDi,t = Indicator variable equal to one when firm i determines the

goodwill balance is impaired and takes a write down in year t

(GDWLIP is less than zero), and zero otherwise;

Compustat

Independent Variables

POSTt = Indicator variable equal to one for fiscal years beginning after

12/15/2011, and zero for fiscal year immediately prior;

Compustat

QUALi = Indicator variable equal to one if firm i discloses that a

qualitative assessment was performed at any point during the

sample period;

SEC EDGAR 10-K filings

Perc_GWi,t-1 =

Ratio of the balance of the total intangible balance

(GDWL+INTANO) to total assets (AT) for firm i in year t-1;

Compustat

GW_WDi,t-1

= The one-year lagged indicator variable of the dependent

variable;

Compustat

SIZEi,t-1 = The natural logarithm of total assets (AT) for firm i in year t-1; Compustat

RETi,t = The annual stock return for firm i minus the value-weighted

cumulative market return during year t;

CRSP

RET2i,t-1 = The two-year stock return for firm i minus the value-weighted

cumulative market return from the beginning of year t-2 to the

end of year t-1;

CRSP

BTMi,t-1 = Ratio of the book value of equity (CEQ) to market value of

equity (PRCC_F * CSHO) for firm i in year t-1;

Compustat

BTM_INDi,t-1 = Indicator variable equal to one if the book-to-market (BTM)

ratio is greater than one for firm i in year t-1, and zero

otherwise;

Compustat

GROWTHi,t-1 = The change in sales (SALE) for firm i from year t-2 to year t-

1, scaled by sales in year t-1;

Compustat

NASDAQAMi,t-

1

= Indicator variable equal to one if firm i's shares are traded on

the NASDAQ or AMEX (EXCHG = 11 or 12) in year

t-1, and zero otherwise;

Compustat

SEGSi,t-1 = The natural logarithm of the count of distinct business segment

IDs (SID) for firm i in year t-1;

Compustat

HERFINDXi,t-1 = The Fama-French-17-industry-year sum of squared sales

shares for firm i in year t-1 (Sum(Share2)), where Share is

equal to firm sales (SALE) divided by lagged total industry-

year sales;

Compustat

INDROAi,t-1 = Average percentage change in firm i's Fama-French 17

industry median return-on-assets ratio over years t-5 to t-1;

Compustat

32

APPENDIX A – Variable Definitions (continued)

Variable Definition Source

Variables Used in Additional Analyses

High_SEGSi = Indicator variable equal to 1 if a firm has four or more

reporting segments during the first year of ASU 2011-08

being effective, and 0 for firms with only one reporting

segment;

Compustat

High_FOLLOWi = Indicator variable equal to 1 if a firm has five or more

analysts issuing EPS forecasts for the first year of ASU

2011-08 being effective, and 0 for firms with no analyst

following;

I/B/E/S

High_CUSHIONi = Indicator variable equal to 1 if a firm is in the top quartile of

market-to-book for the first year of ASU 2011-08 being

effective, and 0 for firms in the bottom quartile of market-to-

book;

Compustat

EARLY_WDi,t = Indicator variable equal to 1 if an impairment charge is

recognized before the fourth quarter of fiscal year t for firm i

(GDWLIPQ is less than zero in the first, second, or third

quarter of the fiscal year), and 0 if an impairment charge is

recognized in only the fourth quarter;

Compustat

EarnCARi,t = Cumulative returns of firm i for trading days -1 to +1 around

the earnings announcement for fiscal year t minus the value-

weighted cumulative market return in the same window;

CRSP

EarnSurpi,t = Actual earnings per share for firm i in fiscal year t minus the

last analyst forecast issued 5 or more days before the

earnings announcement, scaled by stock price;

I/B/E/S, CRSP

LOSSi,t = Indicator variable equal to one for firm i which has negative

net income (NI) in fiscal year t, and zero otherwise;

Compustat

SEC_COMMi,t = Indicator variable equal to one for firm i which has received

an SEC comment letter pertaining to goodwill or intangibles

for fiscal year t, and zero otherwise;

Audit Analytics

MERGEi,t = Indicator variable equal to one for firm i which has a non-

zero balance for acquisitions (AQP) for fiscal year t, and

zero otherwise;

Compustat

BIGNi,t = Indicator variable equal to one for firm i which has engaged

a Big-4 auditor for fiscal year t, and zero otherwise;

Audit Analytics

AFEEi,t = The natural logarithm of the audit fees for firm i in fiscal

year t;

Audit Analytics

ROAi,t = Net income (NI) scaled by total assets (AT) for firm i for

fiscal year t;

Compustat

33

APPENDIX B – Excerpts from ASU 2011-08 about the Updated Impairment Test Approach

A. Flowchart for the new impairment test approach now codified in ASC 350-20-55-25

B. Examples of events and circumstances for the qualitative assessment now codified in ASC 350-

20-35-3C

a. Macroeconomic conditions such as a deterioration in general economic conditions, limitations on accessing

capital, fluctuations in foreign exchange rates, or other developments in equity and credit markets

b. Industry and market considerations such as a deterioration in the environment in which an entity operates, an

increased competitive environment, a decline in market-dependent multiples or metrics (consider in both

absolute terms and relative to peers), a change in the market for an entity’s products or services, or a regulatory

or political development

c. Cost factors such as increases in raw materials, labor, or other costs that have a negative effect on earnings and

cash flows

d. Overall financial performance such as negative or declining cash flows or a decline in actual or planned revenue

or earnings compared with actual and projected results of relevant prior periods

e. Other relevant entity-specific events such as changes in management, key personnel, strategy, or customers;

contemplation of bankruptcy; or litigation

f. Events affecting a reporting unit such as a change in the composition or carrying amount of its net assets, a more-

likely-than-not expectation of selling or disposing all, or a portion, of a reporting unit, the testing for

recoverability of a significant asset group within a reporting unit, or recognition of a goodwill impairment loss

in the financial statements of a subsidiary that is a component of a reporting unit

g. If applicable, a sustained decrease in share price (consider in both absolute terms and relative to peers).

34

APPENDIX C – Examples of Disclosure

Disclosure of the performance of the qualitative assessment (Performing)

Starbucks Corporation, 10-K filing for the fiscal year ended 9/30/2012

(https://www.sec.gov/Archives/edgar/data/829224/000082922412000007/sbux-9302012x10k.htm):

“Goodwill

We test goodwill for impairment on an annual basis during our third fiscal quarter, or more frequently

if circumstances, such as material deterioration in performance or a significant number of store

closures, indicate reporting unit carrying values may exceed their fair values. When evaluating

goodwill for impairment, we first perform a qualitative assessment to determine if the fair value of

the reporting unit is more likely than not greater than the carrying amount. If not, we calculate the

implied estimated fair value of the reporting unit. If the carrying amount of goodwill exceeds the

implied estimated fair value, an impairment charge to current operations is recorded to reduce the

carrying value to the implied estimated fair value.” (Footnote 1, 57)

Disclosure of the option to perform (Mentioning)

Coach, Inc., 10-K filing for the fiscal year ended 6/29/2013

(https://www.sec.gov/Archives/edgar/data/1116132/000114420413047469/v350111_10k.htm):

“Recent Accounting Pronouncements…

In September 2011, Accounting Standards Codification 350-20, “Intangibles — Goodwill and

Other — Goodwill,” was amended to allow entities to assess qualitative factors to determine if it is

more-likely-than-not that goodwill might be impaired, and whether it is necessary to perform the two-

step goodwill impairment test required under current accounting standards. This guidance was

effective for the Company’s fiscal year beginning July 1, 2012. The adoption of this amendment did

not have a material effect on the Company’s consolidated financial statements.”

(Footnote 2, 68)

35

TABLE 1 – Sample Selection

Total Obs. Firms

Observations in Compustat with fiscal years ending between

12/15/2009 and 12/15/2015, and with non-missing, positive

values of total assets (AT) and sales (SALE)

46,393 10,671

Observations remaining after requiring at least one non-missing

value greater than or equal to 1% of total assets of any of the

following variables: goodwill (GDWL), impairment of goodwill,

impairment of goodwill and other intangibles when combined,

and impairment of unamortized intangibles (GDWLIP), and

intangibles (INTANO)

27,306 6,810

Observations remaining after requiring a valid and unique link to

CRSP

18,542 4,483

Observations remaining after merging with hand-collected

qualitative assessment and removing firms that switched between

performing and bypassing the qualitative assessment

18,145 4,413

Observations remaining after removing firms with missing

lagged total assets

17,992 4,386

Observations remaining after removing firms in the Utilities

industry (FF17 = 14) or missing an industry classification

17,427 4,245

Sample starting point for all analyses 17,427 4,245

Observations remaining after requiring non-missing control

variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,

NASDAQAM, SEGS, HERFINDX, INDROA

12,212 2,811

Observations remaining after requiring a firm to appear in all six

years of the sample period (three pre and three post)

7,368 1,228

Final sample for main analysis 7,368 1,228

Firms performing a qualitative assessment (Performing) 2,238 373

Firms disclosing the new option, but not specifying (Mentioning) 3,552 592

Firms not disclosing any information (Silent) 1,578 263

Propensity Score Matched Sample

Performing 2,148 358

Mentioning 2,148 358

Performing 1,206 201

Silent 1,206 201

This table shows the sample selection for the observations used in the analyses with three fiscal years pre- and post-

effective date of ASU 2011-08.

36

TABLE 2 – Descriptive Statistics

Panel A: Partitions by Disclosure Category

Performing Mentioning Silent

Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median

GW_WDi,t 0.113 0.317 0.000 0.118 0.323 0.000 0.113 0.316 0.000

POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500

QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

Perc_GWi,t-1 0.259 0.189 0.220 0.259 0.201 0.204 0.194 0.179 0.129

GW_WDi,t-1 0.137 0.344 0.000 0.140 0.348 0.000 0.128 0.334 0.000

SIZEi,t-1 6.870 1.839 6.831 6.774 1.909 6.793 7.023 2.428 6.875

RETi,t 0.105 0.495 0.022 0.087 0.530 -0.004 0.067 0.587 -0.029

RET2i,t-1 0.157 0.725 0.022 0.132 0.765 -0.008 0.113 0.851 -0.063

BTMi,t-1 0.564 0.506 0.476 0.590 0.549 0.490 0.653 0.634 0.503

BTM_INDi,t-1 0.134 0.341 0.000 0.147 0.354 0.000 0.182 0.386 0.000

GROWTHi,t-1 0.088 0.280 0.058 0.097 0.306 0.061 0.099 0.314 0.060

NASDAQAMi,t-1 0.504 0.500 1.000 0.449 0.497 0.000 0.468 0.499 0.000

SEGSi,t-1 0.781 0.672 1.099 0.747 0.690 1.099 0.668 0.705 0.693

HERFINDXi,t-1 0.027 0.026 0.017 0.027 0.026 0.017 0.027 0.025 0.018

INDROAi,t-1 -0.120 0.638 -0.092 -0.141 0.641 -0.092 -0.111 0.747 -0.109

N. Obs. = 2,238 3,552 1,578

Panel B: Pre- and Post-Effective Date for Firms Performing a Qualitative Assessment

QUALi = 1, POSTt = 0 QUALi = 1, POSTt = 1 Difference

in Means Variable Mean S.D. Mean S.D.

GW_WDi,t 0.113 0.317 0.113 0.317 0.000

Perc_GWi,t-1 0.250 0.190 0.267 0.189 0.017*

GW_WDi,t-1 0.178 0.383 0.097 0.295 -0.081***

SIZEi,t-1 6.757 1.827 6.984 1.845 0.227**

RETi,t 0.158 0.569 0.053 0.401 -0.105***

RET2i,t-1 0.180 0.775 0.135 0.671 -0.045

BTMi,t-1 0.635 0.568 0.493 0.423 -0.142***

BTM_INDi,t-1 0.179 0.383 0.089 0.285 -0.090***

GROWTHi,t-1 0.082 0.318 0.094 0.235 0.012

NASDAQAMi,t-1 0.504 0.500 0.504 0.500 0.000

SEGSi,t-1 0.761 0.678 0.802 0.665 0.041

HERFINDXi,t-1 0.027 0.026 0.027 0.026 0.000

INDROAi,t-1 -0.324 0.752 0.084 0.406 0.408***

N. Obs. = 1,119

1,119

2,238

37

TABLE 2 – Descriptive Statistics (continued)

Panel C: Differences of Means in Firm Characteristics in the Pre Period

Mean in POSTt = 0 Difference in Means

Performing Mentioning Silent Perf. vs Ment. Perf. vs Silent Ment. vs Silent

Variable (1) (2) (3) (2) – (1) (3) – (1) (3) – (2)

GW_WDi,t 0.113 0.111 0.122 -0.002 0.009 0.011

Perc_GWi,t-1 0.250 0.249 0.191 -0.001 -0.059*** -0.058***

GW_WDi,t-1 0.178 0.156 0.139 -0.022 -0.039* -0.017

SIZEi,t-1 6.757 6.642 6.923 -0.115 0.166 0.281**

RETi,t 0.158 0.113 0.103 -0.045* -0.055 -0.010

RET2i,t-1 0.180 0.172 0.192 -0.008 0.012 0.020

BTMi,t-1 0.635 0.639 0.658 0.004 0.023 0.019

BTM_INDi,t-1 0.179 0.178 0.181 -0.001 0.002 0.003

GROWTHi,t-1 0.082 0.092 0.123 0.010 0.041* 0.031*

NASDAQAMi,t-1 0.504 0.449 0.468 -0.055** -0.036 0.019

SEGSi,t-1 0.761 0.734 0.645 -0.027 -0.116*** -0.089**

HERFINDXi,t-1 0.027 0.028 0.028 0.001 0.001 0.000

INDROAi,t-1 -0.324 -0.406 -0.306 -0.082** 0.018 0.100**

N. Obs. = 1,119 1,776 789 2,895 1,908 2,565

Panel D: Propensity Score Matched Samples

Mean in POSTt = 0

Difference in

Means Mean in POSTt = 0

Difference in

Means

Performing Mentioning Perf. vs Ment. Performing Silent Perf. vs Sil.

Variable (1) (2) (3) = (2) – (1) (4) (5) (6) = (5) – (4)

GW_WDi,t 0.113 0.115 0.002 0.109 0.138 0.029

Perc_GWi,t-1 0.253 0.252 -0.001 0.201 0.218 0.017

GW_WDi,t-1 0.174 0.178 0.004 0.174 0.158 -0.016

SIZEi,t-1 6.716 6.702 -0.014 6.817 6.861 0.044

RETi,t 0.139 0.133 -0.006 0.119 0.127 0.008

RET2i,t-1 0.169 0.145 -0.024 0.185 0.200 0.015

BTMi,t-1 0.632 0.659 0.027 0.665 0.633 -0.032

BTM_INDi,t-1 0.176 0.190 0.014 0.189 0.167 -0.022

GROWTHi,t-1 0.080 0.070 -0.010 0.100 0.105 0.005

NASDAQAMi,t-1 0.489 0.486 -0.003 0.453 0.493 0.040

SEGSi,t-1 0.743 0.758 0.015 0.658 0.670 0.012

HERFINDXi,t-1 0.026 0.026 0.000 0.025 0.025 0.000

INDROAi,t-1 -0.398 -0.398 0.000 -0.332 -0.303 0.029

N. Obs. = 1,074 1,074 2,148 603 603 1,206

38

TABLE 2 – Descriptive Statistics (continued)

Panel E: Incidence of Impairments Pre- and Post-Adoption of Qualitative Assessment by

Quintiles of Book-to-Market

Frequency of GW_WDi,t

QUALi = 1 QUALi = 0

Quintile of BTM POSTt=0 POSTt=1 Diff. POSTt=0 POSTt=1 Diff.

1 6.7% 7.4% 0.7% 5.1% 5.1% 0.0%

2 6.3% 7.9% 1.6% 8.1% 6.4% -1.7%

3 8.7% 8.7% 0.0% 10.0% 11.6% 1.6%

4 11.4% 15.5% 4.1% 11.0% 15.0% 4.0%*

5 18.1% 14.0% -4.1% 16.7% 15.1% -1.5%

Total 11.3% 11.3% 0.0% 11.5% 11.8% 0.4%

This table includes descriptive statistics for the sample of companies in our analysis. Panel A reports the descriptive

statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 2,238); 2) Mentioning

the standard (N. Obs. = 3,552); and, 3) Silent about the qualitative assessment (N. Obs. = 1,578), including the three years

before and after the adoption of ASU 2011-08.

Panel B shows a partition of the sample performing the qualitative assessment by pre and post adoption. Panel C displays

the mean of each variable in the three years before the adoption partitioned by group, and Panel D displays the means of

each variable in our propensity score matched samples in the three years before the adoption. Panel E reports the

frequencies of goodwill write downs pre- and post-adoption of ASU 2011-08 for firms performing the qualitative

assessment (QUALi = 1) compared to all other firms (QUALi = 0).

In Panels B, C, D, and E, differences in means are reported, and ***, **, and * indicate statistical significance at 0.01,

0.05, and 0.10 levels, respectively. Refer to Appendix A for variable definitions.

39

TABLE 3 – Determinants of Performing a Qualitative Assessment

Panel A: Determinants in the First Year after ASU 2011-08

Full sample

POST=1

Performing vs

Mentioning

POST=1

Performing vs

Silent

POST=1

(1) (2) (3)

Variables QUALi QUALi QUALi

Perc_GWi,t-1 0.599* -0.007 2.546***

[1.75] [-0.02] [4.54]

GW_WDi,t-1 -0.351 -0.286 -0.531*

[-1.60] [-1.21] [-1.94]

SIZEi,t-1 -0.034 0.002 -0.104**

[-0.86] [0.04] [-2.03]

RETi,t -0.101 -0.101 -0.104

[-0.67] [-0.60] [-0.53]

RET2i,t-1 0.227** 0.263** 0.221

[2.20] [2.22] [1.26]

BTMi,t-1 -0.330* -0.258 -0.479**

[-1.83] [-1.28] [-2.13]

BTM_INDi,t-1 -0.042 0.016 -0.148

[-0.16] [0.06] [-0.42]

GROWTHi,t-1 -0.730* -0.889** -0.845

[-1.75] [-2.01] [-1.35]

GROWTH2i,t-1 0.065 0.052 0.840

[0.22] [0.17] [1.34]

NASDAQAMi,t-1 0.177 0.155 0.310

[1.15] [0.98] [1.53]

SEGSi,t-1 0.083 0.003 0.257*

[0.83] [0.03] [1.85]

HERFINDXi,t-1 4.413 5.723 -13.260

[0.33] [0.44] [-0.39]

INDROAi,t-1 -0.083 0.221 -0.998

[-0.15] [0.39] [-1.32]

Constant -0.937 -0.614 0.906

[-1.36] [-0.87] [0.63]

Industry FE Yes Yes Yes

N. Obs. 1,228 965 636

Pseudo R2 0.031 0.028 0.094

AUC 0.609 0.600 0.699

40

TABLE 3 – Determinants of Performing a Qualitative Assessment (continued)

Panel B: Determinants using the Three Year Pre-period Average

Performing vs

Mentioning

POST=0

Performing vs

Silent

POST=0

(1) (2)

Variables QUALi QUALi

µ(Perc_GWi,t-1) 0.090 2.380***

[0.24] [4.34]

µ(GW_WDi,t-1) 0.283 0.275

[0.99] [0.71]

µ(SIZEi,t-1) 0.013 -0.080

[0.30] [-1.48]

µ(RETi,t) 0.919*** 1.400***

[2.84] [2.85]

µ(RET2i,t-1) -0.444* -0.641*

[-1.79] [-1.74]

µ(BTMi,t-1) -0.132 -0.429

[-0.59] [-1.29]

µ(BTM_INDi,t-1) -0.010 -0.032

[-0.03] [-0.06]

µ(GROWTHi,t-1) 0.249 -1.388

[0.40] [-1.57]

µ(GROWTH2i,t-1) -0.339 0.552

[-0.90] [0.96]

µ(NASDAQAMi,t-1) 0.146 0.153

[0.93] [0.76]

µ(SEGSi,t-1) -0.048 0.201

[-0.43] [1.38]

µ(HERFINDXi,t-1) -0.604 -10.638

[-0.05] [-0.80]

µ(INDROAi,t-1) -0.191 0.551

[-0.44] [1.05]

Constant -0.681 0.539

[-0.99] [0.70]

Industry FE Yes Yes

N. Obs. 965 636

Pseudo R2 0.023 0.083

AUC 0.598 0.691

This table includes results from the logistic regressions on the identified subsets with the following specification:

P(QUALi) =f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (1)

Panel A is limited to first fiscal year when the qualitative assessment became available as an option (post ASU 2011-08’s

effective date). Column 1 uses the whole sample of firms, while column 2 (3) subsets the sample to compare those firms

performing the qualitative assessment to those that are mentioning (are silent about) the option to perform the assessment.

Panel B uses the averages of the determinant variables in the three year pre-adoption, which is then used to perform

propensity score matching between the identified subsets.

Robust z-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.

41

TABLE 4 – Incidence of Impairments Three Years Pre and Post ASU 2011-08

Panel A: Full Sample

Full Sample Performing Full Sample Performing vs

Mentioning

Performing

vs Silent

(1) (2) (3) (4) (5)

Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t

POSTt 0.005 0.011 0.002 0.013 -0.023

[0.66] [0.83] [0.22] [1.31] [-1.38]

QUALi -0.006 0.002 -0.022

[-0.52] [0.17] [-1.49]

POSTt * QUALi 0.010 -0.002 0.036*

[0.66] [-0.14] [1.83]

Perc_GWi,t-1 0.151*** 0.138*** 0.151*** 0.152*** 0.146***

[6.90] [3.65] [6.89] [6.29] [4.89]

GW_WDi,t-1 0.213*** 0.194*** 0.214*** 0.208*** 0.209***

[11.95] [6.41] [11.96] [10.44] [8.71]

SIZEi,t-1 0.007*** 0.002 0.007*** 0.005** 0.005*

[2.97] [0.47] [2.97] [2.14] [1.82]

RETi,t -0.074*** -0.088*** -0.074*** -0.079*** -0.075***

[-9.83] [-5.87] [-9.81] [-8.50] [-7.75]

RET2i,t-1 -0.011** -0.011 -0.011** -0.014** -0.006

[-2.14] [-1.33] [-2.14] [-2.47] [-1.00]

BTMi,t-1 0.038*** 0.026 0.038*** 0.039*** 0.029

[2.98] [1.00] [2.99] [2.65] [1.64]

BTM_INDi,t-1 0.049*** 0.083** 0.050*** 0.049** 0.071***

[2.71] [2.41] [2.71] [2.42] [2.68]

GROWTHi,t-1 0.003 0.007 0.003 -0.007 0.017

[0.29] [0.31] [0.28] [-0.48] [1.12]

NASDAQAMi,t-1 -0.016* 0.010 -0.015* -0.010 -0.005

[-1.66] [0.61] [-1.66] [-1.00] [-0.37]

SEGSi,t-1 0.031*** 0.048*** 0.031*** 0.029*** 0.042***

[4.69] [4.05] [4.70] [3.83] [4.78]

HERFINDXi,t-1 -0.088 -1.903 -0.099 0.280 -1.994*

[-0.15] [-1.06] [-0.17] [0.43] [-1.92]

INDROAi,t-1 0.004 0.001 0.004 0.007 -0.001

[0.67] [0.05] [0.69] [0.93] [-0.16]

Constant -0.009 0.111 -0.007 0.003 0.062

[-0.28] [1.34] [-0.22] [0.07] [1.22]

Industry FE Yes Yes Yes Yes Yes

N. Obs. 7,368 2,238 7,368 5,790 3,816

Adj. R2 0.104 0.102 0.104 0.101 0.109

AUC of Logit Model 0.769 0.778 0.769 0.771 0.774

42

TABLE 4 – Incidence of Impairments Three Years Pre and Post ASU 2011-08 (continued)

Panel B: Propensity Score Matched Sample

Performing vs

Mentioning

Three Years

Performing

vs Silent

Three Years

Performing vs

Mentioning

One Year

Performing

vs Silent

One Year

(1) (2) (3) (4)

Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t

POSTt 0.022* -0.032 0.008 -0.038

[1.81] [-1.56] [0.36] [-1.09]

QUALi 0.001 -0.029 -0.017 -0.059**

[0.08] [-1.52] [-0.83] [-1.98]

POSTt * QUALi -0.012 0.049* 0.019 0.097**

[-0.67] [1.95] [0.58] [2.07]

Perc_GWi,t-1 0.163*** 0.123*** 0.157*** 0.142*

[5.81] [2.95] [3.39] [1.71]

GW_WDi,t-1 0.191*** 0.183*** 0.195*** 0.178***

[8.40] [5.65] [4.22] [2.86]

SIZEi,t-1 0.005* 0.008** 0.009* 0.006

[1.84] [2.20] [1.87] [1.15]

RETi,t -0.080*** -0.071*** -0.098*** -0.074***

[-6.77] [-5.65] [-4.50] [-2.79]

RET2i,t-1 -0.014** -0.004 -0.012 -0.004

[-2.20] [-0.42] [-1.32] [-0.29]

BTMi,t-1 0.041** 0.045* 0.053* 0.049

[2.15] [1.83] [1.95] [1.19]

BTM_INDi,t-1 0.050** 0.059* 0.047 0.029

[2.10] [1.74] [1.13] [0.47]

GROWTHi,t-1 -0.009 0.005 -0.022 -0.003

[-0.48] [0.25] [-0.77] [-0.08]

NASDAQAMi,t-1 -0.001 -0.006 -0.001 -0.006

[-0.11] [-0.37] [-0.05] [-0.24]

SEGSi,t-1 0.031*** 0.045*** 0.023* 0.054***

[3.52] [3.85] [1.74] [2.90]

HERFINDXi,t-1 0.533 -3.958* -2.455 -5.673*

[0.46] [-1.80] [-1.38] [-1.68]

INDROAi,t-1 0.005 -0.004 -0.013 -0.013

[0.54] [-0.31] [-0.57] [-0.61]

Constant -0.000 0.123 0.120 0.161

[-0.00] [1.36] [1.29] [1.17]

Industry FE Yes Yes Yes Yes

N. Obs. 4,296 2,412 1,432 804

Adj. R2 0.099 0.108 0.096 0.073

AUC of Logit Model 0.772 0.775 0.769 0.760

Panel A, Columns 1 and 2 of this table includes results from the linear probability model on the identified subsets with

the following specification:

P(GW_WDi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (2)

Panel A, Columns 3 through 5 and Panel B of this table includes results from the linear probability model on the identified

subsets with the following specification:

P(GW_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (3)

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. The area under the receiver operating characteristic curve (AUC)

is displayed for the same specifications using a logistic regression model in order to assess goodness of fit. Variable

descriptions are included in Appendix A.

43

TABLE 5 – Opportunities to Manage Impairment Charges

Panel A: Number of reporting segments and analyst following in the first year of adoption

Reporting Segments Analyst Following

Number of Firms Perc. Number of Firms Perc.

0 - - 368 30.0

1 500 40.7 214 17.4

2 92 7.5 140 11.4

3 256 20.9 99 8.1

4 173 14.1 71 5.8

5 118 9.6 64 5.2

6 55 4.5 41 3.3

7 or more 34 2.7 231 18.8

Total 1,228 100.0 1,228 100.0

Panel A tabulates reporting segments and analyst following for each unique firm in our sample. The first year of adoption

for ASU 2011-08 is the year chosen to stratify the sample in order to maintain a balanced sample throughout all six years

of the analysis. For the subsamples in this analysis, we compare firms with only one reporting segment to firms with four

or more reporting segments, and firms with no analyst following to firms with five or more analysts following. Firms in

the respective top portion of the cross-section are assigned a one for the respective Highi variable, and firms in the bottom

are assigned a zero.

44

TABLE 5 – Opportunities to Manage Impairment Charges (continued)

Panel B: Regression analysis – High and low number of reporting segments

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (4)

Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t

High_SEGSi 0.009 0.039 -0.007 0.055

[0.33] [0.80] [-0.23] [0.86]

POSTt 0.007 0.033* -0.000 -0.021

[0.71] [1.93] [-0.02] [-0.83]

POSTt * High_SEGSi 0.003 -0.018 0.008 -0.018

[0.20] [-0.55] [0.42] [-0.39]

QUALi -0.034** -0.039*

[-2.57] [-1.84]

POSTt * QUALi 0.027 0.052*

[1.50] [1.70]

High_SEGSi * QUALi 0.049* -0.019

[1.80] [-0.37]

POSTt * High_SEGSi * QUALi -0.020 0.062

[-0.54] [0.94]

Perc_GWi,t-1 0.149*** 0.172*** 0.148*** 0.152***

[5.89] [3.67] [5.89] [3.11]

GW_WDi,t-1 0.237*** 0.190*** 0.237*** 0.172***

[10.82] [5.65] [10.78] [4.37]

Controls Included Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

N. Obs. 5,280 1,506 5,280 1,764

Adj. R2 0.123 0.112 0.123 0.122

Panel C: Regression analysis – High and low number of analysts following

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (4)

Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t

High_FOLLOWi -0.005 0.023 -0.007 -0.048

[-0.28] [0.77] [-0.39] [-1.40]

POSTt -0.013 0.033 -0.029* -0.084***

[-0.99] [1.43] [-1.85] [-2.69]

POSTt * High_FOLLOWi 0.023 -0.015 0.037* 0.088*

[1.29] [-0.50] [1.69] [1.77]

QUALi -0.038** -0.052

[-2.04] [-1.59]

POSTt * QUALi 0.052** 0.090**

[2.01] [2.12]

High_FOLLOWi,* QUALi 0.015 0.035

[0.53] [0.72]

POSTt * High_FOLLOWi * QUALi -0.047 -0.077

[-1.28] [-1.19]

Perc_GWi,t-1 0.136*** 0.121*** 0.138*** 0.113**

[4.56] [2.62] [4.62] [2.10]

GW_WDi,t-1 0.204*** 0.226*** 0.204*** 0.187***

[8.36] [5.31] [8.35] [4.35]

Controls Included Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

N. Obs. 4,224 1,356 4,224 1,548

Adj. R2 0.089 0.106 0.089 0.111

45

TABLE 5 – Opportunities to Manage Impairment Charges (continued)

Panel D: Regression analysis – High and low cushion

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (5)

Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t

High_CUSHIONi -0.005 0.013 -0.011 -0.027

[-0.27] [0.30] [-0.53] [-0.73]

POSTt 0.020 0.027 0.017 -0.011

[1.23] [0.74] [0.96] [-0.23]

POSTt * High_CUSHIONi -0.022 -0.036 -0.014 -0.001

[-1.13] [-0.87] [-0.65] [-0.02]

QUALi -0.014 -0.037

[-0.50] [-0.80]

POSTt * QUALi 0.011 0.022

[0.30] [0.36]

High_CUSHIONi,* QUALi 0.022 0.017

[0.62] [0.30]

POSTt * High_CUSHIONi * QUALi -0.028 0.005

[-0.61] [0.07]

Perc_GWi,t-1 0.184*** 0.135*** 0.185*** 0.093*

[6.31] [2.73] [6.32] [1.74]

GW_WDi,t-1 0.214*** 0.196*** 0.214*** 0.182***

[8.68] [3.94] [8.69] [3.71]

Controls Included Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

N. Obs. 3,570 924 3,570 1,770

Adj. R2 0.124 0.116 0.123 0.101

Panel B, C, and D includes results from the following OLS regressions on the identified subsets with the following

specifications:

P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, Controlsi,t, Fixed Effectsi,t, ei,t) (4)

P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, QUALi, POSTt*QUALi, Highi*QUALi,t, POSTt*Highi*QUALi,

Controlsi,t, Fixed Effectsi,t, ei,t) (5)

Where Highi is replaced with the indicator variable of interest (segments, analyst following, or cushion) which identify a

cross-section of firms with a higher likelihood of exhibiting opportunistic behavior in impairment assessments.

High_SEGSi and High_FOLLOWi are defined in Panel A; High_CUSHIONi is an indicator variable equal to one if a firm

is in the top quartile of the market-to-book ratio in the first year after adoption of ASU 2011-08, and zero if a firm is in

the lowest quartile of market-to-book in that first year. These indicator variables are assigned to the same firms for all six

years in order to maintain a balanced sample.

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.

46

TABLE 6 – Predictive Modeling of Impairments

Panel A: Sample selection

Sample starting point for all analyses 17,427 4,245

Observations remaining after requiring non-missing control

variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,

NASDAQAM, SEGS, HERFINDX, INDROA

12,212 2,811

Final sample for extended analysis 12,212 2,811

Firms performing a qualitative assessment (Performing) 3,366 731

Firms disclosing the new option, but not specifying (Mentioning) 5,859 1,346

Firms not disclosing any information (Silent) 2,987 734

Panel B: Logistic Regression Model

POST=0 POST=1

(1) (2)

Variables GW_WDi,t GW_WDi,t

Perc_GWi,t-1 1.665*** 1.452***

[8.01] [6.48]

GW_WDi,t-1 1.533*** 1.420***

[14.06] [12.16]

SIZEi,t-1 0.088*** 0.088***

[3.56] [3.37]

RETi,t -1.091*** -1.104***

[-8.56] [-6.85]

RET2i,t-1 -0.066 -0.265***

[-0.92] [-3.10]

BTMi,t-1 0.633*** 0.111

[5.73] [1.13]

BTM_INDi,t-1 0.196 0.585***

[1.26] [3.42]

GROWTHi,t-1 0.131 -0.188

[1.06] [-1.26]

NASDAQAMi,t-1 -0.045 -0.255**

[-0.47] [-2.45]

SEGSi,t-1 0.282*** 0.396***

[4.14] [5.43]

HERFINDXi,t-1 2.162 10.406*

[0.31] [1.91]

INDROAi,t-1 0.284*** -0.037

[2.81] [-0.22]

Constant -3.958*** -3.846***

[-11.00] [-10.62]

Industry FE Yes Yes

N. Obs. 6,033 6,179

Pseudo R2 0.153 0.135

AUC 0.778 0.764

47

TABLE 6 – Predictive Modeling of Impairments (continued)

Panel C: Type I and II Errors of Predicted Impairments Post ASU 2011-08

Full Sample GW_WDi,t

POSTt = 1 =0 =1 Total

Pred.

Imp.

=0 81.5% 7.5% 89.0%

=1 7.3% 3.7% 11.0%

Total 88.8% 11.2% 100.0%

QUALi = 1 GW_WDi,t

POSTt /= 1 =0 =1 Total

Pred.

Imp.

=0 83.6% 8.2% 91.8%

=1 5.8% 2.4% 8.2%

Total 89.4% 10.6% 100.0%

QUALi = 0 GW_WDi,t

POSTt = 1 =0 =1 Total

Pred.

Imp.

=0 80.7% 7.3% 88.0%

=1 7.9% 4.1% 12.0%

Total 88.6% 11.4% 100.0%

Two-sample test of proportions QUALi = 1 QUALi = 0 Difference

False Positive Rate (Type I) 6.5% 8.9% -2.4%***

False Negative Rate (Type II) 77.3% 64.0% 13.3%***

Panel A shows the sample selection for the observations used in the prediction modeling of impairment analysis. Panel

B reports the results of the from a logistic regression model on all observations pre- and post-adoption of ASU 2011-

08 in columns 1 and 2, respectively, using the following specification:

P(GW_WDi,t) = f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (Equation 2 without POSTt)

Robust z-statistics are shown in the brackets. Standard errors are clustered by firm, and the area under the receiver

operating characteristic curve (AUC) is displayed in order to assess goodness of fit.

Panel C displays the analysis of type I and II errors. The estimated coefficients from Panel B, column 1 (the pre-period)

are used to estimate the likelihood of an impairment for every observation in the post-period. Firm-year observations in

the highest 11% of estimated probabilities in each year of the post-period are assigned a value of 1 for predicted

impairment, while the remaining firm-year observations are assigned a 0. Two-by-two contingency tables are displayed

comparing the predicted impairment to actual impairments for the full sample in the post-period, as well as separately

for qualitative assessment firms (QUALi = 1) and for the remainder of firms (QUALi = 0). Lastly, a two-sample test of

proportions is performed to evaluate the difference between the type I and type II error rates between the two sub-

samples. Throughout the table, ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively.

Variable descriptions are included in Appendix A.

48

TABLE 7 – Impairment Timeliness and Qualitative Assessments

Panel A: Sample selection

Sample starting point for all analyses from Table 1 17,427 4,245

Observations remaining after requiring non-missing control

variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,

NASDAQAM, SEGS, HERFINDX, INDROA

12,212 2,811

Observations remaining after restricting the sample to firm-years

with an impairment charge (GW_WDi,t=1)

1,416 897

Final sample for impairment timeliness analysis 1,416 897

Firms performing a qualitative assessment (Performing) 365 234

Firms disclosing the new option, but not specifying (Mentioning) 722 463

Firms not disclosing any information (Silent) 329 200

Panel B: Descriptive statistics

Performing Mentioning Silent

Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median

EARLY_WDi,t 0.447 0.498 0.000 0.429 0.495 0.000 0.331 0.471 0.000

POSTt 0.507 0.501 1.000 0.481 0.500 0.000 0.486 0.501 0.000

QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

Perc_GWi,t-1 0.293 0.199 0.260 0.300 0.210 0.250 0.210 0.182 0.158

GW_WDi,t-1 0.356 0.480 0.000 0.368 0.483 0.000 0.362 0.481 0.000

SIZEi,t-1 6.987 1.740 6.892 6.804 1.896 6.803 7.523 2.387 7.234

RETi,t -0.065 0.523 -0.119 -0.097 0.600 -0.176 -0.129 0.556 -0.228

RET2i,t-1 -0.031 0.654 -0.145 -0.074 0.803 -0.213 -0.107 0.761 -0.249

BTMi,t-1 0.748 0.634 0.630 0.830 0.726 0.701 0.963 0.805 0.774

BTM_INDi,t-1 0.260 0.439 0.000 0.285 0.452 0.000 0.337 0.474 0.000

GROWTHi,t-1 0.101 0.332 0.050 0.080 0.335 0.031 0.104 0.324 0.048

NASDAQAMi,t-1 0.512 0.501 1.000 0.418 0.494 0.000 0.459 0.499 0.000

SEGSi,t-1 0.966 0.660 1.099 0.823 0.691 1.099 0.920 0.724 1.099

HERFINDXi,t-1 0.029 0.029 0.017 0.028 0.026 0.017 0.032 0.031 0.018

INDROAi,t-1 -0.149 0.609 -0.120 -0.159 0.644 -0.120 -0.148 0.719 -0.184

N. Obs. = 365 722 329

Panel A shows the sample selection for the observations used in the impairment timeliness analysis. Panel B reports the

descriptive statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 365); 2)

Mentioning the standard (N. Obs. = 722); and, 3) Silent about the qualitative assessment (N. Obs. = 329), including only

firm-year observations with an impairment charge (GW_WDi,t=1) in the three years before and three years after the

adoption of ASU 2011-08. Variable descriptions are included in Appendix A.

49

TABLE 7 – Impairment Timeliness and Qualitative Assessments (continued)

Panel C: Mean early impairment charges between groups, pre and post

POSTt =0 POSTt =1 Combined

Mean S.D. Mean S.D. Mean S.D.

Performing EARLY_WDi,t 0.428 0.496 0.465 0.500 0.447 0.498

N. Obs. = 180 185 365

Mentioning EARLY_WDi,t 0.427 0.495 0.432 0.496 0.429 0.495

N. Obs. = 375 347 722

Silent EARLY_WDi,t 0.367 0.483 0.293 0.457 0.331 0.471

N. Obs. = 169 160 329

Combined EARLY_WDi,t 0.413 0.493 0.409 0.492

N. Obs. = 724 692

Panel D: Impairment timeliness between groups, pre and post

Full Sample Performing Full Sample Performing

vs Silent

(1) (2) (3) (4)

Variables EARLY_WDi,t EARLY_WDi,t EARLY_WDi,t EARLY_WDi,t

POSTt 0.016 0.082 -0.006 -0.036

[0.56] [1.26] [-0.18] [-0.71]

QUALi 0.003 0.023

[0.05] [0.37]

POSTt * QUALi 0.080 0.119

[1.26] [1.57]

Perc_GWi,t-1 0.229*** 0.182 0.224*** 0.259**

[3.11] [1.24] [3.05] [2.23]

GW_WDi,t-1 0.035 -0.015 0.039 0.045

[1.21] [-0.25] [1.33] [1.07]

Controls Included Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

N. Obs. 1,416 365 1,416 694

Adj. R2 0.013 -0.038 0.014 0.010

Panel C reports the mean of (percentage of) companies taking an impairment charge in the first three quarters for each of

the subsamples of firms in the pre- and post-periods.

Panel D includes results from the following linear probability models on the identified subsets with the following

specification:

P(EARLY_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (6)

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.

50

TABLE 8 – Audit Fees and Qualitative Assessments

Panel A: Sample selection

Sample starting point for all analyses from Table 1 17,427 4,245

Observations remaining after requiring non-missing control

variables AFEE, SIZE, BIGN, ROA, BTM

12,129 3,018

Observations remaining after requiring a firm to appear in all six

years of the sample period (three pre and three post)

6,846 1,141

Final sample for audit fees analysis 6,846 1,141

Firms performing a qualitative assessment (Performing) 2,202 367

Firms disclosing the new option, but not specifying (Mentioning) 3,378 563

Firms not disclosing any information (Silent) 1,266 211

Panel B: Partitions by each type

Performing Mentioning Silent

Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median

AFEEi,t 14.271 1.101 14.206 14.187 1.153 14.210 14.265 1.539 14.101

POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500

GW_WDi,t 0.114 0.317 0.000 0.128 0.334 0.000 0.117 0.321 0.000

QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

Perc_GWi,t-1 0.260 0.189 0.216 0.264 0.205 0.212 0.212 0.185 0.143

SIZEi,t-1 6.996 1.812 6.948 6.843 1.876 6.902 7.290 2.532 7.086

BIGNi,t 0.796 0.403 1.000 0.798 0.401 1.000 0.828 0.378 1.000

ROAi,t 0.043 0.097 0.053 0.032 0.126 0.049 0.025 0.137 0.048

BTMi,t-1 0.529 0.453 0.448 0.576 0.510 0.480 0.611 0.563 0.482

N. Obs. = 2,202 3,378 1,266

Panel A shows the sample selection for the observations used in the audit fee analysis. Panel B reports the descriptive

statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 2,202); 2) Mentioning

the standard (N. Obs. = 3,378); and, 3) Silent about the qualitative assessment (N. Obs. = 1,266), including three years

before and three years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.

51

TABLE 8 – Audit Fees and Qualitative Assessments (continued)

Panel C: Regression analysis

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (4)

Variables AFEEi,t AFEEi,t AFEEi,t AFEEi,t

POSTt 0.003 0.027* -0.004 -0.014

[0.34] [1.89] [-0.44] [-0.51]

QUALi 0.041 0.178***

[1.37] [2.96]

POSTt * QUALi 0.023 0.032

[1.57] [0.97]

Perc_GWi,t-1 -0.041 -0.139 -0.046 0.022

[-0.55] [-0.98] [-0.63] [0.12]

GW_WDi,t 0.050* 0.066* 0.050* 0.039

[1.90] [1.66] [1.91] [0.70]

SIZEi,t-1 0.532*** 0.518*** 0.532*** 0.533***

[59.69] [31.95] [59.64] [29.70]

BIGNi,t 0.298*** 0.315*** 0.300*** 0.214***

[8.05] [4.74] [8.12] [2.84]

ROAi,t -0.509*** -0.666*** -0.519*** -0.597***

[-5.03] [-3.78] [-5.13] [-3.06]

BTMi,t-1 -0.112*** -0.092** -0.109*** -0.090**

[-4.48] [-2.06] [-4.36] [-2.08]

Constant 10.212*** 10.281*** 10.198*** 10.007***

[110.30] [47.76] [110.12] [34.69]

Industry FE Yes Yes Yes Yes

N. Obs. 6,846 2,202 6,846 1,596

Adj. R2 0.836 0.815 0.837 0.853

Panel C includes results from the following OLS model on the identified subsets with the following specification:

AFEEi,t = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (7)

AFEEi,t = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (8)

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.

52

TABLE 9 – SEC Comment Letters and Qualitative Assessments

Panel A: Sample selection

Sample starting point for all analyses from Table 1 17,427 4,245

Observations remaining after requiring non-missing control

variables SEC_COMM, MERGE, BIGN, LOSS

16,717 4,112

Observations remaining after requiring a firm to appear in all six

years of the sample period (three pre and three post)

10,134 1,689

Final sample for comment letter analysis 10,134 1,689

Firms performing a qualitative assessment (Performing) 3,090 515

Firms disclosing the new option, but not specifying (Mentioning) 4,938 823

Firms not disclosing any information (Silent) 2,106 351

Panel B: Partitions by each type

Performing Mentioning Silent

Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median

SEC_COMMi,t 0.086 0.280 0.000 0.082 0.274 0.000 0.025 0.157 0.000

POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500

GW_WDi,t 0.111 0.315 0.000 0.117 0.321 0.000 0.119 0.324 0.000

QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

MERGEi,t 0.396 0.489 0.000 0.375 0.484 0.000 0.220 0.415 0.000

BIGNi,t 0.790 0.407 1.000 0.777 0.416 1.000 0.852 0.355 1.000

LOSSi,t 0.189 0.391 0.000 0.218 0.413 0.000 0.238 0.426 0.000

N. Obs. = 3,090 4,938 2,106

Panel A shows the sample selection for the observations used in the SEC comment letter analysis. Panel B reports the

descriptive statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 3,090); 2)

Mentioning the standard (N. Obs. = 4,938); and, 3) Silent about the qualitative assessment (N. Obs. = 2,106), including

three years before and three years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.

53

TABLE 9 – SEC Comment Letters and Qualitative Assessments (continued)

Panel C: Regression analysis

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (4)

Variables SEC_COMMi,t SEC_COMMi,t SEC_COMMi,t SEC_COMMi,t

POSTt -0.046*** -0.053*** -0.042*** -0.027***

[-9.01] [-5.34] [-7.18] [-2.96]

QUALi 0.025*** 0.067***

[2.65] [4.17]

POSTt * QUALi -0.012 -0.016

[-1.07] [-0.88]

GW_WDi,t 0.050*** 0.007 0.050*** 0.040**

[4.99] [0.40] [4.99] [2.16]

MERGEi,t 0.032*** 0.021* 0.031*** 0.017

[5.33] [1.93] [5.17] [1.46]

BIGNi,t 0.010 0.025** 0.010 0.009

[1.53] [2.04] [1.60] [0.62]

LOSSi,t 0.012* 0.014 0.012* -0.010

[1.68] [0.92] [1.78] [-0.90]

Constant 0.032*** 0.059** 0.025** -0.018

[2.97] [2.54] [2.26] [-0.98]

Industry FE Yes Yes Yes Yes

N. Obs. 10,134 3,090 10,134 2,196

Adj. R2 0.025 0.023 0.026 0.027

Panel C includes results from the following linear probability model on the identified subsets with the following

specification:

P(SEC_COMMi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (9)

P(SEC_COMMi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (10)

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.

54

TABLE 10 – Market Reaction to Earnings of Users of Qualitative Assessments

Panel A: Sample selection

Sample starting point for all analyses from Table 1 17,427 4,245

Observations remaining after requiring non-missing control

variables EarnCAR, EarnSurp, LOSS

11,584 3,376

Observations remaining after requiring a firm to appear in all six

years of the sample period (three pre and three post)

5,070 845

Final sample for ERC analysis 5,070 845

Firms performing a qualitative assessment (Performing) 1,626 271

Firms disclosing the new option, but not specifying (Mentioning) 2,364 394

Firms not disclosing any information (Silent) 1,080 180

Panel B: Partitions by each type

Performing Mentioning Silent

Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median

EarnCARi,t 0.007 0.062 0.006 0.006 0.063 0.002 -0.000 0.055 -0.001

EarnSurpi,t 0.000 0.021 0.000 0.001 0.020 0.000 0.000 0.039 0.000

POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500

QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

LOSSi,t 0.125 0.331 0.000 0.123 0.328 0.000 0.151 0.358 0.000

N. Obs. = 1,626 2,364 1,080

Panel A shows the sample selection for the observations used in the ERC analysis. Panel B reports the descriptive statistics

for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 1,626); 2) Mentioning the standard

(N. Obs. = 2,364); and, 3) Silent about the qualitative assessment (N. Obs. = 1,080), including three years before and three

years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.

55

TABLE 10 – Market Reaction to Earnings of Users of Qualitative Assessments (continued)

Panel C: Regression analysis

Full Sample Performing Full Sample Performing vs

Silent PSM

(1) (2) (3) (4)

Variables EarnCARi,t EarnCARi,t EarnCARi,t EarnCARi,t

EarnSurpi,t 0.274*** 0.363*** 0.245*** 0.285***

[5.32] [3.56] [4.21] [2.76]

POSTt -0.003* -0.004 -0.003 0.011*

[-1.88] [-1.20] [-1.41] [1.89]

POSTt * EarnSurpi,t -0.148** -0.137 -0.140* -0.267***

[-1.98] [-0.75] [-1.66] [-3.59]

QUALi 0.003 0.013**

[1.07] [2.35]

POSTt * QUALi -0.001 -0.016*

[-0.16] [-1.86]

EarnSurpi,t * QUALi 0.117 0.816*

[1.02] [1.94]

POSTt * EarnSurpi,t * QUALi 0.001 -0.415

[0.00] [-0.55]

LOSSi,t 0.001 -0.001 0.001 0.002

[0.40] [-0.28] [0.42] [0.25]

Constant 0.005 0.012 0.004 -0.007

[1.00] [1.01] [0.79] [-1.63]

Industry FE Yes Yes Yes Yes

N. Obs. 5,070 1,626 5,070 1,050

Adj. R2 0.010 0.008 0.010 0.017

Panel C includes results from the following OLS regressions on the identified subsets with the following specification:

EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, LOSSi,t, Fixed Effectsi,t, ei,t) (11)

EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, QUALi, POSTt * QUALi, EarnSurpi,t * QUALi,t, POSTt

* EarnSurpi,t * QUALi, LOSSi,t, Fixed Effectsi,t, ei,t) (12)

Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical

significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.