does classifying and disaggregating financial statement...

Does classifying and disaggregating financial statement information help credit analysts recognize firms’ cost structures?

Robert Bloomfield Cornell University Johnson School of Management

Frank D. Hodge

University of Washington Foster School of Business

Patrick Hopkins Indiana University Kelley School of Business

Kristina Rennekamp

Cornell University Johnson School of Management

March 2010

Solely for presentation at the University of Chicago, April2010. Please do not distribute, cite or quote this version.

We thank Sue Bielstein, Robert Lipe, Tom Linsmeier, Ray Pfeiffer, Kim Petrone, Chandy Smith, Wesley Smyth, and Gerry White for helpful comments. We also thank Scott Asay and Tim Brown for their coding assistance. Rob Bloomfield acknowledges support of the Nicholas H. Noyes Professorship, Frank Hodge acknowledges support of the Herbert O. Whitten Professorship, and Pat Hopkins acknowledges support of the Deloitte Foundation. This research project was conducted with the assistance of the Financial Accounting Standards Board (FASB), under the auspices of the Financial Accounting Standards Research Initiative (FASRI). The first author has received financial compensation for serving as Director of FASRI, while the others have received financial compensation for a separate report written for the FASB based on the experiment described herein. All views expressed in this document are those of the authors, and do not represent the views of any FASB staff or Board members, nor any official positions of that body.

Does classifying and disaggregating financial statement information help credit analysts recognize a firms’ cost structures?

ABSTRACT In October 2008, the Financial Accounting Standards Board and the International Accounting Standards Board issued a discussion paper entitled Preliminary Views on Financial Statement Presentation. In the discussion paper, the Boards propose that firms classify their assets and liabilities by activity (operating, investing and financing), and then maintain those classifications cohesively across the statement of financial position, the statement of comprehensive income, and the statement of cash flows. In addition, the Boards propose that the statement of comprehensive income and the statement of cash flows list separately income and expense items that differ by function or nature. We report experimental results suggesting that cohesive classification across the financial statements, together with disaggregation, helps analysts identify key elements of a firm’s cost structure. We also find that disaggregation on the face of the financial statements, when cohesive classification is not provided in the financial statements, is only modestly effective in helping analysts recognize a firm’s cost structure, and that classification on the face of the financial statements with disaggregation provided in the footnotes actually inhibits analysts’ ability to recognize a firm’s cost structure. The results support theories emphasizing the proximity of related information, and provide an important counterexample to the more common result that users respond more to information on the face of financial statements than to information disclosed in footnotes. Keywords: Financial statement presentation, classification, disaggregation, proximity, experiment, standard setting. Data availability: Contact the authors.

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I. INTRODUCTION

In October 2008, the Financial Accounting Standards Board (FASB) and the International

Accounting Standards Board (IASB) issued their Preliminary Views on Financial Statement

Presentation [FASB 2008, hereafter FSP]. The FSP proposes that firms classify assets and

liabilities on the Statement of Financial Position by activity (operating, investing and financing),

and then cohesively maintain those classifications across the Statement of Comprehensive

Income and the Statement of Cash Flows. In addition, the boards propose that the Statement of

Comprehensive Income and the Statement of Cash Flows separately list income and expense

items that do not respond equally to similar economic events.

Cohesive classification and disaggregation both provide additional information to

investors that could potentially help them make more informed judgments and decisions.

Standard setters continue to debate, however, whether such information is useful enough to

outweigh the costs of providing it and, if so, whether it is most appropriately placed on the face

of financial statements or in footnotes. Most studies on financial statement presentation predict

and find that investors attend more carefully to single items of information when they are placed

on the face of financial statements rather than in the footnotes (see, for example, Hirst and

Hopkins 1998 and Hirst, Hopkins and Wahlen 2004). More recent research emphasizes the

importance of information items’ proximity in determining the extent to which financial

statement users are able to completely process multiple items of related information (Hodge,

Hopkins and Wood 2010). We extend this line of research by examining analyst judgments

when classification and disaggregation alter the proximity and cohesiveness of multiple

information items.

Consistent with the FSP’s explicit objective to provide information about companies’

financial flexibility (FASB 2008, para 2.13), we conduct an experiment in which 60 experienced

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credit analysts are asked to identify differences between two manufacturing firms that are

similar, except for one key factor: one firm produces all of its goods internally, while the other

outsources most of its production. Our study includes two independent variables. First, we

manipulate whether items are cohesively classified by activity on the face of the financial

statements. Second, we manipulate whether expenses and cash flows are disaggregated on the

face of the financial statements or in footnotes. We provide participating analysts with

comprehensive financial information for both firms, and ask them to conduct analyses closely

modeled on their typical job duties.

Our results suggest that providing analysts with cohesively classified information and

disaggregated expenses on the face of the financial statements facilitates their ability to identify

key differences between the firms. Further, these effects are also present in the condition in

which we provide analysts with disaggregated information in the notes, but when classification is

absent from the financial statements. In these two cases, when analysts respond to an open-

ended question asking them to justify their judgment, half of our participants identify differences

in the cost structures of the firms and half of our participants identify differences in the two

firms’ abilities to dispose of fixed assets without impinging on operations. These proportions

drop dramatically when the financial statements are cohesively classified, but disaggregated

information is provided in the notes. We also find a significant decrease in this proportion when

disaggregated information is provided on the face of the financial statements but without related

information on classification.

Our results suggest that reporting related information in close proximity, and maintaining

cohesive classifications of related items across financial statements, helps analysts identify key

differences between firms. Specifically, in both settings where analysts are better able to identify

the cost structures of the firms, related information is reported in close proximity (either on the

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face or in the notes). In the settings where related information is either (1) spread across a

variety of locations (i.e., classification on face, disaggregation in notes) or (2) is located on the

face of the financial statements but lacks a consistent representation of the firms’ cost structures

(i.e., disaggregation on face, but without classification), analysts are not as good at identifying

the cost structures of the firms.

Despite these results, we find highly accurate awareness of the two firms’ cost structures

when we examine analysts’ forecasts of revenue and income, from which we calculate the

forecasted percentage drop in expenses divided by the percentage drop in sales (i.e., the Expense

Variability Ratio, or EVR). Analysts who believe that all expenses are fixed will forecast

revenue and income that result in an EVR of zero, while analysts who believe that all expenses

are variable will forecast revenue and income that result in an EVR of one. Almost all of the

analysts in our study forecast higher EVR for the outsourcing firm than for the insourcing firm,

demonstrating a fundamental understanding of the differences in the two firms’ cost structures.

Analysts’ EVR projections reflect an interaction between cohesive classification and

disaggregation. Disaggregating items on the face of the financial statements rather than in the

notes improves forecasting performance when items are also cohesively classified on the face,

but not when cohesive classification is absent. Similarly, cohesive classification on the face of

the financial statements reduces forecasting performance only when items are disaggregated in

the notes, but not when they are disaggregated on the face of the financial statements. These

results further suggest that reporting related information in close proximity, and maintaining

cohesive classification of related information across financial statements, helps analysts identify

key differences between firms.

Our results have implications for both policy and theory. Most research on financial

statement presentation investigates whether information has greater impact on judgments and

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decisions when it is presented on the face of financial statements rather than in the notes (and

typically concludes that it does). Our results suggest that policy makers should also consider the

relations between disclosure information and, perhaps, find ways to present related information

in a common location. Our results also suggest a number of directions for future research,

including how users process and integrate information included in financial statements and

disclosures, as well as how to most effectively present related disclosures in a common location.

Our study includes some procedural and methodological innovations that future research

should seek to refine. We worked closely with the FASB’s Financial Statement Presentation

Project team to identify research questions that would be helpful for Board deliberations, and to

gain access to credit analysts with the skills required to complete our study. In addition, we

provided analysts with materials that were extremely rich and detailed compared to most

experiments; materials designed to parallel the settings in which they actually assign credit

ratings. These innovations help us to draw inferences about how credit analysts might actually

respond to the changes proposed by the FSP project, but also reduce the power of our tests

because they add noise to our dependent measures.

The remainder of the paper is organized into five sections. We discuss the motivation for

our study and prior research on financial statement presentation in Section II, describe our

experiment in Section III, discuss our results in Section IV, and conclude in Section V.

II. Motivation and Theory

The Financial Statement Presentation Project

The vast majority of financial accounting standards address issues of recognition,

measurement and disclosure: whether an item should be recognized as an asset, liability, income

or expense item, how that item should be measured to derive a dollar value, and whether

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additional disclosures are required in the notes to financial statements. In contrast, accounting

standards include relatively little authoritative guidance on how to present financial statements to

users. The FASB and IASB initiated the joint Financial Statement Presentation Project to provide

unified guidance on presentation-related issues.

In this study, we examine two important dimensions on which guidance is lacking:

classification and degree of disaggregation. While a variety of standards touch on classification

issues, the FASB notes in “Preliminary Views on Financial Statement Presentation” (FASB

2008) that this guidance tends to be piecemeal, making it difficult for users to comprehend how

financial statements interrelate.1 Guidance regarding disaggregation is even less prevalent. An

Exposure Draft of “The Objective of Financial Reporting and Qualitative Characteristics and

Constraints of Decision-Useful Financial Reporting Information” (FASB 2008) mentions

aggregation only once, stating “a single depiction in financial reports may represent multiple

economic phenomena. For example, the presentation of the item called plant and equipment in a

financial statement may represent an aggregate of all of an entity’s plant and equipment.” The

Exposure Draft does not provide an indication of how much disaggregation is desirable.

1“Transactions or events recognized in financial statements today are not described or classified in the same way in each of the statements. That makes it difficult for users to understand how the information in one statement relates to information in the other statements. For example, the Boards’ standards on the statement of cash flows require a section for operating activities, but International Financial Reporting Standards (IFRSs) and U.S. GAAP do not provide a section for operating activities in the statement of comprehensive income or the statement of financial position. That makes it difficult, for example, for users who want to assess the quality of an entity’s earnings by comparing operating income with operating cash flows. Even though financial statements sometimes articulate at a high level (for example, the change in the cash account relates to the statement of cash flows), users have asked for improved linkages between and among statements.” [FSP, Para. 1-11].

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In response to concerns about the lack of specific regulation on the extent of

disaggregation in financial statements,2 the Preliminary Views document states three objectives

of financial statement presentation:

Cohesiveness Objective: An entity should present information in its financial statements in a manner that portrays a cohesive financial picture of its activities.

Disaggregation Objective: An entity should disaggregate information in its financial statements in a manner that makes it useful in assessing the amount, timing, and uncertainty of its future cash flows.

Liquidity and Financial Flexibility Objective: An entity should present information in its financial statements in a manner that helps users to assess the entity’s ability to meet its financial commitments as they become due and to invest in business opportunities.

The Boards propose to address the cohesiveness objective by consistently classifying

items into the same sections across the financial statements. In particular, the Statement of

Financial Position, Statement of Comprehensive Income and Statement of Cash Flows would all

have sections distinguishing between business and financing activities, and within business

activities, items would be classified as either operating or investing activities.3 These

classifications would be “cohesive,” which means that an asset classified as associated with (for

example) a business investing activity would generate business investing income on the

Statement of Comprehensive Income, and business investing cash flows on the Statement of

Cash Flows. The definitions of the sections are provided in Appendix A. For our purposes, the

distinction between operating and investing activities is most important: business operating

2 “Even though IAS 1 and Regulation S-X address presentation issues, IFRSs and U.S. GAAP provide little specific guidance on the presentation of line items in financial statements, such as the level of detail or number of line items that should be presented. The resulting variation and inconsistency in presentation formats create difficulties for users who want to understand and analyze an entity’s activities. For example, some entities disaggregate direct product costs (such as materials and labor) as well as general and administrative costs (such as rent and utilities) in their statement of comprehensive income. However, other entities present both product costs and general and administrative costs in the aggregate. Such aggregation makes it difficult for users to study the relationship between revenue and costs for an entity’s principal activities as well as to perform a benchmark analysis of those activities across an industry.” [FASB 2008, para 1.14] 3 There are also sections for income taxes and discontinued operations, but these are not the focus of our study.

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activities are those “related to the central purpose(s) for which the entity is in business” (FASB

2008, para. 2.32), while investing activities are “unrelated to the central purpose for which the

entity is in business” (FASB 2008, para. 2.33).

The FSP also proposes that income and expense items be disaggregated by function and

by nature. Function “refers to the primary activities in which an entity is engaged, such as

selling goods, providing services, manufacturing, advertising, marketing, or business

development or administration” (FASB 2008, para. S11), while Nature “refers to the economic

characteristics or attributes that distinguish assets, liabilities, and income and expense items that

do not respond equally to similar economic events.” (FASB 2008, para. S11)

Effects of Classification and Disaggregation

How best to aggregate the overwhelming amount of information produced by a

company’s accounting system is a fundamental problem managers face when compiling and

presenting their financial statements. Lev (1968, 1970) characterized an efficient degree of

aggregation as that which results in acceptable and quantifiable levels of information loss to

users of financial statements.4 Lev (1968, 247) “assume[s] that users of financial statements

would in principle prefer to have access to detailed rather than aggregated figures in their

decision making…aggregation result[s] in loss of information.” However, Demski (1973) uses

Blackwell’s theorem to argue that no efficient degree of aggregation exists—or at least, not

without standard setters making strong assumptions about which users they prefer to help at the

expense of others. Demski (1973) notes that the cost of information lost through aggregation

will vary across users, depending on the decisions they must make, how sensitive their decisions

are to available information, and how sensitive their payoffs are to deviations from the optimal

4 Our focus is restricted to the effects of accounting aggregation on users of financial statements. Other studies (e.g., Dye and Sridhar 2004) investigate determinants of aggregation procedures adopted by accountants and managers.

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decision. This implies that any aggregation of accounting information by standard setters will be

sub-optimal for at least some users. The only solution to the problem that Demski (1973)

outlines is to completely disaggregate information for all users.

From this purely informational perspective, disaggregation can improve user decisions by

reversing some of the information loss caused by aggregation. For example, the Statement of

Financial Position typically aggregates all Property, Plant and Equipment (PPE) into a single line

item. Disaggregating this line item into operating and investing categories is one step closer to a

complete listing of all PPE assets, and conveys one particularly important dimension on which

PPE assets differ: investing assets are, by definition, easier to dispose without affecting the core

activities of the company. Similarly, the typical Statement of Comprehensive Income aggregates

Cost of Goods Sold into a single line item. Disaggregating that line item into categories that

differ by function and nature (e.g., costs of depreciating and maintaining PPE vs. energy costs)

conveys information about the company’s fixed and variable cost structure; information that is

useful in predicting cash flows.

The Information-Processing Perspective

In their critique of Lev (1970), Bernhardt and Copeland (1970) note that Ijiri, Jaedicke

and Knight (1966) and Sorter (1970) “hypothesize that merely changing the form of the financial

statement can affect users’ decisions.” If true, this calls into question the validity of the strictly

informational, non-contextual view of financial-statement aggregation forwarded by Lev (1968,

1970). Ronen and Falk (1973) provide evidence confirming Bernhardt and Copeland’s (1970)

perspective. Specifically, in a series of experiments testing Lev’s (1968, 1970) measure of

aggregation-related information content, they find that financial statement format and labeling

are important determinants of judgment performance in a financial statement analysis task

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performed by MBA student subjects. This research began a voluminous literature examining

whether classification, disaggregation and other financial statement presentation choices alter

how users process financial information.

As described in Bonner (2008, 190-191) and Libby, Bloomfield and Nelson (2002), much

of the literature on financial statement presentation before the mid-1980s struggled to

discriminate information-processing effects from information content effects. For example,

Stallman (1969) provided analyst subjects with either a condensed annual report or a condensed

annual report with supplemental disaggregated condensed divisional financial statements.

Subjects who received the aggregated financial information could not infer the information

content of the (disaggregated) divisional statements. As a result, this design confounds the

effects of presenting information in different ways with providing subjects with additional

information.

More recent research has shown that users are influenced by both classification and

disaggregation, even after carefully controlling for informational content effects by making sure

the same information is available in all conditions. For example, several studies show that user

judgments and decisions reflect information less when it is disclosed in the footnotes relative to

when it is recognized on the face of the financial statements (Hirst and Hopkins 1998; Harper,

Mister, and Stawser 1987, 1991; Sami and Schwartz 1992; Wilkins and Zimmer 1983). Hirst,

Hopkins and Wahlen (2004) show that recognition versus disclosure of the same underlying

information even affects the judgments and decisions of professional analysts with industry- and

task-specific expertise. These studies demonstrate that footnote disclosure is not an effective

substitute for financial statement recognition, even when controlling for the fact that the

information in question is presented (one way or another) to all subjects.

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Changing the location of information within the financial statements and footnotes can

affect how users process the information for several reasons. Two reasons that are particularly

relevant to our study are conceptual categorization and information proximity. Hopkins (1996)

provides evidence that the financial statements provide structured, predictable conceptual

categories that facilitate the ability of expert users to comprehend and utilize the information

reported in the body of the financial statements. His results support prior findings that users

acquire and comprehend information more easily when it is provided in conceptual categories

(Bransford and Johnson 1972, Kozminky 1977), and that how information is structured affects

how it is interpreted and used by decision makers (Voss and Bisanz 1985). To the extent that

cohesively classifying items as operating, investing and financing is a conceptually meaningful

way to organize information, doing so should help users more easily acquire and use the

information relative to when the information does not have this organizational structure.

One theory from human factors research that is particularly well suited for explaining

why cohesively classifying information influences its use is the “proximity compatibility

principle” (Wickens and Carswell 1995; Carswell and Wickens 1996). The proximity

compatibility principle “proposes that visually unitary configurations of data values, such as

object displays, will support information integration better than will more separable formats”

(Carswell and Wickens 1996, 1). In other words, when a task requires uses to integrate

information from multiple sources, their ability to do so will improve when that information is

displayed in close proximity (Andre and Wickens 1989, Lipe and Salterio 2000, Hodge et al.

2004).

Cognitive load theory (Sweller 1988, 1989) helps us understand how memory limitations

likely give rise to the proximity compatibility principle. Cognitive load theory explains that

individuals have a limited working (short-term) memory making it difficult to integrate multiple

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pieces of information, especially when the information comes from various locations. This is the

case when information is located in separate documents or on separate pages within the same

document. It is also likely the case in an electronic world when information is presented on

separate screens.

Applying the proximity compatibility principle and cognitive load theory to the analysis

of financial-statements suggests that users will more effectively integrate financial information

when it is presented in closer proximity. One way to improve the proximity of related financial

information is to present it in a single display (e.g., a single financial statement), which should

facilitate users’ ability to integrate the information thereby leading to higher quality (or at least

more informed) judgments and decisions.

Consistent with this theory, Hodge, Hopkins and Wood (2010) provide evidence that the

proximity of forecast-relevant information can influence financial statement users’ forecast

accuracy, even after controlling for perceived reliability differences between information

provided in the body of the financial statements and the footnotes. These results suggest that

enhancing the proximity of disaggregated information with meaningful conceptual categories

will increase the likelihood that users acquire, understand, and use the information to make

informed judgments and decisions.

In addition to presenting related information in close proximity, cohesively linking

related information in a firm’s financial reports can improve user judgments (Maines, McDaniel

and Harris 1997; Hodge, Kennedy and Maines 2004). Financial statements often provide similar

information in multiple locations (for example, a change in accounts receivable is presented in

both the Statement of Cash Flows and the Statement of Financial Position). In the context of our

study, cohesive classification reinforces the distinction between operating and investing activities

by reporting operating and investing assets on the Statement of Financial Position, and reporting

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depreciation stemming from those assets in the operating and investing sections of the Statement

of Comprehensive Income.

As a whole, the above research suggests that disaggregating information and cohesively

classifying related information will help analysts make more informed judgments and decisions.

It also suggests that the greatest benefit to users will come from cohesively classifying

disaggregated information in a single location. Users will recognize fewer benefits when, for

example, the disaggregated information is presented in the footnotes while the cohesively

classified information is presented on the face of the financial statements, or when disaggregated

information is presented on the face of the financial statements without cohesive classification of

the information.

Information-Usefulness Perspective

The information-processing perspective discussed above suggests that how management presents

information influences a user’s cognitive ability to process that information. The information-

usefulness perspective argues that users look to management’s presentation choices as a signal

about the usefulness of the information. The Conceptual Framework (particularly Statement of

Financial Accounting Concepts 5 (SFAC 5), FASB 1984) supports this perspective by stating

criteria for recognition;5 those criteria allow users to infer that relevant, measurable information

that meets the definition of a financial statement element is likely to be more reliable if it is

recognized on the face of the financial statements than if it is disclosed in the notes.6 SFAC 5

5 The criteria are: Definitions. The item meets the definition of an element of financial statements. Measurability. It has a relevant attribute measurable with sufficient reliability. Relevance. The information about it is capable of making a difference in user decisions. Reliability. The information is representationally faithful, verifiable, and neutral. [p. 3]

6 As one example of such inferential reasoning, Statement of Financial Accounting Standards 5 (FASB 1975) requires firms to recognize contingent liabilities on the face of the financial statements if they are sufficiently likely

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provides a basis for users to draw similar inferences from how items are classified and

disaggregated in the financial statements. Paragraph 20 recognizes that “classification in

financial statements facilitates analysis by grouping items with essentially similar characteristics

and separating items with essentially different characteristics.” However, the next paragraph

further recognizes that “Financial statements result from processing vast masses of data and

involve needs to simplify, to condense, and to aggregate.” Based on this, users can infer that

management’s choice to classify and disaggregate certain information, while not classifying or

disaggregating other information, says something about the information’s usefulness relative to

the cost of providing the information.

In our study, we do not attempt to disentangle the relative influence of the information-

processing perspective and the information usefulness perspective on user judgments and

decisions. We did, however, carefully consider the two perspectives in creating our materials.

Following Bloomfield’s (2008) application of the philosophy of language called Pragmatics

(Grice 1975), we recognize that users will draw different inference depending on whether

classification and disaggregation decisions are made by standard-setters or reporting firms (who

have different incentives). To avoid the complexities of strategic representations by

management, we clearly explain in our materials that the financial statement presentation

decisions were mandated by standard-setters, not voluntarily chosen by management.

III. THE EXPERIMENT

Experimental Goals and Design

and sufficiently estimable, and requires footnote disclosure for contingencies that meet a lower standard of proof. Thus, management’s choice on where to report a contingent liability conveys information about the liability. In addition, Libby, Nelson and Hunton (2006) show that where management chooses to report an item influences how auditors view the item; auditors are more likely to waive errors in footnote disclosures than in numbers reported on the face of the financial statements.

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The goal of our experiment is to examine how the presence of cohesively classified data

and the location of disaggregated data affect the judgments and decisions of professional credit

analysts. We do this by providing participants with financial statements for two apparel firms

that differ in one key characteristic: one firm manufactures its own products, while the other

outsources production to a third party, retaining any associated fixed assets for investment

purposes pending a decision on disposition. Absent classified and disaggregated information, the

financial statements for the two firms provide little indication of this very important difference.

The two firms report similar Revenues, Cost of Goods Sold, and Property, Plant and Equipment.

However, classification reveals that much of the outsourcing firm’s Property, Plant and

Equipment constitute investment assets rather than operating assets, while disaggregation reveals

that their Cost of Goods Sold includes many costs that are likely to vary with revenue. As a

result, the outsourcing firm’s cost structure shelters it from losses and allows for greater

flexibility in disposing of fixed assets if demand declines. We amplify the importance of this

difference by providing information that made a decline in demand appear likely.

To assess how the presence of classified data and location of disaggregated data affects

judgments and decisions, we use a full-factorial 2 x 2 between-subjects design with classification

(presence vs. absence of cohesive classification) and location of disaggregated information

(disaggregated on face vs. disaggregated in notes) as independent variables.7 Across all

conditions, analysts receive financial information for both the outsourcing firm (called Apex

Apparel) and the insourcing firm (called Zen Apparel).

We examine four primary dependent variables. The first three capture key inputs to

comparing two firms’ credit worthiness: identification of differences between the firms

7 We ran one additional condition where cohesive classification and disaggregated information were both absent. This condition most closely resembles the way firms report financial information today. We discuss results pertaining to this condition at the end of our results section.

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(assessed through responses to open-ended questions); forecasts based on an understanding of

the firms’ cost structures; and a decision on which firm is more deserving of a credit rating

downgrade. The fourth dependent variable we examine is the analysts’ perception of the

transparency of the financial statements.

Participants

Sixty professional credit analysts from multiple credit rating firms completed the

experiment during the early months of 2009. On average, the analysts had 8.47 years of work

experience. We randomly assigned analysts to experimental conditions. Analysts were solicited

through emails from the research team, which included statements from the Chairman of the

FASB and the Director of FASRI indicating the value of participating (but no indication of

preferred results). The FASRI Director personally administered the studies through visits to the

offices of each firm. Participation was followed by a brief update on the status of the Financial

Statement Presentation Project.

We chose a sophisticated subject pool because less sophisticated users of financial

statements tend to sequentially process financial statements and related disclosures (Maines and

McDaniel 2000). This sequential processing strategy means that unsophisticated users of

financial statements are likely more susceptible to the potentially debilitating effects of

information overload when analyzing a complete set of financial statements and related footnotes

like those used in our experiment. When experiencing information overload, novice users are not

likely to benefit from disaggregated disclosures, and may even become further incapacitated by

the additional information. In contrast, Hunton and McEwen (1997) report that experienced sell-

side equity analysts appear to use non-linear, directed information search strategies when

processing financial statement and related footnote information. This schema-driven processing

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of financial information suggests that professional analysts possess the skills and ability

necessary to benefit from the additional information embedded in disaggregated displays,

especially if those displays are also cohesively formatted to facilitate extracting decision-relevant

information.

Case Materials and Procedures

Information

We provided analysts with a cover page stating that their primary goal while completing

the task was to forecast information (e.g., sales and EBIT) for Apex and Zen in the context of

providing a rating on each firm’s unsecured debt. We then provided analysts with a cover page

that described that both Apex and Zen operate in the same industry and sell similar products in

the same geographic locations. After reading this general information, analysts analyzed a

Statement of Comprehensive Income, a Statement of Financial Position, a Statement of Cash

Flows, and footnotes for each firm. We modeled the financial information for both firms after the

financial statements and footnotes of a publicly-traded apparel manufacturing and retail firm. We

altered all identifiers in order to mask the actual company’s name so that analysts would focus on

the reported information instead of other non-financial factors (e.g., the company’s reputation).

We further altered Zen’s financial statements by making cosmetic changes to account names and

footnote descriptions, as well as by scaling any numbers reported in Zen’s financial statements

so that they were four percent smaller than the corresponding numbers reported for Apex.

We held constant the underlying facts about the firms when creating the financial

statements for each condition. Both firms hold roughly similar fixed assets, but footnotes for

Apex, the outsourcing firm, state in all conditions that that amount “includes assets that are no

longer being used for production purposes but are rented to others pending a strategic decision to

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employ the assets in operations or dispose of them.” Both firms have Cost of Goods Sold that is

53 percent of sales in 2006. However, Apex, the firm that outsources production, has the bulk of

its costs in the form of materials (46 percent of sales), while Zen has the bulk of its costs in the

form of labor, overhead, freight, transportation and handling (37 percent of sales). Both firms

experience a sales decline in 2007 of 8.4 percent. Apex, due to its variable cost structure, has a

corresponding drop in cost of goods sold of 7.6 percent. Zen, due to its fixed cost structure, is not

able to adapt to changing market conditions as quickly as Apex and therefore has a

corresponding drop in costs of goods sold of only 4.8 percent. If analysts recognize the

difference in the firms’ assets and cost structures, they should realize that Apex has more

operational flexibility, holds fixed assets that could be disposed of without impinging on

operations, and will likely see a greater decline in expenses than Zen, in the face of declining

demand.

After reviewing the financial information for both firms we asked the analysts to assume

that today’s date is 60 days after the end of the fiscal year for both Apex and Zen. In addition, we

asked them to assume that both companies publicly released their audited annual financial

statements today and that both companies successfully filed their respective Form 10-Ks with the

Securities and Exchange Commission (SEC). We then introduced XYZ Retail Group.

We described XYZ as one of the largest retail customers of both Apex and Zen. Similar

to the financial information for Apex and Zen, we made slight adjustments to the information for

XYZ to mask the identity of the retailer. We then stated that a prominent rating service has

downgraded XYZ two ratings notches affecting approximately $5.0 billion of outstanding debt,

and the rating service revised its outlook for XYZ from “Stable” to “Negative.” We stated that

the downgrade was the result of the following information. First, XYZ’s operating and credit

metrics deteriorated last year and there is potential for additional deterioration in the near to

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intermediate term. Second, XYZ’s comparable same-store sales declined 1.3 percent last year

causing EBITDA (excluding onetime items) to decline by 4 percent. Third, the current overall

slowdown in discretionary name-brand apparel and home-related spending will likely continue to

hurt top-line growth and may impact same-store sales by as much as 4-5 percent. After reviewing

this information analysts completed our main questionnaire.

Classification manipulation

In the conditions where items are classified, Apex and Zen classify all assets and

liabilities into operating, investing and financing activities on the Statement of Financial

Position, and carry that classification cohesively to the Statement of Cash Flows and Statement

of Comprehensive Income. In the conditions where items were not classified, the two firms

classify items into operating, investing and financing activities only on the Statement of Cash

Flows according to existing requirements. We provide examples from our two classification

conditions in Appendix B.

Disaggregation Manipulation

In the conditions where items were disaggregated on the face of the financial statements,

the two firms provide details on the nature and function of expenses in the Statement of

Comprehensive Income and the Statement of Cash Flows. In the conditions where items were

disaggregated in the notes, the same information is provided in footnote disclosures. We provide

examples from our two disaggregation conditions in Appendix C.

Interactions between Classification and Disaggregation

We argued previously that users will benefit the most when information is cohesively

classified with disaggregated information presented in close proximity. Below we detail how

19

three key indicators in each condition differ on these important dimensions. The indicators are:

the breakdown of Fixed Assets; the breakdown of Cost of Goods Sold; and the breakdown of

Other Income.

When the financial statements are classified and include disaggregated expenses, all three indicators can be found on the Statement of Comprehensive Income. Depreciation is clearly split between Cost of Goods Sold (a small number) and investing (a large number); Cost of Goods Sold includes large variable costs (such as materials) and small fixed costs (such as other overhead); and Other Investment Income clearly identifies Rent as a source of income. Furthermore, the distinction between operating and investing assets carries over to the Statement of Financial Position. Related information is located in close proximity so we expect relatively strong performance.

When the financial statements are not classified and expenses are disaggregated

in the notes, all three indicators can be found in three footnotes: The PPE footnote disaggregates depreciation by operating and investing; the Cost of Goods Sold footnote shows fixed and variable costs; and the Other Income/Expense footnote identifies Rent and Depreciation expense. As above, related information is located in close proximity so we expect relatively strong performance.

When the financial statements are classified, but expenses are disaggregated in

the notes, users must attend carefully to both the face of the statements and the notes. In particular, the division of operating and investing income and expenses is shown on the face of the Statement of Comprehensive Income, but users must refer to the footnotes for details on the disaggregated components of each. Related information is dispersed so we expect relatively poor performance.

When the financial statements are not classified, but include disaggregated

expenses, Cost of Goods Sold is disaggregated on the Statement of Comprehensive Income into fixed and variable items, and depreciation associated with operations is shown as a component of Cost of Goods Sold and General and Administrative Expenses. Depreciation associated with investing assets is also shown on the Statement of Comprehensive Income, but the distinction between operating and investing assets is not as clearly delineated as when the statements are cohesively classified. Furthermore, the distinction between operating and investing assets does not carry over to the Statement of Financial Position. Because the relationship between operating and investing assets and income is not made explicit in a single location, we expect relatively poor performance.

See Appendix D for excerpts from our materials highlighting the differences described

above.

20

Questionnaire and Dependent Measures

Questionnaire

We developed our questionnaire after conducting informal interviews with people

responsible for overseeing credit analysis at several firms. Our interviews indicated analysts at

the firms follow a similar process. Analysts are initially provided with a preliminary rating based

on algorithmic calculations drawn from financial statements. The analysts then review the

statements in detail to understand any characteristics of the firm that might affect

creditworthiness, but that might not be captured by the standard calculations, and adjust the

preliminary rating upward or downward accordingly. Adjustments rarely alter a firm’s rating by

more than one or two levels.

To map our task into the real-world protocol followed by analysts at both firms, we asked

analysts to review the information provided before opening a separate envelope with our first

questionnaire. We anticipated that they would use that time to identify the characteristics that

make the two firms differ in creditworthiness. The first questionnaire started by capturing any

quantitative implications of that evaluation, by asking analysts to forecast sales revenue and

earnings before interest and taxes (EBIT) for Apex and Zen for next fiscal year as well as to

provide detailed support for their forecasted amounts. We then asked analysts how confident

they were in their EBIT forecasts and how well the information presented helped them

understand each firm’s cost structure. We followed this by asking analysts to assume that their

firm’s debt-rating algorithm mechanically assigned identical ratings to both Apex and Zen prior

to release of the information about XYZ. We then asked the analysts to downgrade either Apex

or Zen by one or two notches below the mechanically assigned rating and to provide three key

pieces of information that determined which firm they chose to downgrade.

After making their downgrade choice, analysts responded to several questions asking

21

them to describe any additional information they would have liked to have had, whether Apex or

Zen is better able to shed costs in the face of declining revenues, whether Apex or Zen is more

creditworthy, and whether Apex or Zen will have stronger “core” operating performance in the

coming year. At the end of the questionnaire, we asked analysts to compute several ratios

(expense ratio, interest coverage ratio and return on assets) for each firm and to list three ratios

that they consider to be the most important in evaluating the appropriateness of a company’s

credit rating.

After completing the main questionnaire, analysts completed a post-experiment

questionnaire containing demographic questions, questions related to assessing a firm’s earnings

quality, and questions about what information in the materials facilitated and what information in

the materials impeded their analysis. Finally, we asked analysts to briefly describe three changes

to the credit rating industry currently being considered by the SEC and three changes to financial

reporting currently being considered by the FASB. Most analysts completed their analysis and

questionnaires in 75-90 minutes.

Dependent Variables:

Our first three dependent variables examine analysts’ decision making, and the last

examines the analysts’ assessments of the transparency of the financial statements.

Identification of Firm Characteristics

Our first dependent variable captures analysts’ ability to identify the key differences

between two firms that would drive their forecasts and credit ratings. To avoid any demand

effects, we solicited responses by asking analysts “In the space that follows, please provide the

three key pieces of information that determined the firm you selected for your rating-change

recommendation.” We asked similar open-ended questions after soliciting analyst forecasts.

22

Expense Variability Ratio

We captured the difference in the cost structure of the two firms by converting analysts’

forecasts of sales and income into a summary measure we call the expense variability ratio

(EVR). We define EVR as:

EVR = Percentage Change in Expenses / Percentage Change in Sales.

(We calculated expenses as Revenue – Profit.) A firm with only fixed expenses and no variable

expenses would have an EVR of zero, while a firm with only variable expenses and no fixed

expenses would have an EVR of one. Based on our materials, Apex, the outsourcing firm, has

an EVR of 0.86 in the two most recent years, which indicates far more variable expenses than

Zen with an EVR of only 0.58.

Downgrade Decisions

To assess downgrade decisions, we asked analysts to “Assume that your firm’s debt-

rating algorithm mechanically assigned identical ratings to both Apex Apparel and Zen Apparel

prior to release of the information about XYZ Retail Group, Inc. Please pick one of the

companies to downgrade, and indicate if you would downgrade the company one or two

notches”.

Assessments of Transparency

Our final dependent variable focuses on how analysts viewed the quality of the financial

information they analyzed in completing the experimental task. Specifically, we asked

“Compared to the financial statement information I typically review, I found the financial

statement information reported by Apex to be transparent.” Analysts responded on a scale

ranging from -7 (strongly disagree) to 7 (strongly agree)”.

23

IV. RESULTS AND DISCUSSION

Manipulation Checks

The materials in our study are substantially more complex than participants would

typically receive in an experiment. Thus, we first verify that participants in our study understand

the operating environment faced by Apex (the outsourcing firm) and Zen (the insourcing firm),

as well as the differences in the two firms’ cost structures. Of the 60 analysts who participated in

our study, 95 percent (57/60) estimated that 2008 revenues would be lower than 2007 revenues

for both firms. Consistent with this result, 93 percent (56/60) estimated that EBIT would be

lower in 2008 than 2007 for Apex, and 92 percent (55/60) estimated a reduction in EBIT in 2008

for Zen. Combined, this evidence suggests that analysts in our experiment understand the

deteriorating operating environment faced by both firms.

We also find that analysts in all conditions made forecasts of revenue and income that

resulted in a higher EVR for Apex than Zen, with a median difference between the two firms of

6.5%. This difference is significantly greater than 0 (Wilcoxon signed-rank test statistic = 461,

p<0.001, one-tailed), suggesting that on average analysts were indeed able to assess the

difference in cost structures between the two firms. Our results for the downgrade variable show

a strong consensus that Zen should be downgraded; 82 percent of analysts downgraded Zen. This

further confirms our interpretation from the overall EVR difference that analysts are able to

understand the difference in the firms’ cost structures.

Identification of Differences

We begin by examining how our manipulations affect analysts’ ability to identify

characteristics that differ across the two firms. We do this by coding answers to the question

“Please provide the three key pieces of information that determined the firm you selected for

24

your rating-change recommendation.” Prior to analyzing analysts’ responses, we created two

categories to reflect the key differences between Zen and Apex (Shaded areas in Figure 1):

(a) The outsourcing firm (Apex) has a more favorable cost structure during bad times

(more variable costs and fewer fixed costs), and

(b) The outsourcing firm (Apex) has greater asset disposition flexibility (because they

can sell off their investment assets without harming operations).

We create four additional categories (Unshaded areas in Figure 1) to capture other reasons

mentioned by analysts for their downgrade decisions. Two research assistants, who knew

nothing about the purpose of the study or the version each analyst saw, independently coded

analysts’ responses to the appropriate categories. Initial agreement between coders was 90

percent. For the other 10 percent, the coders worked together to resolve their differences and

come to a final agreement on the appropriate coding of the responses.

(Insert Figure 1 about here)

Results from Panels B of Tables 1 and 2 show that, individually, the presence of

classification and the location of disaggregated information have no effect on the likelihood that

analysts would identify either cost structure flexibility or asset disposition flexibility (all p-values

> 0.40). However, we do see a strong interaction. Cost structure flexibility was mentioned by 14

of 28 analysts (50%) who viewed classified and disaggregated information on the face of the

financial statements, or who viewed disaggregated information in the notes without information

being classified on the face of the financial statements. In contrast, cost structure flexibility was

mentioned by only 7 of 32 analysts (22%) who viewed classified information on the face of the

financial statements with disaggregated information in the notes, or who viewed disaggregated

information on the face of the financial statements without information being classified on the

face of the financial statements (χ2 = 5.2570, p=0.022, two-tailed).

25

We obtain similar results when we examine asset disposition flexibility. Asset disposition

flexibility was mentioned by 9 of 28 analysts (32%) who viewed classified and disaggregated

information on the face of the financial statements, or who viewed disaggregated information in

the notes without information being classified on the face of the financial statements. In contrast,

asset disposition flexibility was mentioned by only 3 of 32 analysts (9%) who viewed classified

information on the face of the financial statements with disaggregated information in the notes,

or who viewed disaggregated information on the face of the financial statements without

information being classified on the face of the financial statements (χ2 = 4.8380, p=0.028, two-

tailed).

The interactions reported above for both cost structure flexibility and asset disposition

flexibility support the importance of cohesively presenting related information in close

proximity. Analysts are better able to identify key differences between the firms when the

financial statements are cohesively classified and provide disaggregated information on their

face. Analysts also perform well when disaggregated information is provided in the footnotes

without cohesive classification on the face of the financial statement: a combination that also

places the related information in one place (in the footnotes). Analysts perform poorly when

information is classified on the face of the financial statements, but disaggregated information is

provided in the footnotes. In this condition, the Statement of Financial Position provides a

breakdown between operating and investing fixed assets, but related expenses are disaggregated

only in the footnotes. Similarly, when disaggregated information is presented on the face of the

financial statements, but there is no cohesive classification, the Statement of Comprehensive

Income provides a breakdown of Cost of Goods Sold that indicates the firms’ cost structures, but

this information is not reinforced in the Statement of Financial Position, which fails to classify

assets as operating or investing.

26

Only one of the other categories created from analysts’ open-ended responses

approaches significance. Table 3 shows that 7 of the 31 analysts (23%) who viewed information

classified on the face of the financial statements mentioned that the outsourcing firm has a

diversified stream of income (from rental income and operations), while only 2 of 29 analysts

(7%) who viewed information that was not classified on the face of the financial statements

mentioned this issue (Fisher’s P = 0.089, one-tailed8). This result likely reflects that

classification helps analysts see that the outsourcing firm has two types of fixed assets. The lack

of an interaction between classification and presentation is likely because disaggregation pertains

exclusively to expense items, and provides no information about the diversified revenue stream.

Expense Variability Ratio (EVR) Forecasts

EVR reflects the forecasted percentage drop in expenses divided by the percentage drop

in sales (the Expense Variability Ratio, or EVR). This measure directly assesses financial

flexibility in the face of declining sales.

As shown in Panel B of Table 4, results of a non-parametric Wilcoxon test show that the

median difference between the EVR for Apex and Zen is significantly larger when disaggregated

information is provided on the face of the financial statements rather than in the footnotes

(Wilcoxon Z = 1.3434, p=0.090, one-tailed) 9. To our surprise, results also show that classifying

information on the face of the financial statements reduces analysts’ estimates of the difference

in EVR between firms. This result is significant (Wilcoxon Z = -1.9657, p = 0.050, two-tailed),

but opposite our expectation (so we report a two-tailed test).

Analyses of simple effects provide additional support for the importance of reporting

related information in close proximity. When information is classified on the face of the

8 Fisher’s exact test is used in this analysis because of cases where cell frequencies were less than five. 9 We use non-parametric tests in the analyses of EVR because the data is not normally distributed.

27

financial statements, moving disaggregated information from the notes to the face of the

financial statements helps forecast performance (Wilcoxon Z=1.7267, p=0.084, two-tailed,

untabulated). In the absence of classification, moving disaggregated information from the notes

to the face of the financial statements does not help forecast performance (Wilcoxon Z=0.0230,

p=0.982, two-tailed, untabulated). Providing classified information on the face of the financial

statements harms forecasting performance only when disaggregated information is in the notes

(Wilcoxon Z=1.8458, p=0.062), but not when it is on the face (Wilcoxon Z=0.6662, p=0.505,

two-tailed).

Downgrade Decisions

We coded analysts’ downgrade decisions as -1 or -2 (+1 or + 2) if an analyst indicates

that the Apex (Zen) should be downgraded 1 or 2 notches, respectively. Thus, higher values

indicate that it is more appropriate to downgrade Zen than Apex. Contrary to our predictions,

disaggregating information on the face of the financial statements did not affect analysts’

downgrade decisions. As shown in Panel B of Table 5, there is no difference in downgrade

decisions between the two disaggregation conditions (both have a mean of 0.9285). As

previously discussed, there was a strong consensus among all analysts that Zen should be

downgraded rather than Apex. Across all conditions, the average downgrade decision is equal to

0.9285, which is significantly greater than 0 (t = 11.6065, p<0.001, one-tailed). Given the

effects of disaggregation on other variables, it appears that our method of eliciting downgrade

decisions was simply too coarse to detect an effect. (A more precise elicitation might have been

to ask analysts to rank, on a 100-point scale, how strongly they would advocate downgrading the

firm.)

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Transparency

To assess transparency, we asked analysts to compare the transparency of the financial

information reported by Apex to the financial information they typically review.

As shown in Panel B of Table 6, providing disaggregated information on the face of the

financial statements rather than in the footnotes has no statistically significant effect on analysts’

transparency ratings (t = 0.4246, p = 0.336, one-tailed), while classifying information on the face

of the financial statements does (t = 1.9714, p = 0.027, one-tailed). This result is surprising, in

that classification alone has no effect on analysts’ ability to identify differences between the

firms and even harms forecasting performance. However, the results further support the

importance of presenting classified and disaggregated information in a common location.

Transparency ratings in the cell where both classified and disaggregated information are

provided on the face of the financial statements are significantly higher than transparency ratings

in the other three cells (see Panel C of Table 6, t = 1.9906, p = 0.026, one-tailed). This is also

the only cell that is rated as more transparent than the financial statements the analysts typically

review (mean = 1.5, t = 2.5148, p = 0.0136, one-tailed). Consistent with previous results, these

results further emphasize the interactive effects of classification and disaggregation; the greatest

benefits accrue to users when information is cohesively classified and disaggregated on the face

of the financial statements.

(Insert Table 6 about here)

Supplemental Analyses

Although we randomly assigned participants to experimental conditions, analysis of our

demographic variables reveals that analysts who received information that was disaggregated on

the face of the financial statements had significantly less experience than analysts who received

29

information that was disaggregated in the notes (5.5 years vs. 11.5 years; t = 3.3650, p = 0.001,

two-tailed, untabulated). Further analysis reveals that among the analysts who received

disaggregated information on the face of the financial statements, those with more experience

were better able to use the disaggregated information to improve their judgments (as measured

by an increase in their EVR score for Apex relative to Zen). This suggests that the less

experienced analysts in this condition are weakening our results, thereby understating the

benefits of receiving disaggregated information on the face of the financial statements.

In addition to our four main conditions, we collected data from 14 additional participants.

These participants received financial statements identical to those in the other conditions without

classification, but the footnotes they received were more limited. The footnotes indicated the

amount of Property, Plant and Equipment held for investment, and the breakdown of inventories,

but did not provide disaggregated expense information. We found few differences across our

dependent variables when we compared these participants to the participants who received the

same financial statements but more detailed footnotes. This suggests that these participants were

able to identify the differences between the firms despite receiving less information in the

footnotes. The analysts in this condition, however, were more likely to demand additional

information about the cost structure and cash flows of Zen, the insourcing firm, than those who

received the same financial statements but more detailed footnotes (p = 0.098 on both measures,

one-tailed, untabulated). These analysts were also less confident in their assessments of the cost

structure of both Apex and Zen (p = 0.006 and p = 0.002, respectively, one-tailed and

untabulated). Thus, it appears that these statements allowed analysts enough information to draw

reasonable conclusions, but that the lack of detail dampened their confidence in those

conclusions.

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V. DISCUSSION

Should firms be required to more cohesively classify financial information on the face of

their financial statements? Should firms be required to provide disaggregated information on the

face of their financial statements or in their footnotes? Our experimental results suggest that

these decisions are not independent, and that cohesively classified statements are most useful

when expenses are also disaggregated on the face of financial statements.

We find that analysts’ forecasts and ratings of financial statement transparency reflect

differing benefits of locating related items in close proximity. Cohesive classification on the face

of the financial statements actually makes financial forecasts less sensitive to firms’ financial

flexibility, but this is primarily driven by the deleterious effects of presenting disaggregated

information in the footnotes (i.e., so the two forms of information are in different locations).

Disaggregation on the face of the financial statements improves forecasting performance, but

only when information is also cohesively classified on the face (i.e., so the two forms of

information are in a common location and tell a cohesive story). Analysts rate financial

statements as most transparent when both forms of information are presented on the face of the

financial statements; this is the only cell in which analysts rate the financial statements as

significantly more transparent that the statements they typically read. Providing disaggregated

information in the notes but without cohesive classification does not result in high transparency

ratings; even though it allows more analysts to identify differences in cost structure and financial

flexibility. This suggests that providing information together in the footnotes may force analysts

to work harder. Alternatively, analysts may believe that information is more reliable when it is

provided on the face of financial statements (a claim that is supported by Libby et al. 2006).

In addition to their direct implications for the FSP project, our results also suggest some

interesting directions for further academic theory and research. Most research on financial

31

statement presentation tests (and usually supports) the theory that information has more impact

on decision-makers when it is presented on the face of financial statements, rather than in the

notes. However, most studies examine a single piece of information. Our results show that

when two pieces of information are related, the proximity of the two pieces of information can

be even more important. We believe there is an opportunity for additional research—grounded in

cognitive psychology, financial analysis or both—to develop testable theories of how items

reported in the financial statements are related and how those relations will mediate the impact of

their presentation.

Future research might also explore how and why financial statement presentation might

have differing effects on different dimensions of performance (such as identifying qualitative

characteristics of firms vs. forecasting future outcomes), on the effort required to achieve such

performance, and on subjective assessments of the quality of financial reporting.

32

TABLE 1 Mean Cost Structure Flexibility Assessments by Condition

Panel A: Number of Analysts Providing Cost Structure Flexibility Justifications for Downgrade Decisionsa

Classification In Notes On Face Row Data

Absent 7 out of 14 3 out of 15 10 out of 2950.00% 20.00% 34.48%

Present 4 out of 17 7 out of 14 11 out of 3123.53% 50.00% 35.48%

Column Data 11 out of 31 10 out of 2935.48% 34.48%

Panel B: Planned Comparisons for

Contrast N Χ2-statistic b Probability c

Main effect of Disaggregation (across Classification) 60 0.0070 0.935

Main effect of Classification (across Disaggregation) 60 0.0070 0.935

Interaction between Classification and Disaggregation 60 5.2570 0.022(Classification Present/Disaggregation on Face & Classification Absent/Dissagregation in Notes >

Classification Absent/Disaggregation on Face & Classification Present/Disaggregation in Notes)

c Two-tailed.

Disaggregation

a Percent of analysts in each condition mentioning “cost structure” in response to the question “Please provide the three key pieces of information that determined the firm you selected for your rating-change recommendation.”b χ2 tests were used in these analyses since they involved comparisons of frequency data.

33

TABLE 2 Mean Asset Disposition Assessments by Condition

Panel A: Number of Analysts Providing Asset Disposition Justifications for Downgrade Decisionsa





Panel B: Planned Comparisons

Contrast N Χ2-statistic b Probability c





c Two-tailed.

Disaggregation

a Percent of analysts in each condition mentioning “asset disposition flexibility” in response to the question “Please provide the three key pieces of information that determined the firm you selected for your rating-change recommendation.”

b χ2 tests were used in these analyses since they involved comparisons of frequency data.

34

TABLE 3 Mean Diversification Assessments by Condition

Panel A: Number of Analysts Providing Diversification Justifications for Downgrade Decisionsa






Contrast d.f. Fisher's P b

Main effect of Disaggregation (across Classification) 58 0.203

Main effect of Classification (across Disaggregation) 58 0.089

Interaction between Classification and Disaggregation 56 0.588(Classification Present/Disaggregation on Face & Classification Absent/Dissagregation in Notes >


b One-tailed Fisher exact tests were used in these analyses due to cases where cell frequencies were below 5.

Disaggregation

a Percent of analysts in each condition mentioning “diversification” in response to the question “Please provide the three key pieces of information that determined the firm you selected for your rating-change recommendation.”

35

TABLE 4 Median Expense Variability Ratio Differences between the Outsourcer and Insourcer by

Condition

Panel A: Median Apex EVR less Zen EVR [Mean] (Standard Deviation)a


Absent 0.1473 0.1350 0.1386[0.0770] [0.2078] [0.1424](0.4341) (0.2975) (0.3712)

n =14 n = 14 n = 28

Present 0.0429 0.0925 0.0525[-0.0705] [0.1210] [0.0160](0.3924) (0.2382) (0.3407)n = 17 n = 14 n = 31

Column Data 0.0525 0.1308[-0.0039] [0.1644](0.4115) (0.2682)n = 31 n = 28


Contrast S Z-Statistic b Probability

Main effect of Disaggregation (across Classification) 929 1.3434 0.090c

Main effect of Classification (across Disaggregation) 970 -1.9657 0.050d

Interaction between Classification and Disaggregation 915 1.1309 0.129c

(Classification Present/Disaggregation on Face & Classification Absent/Dissagregation in Notes >


c One-tailed.d Two-tailed. In cases where our predictions were clearly not supported, a two-tailed p-value is provided to denote whether or not a significant

difference (regardless of direction) was detected.

Disaggregation

a The EVR for each firm is calculated as the percentage change in Expenses divided by the percentage change in Sales. Higher numbers indicate costs that are more variable and less fixed. Values shown in the table above are the outsourcer EVR less the insourcer EVR, so that higher values indicate greater estimated differences in the cost structures of the two firms. Note that the difference between two medians is not the same as the median of the difference.b Wilcoxon tests were used in these analyses due to the fact that the EVR data was not normally distributed.

36

TABLE 5 Mean Downgrade Decisions by Condition

Panel A: Mean Downgrade Decisions (Standard Deviation)a


Absent 1.0769 1.0000 1.0370(0.4935) (0.5547) (0.5175)n = 13 n = 14 n = 27

Present 0.8000 0.8571 0.8276(0.7746) (0.5345) (0.6584)n = 15 n = 14 n = 29

Column Data 0.9285 0.9285(0.6627) (0.5394)n = 28 n = 28


Contrast d.f. t-Statistic Probability

Main effect of Disaggregation (across Classification) 54 0.0000 1.000c

Main effect of Classification (across Disaggregation) 54 -1.3169 0.193c

Interaction between Classification and Disaggregation 52 0.414 0.340b

(Classification Present/Disaggregation on Face & Classification Absent/Dissagregation in Notes >


b One-tailed.c Two-tailed. In cases where our predictions were clearly not supported, a two-tailed p-value is provided to denote whether or not a significant

difference (regardless of direction) was detected.

Disaggregation

a Downgrade decisions are coded as -1 or -2 (+1 or + 2) if the analyst indicates that the outsourcer (insourcer) should be downgraded 1 or 2 notches, respectively. Thus, higher values indicate a greater understanding that it is more appropriate to downgrade the insourcer than the outsourcer.

37

TABLE 6 Mean Financial Statement Transparency Assessments by Condition

Panel A: Mean Transparency Ratings (Standard Deviation)a


Absent -0.5833 -0.7667 -0.6852(2.8670) (2.8652) (2.8119)n = 12 n = 15 n = 27

Present 0.3235 1.5000 0.8333(3.4683) (2.1506) (2.9837)n = 17 n = 13 n = 30

Column Data -0.0517 0.2857(3.2109) (2.7636)n = 29 n = 28


Contrast d.f. t-Statistic Probability b





Panel C: Classification Present & Disaggregation on Face vs. Other Conditions

Contrast d.f. t-Statistic Probability b

Classification Present/Disaggregation on Face > Other Three Conditions 53 1.9906 0.026

b One-tailed.

Disaggregation

a Responses to the question “Compared to the financial statement information I typically review, I found the financial statement information reported by Apex to be transparent,” with a response ranging from -7 (strongly disagree) to 7 (strongly agree).

38

FIGURE 1

Descriptions of Downgrade Decision Justifications

Any indication that the Outsourcer has rent income which may differ from retail income (or the words diversify or balance) counts in this category.

Diversification

Any indication that the Insourcer, because it has high operating or production or overhead costs, will be able to reduce those costs, counts in this category.

Justification

Any reference to ‘cost structure’ or synonyms; the fact that the Insourcer will be less able to shed costs; the fact that the Outsourcer will be able to shed costs in light of a decline. These need not be comparative.

DescriptionCost Structure Flexibility

Cost Reduction Flexibility

Any comment that margins, EBIT, cash flows, or other performance metrics are worse for the Insourcer counts in this category.

Past Performance

Any indication that the Outsourcer has a flawed strategy because it is holding investment properties, is outsourcing production, or that the Insourcer has a good strategy because it is making its own goods and does not hold investment properties, counts in this category.

Capital Structure Flexibility

Any reference to the Insourcer’s ability to sell off assets counts in this category.

Superior Strategy

39

APPENDIX A. Definitions of Sections and Categories to be Included in the Financial Statements

(Source: Financial Accounting Standards Board. 2008. Preliminary views on financial statement presentation. Norwalk, CT.)

2.31. The business section should include assets and liabilities that management views as part

of its continuing business activities and changes in those assets and liabilities. Business activities are those conducted with the intention of creating value, such as producing goods or providing services. The business section normally would include assets and liabilities that are related to transactions with customers, suppliers, and employees (in their capacities as such) because such transactions usually relate directly to an entity’s value-creating activities.

2.32. The operating category within the business section should include assets and liabilities

that management views as related to the central purpose(s) for which the entity is in business. An entity uses its operating assets and liabilities in its primary revenue and expense-generating activities. All changes in operating assets and liabilities should be presented in the operating category in the statement of comprehensive income (unless existing standards require a change to be recognized as an item of other comprehensive income) and the statement of cash flows.

2.33. The investing category within the business section should include business assets and

business liabilities, if any, that management views as unrelated to the central purpose for which the entity is in business. An entity may use its investing assets and liabilities to generate a return in the form of interest, dividends, or increased market prices but does not use them in its primary revenue- and expense-generating activities. All changes in investing assets and liabilities should be presented in the investing category in the statement of comprehensive income (unless existing standards require a change to be recognized as an item of other comprehensive income) and the statement of cash flows.

2.34. The financing section should include a financing asset category and a financing

liability category. Financing assets and financing liabilities are financial assets and financial liabilities (as those terms are defined in IFRSs and U.S. GAAP) that management views as part of the financing of the entity’s business and other activities. In determining whether a financial asset or financial liability is part of an entity’s financing activities, management should consider whether the item is interchangeable with other sources used to fund its business activities. For example, an entity could acquire equipment using cash, a lease, or a bank loan. The financing section would normally include liabilities that originated from an entity’s capital-raising activities (for example, a bank loan or bonds) because capital is usually raised to fund value-creating (business) activities. However, as discussed in paragraph 2.79, because of the management approach to classification used in the proposed presentation model, items classified in the financing section by a manufacturing entity may differ from those classified in that section by a financial services entity. All changes in financing assets and financing liabilities should be presented in the financing asset and financing liability categories, respectively in the statement of comprehensive income (unless existing standards require a change to be recognized as an item of other comprehensive income) and the statement of cash flows.

40

APPENDIX B. Classification Examples

A) Excerpts from Materials for Condition with Classification on the Face of the Financial Statements

41

APPENDIX B, CONTINUED

42


B) Excerpts from Materials for Condition with Classification Absent

43


44

APPENDIX C. Disaggregation Examples

A) Excerpts from Materials for Condition with Disaggregation on the Face of the Financial Statements

45

APPENDIX C, CONTINUED

46


B) Excerpts from Materials for Condition with Disaggregation in the Notes to the Financial Statements

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48

APPENDIX D. Examples Highlighting Differences in Each Condition

A) Income Statements Income Statement for Classification Absent/Disaggregation in Notes Condition:

Income Statement for Classification Absent/Disaggregation on Face Condition:

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APPENDIX D, CONTINUED Income Statement for Classification Present/Disaggregation in Notes Condition:

Income Statement for Classification Present/Disaggregation on Face Condition:

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APPENDIX D, CONTINUED

B) Balance Sheets

Balance Sheet for Classification Absent/Disaggregation on Face Condition:

Balance Sheet for Classification Present/Disaggregation on Face Condition:

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APPENDIX D, CONTINUED

C) Footnotes PPE Footnote for Disaggregation in Notes Condition:

PPE Footnote for Disaggregation on Face Condition:

Cost of Goods Sold Footnote for Disaggregation in Notes Condition:

Cost of Goods Sold Footnote for Disaggregation on Face Condition: [NONE PROVIDED – INFO IN INCOME STATEMENT] Other Income/Expenses Footnote for Classification Absent/ Disaggregation in Notes Condition:

Other Income/Expenses Footnote for Classification Present/ Disaggregation in Notes Condition:

Other Income/Expenses Footnote for Disaggregation on Face Condition: [NONE PROVIDED – INFO IN INCOME STATEMENT]

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