j. account. public policy - student.zy.cdut.edu.cn

J. Account. Public Policy xxx (xxxx) xxx

Contents lists available at ScienceDirect

J. Account. Public Policy

journal homepage: www.elsevier .com/locate / jaccpubpol

Managerial discretion and the comparability of fair valueestimatesq

https://doi.org/10.1016/j.jaccpubpol.2021.1068780278-4254/� 2021 Elsevier Inc. All rights reserved.

q We thank Marco Trombetta (editor-in-chief), two anonymous reviewers, Ahmad Hammami, Zach Kaplan, and workshop participants aUniversity, University of Iowa, Southern Methodist University, National Taipei University, Tamkang University, the 2018 AAA Midwest Meeting2018 Hawaii Accounting Research Conference for helpful comments and suggestions.⇑ Corresponding author at: Neeley School of Business, Texas Christian University, 2900 Lubbock, Fort Worth, Texas 76129.

E-mail addresses: [email protected] (J. Black), [email protected] (J.Z. Chen), [email protected] (M. Cussatt).

Please cite this article as: J. Black, Jeff Zeyun Chen and M. Cussatt, Managerial discretion and the comparability of fair value estimAccount. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2021.106878

Jonathan Black a, Jeff Zeyun Chen b,⇑, Marc Cussatt c

aKrannert School of Management, Purdue University, United StatesbNeeley School of Business, Texas Christian University, United StatescWilber O. and Ann Powers College of Business, Clemson University, United States

a r t i c l e i n f o

Article history:Available online xxxx

Keywords:Fair value hierarchyComparabilityFair value measurementSFAS 157

a b s t r a c t

We explore how discretion over fair value measurement affects the comparability of fairvalue estimates in the financial industry. We find that greater exposure to Level 2 (Level3) measurement enhances (diminishes) the comparability of fair value estimates acrossfirms. These contrasting results reflect a nuanced relation between discretion over fairvalue measurement and comparability and suggest that managers convey useful informa-tion through Level 2 estimates, whereas Level 3 measurement is subject to error and man-agerial opportunism. Cross-sectional analyses show that fair value estimates are lesscomparable when managers have stronger incentives to introduce discretion and morecomparable when investor monitoring is stronger. Additional analyses demonstrate thatthe comparability of fair value estimates is negatively associated with non-agency mort-gage backed security holdings, the asset class most likely to be held at Level 3 by our sam-ple firms, and that our primary results hold for alternative measures of comparability.Taken together, our results highlight the critical role of discretion in shaping the compara-bility of fair value estimates.

� 2021 Elsevier Inc. All rights reserved.

1. Introduction

Comparability is crucial to high quality financial reporting. Creditors and investors must be able to compare financialinformation across firms in order to allocate resources properly. Indeed, the Financial Accounting Standards Board (FASB)claims that comparability is a desirable property of financial statements (FASB, 2010). While prior literature suggests thataccounting standards influence comparability, we know relatively little about its other determinants (Lang et al., 2010;DeFond et al., 2011; Barth et al., 2012; Yip and Young, 2012; Brochet et al., 2013). For example, measurement also playsan important role in comparability, but it is often overlooked in the literature (Barlev and Haddad, 2007; Barth, 2013).

In this study, we examine the relation between discretion over measurement and the comparability of fair value esti-mates across firms in the financial industry. For the purpose of our study, we define fair value comparability as the degreeto which the fair value component of comprehensive income (i.e., the sum of realized and unrealized fair value gains and

t Purdue, and the

ates, J.

https://doi.org/10.1016/j.jaccpubpol.2021.106878

mailto:[email protected]




http://www.sciencedirect.com/science/journal/02784254

http://www.elsevier.com/locate/jaccpubpol


J. Black, Jeff Zeyun Chen and M. Cussatt J. Account. Public Policy xxx (xxxx) xxx

losses) for a pair of firms covaries over time. This definition is in the same spirit as De Franco et al. (2011), who argue thatearnings comparability may be achieved if two firms’ earnings covary over time such that one firm’s earnings can be infor-mative to investors interested in predicting the other’s earnings. We focus on fair value comparability for two reasons. First,comparability is one of the main perceived benefits of fair value accounting (Barth, 2013). However, fair value measurementis challenging for some items and estimation techniques may vary across firms (Riedl and Serafeim, 2011). Accordingly, crit-ics argue that allowing unverifiable estimates into accounting information reduces its usefulness and may increase the like-lihood of managerial opportunism (Ramanna, 2008; Ramanna and Watts, 2012; Ball et al., 2015).

Second, available-for-sale (AFS) investment securities represent a significant portion of assets in the financial industry.These assets result in both realized fair value gains and losses reported in income and unrealized holding gains and lossesreported in other comprehensive income (OCI). Since prior comparability studies primarily focus on bottom line earnings(e.g., De Franco et al., 2011; Francis et al., 2014), their results may not be generalizable to the comparability of these fairvalue estimates. Furthermore, while prior research finds that OCI is value relevant (Jones and Smith, 2011) and that the fairvalue adjustments included in OCI can predict future earnings (Bratten et al., 2016), there is little research on the compara-bility of OCI.1 By studying the comparability of fair value estimates we also learn about the determinants of the comparability ofOCI.

When prices from an efficient market are unavailable, fair values can be difficult to measure and their estimation requiressubjective choices. On one hand, managers may use this discretion opportunistically and report biased, inaccurate fair valueestimates (Huizinga and Laeven, 2012; Hanley et al., 2018). Moreover, even in the absence of managerial manipulation weexpect unintentional variation (i.e., errors) to occur when fair values are not obtained from an efficient market. Thus, differ-ences in the choice of valuation model and inputs, which can be unverifiable and/or unobservable, may make it difficult tocompare estimates of fair value across firms, reducing the decision usefulness of these estimates.2

On the other hand, managerial discretion could be used to communicate private information (Watts and Zimmerman,1986; Healy and Palepu, 1993). For example, Altamuro and Zhang (2013) find that the fair value of mortgage service rightsbased on Level 3 inputs can better predict future cash flow than that based on Level 2 inputs. Magnan et al. (2015) find thatLevel 2 estimates are associated with more accurate analyst forecasts. Badia et al. (2017) detect evidence of conditional con-servatism in Level 2 and Level 3 estimates, consistent with managers trying to reduce information asymmetry with inves-tors. Therefore, it is also possible that managerial discretion over fair value measurement does not impair comparability, andin fact may improve the comparability of certain fair value estimates across firms.

We use the fair value hierarchy required under Statement of Financial Accounting Standards (SFAS) 157 to identify vari-ation in the potential for managerial discretion in the fair value measurement process. Specifically, SFAS 157 establishes ahierarchy for fair value estimates according to the measurement method and inputs used to generate the estimates. Withinthis hierarchy, measurement difficulties and room for managerial discretion increase monotonically from Level 1 (mark-to-market), to Level 2 (market inputs), to Level 3 (mark-to-model) fair value designations.

Our main measure of comparability is the correlation of the fair value component of comprehensive income (FVCI)between two firms in the financial industry. Given that our research question focuses on the role of discretion over measure-ment in shaping comparability, we control for economic (dis)similarities between firm-pairs that may affect the compara-bility of fair value estimates across firms. This approach isolates comparability due to the consistency with which differentfirms apply accounting rules (Francis et al., 2014). We take several steps to effectively control for economic drivers of com-parability. First, as previously noted, we focus on the financial industry because of its broad exposure to fair value estimation.An additional benefit of focusing on one industry is that firm-pairs in the same industry and the same fiscal year are subjectto the same general economic shocks.3 Second, similar to Francis et al. (2014), we control for differences in observable firm-paircharacteristics (e.g., firm size, growth, and asset risk) and firm-pair correlations of stock returns, because their co-movementsrepresent common shocks to firm fundamentals. Finally, we control for the correlation of the non-fair value components of com-prehensive income.

We find a negative association between the proportion of Level 3 fair value assets and comparability of fair value esti-mates across firms. In contrast, we find a positive association between the proportion of Level 2 assets and comparabilityof fair value estimates across firms. The latter result is particularly interesting given that managers face uncertainties andmust exercise discretion over Level 2 measurement, albeit to a lesser extent than Level 3 measurement. The contrastingresults reflect a nuanced relation between comparability and managerial discretion over fair value measurement, with Level

1 Unrealized holding gains and losses reported in OCI represent the amount that management is forgoing by holding the asset or the amount thatmanagement could receive from selling the asset under current market conditions. According to Leisenring et al. (2012), ‘‘this information is vital tounderstanding the amount, timing and uncertainty of future cash flows to the entity from financial assets because impediments to changing methods of valuerealization for these assets are low and incentives to change methods of value realization are high when economic conditions change.”

2 Research on fair value accounting in Europe conducted by the European Central Bank also points out that ‘‘At present a variety of (market) valuation modelscoexist with varied inputs and assumptions, and this may significantly reduce comparability if used indiscriminately across banks and across balance-sheet items.”(Enria, 2004)

3 Christensen (2020) points out that there are many advantages of using a narrow-sample approach to study the effect of disclosure regulation. A keyadvantage is that it allows researchers to more precisely identify the changes caused by the regulation in the specific institutional setting. We believe that,given the unique incentives to hold fair value assets and the prevalent use of fair value measurement for financial firms, a narrow-sample approach allows us tomore effectively assess how discretion over measurement impacts the comparability of fair value estimates, relative to investigating this question using abroad-sample approach.

2


2 measurement producing estimates that improve the information environment and, thereby, enhance comparability,whereas Level 3 measurement generates lower quality fair value estimates with reduced comparability.

We perform several cross-sectional tests that exploit situations where management intent and ability to manipulatefinancial statements are likely to influence the comparability of fair value estimates. These tests attempt to hold uninten-tional measurement errors constant and isolate intentional/opportunistic discretion over measurement. First, we considerhow capital constraints affect the comparability of fair value assets. Prior research documents that investors and regulatorsperceive sufficiently capitalized banks to be less risky and more capable of withstanding market turmoil (Berger andBouwman, 2013) and excess capital is associated with higher bank valuation (Mehran and Thakor, 2011). Thus, managersof undercapitalized banks have an incentive to inflate their balance sheets to appear better capitalized. We find that the neg-ative (positive) relation between Level 3 (Level 2) measurement and comparability is more (less) prominent in firm-pairsthat are facing capital constraints.

Next, we consider the effect of option-based compensation that incentivizes managerial risk taking. Option-based com-pensation, and in particular CEO’s vega from option grants, have been shown to encourage earnings management (Shu andThomas, 2019; McAnally et al., 2008; Bergstresser and Philippon, 2006; Cheng et al., 2011; Armstrong et al., 2013). We findthat the negative relation between Level 3 measurement and comparability is more prominent for firm-pairs with managerswho hold more stock options or have a higher vega. These results provide strong evidence that managerial compensationincentives engender opportunistic use of discretion over fair value estimates that impairs fair value comparability.

Finally, we consider whether investor monitoring affects the comparability of fair value estimates. Long-term, dedicatedinstitutional investors are more likely to engage in monitoring (Bushee, 1998). Similarly, investors are better able to monitormanagement when information asymmetry is low (Healy and Palepu, 2001). We find that the negative relation betweenLevel 3 measurement and comparability is less prominent for firm-pairs with higher levels of dedicated institutional own-ership. The relation between Level 2 measurement and comparability is significantly more positive when investors facelower information asymmetry (measured by bid-ask spreads). These results suggest that investor monitoring effectivenesshas a significant impact on the relation between fair value comparability and firm exposure to less-verifiable fair value esti-mates. Taken together, our cross-sectional tests reinforce the notion that managerial opportunism plays a significant role inthe comparability of fair value estimates.

We also consider several additional analyses. First, we analyze the comparability of an individual asset class subject to fairvalue measurement. Pushing down the analyses to an individual asset class holds the investment decision constant, whichreduces the possibility that economic effects explain away our results. Extending the methodology of Iselin and Nicoletti(2017), we identify that the most common Level 3 asset for firms in our sample is non-agency mortgage backed securities(MBSNAs). Next, we confirm that firm-pair exposure to MBSNA fair value estimates is negatively associated with fair valuecomparability. Additional untabulated analyses use a two-stage model that allows us to disaggregate a firm’s MBSNA hold-ings into a predicted (i.e., expected) portion and an unexpected portion, which is more likely to capture managerial discre-tion in the measurement of MBSNAs. We find that the unexpected portion of MBSNAs is negatively related to thecomparability of fair value estimates.

Next, we consider alternative measures of comparability by adapting another methodology used in De Franco et al.(2011), which measures accounting comparability as similarity in the mapping of economic events to earnings. We modifythis methodology to assess the similarity in the mapping of economic events to FVCI between two firms. We consider twosets of economic events. First, similar to De Franco et al. (2011) we assess the mapping from stock returns to FVCI, after con-trolling for the non-fair value component of comprehensive income. However, most AFS assets are debt instruments andtherefore are likely more sensitive to macroeconomic conditions relative to economic factors that directly impact stockreturns. Thus, our second alternative measure considers the mapping of macroeconomic factors (changes in the federal fundsrate, unemployment rate, and GDP) to FVCI.4 Our main results are qualitatively similar under both alternative measures of fairvalue accounting system comparability.

Finally, we examine the effect of the adoption of SFAS 157 on fair value comparability. SFAS 157 standardized the defi-nition of fair value and its estimation, reducing the amount of managerial discretion over fair value estimates. A key com-ponent of SFAS 157 required firms to provide expanded disclosures of valuation models and the inputs used to derive fairvalue estimates, allowing more intense scrutiny by investors and further reducing the ability for managers to manipulatethese estimates. While the fair value hierarchy was not available prior to SFAS 157, we estimate exposure to level 2 and3 assets prior to SFAS 157 and find that the association between Level 2 asset holdings and comparability increased signif-icantly after the adoption of SFAS 157, consistent with reduced managerial opportunism in fair value estimation followingSFAS 157. However, we do not detect a similar pattern for Level 3 estimates.

Our study contributes to two streams of literature. First, our study extends the comparability literature. Many studiesdemonstrate the value of comparability to financial statement users (De Franco et al., 2011; Kim et al., 2013, 2016; Chenet al., 2018). However, we know relatively little about determinants of comparability at the firm level. Prior literature hasfocused on the role of accounting standards (Lang et al., 2010; DeFond et al., 2011; Barth et al., 2012; Yip and Young,2012; Brochet et al., 2013) and the role of auditors (Francis et al., 2014; Chen et al., 2020) in achieving comparability. Animportant underexplored question is how accounting measurement affects comparability (Barth, 2013). We take an initial

4 We thank an anonymous reviewer for suggesting this approach to modeling the fair value accounting system.

3


step toward filling this void by exploiting variation in discretion over fair value measurement provided by the three-level fairvalue hierarchy. Our findings should be of interest to regulators hoping to establish guidelines for fair value measurement aswell as to investors, lenders, and others who hope to compare fair value information across companies.

Second, our paper extends the fair value literature by providing the first evidence that links comparability to fair valuemeasurement. While prior studies focus on firm-specific attributes, such as reliability and verifiability, of fair value esti-mates, which are calculated independently of the attributes of other firms, comparability captures similarities in these attri-butes across firms (Song et al., 2010; De Franco et al., 2011; Riedl and Serafeim, 2011; Arora et al., 2014) and is arguably oneof the most important perceived benefits from fair value accounting (Barth, 2013).

2. Background and literature review

The application of the fair value measurement in financial reporting involves a tradeoff between relevance and verifiabil-ity. SFAS 157 recognizes financial statement users’ demand for information about the trustworthiness of fair value estimatesand prescribes a fair value hierarchical order based on the attributes of the specific asset and liability and the quality of theinputs used in deriving fair value. Level 1 inputs are unadjusted quoted prices in active markets for identical assets or lia-bilities. In the absence of Level 1 inputs, Level 2 inputs are used to calculate fair value. They include either observable pricesin an active market for similar assets and liabilities, observable market prices in inactive markets for identical assets andliabilities, or pricing models whose inputs are observable for substantially the full term of the asset. Thus, Level 2 measure-ment offers management some discretion over which inputs and models it uses. Level 3 inputs are used only if the manage-ment cannot find Level 1 and Level 2 inputs. They are not observable from sources external to the reporting entity and reflectmanagement’s assessment of the assumptions that market participants would use to value the asset or liability. As a result,management has considerable discretion in fair value measurement when they use Level 3 inputs. Appendix A provides anexample of variation in managerial discretion over Level 3 estimates across different firms.5

2.1. Prior literature on the fair value hierarchy

Song et al. (2010) is one of the first attempts to demonstrate the value relevance of the fair value hierarchy informationafter SFAS 157. They find that Level 3 estimates are significantly less value relevant than Level 1 and Level 2 estimates, sug-gesting that the market discounts the usefulness of unverifiable information contained in Level 3 estimates. Goh et al. (2015)further show that the difference between the pricing of the different estimates is more pronounced during the 2008 financialcrisis. Riedl and Serafeim (2011) hypothesize that information risk related to uncertainty about the payoff distribution of fairvalue assets increases in the Level 1, 2, and 3 fair value designations. They derive an empirical model allowing asset specificestimates of implied betas and find that firms with a greater exposure to Level 3 assets have higher betas relative to theimplied betas for Level 1 or Level 2 assets. Magnan et al. (2015) find a more nuanced relation between fair value measure-ment and the quality of the information environment faced by analysts. Specifically, they find that higher proportions ofLevel 2 assets are associated with more accurate forecasts, while higher proportions of Level 3 assets are associated withincreased forecast dispersion.

Recent research finds that management takes actions to avoid bearing agency costs associated with reporting less-verifiable fair value estimates. For example, Badia et al. (2017) find that firms holding more Level 2 and Level 3 assets reportmore conservative fair value gains and losses. Black et al. (2018) focus on the financial industry and reports evidence of con-servatism in bottom-line earnings in response to firms’ reporting Level 2 and Level 3 assets. Iselin and Nicoletti (2017) findthat public banks attempt to avoid disclosure of Level 3 assets through changes in both investment portfolio composition andclassification.

We extend the fair value accounting literature by examining the impact of fair value measurement on comparability.Comparability is an enhancing characteristic that increases the usefulness of financial information (FASB, 2010). Unlike rel-evance or reliability, comparability pertains to two or more items between two reporting entities. Comparable informationallows users of that information to ‘‘identify and understand similarities in, and differences among, items” (FASB, 2010). Weexamine whether measurement discretion enhances or impairs fair value comparability, thereby affecting the decision use-fulness of fair value estimates.

2.2. Prior literature on comparability

Regulators, practitioners, investors, and academics invariably emphasize the role of comparability across financial state-ments in assessing a firm’s performance. De Franco et al. (2011) develop novel measures of earnings comparability and showthat they are positively related to analyst following and forecast accuracy. Using De Franco et al. (2011)’s measures, subse-

5 We examine the 10Ks of Bank of America, Chase, PNC, and Wells Fargo for fiscal year 2014 and identify a common Level 3 asset for these four firms:residential mortgage-backed securities and loans (MBS). We find the use of different valuation techniques across firms. Chase and PNC utilize a discounted cashflow (DCF) model, while Bank of America and Wells Fargo use a combination of a DCF model and market comparable pricing. The ranges of inputs used in eachfirm’s DCF model vary significantly across firms. Interestingly, Bank of America does not disclose the inputs to the market comparable technique, while WellsFargo does. Lastly, PNC is the only firm to utilize a third-party vendor in pricing their MBS.

4


quent research finds strong implications of comparability for credit risk pricing (Kim et al., 2013), investment decisions(Chen et al., 2018), and bad news hoarding (Kim et al., 2016).

Mandatory IFRS adoption in the European Union in 2005 offers an opportunity for empirical research on whetheraccounting standards influence comparability. Yip and Young (2012) find that mandatory IFRS adoption improves cross-country information comparability. In a similar vein, Barth et al. (2012) show that the adoption of IFRS by non-US firmsenhances comparability with US firms. Cascino and Gassen (2015) further document that firms from countries with stricterreporting enforcement experience more prominent IFRS comparability effects. Regarding economic consequences, DeFondet al. (2011) find that foreign mutual fund ownership increases because of IFRS-induced comparability improvement.Brochet et al. (2013) find that such enhanced comparability reduces insiders’ ability to exploit private information.

While many recent studies consider the consequences of comparability, much remains to be known about what firmcharacteristics contribute to it. As Ball et al. (2003) point out, focusing on accounting standards alone is perhaps incompletefor researchers to draw conclusions about the economic forces affecting financial reporting quality. Like other qualitativecharacteristics of financial reporting, comparability is sensitive to the incentives of the managers who are responsible forfinancial statement preparation. Our study extends the comparability literature by focusing on the often-overlooked rolethat measurement plays in generating comparable estimates (Barth, 2013). Fair value accounting can enhance comparabilityif fair values accurately reflect economic similarities and differences across firms. However, managers face significant chal-lenges estimating some fair value assets (e.g., Level 3 assets) which could result in error. If estimation errors (intentional and/or unintentional) cause a departure from the underlying economics, comparability may be impaired.

3. Hypothesis development

Barth (2014) argues that fair value accounting produces more comparable financial statements than either modified orunmodified historical cost accounting because the same (different) financial assets should have the same (different) fair val-ues at any point in time. However, in practice, the ‘‘true” fair value of an asset is rarely observable and its estimation is oftenambiguous. For many items held at fair value, management has discretion to use various inputs and measurement tech-niques that may affect the comparability of reported fair values.

Our first hypothesis considers how discretion over measurement affects the comparability of fair value estimates. The fairvalue hierarchy classifies fair value measurement into three categories, which provide variation in the methods and inputsused to value the underlying assets. Level 1, 2, and 3 fair value estimates have progressively more subjective inputs and val-uation models used to derive fair value estimates, thus as we move along the hierarchy we expect assets to have increasingmeasurement error (intentional and unintentional) and information asymmetry between managers and investors. Level 3assets, in particular, rely on models and assumptions that likely vary across firms and are neither observable nor verifiableby outsiders. Thus, we expect that fair value estimates that are more subject to managerial discretion exhibit lowercomparability.

Our first hypothesis in the alternative form is:

H1. Level 2 and Level 3 measurement are associated with reduced comparability of fair value estimates

The null hypothesis is that fair value hierarchy is not associated with variation in fair value comparability. While we donot expect this to be the case, it cannot be ruled out a priori. Further, managers could use discretion in fair value estimates toinform investors and correct market mispricing, leading to more comparable estimates. For example, Song et al. (2010) findthat value relevance of Level 2 assets is about the same as that of Level 1 assets and Magnan et al. (2015) find that analystsissue more accurate forecasts for firms holding a higher proportion of Level 2 assets. These results suggest that managersmay convey useful information through Level 2 estimates and it is likely they could do the same using Level 3 estimates.Indeed, Altamuro and Zhang (2013) find that fair value estimates of mortgage servicing rights based on Level 3 inputs areof higher quality than those based on Level 2 inputs. This suggests that at least some fair values based on Level 3 inputsmay incorporate fundamental information more effectively than fair values based on market inputs. Therefore, if managerialdiscretion, on average, enhances the decision usefulness of fair value estimates when the market for the underlying asset isinactive, then higher exposure to unverifiable fair value measurement may not necessarily lead to less comparable fair valuegains and losses.

Our second and third hypotheses examine whether the association between comparability and the exposure to Level 2and Level 3 measurement is affected by managers’ incentives to manipulate fair values and inflate balance sheets. With pro-gressively more subjective inputs and valuation models used to derive the fair value estimates, information asymmetrybetween managers and investors is likely to increase across portfolios of assets designated as Level 1, 2, and 3 fair values.Therefore, moral hazard problems are more likely to arise from Level 2 or Level 3 measurement if managers use private infor-mation to their personal advantage by manipulating the reported fair values they disclose to investors. We expect agencyproblems for Level 3 assets to be greatest because they have various risk characteristics that are not observable or verifiableby outsiders.

Bank managers are keenly aware of their capital reserves because they must maintain adequate capital to remain solventif they incur losses on risky financial assets. Prior research documents that investors and regulators perceive sufficiently cap-italized banks to be less risky and more capable of withstanding market turmoil (Berger and Bouwman, 2013). Furthermore,

5


most banks maintain capital ratios in excess of regulatory limits and excess capital is associated with higher bank valuation(Mehran and Thakor, 2011). Indeed, even though the majority of fair value assets (those designated available-for-sale) arenot directly monitored by the Federal Reserve in regulatory capital, Huizinga and Laeven (2012) provide evidence that banksfacing capital constraints during the financial crisis overstate fair value assets to inflate balance sheets. In the insuranceindustry, Hanley et al. (2018) also document that regulatory capital reporting incentives play an important role in insurers’strategic estimation of asset fair values.

We conjecture that managers are more likely to utilize aggressive models to measure Level 2 and/or Level 3 assets whenbanks have low levels of capital reserves. To the extent that opportunistic use of discretion over measurement reduces thecomparability of fair value estimates, firms with strong incentives to manipulate fair values are likely to have less compa-rable Level 2 and/or Level 3 measurement relative to firms with little incentive to manipulate their fair values. Thus, our sec-ond hypothesis stated in the alternative form, is as follows:

H2. The relation between the exposure to Level 2 and/or Level 3 measurement and the comparability of fair value gains andlosses is impaired for firm-pairs with lower capital reserves.

Next, we consider the role of CEO compensation incentives. Agency problems between investors and managers give riseto opportunistic risk taking in bank investment portfolios. While excessive risk should be revealed to investors through vola-tile earnings, prior studies have found that managers manipulate their accounting to smooth earnings and appear less risky(Shu and Thomas, 2019). Thus, it is likely that when agency problems exist, managers will also manipulate fair values to hiderisk taking.

Prior literature argues that option holdings exacerbate agency concerns by encouraging opportunistic risk taking by man-agers (e.g., Cheng and Farber, 2008). Specifically, options exhibit a convex payoff structure that increases the managers’return for taking risk (i.e., vega). Armstrong et al. (2013) find strong evidence of a positive relation between vega and mis-reporting, consistent with option-based compensation incentivizing managers to manipulate financial reports.6 Thus, basedon Cheng and Farber (2008) and Armstrong et al. (2013), we argue that option-based compensation may motivate bank man-agers to reduce comparability in an attempt to hide such opportunistic risk taking. This leads to our third hypothesis, stated inthe alternative form:

H3. The relation between the exposure to Level 2 and/or Level 3 measurement and the comparability of fair value gains andlosses is impaired for firm-pairs with higher CEO option ownership and vega.

The last two hypotheses, hypothesis 4a and hypothesis 4b (hereafter, H4a and H4b), examine the effect of cross-sectionalvariation in external monitoring on the relation between fair value measurement and comparability. Chung et al. (2002) finda negative relation between institutional monitoring and opportunistic earnings management toward managers’ desiredlevel or range of profits. Black et al. (2018) find that the association between Level 2 and Level 3 fair value assets and thedemand for conditional accounting conservatism is stronger in the presence of high levels of monitoring institutional own-ership. These results suggest that strong institutional monitoring can constrain managerial opportunism in Level 2/Level 3measurement, mitigating the negative impact of unverifiable fair value measurement on comparability across firms. Ournext hypothesis, H4a, stated in the alternative form, is as follows:

H4a. The relation between the exposure to Level 2 and/or Level 3 measurement and the comparability of fair value gains andlosses is improved for firm-pairs with higher levels of dedicated institutional ownership.

Similarly, when there is low information asymmetry between managers and investors, it is easier for investors to monitormanagement and, thus, costlier for managers to engage in fair value manipulation. Consistent with this line of reasoning,Riedl and Serafeim (2011) find evidence that high-quality disclosure mitigates information risk across the fair value desig-nations. Further, Chung et al. (2017) provide evidence that when firms provide more voluntary disclosure related to theirportfolio of fair value assets, they have higher market pricing and lower information risk related to Level 3 estimates. Accord-ingly, we conjecture that if firms with strong information environments have less management induced bias in their unver-ifiable fair value estimates, then higher exposure to Level 2 and/or Level 3 measurement is less likely to impair comparabilityfor firms with strong information environments. Our last hypothesis, H4b, stated in the alternative form, is as follows:

H4b. The relation between the exposure to Level 2 and/or Level 3 measurement and the comparability of fair value gains andlosses is improved for firm-pairs with lower information asymmetry.

6 Armstrong et al. (2013) also note that options can increase the sensitivity of managers’ wealth to fluctuations in stock price (i.e., delta). Therefore, optionsdo not unequivocally encourage risk taking if managers are risk averse.

6


4. Research design

4.1. Measure of fair value comparability

One of De Franco et al. (2011)’s comparability measures focuses on the degree to which earnings for two firms covary overtime.7 In our setting, we anticipate that more managerial opportunism in fair value measurement across firms will lead todecreased co-movement of items measured at fair value and thus less comparable fair value gains and losses. We modify DeFranco et al. (2011)’s measure to focus on the portion of comprehensive income that is affected by fair value measurement.Specifically, we calculate our fair value comparability measure for all firm-pairs within the financial industry (SIC 6000 –6300) by saving the adjusted R2 from the following regression:

7 DeearningearningrationalidenticaearningaccountSection

8 In amagnitu

9 Spereturns

FVCIit ¼ b0 þ b1FVCIjt þ eijt ð1Þ

where FVCI is the fair value component of comprehensive income for firm i and firm j in a pair for quarter t, scaled by averagetotal assets of each firm. We run Eq. (1) for each firm-pair over a non-overlapping 16-quarter period (2008–2011 and 2012–2015).8 Throughout the rest of this paper, we refer to the adjusted R2 from this regression as FVCOMP, the co-movement of FVCI.Higher values of FVCOMP indicate greater comparability of fair value estimates between the two firms in a pair.

4.2. Main test (H1)

We test H1 by estimating the following model:

FVCOMPijt ¼ b0 þ b1Lev2 Avgijt þ b2Lev3 Avgijt þ Controlsijt þ eijt ð2Þ

where Lev2_Avg (Lev3_Avg) is the average proportion of Level 2 (Level 3) fair value assets to total fair value assets for firm iand firm j. Since each firm-pair observation of FVCOMP requires 16 quarters of data, we calculate Lev2_Avg and Lev3_Avg asthe average proportion of fair value assets to total fair value assets for each 16-quarter window. H1 predicts that b1 and b2should be negative if greater exposure to Level 2 and Level 3 measurement is associated with less comparable fair value gainsand losses.

For Eq. (2) to capture the effect of fair value measurement on comparability, it is crucial that we control for differences inunderlying economic fundamentals between firm-pairs. Our approach to controlling for firm-pair economics is similar tothat used in Francis et al (2014). First, by looking at only the financial industry we identify a group of similar firms thatare exposed to similar economic shocks. Second, we control for NonFVCOMP (the firm-pair correlation of quarterly incomefrom sources other than fair value items), and RETCOMP (the firm-pair correlation of the contemporaneous monthly stockreturns). We generate these measures in a similar manner to FVCOMP.9 The inclusion of NonFVCOMP as a control helps removeany systematic relation between the two components of comprehensive income driven by economic fundamentals. Finally,because returns incorporate all value-relevant information about a company’s fair value assets, RETCOMP captures both differ-ences in asset structure and performance. By including these variables, we endeavor to remove variation in FVCOMP that isrelated to firm-pair economic comparability.

We also control for a set of firm characteristics related to the economic fundamentals of each firm-pair that may affect thecomparability of FVCI for a given pair of firms. These firm characteristics include overall fair value exposure (i.e., fair valeassets scaled by total assets), firm size, capital ratio, leverage, market-to-book ratio, probability of loss, loan loss provisions,and loan loss allowances. Following prior research that has used firm-pair observations (De Franco et al., 2011; Francis et al.,2014), we include both the difference and the average of each control variable and make no predictions as to what the signsof the coefficients should be. In addition, to remove the impact of the cross-firm variation in the investments on Level 2 andLevel 3 assets on economic comparability, we include the differences in Level 2 and Level 3 asset holdings between firm-pairs (Lev2_Diff and Lev3_Diff) as control variables in Eq. (2). Similar to Lev2_Avg and Lev3_Avg, we calculate each controlvariable by averaging it over each 16-quarter window. The differences (_Diffi,j) are measured as the absolute value of the dif-ference between firm i and firm j. The averages (_Avgi,j) are measured as average of firm i or firm j. Appendix B providesdetailed definitions of the control variables.

Franco et al. (2011) propose two measures of comparability. One is based on the similarity of the mapping of stock returns to earnings across firms (thes-returns measure), and the other is based on the covariation in earnings across firms (the earnings covariation measure). The advantage of thes-returns measure is that the economic event is held constant (by construction), in an attempt to isolate comparability of the accounting system. Thee for the earnings covariation measure is that earnings can fulfil a comparability role to investors even when the accounting functions per se are notl. An advantage of this measure is that it does not require researchers to specify and estimate the accounting function, which is a limitation of thes-returns measure. For our main analyses, we use a covariation framework because we lack strong theoretical guidance to specify and estimate theing function that maps banks economic fundamentals to their fair value gains and losses, especially for Level 2 and Level 3 estimates. Nevertheless, in7.2 we perform additional analyses in an attempt to model the fair value accounting function and assess the robustness of our main results.n additional analysis we estimate Eq. (1) over a single 32 quarter period (2008–2015) and find that our main results are similar in significance andde. See Section 7.2.1.cifically, to construct NonFVCOMP and RETCOMP, we replace FVCI in Eq. (1) with net income less fair value items scaled by total assets, and monthly, respectively.

7


4.3. Cross-sectional tests (H2, H3, and H4)

H2 predicts that the relation between fair value comparability and exposure to Level 2 and Level 3 measurement isimpaired for firm-pairs with low capital reserves. We identify firm-pairs containing a capital-constrained firm by creatingan indicator variable, LowCapital, equal to one when either firm in a given pair has a capital ratio below the sample median.Capital ratios are calculated as the firm average over each 16-quarter window.

H3 predicts that the relation between fair value comparability and exposure to Level 2 and Level 3 measurement isimpaired for firm-pairs with greater CEO incentive to take risk. To test H3, we create the indicator variables HighVega andHighOption equal to one if CEOs for either firm in a firm-pair have above median vega and option ownership, respectively.We obtain estimates of vega, sensitivity of manager wealth to risk, from Lalitha Naveen’s web page (https://sites.temple.edu/lnaveen/data/).10 Vega creates a consistent incentive to take risks and, potentially, manipulate fair value estimates. Whileoptions typically increase vega, it is possible they have other incentive effects (Armstrong et al., 2013).

H4a and H4b predict that the relation between fair value comparability and exposure to Level 2 and Level 3 measurementis improved for firm-pairs that have higher levels of dedicated institutional ownership and lower information asymmetry,respectively. To test H4a, we create an indicator variable, DedicatedII, equal to one if either firm in a firm-pair has at least5% ownership by dedicated institutional investors, as defined by Bushee (2001), over the 16-quarter window. To test H4b,we create an indicator variable, LowBidAsk, if either firm in a firm-pair has a bid-ask spread below the sample median.

In order to test H2 through H4, we augment model (2) by allowing the relation between FVCOMP and exposure to Level 2and Level 3 measurement to vary with LowCapital (H2), HighVega (H3), HighOption (H3), DedicatedII (H4a), and LowBidAsk(H4b):

10 Thi11 For

FVCOMPijt ¼ b0 þ b1Lev2 Avgijt þ b2Lev3 Avgijt þ b3Lev2 Avg � ðLowCapitalijt or CEOCompijt or MonitorijtÞþ b4Lev3 Avg � ðLowCapitalijt or CEOCompijt or MonitorijtÞþ b5ðLowCapitalijt or CEOCompijt or MonitorijtÞ þ Controlsijt þ eijt ð3Þ

where CEOComp is a placeholder for HighVega and HighOption and Monitor is a placeholder for DedicatedII and LowBidAsk.Support for H2 and H3 (H4a and H4b) would require negative (positive) and significant coefficient estimates for b3 and b4.

5. Sample and descriptive statistics

Our sample period is from Q1 2008 to Q4 2015. We obtain quarterly financial statement data from the COMPUSTAT quar-terly fundamentals database. Similar to Song et al. (2010) and Riedl and Serafeim (2011), we focus on firms in the financialindustry (SIC 6000–6300) because fair value standards relate most directly to financial instruments, which constitute theprimary operating structure of financial institutions. Following De Franco et al (2011) and Francis et al. (2014), we drop firmswith less than $10 million in total assets and firms whose fiscal quarters are not aligned with the calendar (i.e., not ending ineither March, June, September, or December). This procedure yields an initial sample of 20,762 firm-quarters (933 uniquefirms).

We exhaustively form unique firm pairs in each quarter.11 The resultant sample has about 4 million pairs. Of these 4 millionpairs, 1.6 million firm-pair-quarter combinations have data for all 32 quarters from 2008 to 2015. To mitigate concerns aboutnon-independence of error terms in the regression analysis, we follow Francis et al. (2014) and use firm-pairs with non-overlapping 16-quarter periods to estimate correlation of FVCI at the firm-pair level. The first (second) 16-quarter period overwhich we measure fair value comparability is from Q1 2008 (2012) to Q4 2011 (2015). The above pairing procedure results in102,890 firm-pair observations. We drop 22,822 firm-pair observations that are missing fair value hierarchy data leaving 80,068firm-pair combinations over the two 16-quarter periods. We then drop 26,519 firm-pair observations with missing data to cal-culate control variables in the regression analysis. Our final sample consists of 53,549 firm-pair observations (20,909 for the 16quarters ended Q4 2011 and 32,640 for the 16 quarters ended Q4 2015) corresponding to 262 unique firms. To address outliers,we winsorize all continuous variables at the top and bottom 1% level.

Table 1 summarizes descriptive statistics. On average, the proportion of total fair value assets to total assets for our pair-wise combinations (FVA_Avg) is 18.6%, reflecting the economic importance of fair value assets for financial firms. The averageproportion of fair value assets according to the three-level hierarchy reveals that Level 2 assets are by far the most commontype of asset class held by firms in our sample, with a mean Lev2_Avg of 93.3%. Level 1 and Level 3 assets make-up a muchsmaller proportion of fair value assets with mean Lev1_Avg and Lev3_Avg of 4.4% and 2.3%, respectively. The mean (median)of FVCOMP is 0.290 (0.228). In contrast, the mean (median) of NonFVCOMP is only 0.134 (0.056), suggesting that the fair valuecomponent of comprehensive income exhibits a higher degree of co-movement than non-fair value earnings for financialfirms.

s is an updated dataset from Coles et al. (2006) and is based on the methodology from Core and Guay (2002).example, if there are four firms A, B, C, and D in a given quarter, we produce six unique firm-pairs: A-B, A-C, A-D, B-C, B-D, and C-D.

8

https://sites.temple.edu/lnaveen/data/

https://sites.temple.edu/lnaveen/data/

Table 1Descriptive statistics.

Variable N Mean Std. Dev. Q3 Median Q1

Comparability MeasuresFVCOMP 53,549 0.290 0.258 0.490 0.228 0.050CompAcct_RET 53,549 �0.014 0.031 �0.002 �0.005 �0.012CompAcct_Macro 53,549 �0.007 0.009 �0.002 �0.004 �0.008Firm-Pair Fair Value VariablesFVA_Avg 53,549 0.186 0.073 0.226 0.176 0.136Lev1_Avg 53,549 0.044 0.082 0.048 0.015 0.003Lev2_Avg 53,549 0.933 0.088 0.987 0.963 0.914Lev3_Avg 53,549 0.023 0.032 0.029 0.010 0.002FVA_Diff 53,549 0.110 0.096 0.153 0.085 0.039Lev1_Diff 53,549 0.071 0.146 0.068 0.019 0.004Lev2_Diff 53,549 0.094 0.146 0.109 0.041 0.013Lev3_Diff 53,549 0.035 0.053 0.042 0.013 0.002Firm-Pair Economic Control VariablesSize_Diff 53,549 1.659 1.425 2.294 1.285 0.596Size_Avg 53,549 8.077 1.079 8.668 7.911 7.297Capr1_Diff 53,549 2.936 2.401 4.138 2.353 1.088Capr1_Avg 53,549 13.011 1.918 14.149 12.871 11.723Lev_Diff 53,549 1.112 1.712 1.242 0.653 0.297Lev_Avg 53,549 1.324 0.966 1.611 1.098 0.745MB_Diff 53,549 0.456 0.422 0.617 0.345 0.160MB_Avg 53,549 1.134 0.300 1.300 1.106 0.936LossProb_Diff 53,549 0.141 0.194 0.188 0.063 0.000LossProb_Avg 53,549 0.103 0.141 0.156 0.031 0.000Provision_Diff 53,549 0.001 0.001 0.002 0.001 0.000Provision_Avg 53,549 0.001 0.001 0.001 0.001 0.000Allowance_Diff 53,549 0.005 0.005 0.007 0.004 0.002Allowance_Avg 53,549 0.011 0.004 0.013 0.011 0.009NonFVCOMP 53,549 0.134 0.179 0.185 0.056 0.012RETCOMP 53,549 0.133 0.153 0.201 0.073 0.016

Table 1. provides descriptive statistics on the fair value asset proportions by level and on other key variables used in our analyses. All variables are definedin Appendix B.


6. Main results

6.1. Results of H1

The first column of Table 2 reports baseline regression results for Eq. (2) with only fair value variables and firm and yearfixed effects as controls. The second column of Table 2 reports the results of estimating Eq. (2) that contains all control vari-ables. Because the results are similar across the two columns, for brevity we only discuss the results in the second column.Consistent with H1, we find a negative and significant coefficient estimate for Lev3_Avg (b2 = �0.587, p-value < 0.01), sug-gesting that greater exposure to Level 3 measurement impairs fair value comparability. In contrast, and in opposition toour a priori expectations, we observe a positive and significant relation between Lev2_Avg and FVCOMP (b1 = 0.139, p-value < 0.01), indicating that greater exposure to Level 2 estimates is associated with increased comparability of fair valueestimates across firms. Together, the results reveal a nuanced relation between discretion in fair value measurement andcomparability. While Level 3 measurement is more likely to be subject to managerial opportunism and estimation error,reducing fair value comparability, Level 2 measurement contains high quality estimates that increase comparability. The lat-ter finding suggests that there are comparability benefits from the informative use of discretion in Level 2 fair valuemeasurement.12

Turning to other fair value variables, we find that larger firm-pair differences in total fair value asset exposures and specif-ically, differences in Level 2 and Level 3 asset exposures are associated with reduced fair value comparability (coefficient onFVA_Diff = �1.014, p-value < 0.01; coefficient on Lev2_Diff = �0.058, p-value = 0.015; coefficient on Lev3_Diff = �0.135, p-value = 0.066). In contrast, we find that higher levels of exposure to total fair value estimates are associated with increasedcomparability (coefficient on FVA_Avg = 1.182, p-value < 0.01), suggesting that the comparability of fair value assets mayimprove with scale.

12 The significant exposure to Level 2 assets in the financial industry (on average 93.3% of all fair value assets) likely attracts greater regulatory and capitalmarket scrutiny, which may incentivize managers to use their private information to provide better estimates of Level 2 fair values. In contrast, the unverifiablenature of Level 3 estimates makes it easier to engage in opportunistic manipulation of fair values, and more difficult for managers to signal private informationbecause the market cannot assess the quality of such signals. Furthermore, auditors face significant challenges in auditing fair value estimates under extremeestimation uncertainty (Christensen et al., 2012; Glover et al., 2017). Thus, it is possible that audit quality is higher for Level 2 estimates than Level 3 estimates.However, identifying the specific mechanism(s) that results in Level 2 and Level 3 estimates exhibiting opposite relations with fair value comparability isbeyond the scope of our study. We invite future research to conduct a more thorough investigation of this issue.

9

Table 2The association between exposure to Level 2 and Level 3 measurement and the comparability of the fair value component of comprehensive income.

Variables Dep. Var. = FVCOMP Dep. Var. = FVCOMP

Coeff. t-stat Coeff. t-stat

Lev2_Avg 0.099** 2.36 0.139*** 3.30Lev3_Avg �0.460*** �3.23 �0.587*** �3.80Lev2_Diff �0.095*** �4.12 �0.058** �2.45Lev3_Diff �0.219*** �2.97 �0.135* �1.85FVA_Diff �1.095*** �27.56 �1.014*** �26.91FVA_Avg 1.138*** 19.82 1.182*** 19.87Size_Diff �0.006*** �5.17Size_Avg 0.010*** 3.83Capr1_Diff �0.004*** �4.83Capr1_Avg �0.000 �0.27Lev_Diff �0.010*** �5.78Lev_Avg 0.000 0.11MB_Diff 0.002 0.45MB_Avg 0.032*** 3.84LossProb_Diff 0.039** 2.60LossProb_Avg �0.121** �2.50Provision_Diff 2.045 0.85Provision_Avg 2.524 0.47Allowance_Diff �4.400*** �9.33Allowance_Avg 8.255*** 8.16NonFVCOMP 0.005 0.52RETCOMP 0.542*** 5.44Intercept 0.058 1.33 �0.164*** �3.33

Firm & Year FE Included IncludedNum. of obs. 53,549 53,549Adjusted R2 0.254 0.285

Table 2 provides the results of estimating equation (2). The dependent variable is FVCOMP in both columns. Standard errors are robust to heteroscedasticityand clustered at the firm level. ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. All variables are defined in Appendix B.


In untabulated additional analysis, we allow the relation between FVCOMP and Lev2_Avg and the relation betweenFVCOMP and Lev3_Avg to vary between the two 16-quarter windows over which we calculate FVCOMP (i.e., 2008–2011and 2012–2015). The first 16-quarter window contains the financial crisis, which had a significant impact on the liquidityof many investment securities and, hence, could have affected the co-movement of their fair values. Furthermore, firms’investment strategies are likely to be different during financial crises and uncertain market conditions.13 However, we finda negligible difference in the relation between Level 2 and Level 3 exposure and comparability of fair value estimates betweenthe two 16-quarter periods.

6.2. Results of cross-sectional tests (H2, H3, and H4)

We report our cross-sectional results in Table 3.14 Panel A presents results of testing H2. The main effect of Lev2_Avgremains positive and significant (b1 = 0.432, p-value < 0.01), suggesting that firm-pairs with sufficient capital convey useful fairvalue information through Level 2 estimates enhancing comparability across firms. The coefficient for Lev3_Avg is not reliablydifferent from zero (b1 = �0.259, p-value > 0.10), thus we do not find a statistically significant relation between fair value com-parability and exposure to Level 3 assets when firm-pairs have sufficient capital. Turning to the interaction effect, we find anegative and significant coefficient estimate for LowCapital*Lev2_Avg (b3 = �0.343, p-value < 0.01) and LowCapital*Lev3_Avg(b4 = �0.448, p-value < 0.10), consistent with H2. The results suggest that when managers have capital adequacy incentivesto inflate the balance sheet (Huizinga and Laeven, 2012), they are more likely to manipulate unverifiable fair value measure-ments, resulting in less comparable fair value gains and losses across firms.

Table 3 Panel B displays the results from our test of H3. Specifically, we estimate Eq. (3) where CEOComp is equal to High-Vega (Column 1) and HighOption (Column 2). In both columns, the main effect of Lev2_Avg (Lev3_Avg) remains positive (neg-ative) and significant, providing evidence of a positive (negative) association between Lev2_Avg (Lev3_Avg) and fair valuecomparability for firm-pairs with below median CEO compensation incentives. Turning to the interaction effects, in Column1 of Panel B we find a negative and significant result for CEOComp*Lev3_Avg (b4 = �0.830, p-value < 0.10). In Column 2, wealso find a negative and significant coefficient for CEOComp*Lev3_Avg (b4 = �0.991, p-value < 0.01). Consistent with H3, these

13 We find that FVCOMP is significantly smaller (i.e., the fair value component of comprehensive income is, on average, less comparable across firm-pairs) inthe 2008–2011 period compared to the 2012–2015 period, suggesting that the financial crisis negatively impacted fair value comparability. In addition, firmsheld less Level 2 assets but more Level 3 assets during the 2008–2011 period relative to the 2012–2015 period.14 We suppress control variables and their interactions with the cross-sectional variables for brevity.

10

Table 3The association between exposure to Level 2 and Level 3 estimates and the comparability of the fair value component ofcomprehensive income: Cross-sectional partitions.

Panel A: Partitioning on capital constraints

Dep. var. = FVCOMP

Variables Coeff. t-stat

Lev2_Avg 0.432*** 5.24Lev3_Avg �0.259 �1.17LowCapital*Lev2_Avg �0.343*** �4.21LowCapital*Lev3_Avg �0.448* �1.81

Controls (Interacted w/ LowCapital) IncludedFirm & Year FE IncludedNum. of obs. 53,549Adjusted R2 0.290

Panel B: Partitioning on CEO compensation incentives

Dep. var. = FVCOMP

CEOComp = HighVega CEOComp = HighOption

Variables Coeff. t-stat Coeff. t-stat

Lev2_Avg 0.148*** 3.49 0.151*** 3.64Lev3_Avg �0.495*** �3.19 �0.477*** �3.10CEOComp*Lev2_Avg �0.069 �0.37 �0.043 �0.20CEOComp*Lev3_Avg �0.830* �1.92 �0.991*** �2.97

Controls (Interacted w/ CEOComp) Included IncludedFirm & Year FE Included IncludedNum. of obs. 53,549 53,549Adjusted R2 0.288 0.288

Panel C: Partitioning on investor monitoring

Dep. var. = FVCOMP

Monitor = DedicatedII Monitor = LowBidAsk

Variables Coeff. t-stat Coeff. t-stat

Lev2_Avg 0.162*** 3.74 0.019 0.34Lev3_Avg �0.610*** �3.90 �0.304 �1.09Monitor*Lev2_Avg �0.052 �0.44 0.178*** 2.83Monitor*Lev3_Avg 0.402* 1.81 �0.279 �1.04Controls (Interacted w/ Monitor) Included IncludedFirm & Year FE Included IncludedNum. of obs. 53,549 53,549Adjusted R2 0.300 0.292

Table 3 provides the results of estimating equation (3). In Panel A the cross-sectional variable is LowCapital. In Panel Bcolumn (1) CEOComp = HighVega and in Panel B column (2) CEOComp = HighOption. In Panel C column (1) Moni-tor = DedicatedII and in Panel C column (2) Monitor = LowBidAsk. Standard errors are robust to heteroscedasticity andclustered at the firm level. ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. All variables aredefined in Appendix B.


results suggest that CEO incentives to take risk impact the measurement of subjective, Level 3 assets and, thus, these assetshave a more negative association with fair value comparability across firms.

Table 3 Panel C contains our tests of H4a and H4b. Specifically, we estimate Eq. (3) where Monitor is equal to DedicatedII(Column 1) and LowBidAsk (Column 2). In Column 1, the main effect of Lev2_Avg (Lev3_Avg) remains positive (negative) andsignificant, providing evidence of a positive (negative) association between Lev2_Avg (Lev3_Avg) and fair value comparabilityfor firm-pairs with low levels of institutional monitoring. In Column 2, we are unable to detect a significant effect of man-agerial discretion on the comparability of fair values in firm-pairs with below median information asymmetry as the coef-ficients for Lev2_Avg and Lev3_Avg are insignificant. Turning to the interaction effect, in Column 1 we show a positive andsignificant coefficient for Monitor*Lev3_Avg (b4 = 0.402, p-value < 0.10), supporting H4a and suggesting that having highlevels of dedicated institutional ownership improves the comparability of Level 3 fair value estimates across firms. In thesecond column, we detect a positive and significant coefficient estimate for Monitor*Lev2_Avg (b4 = 0.178, p-value < 0.01).Consistent with H4b, we find evidence that when both firms in a firm-pair have low levels of information asymmetry, thepositive relation between fair value comparability and Level 2 measurement is more prominent.

11


7. Additional analyses

7.1. Analysis of specific asset class

At each level of the fair value hierarchy, firms invest in various financial assets serving different business purposes. Topinpoint the measurement effect on comparability of fair value estimates, we identify the specific type of fair value assetthat is most likely to be measured using Level 3 inputs and retest H1 holding the investment decision (with respect tothe specific type of fair value asset) constant between the two firms in a pair.

We obtain data on different types of financial assets that are reported on Federal Reserve Form Y9-C, a regulatory reportfor bank holding companies that contains information on the type of assets held at fair value. Following Iselin and Nicoletti(2017), we estimate the following regression to identify the most likely Level 3 assets in our sample:

15 Eq.descripobserva16 Coe17 Wevalue mfinding

Lev3it ¼ b1Treasuryit þ b2Governmentit þ b3MBSAit þ b4MBSNAit þ b5ABSit þ b6OtherDebtit þ b7Equityit þ eit ð4Þ
where the dependent variable is reported Level 3 fair value assets. Treasury is total treasury securities. Government is totalsecurities issued by the US government agencies.MBSA is total agency issued (by GNMA, FNMA or FHLMC) mortgage-backedsecurities. MBSNA is total non-agency issued mortgage-backed securities. ABS is total asset backed securities. OtherDebt istotal other debt securities. Equity is total mutual funds and other equity securities. All variables in Eq. (4) are deflated by totalassets.
We present the results from estimating Eq. (4) in Table 4 Panel A.15 Similar to Iselin and Nicoletti (2017), we find that theasset type most commonly associated with Level 3 measurement is MBSNA with a coefficient value (0.772) that is almost twiceas large as the next closest asset group, ABS (0.403). Next, we replace Lev3_Avg with MBSNA_Avg and retest H1. Specifically, werun the following firm-pair regression:

FVCOMPijt ¼ b0 þ b1Lev2 Avgijt þ b2MBSNA Avgijt þ Controlsijt þ eijt ð5Þ
We also include the same set of control variables as our main analysis, including firm-pair differences in MBSNA
(MBSNA_Diff). To confirm H1, we expect that the average proportion of MBSNA (MBSNA_Avg) will be negatively associatedwith fair value comparability.

Results from estimating Eq. (5) are reported in Table 4 Panel B.16 As expected, we find that MBSNA_Avg is negatively asso-ciated with fair value comparability (b2 = �0.920, p-value < 0.01) while the coefficient for Lev2_Avg remains significantly pos-itive (b1 = 0.561, p-value < 0.01). These results provide corroborating evidence that exposure to Level 3 measurement isnegatively associated with fair value comparability, holding the investment decision with respect to specific fair value assetclass constant.17

As an additional test, we refine Eq. (5) by using a two-step approach to differentiate comparability attributable to fairvalue measurement from economic comparability related to firms’ MBSNA asset holdings. Specifically, in untabulated anal-yses we exploit the model predicting firm ownership of MBSNA in Iselin and Nicoletti (2017) to separate the expected, fun-damental driven, portion of MBSNA from the unexpected, and discretionary measurement driven, portion of MBSNA. We findthat the unexpected portion of MBSNAs, which is likely attributable to discretionary measurement, is negatively associatedwith fair value comparability.

Overall, our results indicate that, even after holding the asset class constant and removing specific economic drivers ofMBSNA investments across firms, the negative association between Level 3 fair value measurement and comparabilityremains. This provides further evidence that discretion in fair value measurement is driving the negative associationbetween Level 3 fair value measurement and comparability that we document in our tests of H1.

7.2. Alternative fair value comparability measures

7.2.1. Measuring the co-movement of FVCI over a longer time-horizonTo calculate FVCOMP, we estimate Eq. (1) over non-overlapping 16 quarter periods for each firm-pair. This time-horizon is

the same as that used by prior research to measure earnings comparability (De Franco et al., 2011; Francis et al., 2014). Toincrease the efficiency of the estimator and to alleviate concerns over differences between the two time periods, we adopt alonger time-series of quarterly data to calculate the co-movement of FVCI and allow the coefficient estimates to varybetween the periods. Specifically, we estimate Eq. (6) using all 32 quarters of data for each firm-pair (Q1 2008–Q4 2015),allowing the degree of co-movement of FVCI for each firm-pair to vary under different market conditions:

FVCIit ¼ b0 þ b1FVCIjt þ b2Y2012 2015t þ b3FVCIjt � Y2012 2015t þ eijt ð6Þ

(4) is estimated using firm-quarter (not firm-pair) data. Of the 20,762 firm-quarter observations in our COMPUSTAT sample (please see Section 5 for ation of our sample selection), we are able to match 11,315 firm-quarter observations to Form Y9-C data (Table 4 Panel A). The 32,086 firm-pairtions reported in Table 4 Panel B reflects the firm-pair observations that have Y9-C data necessary to estimate Eq. (5).fficient estimates for control variables are suppressed for brevity.suggest that caution be exercised when interpreting these results because some MBSNA assets may be classified as Level 2 assets. To the extent that faireasurement of Level 2 MBSNA assets is more verifiable than that of Level 3 MBSNA assets, combining all MBSNA assets in Eq. (5) is likely to bias againstthe result.

12

Table 4The association between exposure to representative Level 3 fair value assets and the comparability of the fair valuecomponent of comprehensive income.

Panel A: Identifying representative Level 3 assets

Variables Dep. var. = Lev3

Coeff. t-stat

Treasury �0.100 �1.04Government �0.082 �1.53MBSA �0.140*** �4.79MBSNA 0.772*** 3.62ABS 0.403* 1.87OtherDebt 0.031 0.29Equity 0.197 1.53Year Fixed Effects IncludedNum. of obs. 11,315Adjusted R2 0.088

Panel B: Regression of FVCOMP on representative Level 3 assets

Variables Dep. var. = FVCOMP Dep. var. = FVCOMP


Lev2_Avg 0.561*** 9.28MBSNA_Avg �1.049*** �6.36 �0.920*** �5.41Controls Included IncludedFirm & Year FE Included IncludedNum. of obs. 32,086 32,086Adjusted R2 0.293 0.313

Table 4. Panel A provides the results of estimating Eq. (4). Panel B provides the results of estimating Eq. (5). Thedependent variable is FVCOMP. Standard errors are robust to heteroscedasticity and clustered at the firm level in PanelB. ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. All variables are defined in Appendix B.


where Y2012_2015 is a dummy variable equal to 1 for the 16 quarters from Q1 2012 to Q4 2015, and 0 for the 16 quartersfrom Q1 2008 to Q4 2011. If b3 is significant in Eq. (6), then the degree to which FVCI for two firms covaries over time changesin the latter part of the sample period.18 We use the adjusted R2 from Eq. (6) estimated for each firm-pair as an alternativeproxy for FVCOMP.

To test H1, we rerun Eq. (2) using this alternative FVCOMP measure as the dependent variable. Accordingly, we re-calculate all of the explanatory variables in Eq. (2) by calculating their respective mean values (‘‘_avg” variables) and differ-ences in the mean values (‘‘_diff” variables) across the 32 quarters from Q1 2008 to Q4 2015. The sample size for this test isreduced to 17,766 firm-pairs due to requiring non-missing data over a much longer time-horizon. In untabulated results, wecontinue to find a negative (positive) relation between exposure to Level 3 (Level 2) measurement and fair valuecomparability.

7.2.2. Measuring comparability of fair value accounting systemsOur main measure of fair value comparability (FVCOMP) is the co-movement of FVCI for firm-pairs. In this subsection, we

utilize a second notion of comparability based on De Franco et al. (2011) to assess the sensitivity of the results. This approachto comparability compares the estimated accounting output for the same economic events under different accountingsystems.

De Franco et al (2011) estimate the accounting function for each firm in a firm pair by running the following firm-specificregression and saving the coefficient estimates:

18 Toestimat0.408 a16 quarduringfootnot

Earningsit ¼ ai þ biReturnit þ eit ð7aÞ
where Earnings is quarterly income before extraordinary items scaled by beginning of period market value of equity andReturn is the stock return during the quarter. We adjust Eq. (7a) in two different ways to model the mapping of economicevents to FVCI. First, similar to De Franco et al. (2011), we use stock returns as a proxy for the net effect of economic eventsduring a period. We estimate Eq. (7b), which captures the mapping of fair value information in stock returns to FVCI, for eachfirm over two 16-quarter periods (2008–2011 and 2012–2015) and save the coefficient estimates:
assess potential differences in covariation of FVCI between the two periods we average the coefficient estimates for each firm-pair included in theion of Eq. (6) and use a t-test to determine whether they are significantly different from zero. We find that the average coefficient estimate for FVCI isnd statistically significant (p-value < 0.01), suggesting that the fair value component of comprehensive income exhibits positive covariation in the firstter period. The coefficient estimate for FVCI*Y2012_2015 is 0.188 and statistically significant (p-value < 0.01), suggesting that covariation is higherthe second 16 quarter period. This is consistent with our finding that our main measure of FVCOMP is smaller during the financial crisis years (seee 12).

13

19 Poocoeffici20 Poopositive21 Quaunemplrate.htm22 Simvalue acompar


FVCIit ¼ ai þ biReturnit þ ciNonFVCIit þ eit ð7bÞ
Since stock returns represent the net effect of all economic events for a firm over a given period, but FVCI represents only
a portion of a firms’ total income (i.e., it excludes non-fair value gains/losses), we include non-fair value comprehensiveincome (NonFVCI) as a control in Eq. (7b) to remove any variation in bi that is related to economic events that affect non-fair value assets.19

Second, we model FVCI as a function of macroeconomic factors because many fair value assets are debt instruments asopposed to equity securities. Debt instruments are likely to be more sensitive to macroeconomic growth factors relativeto economic factors that influence firm-specific stock returns. Under this approach, we map FVCI to the changes in federalfunds rate, unemployment rate, and U.S. GDP. The extent to which these macroeconomic factors systematically map into fairvalue gains and losses should give us an idea about the firm’s fair value accounting system. Specifically, we estimate model(7c) for each firm over two 16-quarter periods (2008–2011 and 2012–2015) and save the coefficient estimates:

FVCIit ¼ ai þ biDFFit þ ciDUnempit þ diDGDPit þ eit ð7cÞ
where FF is the quarterly federal funds rate, Unemp is quarterly unemployment rate and GDP is quarterly U.S. GDP.20,21
To estimate the comparability of the fair value accounting functions between two firms in a pair, we rely on the assump-tion from De Franco et al. (2011): ‘‘if two firms have experienced the same set of economic events, the more comparable theaccounting between the firms, the more similar their financial statements.” We use firm i’s and firm j’s estimated fair valueaccounting functions to predict their FVCI, assuming they had the same economic events (Return in Eq. (7b) andDFF,DUnempand DGDP in Eq. (7c)). Specifically, for each firm-pair, we use the two estimated fair value accounting functions for each firm(firm i and firm j) with the economic events of a single firm (firm i).

E FVCIð Þiit ¼ ai þ biEconomic Eventsit ð8aÞ

E FVCIð Þijt ¼ aj þ bjEconomic Eventsit ð8bÞ

E(FVCI)iit is the predicted fair value component of comprehensive income for firm i given firm i’s function and firm i’s eco-nomic events in period t. E(FVCI)ijt is the predicted fair value component of comprehensive income for firm j given firm j’sfunction and firm i’s economic events in period t. This methodology allows us to explicitly hold the economic events con-stant. We define the comparability of fair value accounting functions between firm i and firm j, CompAcctijt, as the negativevalue of the average absolute difference between the predicted FVCIs using firm i’s and j’s functions (i.e., CompAcctijt = �|E(FVCI)iit – E(FVCI)ijt|). Therefore, greater values of CompAcctijt indicate greater comparability of fair value accounting functions.When we calculate CompAcctijt using firm-specific returns as proxies for economic events, we refer to it as CompAcct_RETijtwhereas when we calculate CompAcctijt using changes in macroeconomic conditions as proxies for economic events thatinfluence fair value measurement, we refer to it as CompAcct_Macroijt.

We report the results of retesting H1 based on CompAcct_RET (column 1) and CompAcct_Macro (column 2) in Table 5.22

Consistent with H1, we find that greater exposure to Level 3 measurement is associated with a decrease in fair value accountingcomparability (b2 = �0.076, p-value < 0.01 in the CompAcct_RET regression; b2 = �0.036, p-value < 0.01 in the CompAcct_Macroregression). In contrast, we continue to find a significantly positive association between exposure to Level 2 measurement andfair value accounting comparability (b1 = 0.039, p-value < 0.01 in the CompAcct_RET regression; b1 = 0.005, p-value < 0.01 in theCompAcct_Macro regression). Regardless of how we measure the fair value accounting system, we also find a significantly neg-ative relation between fair value comparability and differences in total fair value exposures (FVA_Diff).

7.3. Examining comparability of fair values around SFAS 157

The implementation of SFAS 157 likely reduced information asymmetry surrounding fair value estimation and improvedinvestors’ ability to monitor the use of discretion over fair value estimates. As a further attempt to isolate whether manage-rial discretion affects the comparability of fair value estimates, we compare the relation between fair value comparabilityand firm exposure to fair value estimates before and after the adoption of SFAS 157. This test bolsters our confidence indrawing causal inferences, but we caution that it is subject to at least two caveats. First, SFAS 157 was implemented aroundthe financial crisis, which could contaminate our results. Specifically, the financial crisis had a large and widespread effect onthe liquidity and fair value of investment securities, which may mechanically impact our comparability measure. Second,

led regression results of Eq. (7b) in our sample suggest that returns indeed capture economic events that map into FVCI, with a positive and significantent estimate for bi. The coefficient estimate for ci is not reliably different from zero.led regression results of Eq. (7c) in our sample suggest that all three macroeconomic indicators capture economic events that map into FVCI, withand significant coefficient estimates for bi and ci and a marginally positive and significant coefficient estimate for di.rterly federal funds rate data and GDP data are obtained via the Federal Reserve Bank of St. Louis website (https://fred.stlouisfed.org). Quarterlyoyment data is obtained via the U.S. Bureau of Labor Statistics website (https://www.bls.gov/charts/employment-situation/civilian-unemployment-).ilar to De Franco et al. (2011), we omit controls for economic comparability in Table 5, because both CompAcct variables measure comparability of fairccounting functions, holding economic events constant. In other words, it is unlikely that CompAcct measures are contaminated by economicability between the two firms in a pair.

14

https://fred.stlouisfed.org

https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm

https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm

Table 5The association between exposure to Level 3 measurement and fair value accounting function comparability.

Variables Dep. var = CompAcct_RET Dep. var. = CompAcct_Macro


Lev2_Avg 0.039*** 3.08 0.005*** 2.96Lev3_Avg �0.076*** �3.32 �0.036*** �8.11Lev2_Diff 0.013** 2.21 0.001 1.69Lev3_Diff 0.013 1.31 0.004 1.65FVA_Diff �0.027*** �5.64 �0.002*** �3.21FVA_Avg �0.008 �1.39 �0.019*** �15.34Intercept �0.046*** �3.68 0.002*** 5.75

Firm and Year FE Included IncludedNum. of obs. 53,549 53,549Adjusted R2 0.013 0.071

Table 5 provides the results of tests using CompAcct_RET and CompAcct_Macro as alternative measures of fair value comparability. Standard errors are robustto heteroscedasticity and clustered at the firm level. ***, **, * represent significance at the 1%, 5%, and 10% levels, respectively. All variables are defined inAppendix B.

Table 6The association between exposure to fair value estimates and thecomparability of the fair value component of comprehensiveincome: Pre/Post SFAS 157.

Variables Dep. var. = FVCOMP

Coeff. t-stat

Lev2_Avg �0.153** �2.54Lev3_Avg �0.808*** �4.93SFAS157*Lev2_Avg 0.238*** 2.73SFAS157*Lev3_Avg �0.277 �1.16

Controls (Interacted w/ SFAS157) IncludedFirm & Year FE IncludedNum. of obs. 76,861Adjusted R2 0.389

Table 6 provides the results of estimating Eq. (8). The dependentvariable is FVCOMP. Standard errors are robust toheteroscedasticity and clustered at the firm level. ***, **, * rep-resent significance at the 1%, 5%, and 10% levels, respectively. Allvariables are defined in Appendix B.


there was no fair value hierarchy data before SFAS 157. Thus, we have to estimate Level 2 and Level 3 asset holdings in thepre-SFAS 157 period.

Before SFAS 157, there were different definitions of fair value and limited guidance for applying those definitions in GAAP.SFAS 157 formally defined fair value and established a framework for fair value estimation, thus reducing the amount ofmanagerial discretion over fair value estimates. More importantly, SFAS 157 required expanded disclosures about fair valuemeasurements, allowing more intense scrutiny by investors and further reducing the ability for managers to exert discretion.Thus, we predict that the adoption of SFAS 157 reduced managerial opportunism in fair value estimates and increased fairvalue comparability.

To test the effect of SFAS 157 on fair value comparability, we calculate our comparability measure (FVCOMP) for the eightquarters pre (fiscal years 2005 and 2006) and post (fiscal years 2008 and 2009) SFAS 157.23 In contrast to our primary anal-yses, FVCOMP in our SFAS 157 analyses is based only on fair value gains and losses contained in OCI because data on fair valuegains and losses included in income is unavailable in the pre-SFAS 157 period. To address the lack of fair value hierarchy databefore SFAS 157, we follow Riedl and Serafeim (2011) and assume that the Level 2 and 3 fair value assets in the pre-period arethe same as they are in the first year following SFAS 157 adoption (i.e., fiscal 2008).24 We then estimate the following model:

23 Westateme24 Duestatic is

FVCOMPijt ¼ b0 þ b1Lev2 Avgijt þ b2Lev3 Avgijt þ b3Lev2 Avg � SFAS157ijt þ b4Lev3 Avg � SFAS157ijt

þ Controlsijt þ Firm & Year FEþ eijt ð9Þ

exclude 2007 from our analysis because some early adopters began reporting information required by SFAS 157 in their 2007 fiscal year end financialnts (Chung et al. 2017; Iselin and Nicoletti, 2017).to this assumption, our SFAS 157 analyses are based on 8-quarter windows instead of 16-quarter windows, as the assumption that asset holdings areless likely as the windows are expanded.

15


where SFAS157 is an indicator variable equal to one for fiscal years 2008 and 2009 and zero otherwise. The main effect ofSFAS157 is subsumed by year fixed effects and thus is not included in Eq. (9). The other variables are the same as those inour main model. Results from estimating Eq. (9) are presented in Table 6. We find that b1 and b2 are significantly negative,indicating that before SFAS 157, exposures to Level 2 and Level 3 fair value assets are associated with lower fair value com-parability. Importantly, b2 is significantly more negative than b1 (p-value < 0.01) consistent with fair value estimates subjectto more managerial discretion have a larger impact on fair value comparability. Turning to the interaction coefficients ofinterest, we find a significantly positive coefficient estimate for Lev2_Avg*SFAS157ijt, consistent with our prediction that SFAS157 reduced managerial opportunism in fair value estimation. In fact, the sum of b1 and b3 becomes positive (p-value = 0.026), reinforcing the notion that managerial discretion contained in Level 2 estimates enhances fair value compa-rability following SFAS 157. However, we fail to detect a significant coefficient estimate for Lev3_Avg*SFAS157ijt, indicatingthat SFAS 157 had a limited effect in reducing managerial discretion of Level 3 estimates. The sum of b2 and b4 remains sig-nificantly negative (p-value < 0.01).

8. Conclusion

In this paper, we examine whether managerial discretion over fair value measurement affects comparability of fair valueestimates across firms in the financial industry. Our results reveal a nuanced relation between managerial discretion in fairvalue measurement and comparability. Specifically, discretion available in Level 2 measurement enhances comparabilitywhereas discretion available in Level 3 measurement impairs comparability. To better understand whether these tests trulycapture managerial discretion, we consider several cross-sectional tests based on managers’ incentives and investor moni-toring. We find that Level 2 and 3 fair value estimates are less comparable when managers have incentives to manipulatetheir accounting, suggesting that managerial opportunism impairs fair value measurement. Furthermore, monitors appearto rein in opportunistic measurement and can mitigate the measurement effect on fair value comparability.

A key takeaway of our results is that the comparability benefits of fair value accounting are contingent on whether man-agerial discretion in fair value measurement improves the usefulness of information or adds noise and bias to the reportedamount. Our findings may be of interest to standard setters and auditors when they decide how much discretion they arewilling to allow management when estimating fair values. They may also be important to investors and other stakeholdersattempting to compare financial statements across firms with significant exposures to fair value estimates.

There are at least two limitations of our study. First, while we diligently control for economic (dis)similarities betweenfirm-pairs in our regression models, we are unable to perfectly match on the portfolio of financial assets held by each firmat Level 1, 2, and 3. Doing so would require more granular data regarding each firm’s specific investment holdings at eachlevel. Thus, we cannot completely rule out the possibility that our results are attributable to the fact that there are simplymore (economically) homogenous Level 2 assets than Level 3 assets. Second, it is possible that comparability causes differ-ences in firm-level investment decisions, rather than fair value asset holdings shaping comparability. In different investmentsettings, Chen et al. (2018) find a positive effect of accounting comparability on the efficiency of acquisition decisions andChircop, Collins, Hass, Hguyen (2020) document similar results for innovation investments.

Finally, we make suggestions for future research. While we focus on the comparability of comprehensive income attribu-table to fair value estimates, fair value estimates affect not only comprehensive income, but also the balance sheet. Theimplications of fair value accounting for comparability of balance sheet-based performance metrics is beyond the scopeof our study but is an interesting area for future research. Furthermore, future research could help us understand the specificfinancial instruments, economic events, and accounting treatments that are responsible for the opposing comparabilityeffects of Level 2 and Level 3 fair value estimates identified in this paper. A more thorough understanding of the mechanisms(economic or accounting) behind the effect of different fair value assets on comparability will help investors price theseassets more efficiently and help regulators develop more targeted fair value disclosure rules.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could haveappeared to influence the work reported in this paper.

Acknowledgements

We thank Marco Trombetta (editor-in-chief), two anonymous reviewers, Ahmad Hammami, Zach Kaplan, and workshopparticipants at Purdue University, University of Iowa, Southern Methodist University, National Taipei University, Tamkang

16


University, the 2018 AAAMidwest Meeting, and the 2018 Hawaii Accounting Research Conference for helpful comments andsuggestions.

Appendix A. Comparison of significant unobservable input disclosures for residential mortgage-backed securities andloans

Valuation technique
Significant unobservableinputs
17

Ranges ofinputs

Weightedaverage

Bank ofAmerica

Discounted cash flows
Yield 0% to 25% 6% Prepayment speed 0% to 35% 14% Default rate 2% to 15% 7% Loss severity 26% to 100% 34%
Market comparables
Not disclosed Not disclosed Not disclosed Chase Discounted cash flows Yield 1% to 25% 5%
Prepayment speed
0% to 18% 6% Conditional default rate 0% to 100% 22% Loss severity 0% to 90% 27%
PNC
Priced by a third-party vendorusing adiscounted cash flow pricingmodel
Spread over the benchmarkcurve

249bps

Constant prepayment rate
1% to 28.9% 6.80% Constant default rate 0% to 16.7% 5.60% Loss severity 6.1% to 100% 53.10%
Wells Fargo
Discounted cash flows Discount rate 1.1% to 7.7% 5.20% Prepayment rate 2% to 15.5% 8.10% Default rate 0.4% to 15% 2.60% Loss severity 0.1% to 26.4% 18.30%
Market comparable pricing
Comparability Adjustment �93% to 10% �30%
Amounts in Appendix A are taken from the December 31, 2014 fiscal year end 10-K.

Appendix B. Variable descriptions

Fair Value Comparability Measures
FVCOMP = Adjusted r-squared from a regression of the fair value component of comprehensive income (FVCI)
for each firm-pair firm i and firm j [Eq. (1)].
CompAcct_RET = Abs. value of E(FVCI)iit – E(FVCI)ijt (Eqs. (8a) and (8b), respectively) multiplied by negative one.
CompAcct_RET utilizes firm-specific returns as the proxy for economic events that influence fair valuemeasurement.

CompAcct_Macro
= Abs. value of E(FVCI)iit – E(FVCI)ijt (Eqs. (8a) and (8b), respectively) multiplied by negative one.CompAcct_Macro utilizes changes in macroeconomic conditions as proxies for economic events thatinfluence fair value measurement.
Fair Value Variables
Lev1 = Proportion of level 1 assets to total fair value assets (COMPUSTAT aqpl1q/(aqpl1q + aol2q + aul3q)). Lev2 = Proportion of level 2 assets to total fair value assets (COMPUSTAT aol2q/(aqpl1q + aol2q + aul3q)). Lev3 = Proportion of level 3 assets to total fair value assets (COMPUSTAT aul3q/(aqpl1q + aol2q + aul3q)). FVA = Proportion of fair value assets to total assets (COMPUSTAT (aqpl1q + aol2q + aul3q)/atq). FVCI = Comprehensive income attributable to fair value measurements, deflated by average total assets
(COMPUSTAT (hedgeglq + tfvceq + ciderglq + cisecglq)/avg. atq)

Cross-sectional Variables
DedicatedII = 1 if either firm in the firm-pair has over 5% dedicated institutional ownership, otherwise 0. LowBidAsk = 1 if either firm in the firm-pair has below median bid-ask spread, otherwise 0.
(continued on next page)


HighVega
= 1 if CEOs for either firm in the firm-pair has above median vega, otherwise 0. HighOption = 1 if CEOs for either firm in the firm-pair has above median option ownership, otherwise 0. LowCapital = 1 if either firm in the firm-pair has below median capital ratio, otherwise 0.
Firm Characteristic Control Variables
Size = Natural log of total assets (COMPUSTAT atq). Capr1 = Tier 1 capital ratio (COMPUSTAT capr1q). Lev = Leverage ratio (COMPUSTAT (dlcq + dlttq)/ceqq). MB = Market-to-book (COMPUSTAT (prccq*csho)/ceqq). LossProb = Probability that firm records a loss over the last 16-quarters (COMPUSTAT ibq < 0). Provision = Loan loss provision deflated by total assets (COMPUSTAT pllq/atq). Allowance = Loan loss allowance deflated by total assets (COMPUSTAT rllq/atq). NonFVCOMP = Adjusted r-squared from regression of non-FV component of comprehensive income for each firm-
pair firm i and firm j (COMPUSTAT ciq – (hedgeglq + tfvceq + ciderglq + cisecglq)/avg. atq).
RETCOMP = Adjusted r-squared from regression of contemporaneous monthly stock returns for each firm-pair
firm i and firm j.

Additional Variables
Treasury = Firm-year proportion of AFS treasury securities to total assets reported on form FRY9-c.** Government = Firm-year proportion of available-for-sale government securities to total assets reported on form
FRY9-c.**
MBSA = Firm-year proportion of available-for-sale agency mortgage-backed securities to total assets
reported on form FRY9-c.**
MBSNA = Firm-year proportion of available-for-sale non-agency mortgage-backed securities to total assets
reported on form FRY9-c.**
ABS = Firm-year proportion of available-for-sale asset-backed securities to total assets reported on form
FRY9-c.**
OtherDebt = Firm-year proportion of available-for-sale other debt to total assets reported on form FRY9-c.** Equity = Firm-year proportion of available-for-sale equity to total assets reported on form FRY9-c.** Y2012_2015 = 1 for the 16 quarters from Q1 2012 to Q4 2015, and 0 for the 16 quarters from Q1 2008 to Q4 2011. SFAS157 = 1 if the estimation period is fiscal years 2008 or 2009; 0 if the estimation period is fiscal years 2005
or 2006.

The suffix ‘‘_Avg” (‘‘_Diff”) appended to the end of a variable name indicates that this variable represents the firm-pair average (difference). In order tocalculate firm-pair averages (differences), all variables are first averaged within-firm over each 16-quarter estimation period used to create FVCOMP andthen averaged (differenced) between-firms in each pair. **See Iselin and Nicoletti (2017) for details about which reported numbers are included in thesecategories.

References

Altamuro, J., Zhang, H., 2013. The financial reporting of fair value based on managerial inputs versus market inputs: evidence from mortgage servicingrights. Rev. Acc. Stud. 18 (3), 833–858.

Armstrong, C.S., Larcker, D.F., Ormazabal, G., Taylor, D.J., 2013. The relation between equity incentives and misreporting: the role of risk-taking incentives. J.Financ. Econ. 109 (2), 327–350.

Arora, N., Richardson, S., Tuna, _I., 2014. Asset reliability and security prices: evidence from credit markets. Rev. Acc. Stud. 19 (1), 363–395.Badia, M., Duro, M., Penalva, F., Ryan, S., 2017. Conditional conservative fair value measurements. J. Account. Econ. 63 (1), 75–98.Ball, R., Robin, A., Wu, J.S., 2003. Incentives versus standards: properties of accounting income in four East Asian countries. J. Account. Econ. 36 (1-3), 235–

270.Ball, Ray, Li, X.I., Shivakumar, Lakshmanan, 2015. Contractibility and transparency of financial statement information prepared under IFRS: Evidence from

debt contracts around IFRS adoption. J. Account. Res. 53 (5), 915–963.Barlev, B., Haddad, J.R., 2007. Harmonization, comparability, and fair value accounting. J. Accounting, Auditing & Finance 22 (3), 493–509.Barth, M.E., Landsman, W.R., Lang, M., Williams, C., 2012. Are IFRS-based and US GAAP-based accounting amounts comparable? J. Account. Econ. 54 (1), 68–

93.Barth, M.E., 2013. Global comparability in financial reporting: What, why, how, and when? China J. Account. Stud. 1 (1), 2–12.Barth, M.E., 2014. Measurement in financial reporting: the need for concepts. Account. Horizons 28 (2), 331–352.Berger, A.N., Bouwman, C.H.S., 2013. How does capital affect bank performance during financial crises? J. Financ. Econ. 109 (1), 146–176.Bergstresser, D., Philippon, T., 2006. CEO incentives and earnings management. J. Financ. Econ. 80 (3), 511–529.Black, J., Chen, Z., Cussatt, M., 2018. The association between SFAS No. 157 fair value hierarchy information and conditional accounting conservatism.

Account. Rev. 93 (5), 119–144.Bratten, B., Causholli, M., Khan, U., 2016. Usefulness of fair values for predicting banks’ future earnings: evidence from other comprehensive income and its

components. Rev. Acc. Stud. 21 (1), 280–315.Brochet, F., Jagolinzer, A.D., Riedl, E.J., 2013. Mandatory IFRS adoption and financial statement comparability. Contemp. Account. Res. 30 (4), 1373–1400.Bushee, B., 1998. The influence of institutional investors on myopic R&D investment behavior. Account. Rev. 73 (3), 305–333.Bushee, B., 2001. Do institutional investors prefer near-term earnings over long-run value? Contemp. Account. Res. 18 (2), 207–246.Cascino, S., Gassen, J., 2015. What drives the comparability effect of mandatory IFRS adoption? Rev. Acc. Stud. 20 (1), 242–282.Chen, C.-W., Collins, D.W., Kravet, T.D., Mergenthaler, R.D., 2018. Financial statement comparability and the efficiency of acquisition decisions. Contemp.

Account. Res. 35 (1), 164–202.

18

http://refhub.elsevier.com/S0278-4254(21)00061-2/h0005





























Chen, J.Z., Chen, M.H., Chin, C.L., Lobo, G.J., 2020. Do firms that have a common signing auditor exhibit higher earnings comparability? Account. Rev. 95 (3),115–143.

Cheng, Q., Farber, D.B., 2008. Earnings restatements, changes in CEO compensation, and firm performance. Account. Rev. 83 (5), 1217–1250.Cheng, Q., Warfield, T., Ye, M., 2011. Equity incentives and earnings management: evidence from the banking industry. J. Accounting, Auditing & Finance 26,

317–349.Chircop, J., Collins, D.W., Hass, L.H., Nguyen, N.Q., 2020. Accounting Comparability and Corporate Innovative Efficiency. The Accounting Review 95 (4), 127–

151.Christensen, B.E., Glover, S.M., Wood, D.A., 2012. Extreme estimation uncertainty in fair value estimates: implications for audit assurance. Auditing: J.

Practice Theory 31 (1), 127–146.Chung, R., Firth, M., Kim, J.-B., 2002. Institutional monitoring and opportunistic earnings management. J. Corporate Finance 8 (1), 29–48.Chung, S.G., Goh, B.W., Ng, J., Yong, K.O., 2017. Voluntary fair value disclosures beyond SFAS 157’s three-level estimates. Rev. Acc. Stud. 22 (1), 430–468.Coles, J., Daniel, N., Naveen, L., 2006. Managerial Incentives and Risk-Taking. J. Financ. Econ. 79 (2), 431–468.Core, J., Guay, W., 2002. Estimating the value of employee stock option portfolios and their sensitivities to price and volatility. J. Account. Res. 40 (3), 613–

630.DeFond, M., Hu, X., Hung, M., Li, S., 2011. The impact of mandatory IFRS adoption on foreign mutual fund ownership: the role of comparability. J. Account.

Econ. 51 (3), 240–258.De Franco, G., Kothari, S.P., Verdi, R.S., 2011. The benefits of financial statement comparability. J. Account. Res. 49 (4), 895–931.Enria, A., 2004. Fair value accounting and financial stability. ECB Occasional Paper, No. 13, European Central Bank (ECB), Frankfurt a. M.Financial Accounting Standards Board (FASB), 2010. Conceptual Framework for Financial Reporting: Chapter 1: The Objective of General Purpose Financial

Reporting; and Chapter 3: Qualitative Characteristics of Useful Financial Information.” Concepts Statement No. 8. Norwalk, CT: FASB.Francis, J.R., Pinnuck, M.L., Watanabe, O., 2014. Auditor style and financial statement comparability. Account. Rev. 89 (2), 605–633.Glover, S.M., Taylor, M.H., Wu, Y.J., 2017. Current practices and challenges in auditing fair value measurements and complex estimates: implications for

auditing standards and the academy. Auditing: J. Practice Theory 36 (1), 63–84.Goh, B.W., Li, D., Ng, J., Ow Yong, K., 2015. Market pricing of banks’ fair value assets reported under SFAS 157 since the 2008 financial crisis. J. Account. Public

Policy 34 (2), 129–145.Hanley, K.W., Jagolinzer, A.D., Nikolova, S., 2018. Strategic estimation of asset fair values. J. Account. Econ. 66 (1), 25–45.Healy, P.M., Palepu, K.G., 1993. The effect of firms’ financial disclosure strategies on stock prices. Accounting Horizons 7, 1–11.Healy, P.M., Palepu, K.G., 2001. Information asymmetry, corporate disclosure, and the capital markets: a review of the empirical disclosure literature. J.

Account. Econ. 31 (1–3), 405–440.Huizinga, H., Laeven, L., 2012. Bank valuation and accounting discretion during a financial crisis. J. Financ. Econ. 106 (3), 614–634.Iselin, M., Nicoletti, A., 2017. The effects of SFAS 157 disclosures on investment decisions. J. Account. Econ. 63 (2-3), 404–427.Jones, D.A., Smith, K.J., 2011. Comparing the value relevance, predictive value, and persistence of other comprehensive income and special items. Account.

Rev. 86 (6), 2047–2073.Kim, S., Kraft, P., Ryan, S.G., 2013. Financial statement comparability and credit risk. Rev. Acc. Stud. 18 (3), 783–823.Kim, J.-B., Li, L., Lu, L.Y., Yu, Y., 2016. Financial statement comparability and expected crash risk. J. Account. Econ. 61 (2-3), 294–312.Lang, M., Maffett, M., Owens, E., 2010. Earnings comovement and accounting comparability: The effects of mandatory IFRS adoption. Simon School Working

Paper No. FR 11-03.Leisenring, J., Linsmeier, T., Schipper, K., Trott, E., 2012. Business-model (intent)-based accounting. Account. Bus. Res. 42 (3), 329–344.Magnan, M., Menini, A., Parbonetti, A., 2015. Fair value accounting: Information or confusion for financial markets? Rev. Acc. Stud. 20 (1), 559–591.Mehran, H., Thakor, A., 2011. Bank capital and value in the cross-section. Rev. Financial Stud. 24 (4), 1019–1067.McAnally, M.L., Srivastava, A., Weaver, C., 2008. Executive stock options, missed earnings targets, and earnings management. Account. Rev. 83 (1), 185–216.Ramanna, K., 2008. The implications of unverifiable fair-value accounting: evidence from the political economy of goodwill accounting. J. Account. Econ. 45

(2-3), 253–281.Ramanna, K., Watts, R.L., 2012. Evidence on the use of unverifiable estimates in required goodwill impairment. Rev. Acc. Stud. 17 (4), 749–780.Riedl, E.J., Serafeim, G., 2011. Information risk and fair values: An examination of equity betas. J. Account. Res. 49 (4), 1083–1122.Shu, S.Q., Thomas, W.B., 2019. Managerial equity holdings and income smoothing incentives. J. Manage. Account. Res. 31 (1), 195–218.Song, C.J., Thomas, W.B., Yi, H., 2010. Value relevance of FAS No. 157 fair value hierarchy information and the impact of corporate governance mechanisms.

Account. Rev. 85 (4), 1375–1410.Watts, R., Zimmerman, J., 1986. Positive Accounting Theory. Prentice-Hall Inc., Englewood Cliffs, NJ.Yip, R.W.Y., Young, D., 2012. Does mandatory IFRS adoption improve information comparability? Account. Rev. 87 (5), 1767–1789.

19














































j. account. public policy - student.zy.cdut.edu.cn

Documents