Differences in the Reliability of Fair Value Hierarchy Measurements: A
Cross-Country Study

Abstract
Prior research suggests that there are significant differences in how investors perceive thereliability of fair values across the fair value hierarchy. An unaddressed question in this stream ofresearch is whether cross-country differences in institutional factors are able to mediate
differences in reliability for the fair value hierarchy measurements. Based on an international
sample of banks across 20 different countries, we find that the probability of crash risk is lower
among countries with better financial development infrastructure, greater level of trust, tighter
security regulations and higher level of disclosure requirements. These results apply to Level 1
assets but not to Level 2 and Level 3 assets. We also document that these cross-country factors
improve the trading volume of our sample banks. Our study suggests that while ongoing
concerns toward the more opaque fair values are not fully eliminated by institutional differences,
they matter in influencing investor willingness to trade in these stocks.
Keywords: Fair value accounting, institutional factors, reliability
JEL Classifications: D82, G34, M41

1.

Introduction
This study examines whether institutional country differences are able to influence

investors’ perceptions towards the reliability of fair value hierarchy measurements. Prior
research documents that investors perceive reliability concerns toward the more subjective fairvalue measurements (i.e., Level 2 and 3 fair value measurements) relative to fair value
measurements that are derived directly from market prices (i.e., Level 1 fair value
measurements).
An unaddressed question in this stream of research is whether cross-country institutional
factors are able to mediate differences in reliability across the fair value hierarchy measurements.
Prior research suggests that institutional factors are important toward the enforcement offinancial reporting (e.g., Christensen et al. 2013). In our study, we examine whether differences
across countries in terms of its sophistication of financial development, level of trust in a country,
the extent of security regulations, and the amount of disclosure requirements will affectdifferences in the reliability of reported fair values.
This issue is important because much of the controversy with regard to fair value
accounting stems from concerns regarding the reliability of inputs that are used in measuring fairvalues (Ryan 2008). While Level 1 fair value measurements are less controversial, the use of
Level 2 and Level 3 fair value inputs (otherwise known as the mark-to-model approach) has
generated tremendous concern and interest among standard setters, regulators and academics
because the measurement of fair values based on these inputs are perceived to involve a certain
degree of subjectivity and uncertainty (e.g., Song et al. 2010; Riedl and Serafeim 2011; Magnan
et al. 2014; Chung et al. 2016).

1

Prior research on fair value hierarchy measurements tend to focus on US firms due toavailability of data of reported fair values. We believe we are the first study to examine fair value
hierarchy measurements in an international setting. We obtain our data from the SNL Global
coverage database that provides international coverage of banks’ fair value data both within andoutside US. Based on this database, we are able to obtain cross-country data on fair value
measurements across 20 different countries located in Americas, Europe, and the Asia-Pacificregion.
We use four different country-level measures to test differences regarding the reliabilityof fair value measurements in an international context. Our country variables are based on
various World Bank surveys that provide significant cross-sectional variations to our sample
countries. Our first country level variable measures the extent of financial development in acountry based on the level of sophistication of the country’s stock market and the level offinancial intermediaries in extending credit to the economy. This measure is also viewed as acountry-level financial constraint measure. This is because richer countries which might havehigher quality institutions and laws, in general, tend to be associated with better financial
development regardless of the content of these laws and security enforcement (La Porta 1999).
Our second cross-country level variable measures the overall level of trust in the country.
We believe trust is an important concept in accounting because the value of accounting lies in
how much investors and other stakeholders trust the financial statements. This is especially so in
situations where there appears to be significant concerns toward reported accounting informationthat involves professional estimates and subjective judgments, such as fair value measurements(e.g., Bell and Griffin 2012; Christensen et al. 2012). Fair value measurements, especially thosemeasurements that involve Level 3 inputs, require considerable amount of subjectivity and2measurement uncertainty. Hence, cross-country differences in the overall level of trust might
impact the extent to which market participants rely on fair value measurements in their investing
decisions (e.g., Nanda and Wysocki 2013).
Our third country variable measures the extent of security regulations enforced within the
country. This measure is a proxy for the degree of investor protection through security laws andother legislation. We use a composite measure of the mean of the disclosure index, the liability
standard index, and the public enforcement index to capture this construct (La Porta et al. 2006).
Finally, our fourth country variable measures the amount of disclosure requirements within acountry. Following Hail and Leuz (2006), our disclosure requirements index variable is definedas the arithmetic mean of these various measures - Prospectus, Compensation, Shareholders,
Inside Ownership, Irregular Contracts and Transactions.
We examine whether these four institutional factors will impact the probability of crashrisk, return synchronicity and trading volume in relation to reported fair values in the banks’balance sheets. Crash risk relates to the probability of stock market crash in a firm’s stock priceas a result of a revelation of negative news that lead investors to significantly downgrade theirassessments of a firm’s prospects (e.g., Jin and Myers 2006). Prior studies on crash risk suggestthat this phenomenon is exacerbated when a firm has a greater level of asset opacity in its
financial reporting. To the extent that Level 2 and 3 fair value measurements are less transparent
than Level 1 fair value measurements (market-based prices), information dissemination relating
to changes in these reported amounts might exacerbate stock price crash risk.
Return synchronicity relates to the extent a firm’s stock return varies with market return.
Extant research on stock market synchronicity suggests that the explanatory power of firm’s
stock return with the market return is lower when there is greater transparency and more3complete revelation of firm-specific information (e.g., Morck, Yeung and Yu 2000). With regard
to fair value accounting, fair values are purported to be more transparent and relevant to decision
makers. However, differences across the subjectivity of fair value inputs might impact the extent
to which fair value reporting provides useful information to financial statement readers. To theextent that fair values are reliable and relevant, we expect return synchronicity to be lower. In
contrast, opaque fair values might increase market return synchronicity in a firm’s stock returns.
Our third outcome variable is trading volume, which measures the extent to which investor iswilling to trade in the shares of firms with reported fair values. If investors trust the reporting
reliability of fair values, we expect trading volume to be high. Conversely, if reported fair values
represent significant concerns to market participants, we expect trading volume in the shares offirms with high reported fair values in their balance sheets to be muted.
Our first set of tests examines whether institutional factors account for country variationin stock price crash risk and return synchronicity in reported fair values. We find consistentempirical evidence to suggest that institutional factors are able to mediate the relation between
Level 1 fair value measurements and stock price crash risk. Specifically, the extent of a country’s
financial development, overall level of trust, the extent of security regulations, and the amount of
disclosure requirements are negatively associated with stock price crash risk for reported Level 1
fair values. However, these institutional factors appear to have little effect in mediating therelation towards alleviating stock price crash risk for Level 2 and Level 3 fair value
measurements. Likewise, we find corroborating evidence for Level 1 fair value measurements in
our return synchronicity tests but these institutional factors do not affect how reported Level 2
and 3 fair value measurements affect crash risk and return synchronicity.

4Our second set of tests examines the impact on institutional factors on trading volume.
Specifically, we examine whether institutional factors are able to mediate the relation between
reported fair values and trading volume. In these results, we find strong evidence that
institutional factors have a positive effect on reported fair values across all three fair value
designations. The incremental impact on trading volume is especially striking for the Level 2 and3 fair value measurements. We interpret these results to suggest that institutional factors matter
greatly to market participants in considering whether to trade the shares of banks that reportsignificant amounts of Level 2 and 3 assets in their balance sheets. It appears that investors are
relatively disinclined to invest in the companies of countries that have poor financial
development and security regulation, low overall societal trust and lax disclosure requirements.
We conjecture that the relative lack of investor interest in these firms is because there might bequestionable doubts regarding the reliability of the fair value measurements on these firms’balance sheets.
We contribute to the research on fair value accounting by examining the impact of
institutional factors toward the perceived reliability of fair value measurements in aninternational context. Prior studies have not examined fair value measurements in a crosscountry study presumably because of unavailability of data for reported fair value hierarchymeasurements. Our study provides early evidence suggesting that fair value measurements acrossthe fair value hierarchy are impacted by a country’s institutional background and financial
development as well as the extent of its securities regulation and disclosure level.
The next section develops our hypotheses development and the institutional backgroundsurrounding fair value measurements and the regulatory environment. Section 3 describes the
sample and empirical measures. Section 4 discusses the test results and Section 5 concludes.

5

2.

Literature Review and Hypothesis Development

2.1 Fair value hierarchy
The issuance of FAS 157 (ASC 820) in September 2006 mandated a division of different
classes of assets and liabilities into Level 1 observable inputs from quoted prices in active
markets, Level 2 indirectly observable inputs from quoted prices of comparable items in active
markets, identical items in inactive markets, or other market-related information and Level 3
unobservable, firm- generated inputs. Improved disclosure requirements of methods and inputs
in determining fair value were also made in this accounting standard.
Since the advent of FAS 157, various studies have examined the market behavior towards
the subjectivity of inputs used in each of these fair value levels. Song et al. (2010) and Riedl and
Serafeim (2011) were the earlier studies that investigate the effect of different fair value levels
on market pricing and information risk of assets respectively whereas Goh et al. (2015) and
Chung et al. (2016) provide further cross sectional and longitudinal studies that build upon theearlier studies. There have also been studies that examine how the fair value hierarchymeasurements affect analysts (e.g., Magnan et al. 2014; Barron et al. 2016).
The focus of this literature is primarily confined to firms in the financial sector (e.g.,banks, mortgage companies, insurance companies with a large concentration of financial
instruments reported of fair value that were affected by the implementation of FAS 157).
Notably, all these studies use firms within the US. Our study differs from this stream of researchby being the first paper to examine firms outside the US. In particular, we focus on the impact ofcountries’ institutional factors in influencing investors’ perceptions regarding the reliability ofthese fair value measurements.

62.2 Institutional factors
Extant research examines the impact of securities laws and stock market development on
financial markets (e.g., Barth et al. 2003; La Porta et al. 2006). These studies generally find that
securities laws and regulatory enforcement are important factors in influencing the developmentof financial markets. In addition, the availability of laws to mandate disclosure requirements andfacilitate private enforcement through liability rules has also been shown to benefit stock markets.
On the other hand, in the context of fair value measurements, the link between institutional
environmental factors and the reliability of fair values have not been fully examined.
Over the decades, there is also an emerging literature on trust. Trust plays an importantrole in the social and economic world. It is broadly defined as the subjective probability that anindividual assigns to the events of a potential counterparty performing an action that is beneficial
or at least not harmful to that individual (Gambetta, 1988). A group of researchers investigates
the role of trust in economic development and economic efficiency (La Porta et al., 1997; Zak
and Knack, 2001; Guiso, Sapienza and Zinagles, 2004). In the context of our study, trust has
been alluded to but has not been examined in a fair value accounting setting.
A recent trend in the literature is to use broad measures of trust from existing surveys to
conduct studies related to trust. For example, Nanda and Wysocki (2013) investigate the relation
between financial reporting transparency and societal trust. Trust is an important concept inaccounting because the value of accounting lies in how much investors and other stakeholders
trust the financial statements. Fair value measurements, especially those measurements thatinvolve Level 3 inputs, require considerable amount of subjectivity and measurement uncertainty(Bell and Griffin 2012; Christensen et al. 2012). Recent developments in accounting standards
have also provided firms with more discretion to decide what measurement basis to adopt. With7

a move toward more fair value accounting in financial reporting, investors may become more
skeptical about reported fair value information in the financial statements.
2.3 Crash risk
The theoretical crash risk model in Jin and Myers (2006) postulates that increased opacity
in financial reporting lead to managers being able to withhold bad news from public disclosure.
However, managers are only able to withhold bad news up to certain threshold. Once thethreshold is met, the accumulated bad news are disclosed all at once, and market participants
surprised by these news, will start to exit the market together, resulting in a stock price crash.
A necessary condition for this phenomenon is the ability of managers to control publicaccess to negative information about firm value, either through financial reporting or othermechanisms. Prior research has examined the impact of financial reporting transparency andaccounting standards on crash risk (e.g., Hutton et al. 2009; DeFond et al. 2015). Hutton et al.
(2009) use a firm-specific measure of opacity and find that more opaque firms are more prone tostock price crashes although the effect weakens in the post-Sarbanes-Oxley period. DeFond et al.
(2015) test whether mandatory IFRS adoption affects firm-level crash risk. Their results indicatethat increased transparency from IFRS adoption has the effect of reducing crash risk among
nonfinancial firms, but more selectively among financial firms, presumably because of thecountervailing effects of fair value accounting to crash risk.
On the one hand, fair value accounting might better reflect firms’ true underlying
performance, thereby reducing crash risk (Bleck and Liu 2007). On the other hand, it might also
introduce greater measurement errors in financial reporting, which reduce investors’ ability toobserve firms’ true underlying performance (European Central Bank 2004). In the context of fairvalue hierarchy measurements, the fair value estimates of more opaque assets (i.e., Level 2 and 38assets) may contain relatively more measurement errors, thus increasing financial reporting
opacity.
2.4 Return synchronicity
Extant research on return synchronicity suggests that stock returns are less synchronousin more developed markets. For example, Morck et al. (2000) find that in emerging markets like
China, Malaysia, and Poland, over 80% of stocks often move in the same direction in a given
week. In contrast, Denmark, Ireland, and the United States lack any instances of more than 57%
of the stocks moving in the same direction during any week. The authors suggest that this isbecause stock markets in poor economies are generally less developed. Thus there is poorer
information transfer, which impede the capitalization of firm-specific information into stock
prices. This effect would reduce firm-specific stock price variation, and increase stock return
synchronicity with the market return.
The notion that greater transparency and more complete revelation of firm-specificinformation will reduce return synchronicity has been extensively examined (e.g., Durnev et al.
2003; Ashbaugh et al. 2005). In the context of fair value measurements, it remains to be seen
whether fair values facilitate or worsen information sharing. This is especially so in relation to
institutional factors in an international setting. For example, it is possible that country specificfactors such as the extent of financial development or the effectiveness of security regulations
can have a mediating effect on stock prices through reported fair values.
2.5 Hypotheses development
In this study, we use an international setting to study whether cross-country variation in
institutional factors account for differences in the transparency of Level 2 and Level 3 fair value
measurements. We use four empirical measures, the extent of a country’s financial development,

9

security regulations, disclosure requirements and the overall level of trust in a country, to proxyfor cross-country differences in a firm’s business environment.
We conjecture that the usefulness of fair value accounting depends on individual
countries’ implementation of accounting rules and the proper enforcement of these rules. It also
depends on how much and how willing market participants trust the reliability of fair values. To
the extent that there are cultural and institutional factors determining the level of trust in asociety, our study enables us to examine whether trust plays a mediating role toward theusefulness of fair values.
As previously stated, studies that have examined crash risk and return synchronicity
suggest that opacity in financial reporting will increase the occurrence of crash risk. In the fairvalue accounting research stream, reliability concerns toward reported fair values remain an
ongoing concern. If fair value measurements are able to increase transparency in financial
statements, we will expect the market participants to be informative about firm’s fundamentals
when there is greater use of fair values in the financial statements. In our setting, we areinterested to examine whether there are cross-country differences in the perceived reliability ofreported fair values, and whether these differences will impact market outcomes.
We state our first set of hypotheses, in alternative form, as follows:
H1a: Fair value accounting reduces the probability of crash risk and return synchronicity
when the sophistication of a country’s financial development is higher.
H1b: Fair value accounting reduces the probability of crash risk and return synchronicity
when the overall level of trust in a country is higher.
H1c: Fair value accounting reduces the probability of crash risk and return synchronicity
when the extent of security regulations is greater.

10H1d: Fair value accounting reduces the probability of crash risk and return synchronicity
when there are more disclosure requirements.
A second research question that we examine in this paper is whether market participants
are more willing to trade in the shares of companies with high reported fair values in theirbalance sheets among countries with stronger institutional background (i.e., more well-regulated,better disclosure regimes) than in countries that have relatively weaker institutions andregulations. This question is particularly salient with regard to reported Level 2 and 3 fair values
given the relatively lack of transparency in determining these reported amounts.
We postulate that there is a cross-sectional difference in the willingness of investors totrade in the shares of banks with high reported fair values between countries with richinstitutional background versus countries with relatively poor institutional background. As with
our first set of hypotheses, we examine countries’ institutional factors relating to its financial
development, overall trust level, sophistication of security regulations and level of disclosure
requirements.
We state our second set of hypotheses, in alternative form, as follows:
H2a: Fair value accounting is positively associated with trading volume when thesophistication of a country’s financial development is higher.
H2b: Fair value accounting is positively associated with trading volume when the overalllevel of trust in a country is higher.
H2c: Fair value accounting is positively associated with trading volume when the extent
of security regulations is greater.
H2d: Fair value accounting is positively associated with trading volume when there are
more disclosure requirements.

11

3.

Research Design
We obtain our fair value data from the SNL Global coverage database. This databaseprovides international coverage of banks’ fair value data that is based on the fair value hierarchyclassification, including countries in Americas, Europe, and Asia-Pacific region. We obtain ourcapital market data from Datastream to compute price and other market variables. We exclude
sample firms that do not have a matching ISIN number with the SNL Global coverage database.
Our sample period is from 2007 – 2015. We obtain an initial sample of 6,485 bank-year
observations across 20 different countries with reported Level 1, 2 and 3 assets from this dataset.
We use four different country-level measures in our regression analyses. We develop ourcomposite-level variables from various World Bank surveys. Our first country level measure is
Financial Development (FD). This variable measures the extent of financial development in acountry along two dimensions. Specifically, it is measured as the sum of stock market
development index and financial intermediary development index. The stock market
development index is the sum of market capitalization over GDP, total value traded over GDP,and total value traded over market capitalization. The financial intermediary development index
is the sum of liquid liabilities over GDP and the credit going to the private sector over GDP andis typically regarded as a country-level financial constraint measure.
Our second cross-country level variable is Trust. Our measure of trust is based on theWorld Value Survey, defined by asking respondents the following question: “Generally speaking,would you say that most people can be trusted or that you need have to be very careful in dealing
with people?” The level of trust in each country is based on the percentage of respondents
replying the following response “most people can be trusted”. This measures ranges from 0%(absolute distrust) to 100% (absolute trust).

12

Our third measure is Security Regulation (SR). This is a country-level measure to proxy
for the level of security laws and investor protection. Following Hail and Leuz (2006), thisvariable is defined as the mean of the disclosure index, the liability standard index, and the
public enforcement index (La Porta et al. 2006). Our fourth country level measure is Disclosure.
Following Hail and Leuz (2006), this variable is defined as the arithmetic mean of Prospect,
Compensation, Shareholders, Inside Ownership, Irregular Contracts and Transactions.
We estimate the following regressions to test our hypotheses:
CRASHit = β0 + β 1 FVA1it + β2 FVA23it + β3 CROSS-COUNTRYit ++ β4 FVA1*CROSS-COUNTRYit + β5 FVA23*CROSS-COUNTRYit+ β6 ROAit + β7 LEVERAGEit + β8 M2Bit + β9 SIZEit + εit(1)

SYNit = β0 + β 1 FVA1it + β2 FVA23it + β3 CROSS-COUNTRYit ++ β4 FVA1*CROSS-COUNTRYit + β5 FVA23*CROSS-COUNTRYit+ β6 ROAit + β7 LEVERAGEit + β8 M2Bit + β9 SIZEit + εit(2)

VOLUMEit = β0 + β 1 FVA1it + β2 FVA23it + β3 CROSS-COUNTRYit ++ β4 FVA1*CROSS-COUNTRYit + β5 FVA23*CROSS-COUNTRYit+ β6 ROAit + β7 LEVERAGEit + β8 M2Bit + β9 SIZEit + εit(3)where CRASH is defined as the negative skewness of firm-specific weekly returns overthe fiscal year period based on Jin and Myers (2006). SYN is the return synchronicity of a firm’s
stock returns with market returns. It is calculated as the log (R2 / 1 - R2) estimated from
regressing weekly market return during the fiscal year on the firm’s stock return. VOLUME is themean of weekly trading volume during the fiscal year. FVA1 is Level 1 fair value assets scaled
by total outstanding shares during the fiscal year. FVA23 is the sum of Level 2 and Level 3 fair13

value assets scaled by total outstanding shares during the fiscal year. The cross-country variables
are Financial Development (FD), Trust, Security Regulation (SR), and Disclosure, as previously
defined. In our regression analyses, our cross-country variables are indicator variables that equal
one if their scores are above the median value, and zero otherwise.
Following Hutton et al. (2009), we use the following firm-specific variables as control
variables. SIZE is calculated as the log of total assets, ROA is earnings before extraordinary items
divided by total assets. LEVERAGE is the book value of total liabilities scaled by total assets, andM2B is the ratio of the market value of equity to the book value of equity. We also incorporateyear and country fixed effects in our regression specifications.

4.

Empirical results

4.1 Descriptive statistics and univariate results
Table 1 reports our descriptive statistics for our sample. We report our fair value
measures scaled by the total assets of the bank. The mean (median) value of FVA1 is 0.021(0.000), whereas the mean (median) value of FVA2 is 0.164 (0.145), and the mean (median)value of FVA3 is 0.018 (0.007). The descriptive statistics are generally consistent with the results
reported in prior studies that indicate Level 2 assets represent the largest proportion of fair value
assets on the bank’s balance sheet.
For our dependent variables, the mean (median) value of our crash risk measure is -0.051(-0.055). The mean (median) value of our return synchronicity measure is -2.622 (-2.091).
Finally, the mean (median) value of our trading volume measure is 2.373 (0.020). For ourcountry level variables, the mean (median) value of Financial Development (FD) is 800.39(759.39) There is substantial variation in the level of financial development across our sample

14

countries. Panel B of Table 1 reports the sample distribution of these country variables. Acrossour sample countries, countries with well-developed financial services sectors such as Hong
Kong (1711.42), USA (838.74), Switzerland (799.64), and UK (759.23) scored high on thismeasure, whereas countries such as Pakistan (105.12), and Philippines (188.12) have relatively
lower scores.
The mean (median) value of TRUST is 0.371 (0.382) indicating that about 37-38% of the
population in our sample expressed trust towards others in business dealings. Among thecountries that have higher level of trust are Australia (0.533), Finland (0.588), Netherlands(0.602) and Sweden (0.680). In contrast, Philippines (0.028) and Malaysia (0.085) are countries
that indicate the lowest level of trust towards others in our sample countries. Our third crosscountry variable is Security Regulation (SR). Across our sample countries, high SR scores arefound in countries such as USA (2.900), Canada (2.717), and Singapore (2.527) whereas
countries that report low level of security regulation are Germany (0.633), Sweden (1.358) and
Italy (1.370). Our fourth cross-country variable is Disclosure. Similar to our Security Regulation
variable, we find that countries that have relatively high level of disclosure requirements are
USA (1.000), Singapore (1.000) and Canada (0.917). In contrast, countries with low disclosure
scores include Germany (0.417), Finland (0.500) and Netherlands (0.500).
Table 2 reports the Pearson correlations among our variables used in our analyses.
Among the fair value variables, we find that FVA1 and FVA2 are negatively correlated with eachother whereas FVA2 is negatively correlated with FVA3. With regard to the outcome variables,
crash risk is positively correlated with Financial Development (0.057) and Trust (0.044) but
negatively correlated with Security Regulation (-0.039) and Disclosure (0.039). We also find that
return synchronicity is negatively correlated with all the four institutional factors. Interestingly,15

we find that FVA1 tend to be negatively correlated with the institutional factors while FVA2 tend
to be positively correlated with them. FVA3 is negatively correlated with Financial Development(-0.036) and Trust (-0.062).
Among the cross-country variables, Financial Development is positively correlated with
Trust (0.537), Security Regulation (0.394), and Disclosure (0.435). These correlations are
statistically significant at the 0.01 level. Likewise, Trust is positively correlated with Security
Regulation (0.246) and Disclosure (0.268), These correlations are also statistically significant at
the 0.01 level. Not surprisingly, we find Security Regulation is strongly correlated with
Disclosure (0.953).
4.2 Crash risk and return synchronicity analyses
Table 3 reports the results of our regression analyses from regressing crash risk on our
various cross-country variables. We combine Level 2 and Level 3 assets (FVA23) to differentiate
this variable from FVA1 which uses solely market-based prices when estimating fair values.
Prior literature asserts that Level 1 assets are more reliable because they rely directly on market
prices in determining fair values. In contrast, FVA23 represent fair values of financial assets thatare subject to some form of managerial discretion during the fair value estimation process.
Prior studies on crash risk suggest that greater transparency will reduce the probability ofcrash risk but do not examine the interaction effect of cross-country effects on fair value
measurements. In our tests, we document significant cross-country effects across our interaction
variables of interest for Level 1 assets. In particular, we find a negative association between
Level 1 fair value assets and crash risk among countries with a higher level of Financial
Development, Trust, Security Regulation and Disclosure. The coefficients on our interaction
variables on Level 1 assets are negative and statistically significant for FVA1 x FD (-1.639, t-

16

stat=-2.420), FVA1 x Trust (-1.208, t-stat=-1.672), FVA1 x SR (-1.782, t-stat=-2.571), and FVA1
x Disclosure (-1.712, t-stat=-2.654). FVA1 x Disclosure is statistically significant at the 0.01
level and FVA1 x FD and FVA1 x SR are significant at the 0.05 level whereas FVA1 x Trust issignificant at the 0.10 level.
We interpret these results suggest that there is a cross-country effect for crash risk among
countries with better developed financial development infrastructure, greater security regulations,and higher level of trust and disclosure requirements. However, it appears that these crosscountry improvements impact differences in crash risk for Level 1 assets but not in Level 2 andLevel 3 assets. That is, we do not find any cross-country variables that reduce the probability ofcrash risk inherent in non-market based fair values (i.e., Level 2 and 3 assets) in these tests.
Table 4 reports the results of regressing return synchronicity on our cross-country
variables. These test results corroborate our earlier tests using crash risk as the dependent
variable. We find statistically significant cross-country effects in countries with greater Security
regulation and Disclosure requirements. However, we do not find statistically results for
Financial Development and Trust. Consistent with the results in our crash risk tests, the impact
from cross-country effects on return synchronicity as a result of better security regulations anddisclosure requirements apply only to Level 1 fair value assets. Specifically, the coefficients on
our interaction variables on Level 1 assets are negative and statistically significant for FVA1 x SR(-6.232, t-stat=-4.122), and FVA1 x Disclosure (-2.650, t-stat=-1.880).
On the other hand, we do not find any statistical difference with regard to the incremental
impact to return synchronicity for Level 2 and 3 assets across any of these four cross-country
variables. One interpretation of both sets of results is that cross-country differences in financial
infrastructure, security regulations, and levels of trust and disclosure requirements do not seem to17alleviate reliability concerns toward Level 2 and 3 assets. Hence, asset opacity in Level 2 and 3
assets remain high even in well-developed financial markets.
4.3 Trading volume analyses
Table 5 reports the results of regressing trading volume on our cross-country variables. In
countries with better quality financial infrastructure, greater security regulation, and higher levelof trust and disclosure, there appears to be greater confidence towards reported fair values, whichtranslates to higher trading volume among the shares of these banks. The strongest country
effects reside in differences in security regulations and disclosure requirements. Specifically, we
find positive associations for our FVA1 and FVA2 interaction cross-country variables among
countries with high level of security regulation and disclosure compared with countries with lax
security regulation and poor disclosure requirements. The coefficients for FVA1 x SR and FVA2x SR are 24.81 and 18.57 respectively, both statistically significant at the 0.01 level. Likewise,
the coefficients for FVA1 x Disclosure and FVA2 x Disclosure are 16.33 and 19.59 respectively,
also statistically significant at the 0.01 level. These results suggest that tighter security
regulations and greater disclosure requirements inspire investor confidence to trade in the sharesof banks with a large proportion of fair value assets.
Apart from the above results, we also find that investors are more willing to trade in theshares of banks with Level 2 and 3 assets in countries with more well-developed financial
development infrastructure (FD), and in countries where there is a greater level of trust. Overall,
the results suggest that there is a significant cross-country effect to differences in trading volume.
It appears that investors are less reserved toward banks with greater reported Level 2 and Level 3
fair values if these banks are located in countries with better security regulations and disclosure
requirements. We also find similar results for Level 1 assets in the predicted direction although18the results are not statistically significant. These results suggest that a country’s level of financial
development and the cross-country differences in a country’s level of trust plays a less importantrole in influencing trading volume for banks with different amounts of Level 1 assets,
presumably because there is less concern toward Level 1 assets in the first place.
Overall, we document that there are significant cross-country differences in the level of
trading volume among banks with different amounts of reported Level 2 and Level 3 assets. It
appears that cross-country effects have a mediating effect on trading volume with regard todifferences in reported fair values in banks’ balance sheets.

5.

Conclusion
In this study, we examine how country’s institutional factors are able to play a

mediating role in affecting reliability concerns towards fair value measurements. Using various
empirical measures to proxy for a country’s level of financial development, extent of security
regulations and disclosure requirements, as well as its overall level of trust, we find that these
institutional factors matter to investors when trading the shares of banks with high reported fairvalue figures in their balance sheets. Specifically, our tests reveal that investors are relatively
disinclined to invest in the companies of countries that have poor financial development andsecurity regulation, low overall societal trust and lax disclosure requirements, where there might
be questionable doubts regarding the reliability of the fair value measurements on the firms’balance sheets.
We also show whether these institutional factors mediate the relation between theprobability of crash risk and return synchronicity. The study of tail events such as crash risk isparticularly relevant to debates regarding fair value accounting as it is a policy issue whether

19firms that have more assets that are fair valued in their balance sheets are more likely to sufferfrom higher crash risk owing to concerns regarding the transparency of these assets and thesubjectivity of the fair value estimation processes. We find empirical evidence suggesting thatcountry’s institutional factors are able to mediate the Level 1 fair values, but not Level 2 and 3
fair values.
Our study extends prior research on the fair value hierarchy measurements. We provide
empirical evidence that assess how various countries’ institutional differences might alleviatesignificant reliability concerns toward fair value measurements. In doing so, we shed further
light toward a better understanding of the usefulness of fair value accounting in relation tovarious market outcome factors in an international setting. Our study also contributes to theongoing debate regarding the role of fair value measurements on financial markets.

20References:
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21

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22

TABLE 1: Sample Composition
This table reports the descriptive statistics for our sample banks. Panel A reports the descriptive statisticsof the variables used in our regression analyses and Panel B provides our sample distribution and thevalues of our country variables. FVA1 is Level 1 fair value assets scaled by total assets during the year.
FVA2 is Level 2 fair value assets scaled by total assets during the year. FVA3 is Level 3 fair value assets
scaled by total assets during the year. CRASH is defined as the negative skewness of firm-specific weekly
returns calculated over the fiscal year. SYN is defined as the return synchronicity of a firm’s stock returns
with market returns. It is calculated as the log (R2 / 1-R2) estimated from regressing weekly market return
during the fiscal year on the firm’s stock return. VOLUME is the mean of weekly trading volume during
the fiscal year. FD is the measure for the extent of financial development in a country, TRUST is themeasure for the extent of trust in a country, DISCLOSURE is the measure of the extent of disclosure in acountry, SIZE is calculated as the log of total assets, ROA is earnings before extraordinary items divided
by total assets, LEVERAGE is the book value of total liabilities scaled by total assets, and M2B is the ratioof the market value of equity to the book value of equity.

Panel A: Descriptive statistics

Obs

Mean

Std Dev

5th Pctl

Median95th Pctl

FVA1
FVA2
FVA3
CRASH
SYN
VOLUME
FD
TRUST

6485
6485
6485
4531
3908
4910
6485
6485

0.021
0.164
0.018
-0.051-2.6222.373
800.39
0.371

0.059
0.130
0.049
0.862
2.522
13.04
209.24
0.072

0.000
0.004
0.000
-1.276-7.1820.001
450.69
0.195

0.000
0.145
0.007
-0.055-2.0910.020
759.39
0.382

0.136
0.388
0.062
1.092
0.287
9.067
1171.38
0.396

DISCLOSURE
SIZE
ROA
LEVERAGE
M2B

6485
6485
6485
6475
4415

0.963
14.505
0.004
0.099
1.041

0.104
2.243
0.015
0.086
0.969

0.667
11.823-0.015
0.000
0.181

1.000
13.889
0.006
0.079
0.922

1.000
19.306
0.014
0.260
2.139

23

TABLE 1 (Cont’d)Panel B: Sample distribution

Country

Obs

FD

Trust

SR

Disclose

Australia
Canada763

522.19579.03

0.533
0.421

2.310
2.717

0.750
0.917

FinlandFranceGermany
Hong Kong
Italy
Malaysia
Netherlands
Pakistan

7
106
43
53
133
48
16
2

389.43419.67451.691711.42460.65449.98627.51105.12

0.588
0.187
0.403
0.470
0.292
0.085
0.602
0.239

1.477
1.737
0.633
2.443
1.370
2.343
1.855
1.552

0.500
0.750
0.417
0.917
0.667
0.917
0.500
0.583

Philippines
Singapore
South Africa
South Korea
Spain
SwedenSwitzerland
Thailand

131
22
23
49
53
9
62
13

188.12579.12489.75624.63688.02609.74799.64457.64

0.028
0.385
0.236
0.297
0.196
0.680
0.511
0.326

2.667
2.527
1.743
1.660
1.493
1.358
1.440
1.855

0.833
1.000
0.833
0.750
0.500
0.583
0.667
0.917

UK
USA

66
5,570

759.23838.74

0.304
0.384

2.177
2.900

0.833
1.000

24

TABLE 2

Correlations among key variables

This table presents the Pearson correlations among the variables used in the empirical analyses. The variables are defined in Table 1. *, **, and ***indicate significance at the 10, 5, and 1 percent levels respectively.

FVA1 (1)FVA2(2)
FVA3 (3)CRASH (4)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1.000

-0.032***

0.014

0.015

0.202***

0.226***-0.287***-0.278***-0.501***-0.520***1.000-0.022*-0.0230.034

0.152***

0.053***

0.055***

0.089***

0.089***

1.000

0.013

-0.072***-0.001-0.036***-0.062***-0.003-0.0011.000

0.067***

0.013

0.057***

0.044***-0.039***-0.039***1.000

0.150***-0.017-0.045***-0.228***-0.242***1.000-0.055***-0.088***-0.229***-0.229***1.000

0.537***

0.394***

0.435***

1.000

0.246***

0.268***

1.000

0.953***

SYN (5)
VOLUME (6)FD (7)
TRUST (8)SR (9)DISCLOSURE (10)

1.000

25

TABLE 3: Crash risk analyses
This table presents the results of the regressions from regressing crash risk on various cross-country indicatorvariables. The cross-country variables are FD, Trust, SR, and Disclosure. Our cross-country variables are indicatorvariables that equal one if their scores are above the median value, and zero otherwise. All the variables are definedin Table 1. t-statistics, in parentheses, are indicated below the coefficients. *, **, and *** indicate significance at the10, 5, and 1 percent levels (two-tailed), respectively.
Crash risk

FD
(1)

Trust
(2)

SR
(3)

Disclosure(4)

1.232**(2.007)-0.174(-1.167)
0.110(0.589)-1.639**(-2.420)0.0330(0.159)0.122(1.08)-6.815***(-5.478)-0.335*(-1.743)-0.0115(-0.757)-0.432(-1.414)0.872(1.434)-0.190(-1.281)0.287*(1.837)-1.208*(-1.672)
0.0321(0.155)-0.003(-0.02)-6.850***(-5.506)-0.338*(-1.758)-0.00893(-0.587)-0.634**(-2.269)

1.103**(1.972)-0.202(-1.423)0.112(0.450)-1.782**(-2.571)
0.0718(0.346)-0.112(-0.55)-6.907***(-5.558)-0.373*(-1.944)-0.00633(-0.418)-0.461(-1.383)

1.310**(2.172)-0.172(-1.155)
0.179(0.561)-1.712***(-2.654)0.0118(0.0567)
0.025(0.14)-6.885***(-5.541)-0.331*(-1.721)-0.00685(-0.453)-0.505(-1.453)

Year fixed effects
Country fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations
Adjusted R-squared

4,022
0.02844,022
0.02844,022
0.0286

4,022
0.0289FVA1
FVA23
Cross-country
FVA1 × Cross-country
FVA23 × Cross-country
ROA
LEVERAGE
M2B
SIZE
Constant

26

TABLE 4: Return synchronicity analyses
This table presents the results of the regressions from regressing return synchronicity on various cross-country
variables. The cross-country variables are FD, Trust, SR, and Disclosure. Our cross-country variables are indicatorvariables that equal one if their scores are above the median value, and zero otherwise. All the variables are definedin Table 1. t-statistics, in parentheses, are indicated below the coefficients. *, **, and *** indicate significance at the10, 5, and 1 percent levels (two-tailed), respectively.
Synchronicity

FVA1
FVA23
Cross-country
FVA1 × Cross-country
FVA23 × Cross-country
ROA
LEVERAGE
M2B
SIZE
Constant

FD
(1)

Trust
(2)

SR
(3)

Disclosure(4)-2.926**(-2.155)-1.531***(-4.645)
1.086***(2.662)-0.363(-0.245)-0.0816(-0.178)
2.164(0.790)0.383(0.905)0.210***(5.923)0.923***(45.80)-18.45***(-27.37)-3.422**(-2.539)-1.456***(-4.455)
0.831**(2.422)0.669(0.422)-0.246(-0.539)2.134(0.779)0.384(0.906)0.211***(5.956)0.922***(45.81)-18.14***(-29.42)0.0137(0.0112)-1.673***(-5.348)-0.346(-0.642)-6.232***(-4.122)0.150(0.329)2.264(0.829)0.265(0.627)0.217***(6.154)0.923***(45.93)-16.87***(-23.10)-1.259(-0.949)-1.499***(-4.567)0.880(1.263)-2.650*(-1.880)-0.0412(-0.0899)
2.273(0.830)0.394(0.930)0.207***(5.866)0.919***(45.63)-18.11***(-23.88)

Year fixed effects

Yes

Yes

Yes

Yes

Country fixed effects

Yes

Yes

Yes

Yes

Observations
Adjusted R-squared

3,886
0.447

3,886
0.447

3,886
0.449

3,886
0.447

27

TABLE 5: Trading volume analyses
This table presents the results of the regressions from regressing trading volume on various cross-country variables.
The cross-country variables are FD, Trust, SR, and Disclosure. Our cross-country variables are indicator variables
that equal one if their scores are above the median value, and zero otherwise. All the variables are defined in Table 1.
t-statistics, in parentheses, are indicated below the coefficients. *, **, and *** indicate significance at the 10, 5, and1 percent levels (two-tailed), respectively.
Trading volume

FVA1
FVA23
Cross-country
FVA1 × Cross-country
FVA23 × Cross-country
ROA
LEVERAGE
M2B
SIZE
Constant

FD
(1)

Trust
(2)

SR
(3)

Disclosure(4)

11.65**(2.084)3.575*(1.944)-5.173**(-2.128)9.272(1.500)18.59***(7.388)-26.99*(-1.695)-0.384(-0.162)-0.707***(-3.480)
2.206***(19.10)-33.51***(-8.708)

14.10***(2.619)3.425**(1.962)-6.127***(-4.110)8.837(1.385)20.47***(8.251)-29.69*(-1.867)-0.595(-0.252)-0.732***(-3.607)
2.218***(19.23)-33.01***(-10.30)

9.783**(2.211)4.536***(2.615)-1.516(-0.943)
24.81***(3.431)18.57***(7.259)-26.43*(-1.662)0.298(0.126)-0.765***(-3.754)2.196***(19.01)-37.75***(-11.75)

10.48**(2.354)3.087*(1.726)-6.899***(-2.803)16.33***(2.607)19.59***(7.771)-28.09*(-1.767)-0.506(-0.214)-0.749***(-3.682)
2.215***(19.21)-32.50***(-8.820)

Year fixed effects

Yes

Yes

Yes

Yes

Country fixed effects

Yes

Yes

Yes

Yes

Observations
Adjusted R-squared

4,346
0.290

4,346
0.293

4,346
0.292

4,346
0.292

28