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Has the introduction of IFRS improved accounting quality? A
comparative study of five countries
Corresponding author: Andreas Jansson, Assistant Professor, PhD, School of Business and Economics, Linnaeus University, Växjö, Sweden
e-mail: [email protected]
Phone: +46-470-708230
Fax: +46-772288000
Micael Jönsson, Research Assistant, School of Business and Economics, Linnaeus University, Växjö, Sweden
Christopher von Koch, Assistant Professor, PhD, School of Business and Economics, Linnaeus University, Växjö, Sweden
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Has the introduction of IFRS improved accounting quality? A comparative study of five
countries
Abstract
This paper investigates whether the implementation of International Financial Reporting
Standards (IFRS) has increased accounting quality. Previous research has primarily explored
the effects of IFRS on accounting quality as measured through the use of value relevance,
timely loss recognition and earnings management. In contrast, this paper employs a measure
of accounting quality that is based on the use of accounting information, namely, the
performance of financial analysts. The sample encompasses nearly 2,500 publicly traded
firms, all followed by analysts, from 1996-2009. The sample covers five European countries
(Sweden, Netherlands, France, Germany and the United Kingdom (the UK)), each with
different legal and accounting traditions. We use quantile regressions to estimate the impact
of IFRS while simultaneously considering that most prediction errors are small and are most
likely random and unaffected by the accounting standard being followed. Our results suggest
that IFRS have had no effect on analysts’ average ability to accurately forecast firms’
earnings per share. In all countries except the UK, IFRS have led to higher consistency in
analyst forecasts. The impact of IFRS is not more pronounced in firms that are more affected
by their asset measurement methods. The results suggest that in countries where prior GAAP
differ from IFRS, IFRS may have the effect of presenting more consistent but not more
accurate pictures of firms for analysts.
Keywords: IFRS, accounting quality, analyst forecasts, comparative study
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1. Introduction
With regulation EC No 1606/2002, the European Union (EU) decided that all publicly traded
companies “shall prepare their consolidated accounts in conformity with the international
accounting standards [IAS]” (EU, 2002: Article 4) for each financial year beginning on or
after 1 January 2005. More specifically, this requirement means that these companies must
apply IAS, International Financial Reporting Standards (IFRS) and Standing Interpretations
Committee/International Financial Reporting Standards Interpretation Committee
(SIC/IFRSIC) interpretations issued by the International Accounting Standards Board (IASB).
This requirement most likely constitutes the single most significant change in accounting
standards to have ever occurred in Europe and is popularly referred to as the introduction of
IFRS (for a comprehensive description of the implementation of IFRS, see, for example,
Armstrong et al. 2010). In this paper, we empirically examine the impact of the introduction
of IFRS on the accuracy and dispersion of financial analysts’ forecasts in five EU countries.
The past decade has seen a large amount of empirical research regarding what constitutes
high-quality accounting (see Soderstrom and Sun, 2007 for a review). For many European
countries, the introduction of IFRS has entailed substantial changes in accounting methods,
and this change has prompted a major ‘natural’ opportunity to examine factors thought to
affect accounting quality. Consistently, academics around the world are now extensively
studying the effects of IFRS on accounting quality (see, for example, Armstrong et al., 2010;
Ball, 2006; Barth et al., 2008; Bartov et al., 2005; Byard et al., 2011; Daske and Gebhardt,
2006; Daske et al., 2008; Ding et al., 2007; Hung and Subramanyam, 2007; Jeanjean and
Stolowy, 2008; Jiao et al., 2012). Results from these studies are mixed. On the one hand,
IFRS appear to have a positive effect on accounting quality, but the results are contingent on
country- or firm-specific characteristics. In general, IFRS require more extensive and
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sophisticated disclosures than were afforded by prior local standards, and this requirement
may have a positive influence on the quality of financial reports. On the other hand, certain
aspects of IFRS, such as the greater flexibility in choice of accounting methods that it offers
in comparison with some EU countries’ previous local standards, may be negatively affecting
accounting quality (e.g., Ormrod and Taylor, 2004).
Previous research has commonly used earnings management, timely loss recognition and
value relevance as indicators of accounting quality (cf. Barth et al., 2008), although metrics
such as quality indices and appropriateness also appear. All of these metrics fail to directly
capture the usefulness of the information to accounting users. In this paper, we approach the
effect of IFRS on accounting quality from a different angle by determining whether the
introduction of IFRS has allowed users of accounting information to make better predictions
regarding firm performance. More specifically, we examine whether the introduction of IFRS
has allowed financial analysts to formulate better forecasts of firm performance. This decision
usefulness dimension of accounting quality is in line with a purpose of accounting that is
stressed in the conceptual framework of the IASB, which states that
The objective of general purpose financial reporting is to provide financial
information about the reporting entity that is useful to existing and potential
investors, lenders and other creditors in making decisions about providing
resources to the entity. Those decisions involve buying, selling or holding
equity and debt instruments, and providing or settling loans and other forms of
credit (IASB, 2010: OB2).
Financial analysts are often argued to fulfill an important function in financial markets by
reducing the information asymmetry between firms and investors through their intermediary
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role (Lang and Lundholm, 1996). However, financial analysts’ ability to perform their task is
contingent on the information shared between themselves and the firm not being too
asymmetrical (cf. Krishnaswami and Subramaniam, 1999). Available evidence suggests that
financial analysts rely extensively on accounting information to make forecasts (Block, 1999;
Roger and Grant, 1997); analysts are also considered to be sophisticated users of accounting
information (e.g., Schipper, 1991). Prior research has focused primarily on the role of
voluntary disclosure in reducing this information asymmetry (Barry and Brown, 1985;
Glosten and Milgrom, 1985; Lang and Lundholm, 1996; Merton, 1987), but the introduction
of IFRS also allows for the exploration of impacts that mandatory financial accounting may
have on analysts’ ability to reduce information asymmetry. Higher quality financial
accounting could be expected to allow extensive users of accounting data to formulate higher
quality assessments of firms, leading to less overall information asymmetry.
Because the impact of IFRS on accounting quality appears to vary among countries, it is
unlikely to have the same effects on analysts’ performance in all countries where it is
introduced. Barth et al. (2008), Byard et al. (2011), Daske et al. (2008) and Preiato et al.
(2010), for example, suggest that the enforcement of accounting standards, which can vary
among countries (La Porta et al., 1998), is pivotal for the realization of quality increases
through the introduction of IFRS. Therefore, in this study, we examine the impact of IFRS on
accounting quality in five EU countries: Sweden, the UK, Germany, France and the
Netherlands. These countries have regulatory systems with different origins and with varying
enforcement strength (La Porta et al., 1998; Preiato et al., 2010), which is reflected in their
varying financial accounting traditions (Nobes, 1983).
Our paper is related to a number of previous studies. Ashbaugh and Pincus (2001), who
examined 80 non-US firms that voluntarily adopted IAS during the 1990-93 period, found
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that analysts’ forecast accuracy increases after firms adopt IAS and that the convergence in
firms’ accounting policies that is achieved by adopting IAS is positively associated with a
reduction in analysts’ forecast errors. Some early studies have examined the effects of
mandatory IFRS adoption on analysts’ performance. For example, Byard et al. (2011) found
that forecast errors and forecast dispersion decrease, but only in countries with both strong
enforcement regimes and domestic accounting standards that differ significantly from IFRS.
Similar conclusions are suggested in Horton et al. (2012) and Preiato et al. (2010) who
demonstrate that IFRS can have a positive impact on forecast accuracy if enforcement is
strong. Consistent with this notion, Tan et al. (2011) demonstrate that the accuracy of foreign
financial analysts’ forecasts increases when IFRS are implemented, particularly if the
difference between previous local Generally Accepted Accounting Principles (GAAP) and
IFRS is significant; however, domestic analysts’ accuracy is not affected by IFRS. Beuselinck
et al. (2010) and Yang (2010) examine the impact of IFRS on private and public information
precision. They found a positive association between IFRS and public information precision,
which is consistent with an increase in accounting quality. However, Beuselinck et al. (2010)
suggest that the effect is more significant in countries in which IFRS entail a significant
change, whereas Yang (2010) suggests that the improvement is more significant in countries
that already had high-level disclosure standards. Glaum et al. (forthcoming), who examined
the impact of IFRS on analysts following Germans firms, separated the effect between
improved disclosure and other changes in the firms’ information environments. They found
that improved disclosure had a modest and positive effect on analysts’ accuracy but suggest
that improvements in the quality of earnings, improvements in firms’ investor relations and
changes in analyst behavior have contributed more to the improvement of analyst accuracy.
Overall, the literature suggests that IFRS have a positive but not uniform impact.
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Our results, which are based on a sample comprising 2,447 public companies from 5 EU
countries during the period of 1996-2009, suggest that IFRS have no impact on forecast
accuracy but appear to reduce forecast dispersion. However, the effect of this dispersion is
small. In fact, the effect on dispersion is nonexistent for the UK, the country that exhibits the
smallest difference between previous GAAP and IFRS. Furthermore, the effect is only visible
for the median part of the distribution of forecast dispersions; for large as well as small
dispersions, there is no significant effect. Overall, our results suggest that IFRS appear to
provide a more consistent but not more accurate picture of firms, a conclusion that is also
strengthened by the finding that these effects appear to not be driven by IFRS’ ability to better
represent the underlying economic value of a firm.
Four aspects of our approach distinguish our study from prior empirical studies on the effects
of IFRS. First, we contribute to the literature using a sample of forecast accuracy and forecast
dispersion that encompasses an extensive time period. As Preiato et al. (2010) suggest, analyst
performance differs significantly over time for reasons that are unrelated to accounting
regulation. It is therefore important to include both periods of generally strong and periods of
generally poor analyst performance from both the pre and post IFRS adoption period to better
isolate the effects of IFRS. Second, to analyze our dataset, we use an estimation technique – a
quantile regression model – that is robust to the problem of skewness. As Yang (2010) argues,
skewness, which can bias estimates, is a serious problem with this type of data. Yang (2010)
suggests that a median regression model could remedy this problem. The use of a median
regression model allows us to estimate coefficients without manipulating the dataset, which
would not be possible with an OLS regression model of estimation. Third, our estimation
technique also allows us to predict the effect of independent variables on different magnitudes
of forecast errors and forecast dispersions. This approach allows us to detect the effect of
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IFRS on large and small errors without assuming the same impact across the entire
distribution. It is reasonable to assume that the impact of IFRS is not uniform in this respect
because most small errors or dispersions are likely to be random and therefore impossible to
eliminate through the use of improved accounting. Fourth, we use a different proxy for the
firm-level impact of IFRS. Although prior studies have used industry (e.g., Beuselinck et al.,
2010) and differences in reported earnings in reconciliation accounts (e.g., Horton et al.,
2012) as proxies for the firm-level impact of IFRS, we use an accounting figure that is highly
affected by IFRS as a proxy: intangible assets. Intangible assets, which are difficult for an
analyst to valuate because they are typically unique, are regularly valued at fair value (i.e.,
market value or a proxy thereof) in IFRS; traditionally, these assets have been valued at
historical cost. This method allows us to generate new evidence on what aspect of IFRS might
affect analyst performance.
Our results have a number of theoretical and practical implications. The results indicate the
need to distinguish between two aspects of accounting quality: how it affects users’ accuracy
and how it affects users’ consistency. Accounting quality measures should be developed to
accommodate this distinction. The fact that IFRS primarily affect consistency implies that
fair-value appraisals of asset values are used by analysts when predicting firm performance
and that IFRS have therefore created a more level playing field despite their lack of a
significant effect on predictive value.
2. Hypotheses development
2.1 IFRS and forecast accuracy
Our approach relies on the assumption that higher quality accounting will be reflected in
higher quality forecasts by financial analysts. Revsine et al. (2004) and Schipper (1991) argue
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that analysts are considered to be among the most important and influential users of financial
reports and among the most important information intermediaries between firms and
investors. Considering their information-processing ability and access to resources, analysts
are typically viewed as sophisticated users of accounting information and as being less likely
(than naïve investors) to misunderstand the implications of such information (e.g., Schipper,
1991). Therefore, if financial analysts receive access to higher quality financial reports, they
should be able to make better predictions, measured as higher forecast accuracy.
Lang and Lundholm (1996) provide evidence of the relationship between disclosure and
decreased information asymmetry. Firms with more informative disclosure policies enjoy a
larger analyst following, more accurate earnings forecasts, less dispersion among forecasts,
and less volatility in forecast revisions. Firms that provide firm-specific information, in
particular, are associated with more accurate earnings forecasts and less forecast dispersion.
Soderstrom and Sun (2007) argue that the accounting standard being followed affects
accounting quality. This relationship implies that the introduction of a new accounting
standard should affect the accounting quality of a firm on the margin. The introduction of
IFRS has necessitated an accounting standard change for most EU-member countries and, in
turn, this change should be reflected in accounting quality. In general, the change to IFRS has
entailed a shift toward more valuation of assets according to fair value instead of historical
cost. This shift should mean that IFRS provide a better picture of the underlying economic
value for firms in the EU because changes in the value of assets generally will be accounted
for on a regular basis. However, at the same time, fair value accounting is likely to provide
managers with more discretion in accounting, which might diminish the quality of accounting
because of increased earnings management (Ormrod and Taylor, 2004).
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Empirical research (e.g., Armstrong et al., 2010; Ball, 2006; Barth et al., 2008; Bartov et al.,
2005; Byard et al., 2011; Daske and Gebhardt, 2006; Daske et al., 2008; Ding et al., 2007;
Hung and Subramanyam, 2007; Jeanjean and Stolowy, 2008) has attempted to compare the
quality – measured primarily in terms of value relevance, earnings management and timely
loss recognition – of IFRS and that of previous standards. The overall quality assessment is
not conclusive (cf. Soderstrom and Sun, 2007), perhaps because of the two opposing effects
of IFRS (Ormrod and Taylor, 2004). Nevertheless, in terms of supporting high-quality
decision making by financial analysts, it is reasonable to assume that a better representation of
underlying economic value will outweigh the negative effects of an increase in management
discretion. Thus, our first hypothesis is as follows:
H1: The introduction of IFRS increases the accuracy of analysts’ earnings forecasts
2.2 IFRS and forecast dispersion
Forecast dispersion (measured as the standard deviation in analysts’ forecasts), which is an
indication of the extent of analysts’ disagreement regarding a firm’s upcoming earnings, can
be used as a proxy for investor uncertainty prior to the release of key information (Ramnath et
al., 2008).According to Krishnaswami and Subramaniam (1999), this dispersion is a measure
of information asymmetry. They claim that when information asymmetry between a firm and
its market is high, it is difficult for the market to evaluate or predict the firm’s performance.
This difficulty increases firm uncertainty.
We assume that higher quality accounting will decrease information asymmetry and therefore
decrease firm uncertainty, leading to less forecast dispersion. With their stronger orientation
toward fair value accounting, IFRS are likely to provide a better representation of a firm’s
underlying economic value and should therefore decrease information asymmetry. However,
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increased opportunities for managerial discretion could create a degree of uncertainty. The
effect of IFRS on forecast dispersion may also depend on whether analysts use more public or
private information (Heflin et al., 2003; Irani and Karamanou, 2003). If public information is
the primary source used, there should be less dispersion because public information is
available to all. However, if analysts seek to gain advantage by gathering private information
in response to an increase in public information, policies for the improvement of accounting
information may increase dispersion. It could also be the case that analysts choose to ‘herd’
when earnings are more uncertain, leading to less forecast dispersion for firms with less
predictable earnings (Ramnath et al., 2008). Using the BKLS model, Yang (2010) empirically
tests whether analysts use more public or private information after IFRS adoption in 18
countries. Yang concludes that both public and private information increase after mandatory
IFRS adoption and that the overall effect is a decrease in forecast dispersion among analysts.
Interestingly, public and private information increase more in common law countries than in
civil law countries, indicating that dispersion can increase in some countries while decreasing
in others.
Because available evidence suggests that financial analysts use financial reports as a primary
source (Block, 1999; Roger and Grant, 1997) of firm information and because it is likely that
a better representation of the underlying economic value will outweigh the negative effects of
an increase in management discretion, our second hypothesis is as follows:
H2: The introduction of IFRS reduces the dispersion of analysts’ earnings forecasts
2.3 The relative impact of IFRS
The last decade has seen the emergence of considerable research discussing the influence of
legal and institutional settings on accounting quality (e.g., Byard et al., 2011; Soderstrom and
12
Sun, 2007). In several cases, the research is based on the assumption that these settings have a
significant impact on accounting quality, which, in turn, affects analysts’ ability to make
accurate forecasts. Other studies have also emphasized firm-specific characteristics, as well as
the importance of reporting firms’ incentives as salient factors in the success of IFRS. Byard
et al. (2011) and Jeanjean and Stolowy (2008), for example, stress the importance of firms’
reporting incentives, which are influenced by legal institutions, various market forces, firms’
operating characteristics and the like. In line with Zeff (2007), it is therefore reasonable to
assume that country-specific differences such as business and financial culture, accounting
culture, auditing culture and regulatory culture are likely to affect the success of IFRS
implementation.
We should therefore expect the introduction of IFRS to have different effects on accounting
quality in different legal and institutional settings. For instance, one variable aspect is the
strength of accounting standard enforcement (Preiato et al., 2010), which influences
managerial discretion. Studies by Francis et al. (2003) and Hope (2003), among others, found
that common law countries (i.e., the UK and Ireland) have stronger enforcement mechanisms
than code law countries (i.e., the rest of the EU). The international accounting literature also
indicates that accounting quality is higher in countries with a common law origin (Ali and
Hwang, 2000; Ball et al., 2000; Leuz et al., 2003), and enforcement mechanisms appear to
influence the expected quality of financial reporting under IFRS (Ball et al., 2000; Ball, 2006;
Barth et al., 2008; Byard et al., 2011; Dao, 2005; Daske et al., 2008). Barth et al. (2008)
conclude that the potential benefits of the introduction of IFRS are difficult to attain without
the existence of effective enforcement mechanisms (cf. Byard et al., 2011; Preiato et al.,
2010).
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Another important factor is the quality of the accounting standard previously used in a given
country. If the standard was of low quality, the positive effects of changing to a standard that
better reflects a firm’s underlying economic value would be expected to be more significant
(Byard et al., 2011). The magnitude of differences between IFRS and the local GAAP that
they have replaced varies considerably (Bae et al., 2008; Nobes, 1983), and it is therefore
reasonable to expect IFRS’ effect on analyst performance to vary among countries. Thus, our
third set of hypotheses is as follows:
H3a: The larger the difference between IFRS and the previous GAAP, the more the
introduction of IFRS increases forecast accuracy
H3b: The stronger the enforcement of accounting standards, the more the introduction of
IFRS increases forecast accuracy
H4a: The larger the difference between IFRS and the previous GAAP, the more the
introduction of IFRS decreases forecast dispersion
H4b: The stronger the enforcement of accounting standards, the more the introduction of
IFRS decreases forecast dispersion
2.4 The moderating effect of intangible assets
IFRS imply a higher degree of fair value accounting. In particular, IFRS systematically
employ fair value accounting for classes of assets such as intangible assets, financial
instruments, investment property and biological assets. Therefore, the potential improvement
triggered by IFRS in terms of the correspondence between firms’ accounting and underlying
economic value is likely to be more sizeable for firms with a higher proportion of such assets.
In the absence of fair value accounting data, intangible assets are particularly difficult for
14
analysts to value because they are often unique and, as such, lack official market values.
Therefore, it is reasonable to assume that the higher the proportion of a firm’s intangible
assets, the more analysts’ forecast accuracy will improve and forecast dispersion diminish
following the introduction of IFRS. Our fifth and sixth hypotheses are therefore as follows:
H5: The higher the proportion of intangible assets, the more the introduction of IFRS
increases the accuracy of analysts’ earnings forecasts
H6: The higher the proportion of intangible assets, the more the introduction of IFRS reduces
the dispersion of analysts’ earnings forecasts
3. Method and sample
3.1 Sample
Our sample consists of 2,447 publicly traded companies from five European countries. The
countries were chosen primarily because of differences in the countries’ accounting histories
(Nobes, 1983). Our third and fourth hypotheses identify the two relevant dimensions as being
the difference between previous GAAP and IFRS and the enforcement of accounting
standards. Table 1 reports measures of these two dimensions pertaining to the five countries in
the sample.
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Insert Table 1 about here
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15
The measure of difference between previous GAAP and IFRS (henceforth GAAP-difference)
is provided by Bae et al. (2008), who report an index measuring conformity with IAS
consisting of 21 specific items in 2001. The higher the number, the larger the difference is, the
maximum value being 21. The measure is likely to overstate the change resulting from
mandatory IFRS adoption because many local standard setters began to harmonize local
GAAP with IAS before the mandatory adoption period. However, the measure is likely to
capture the difference between IFRS and the more long-term, prior accounting tradition in
each country, which, according to Kvaal and Nobes (2012), have a persistent influence on
accounting despite IFRS adoption. We also report a measure of accounting standard
enforcement developed by Preiato et al. (2010), which is based on 19 items and a scale
ranging from 0 to 27, capturing effects of the strength of the audit function and the accounting
enforcement body, respectively. The table reports the measure for the year 2005, when IFRS
were made mandatory in our sample countries.
Our sample countries display variation on the dimensions of GAAP-difference and
enforcement. The UK and the Netherlands exhibit the smallest GAAP-difference, whereas
France, Germany and Sweden display larger differences. The UK is regarded as having strong
enforcement. According to Preiato et al. (2010), Sweden and the Netherlands have relatively
weak enforcement, whereas France and Germany have stronger enforcement.
Together, these results suggest four clusters: (i) France and Germany, which display both a
relatively significant GAAP-difference and relatively strong enforcement; (ii) the UK, which
displays a negligible GAAP-difference but strong enforcement; (iii) Sweden, which exhibits a
sizeable GAAP-difference and weak enforcement; and (iv) the Netherlands, which displays a
small GAAP-difference and weak enforcement. This clustering would suggest that based on
hypotheses three and four, we could expect the strongest impact on analyst performance to
16
occur in France and Germany. Conversely, the effect could be expected to be negligible in the
Netherlands. Sweden and the UK appear to be opposites. Therefore, if GAAP-difference is
more important to IFRS impact than enforcement, we would expect the effect on analyst
performance to be more pronounced in Sweden than in the UK and vice versa.
3.2 Variables and descriptive statistics
Analysts’ performance is usually measured in the financial literature in terms of forecast
accuracy and/or the correctness of stock recommendation or price target, depending on how
the final output is viewed. Schipper (1991) discusses the reasonable belief that analysts’
earnings forecasts should relate to their stock recommendations, which suggests that forecasts
and valuation estimates (relative to current price) should be positively related to stock
recommendations. However, research into this area does not support these conjectures.
Bradshaw (2004) demonstrates that recommendations and valuation estimates in the US are
either insignificantly or negatively related, depending on the specification. EPS predictions
are likely to suffer from less bias and better reflect analysts’ use of accounting information
and, therefore, accounting quality.
To make a forecast, an analyst processes much information from a firm; the degree of
accuracy is therefore highly related to the level of informational asymmetry between the
analyst and the firm. Accordingly, forecast accuracy is selected as our first performance
variable. Because the literature also suggests that forecast dispersion could be viewed as a
measure of information asymmetry (Krshnaswami and Subramaniam 1999), forecast
dispersion is our second performance variable.
In accordance with Lang and Lundholm (1996), the first dependent variable, forecast
accuracy, is calculated as the negative of the absolute value of the actual earnings minus the
17
analyst’s earnings forecast, scaled by the stock price at the beginning of the year, and
forecasted EPSt is the mean analyst forecast of the earning per share during period t.
year fiscal theof beginning at the priceStock Accuracy Forecast tt EPSForecastedEPSActual −−=
Forecast accuracy is defined as the negative of the scaled absolute forecast error. In other
words, more accurate forecasts are represented by higher (less negative) values, i.e., lower
forecast error, with zero representing a perfect forecast. Analysts usually forecast the earnings
per share (EPS) of a particular fiscal year several times before the actual figures are released.
The frequency of the forecasts differs in accordance with the analyst. The Institutional
Brokers’ Estimate System (I/B/E/S) collects forecast data from individual analysts around the
world once a month and uses those data to calculate statistics such as the mean, median, and
standard deviation. Only the final estimates of the analysts are included in the monthly
calculation. Thus, the I/B/E/S database provides calculated statistics of analysts’ EPS
forecasts once a month. In this study, we utilize the general methodology for collecting
forecast data (see, for example, Lang and Lundholm, 1996) using the final calculated mean of
an analyst’s EPS forecasts before the first quarterly EPS report is released. For example, for a
firm with a fiscal yearend of December 31, 2009, we use the mean forecast calculated in
March 2009 as the forecast data for the actual EPS on December 31, 2009.
The second dependent variable, forecast dispersion, is the inter-analyst standard deviation of
forecasts, scaled by the stock price at the beginning of the year, also in line with Lang and
Lundholm (1996). Standard deviations are always positive numbers, but we have changed the
sign so that the logic in use for the scale of forecast dispersion is the same as that in use for
forecast accuracy, i.e. so that a lower (more negative) forecast dispersion represents a higher
standard deviation.
18
Because the forecast measures are scaled with stock price, cross-company comparisons are
possible. To test our hypotheses, we use two test variables, ‘accounting standard followed’
and ‘interaction term,’ which is the interaction between the standard followed and the
proportion of intangible assets, and six control variables. Accounting standard followed is a
dummy variable for which 1 is used for IFRS and 0 is used for every other accounting
standard. This variable is a firm-level variable, which means that early IFRS adopters are
identified as IFRS users even though this usage is not mandatory. The variable is set to 1 one
year after a firm’s implementation of IFRS because analysts could not have based their
adoption-year predictions on IFRS accounting, as the EPS predictions we use were formulated
before the first IFRS-based quarterly report. We obtain the interaction term by multiplying the
dummy variable by the proportion of intangible assets to total assets. The six control variables
(see Table 2), selected on the basis of prior research into factors that normally affect analysts’
performance (Lang and Lundholm 1996), are as follows: number of analysts, market value,
trading volume, earnings surprise, profit/loss, and standard deviation of return on equity (std
ROE).
-----------------------------------------------------------------------------------
Insert Table 2 about here
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The number of analysts is determined by simply a count of those following the company and
providing earnings forecast, again in line with Lang and Lundholm (1996). We control for
firm size using market value and trading volume. Firm size is used in the literature as a proxy
for several factors. Size should reflect information availability and therefore be positively
related to forecast accuracy. Brennan and Hughes (1991) also found empirical evidence
19
between firm size and analysts following a firm, and Lang and Lundholm (1993) found that
firm size and performance variability likely correlate with disclosure policy. Market value is
measured as the company’s market value at the beginning of the fiscal year and is commonly
used to control for size. However, we also utilize trading volume as a control for size because
it may be more indicative of the number of analysts following a firm, as analysts are often
paid indirectly through trading activity. Trading volume refers to the company’s absolute
daily trading volume during the first month of the fiscal year. Earnings surprise, which is the
variation in a firm’s results from one year to another, is calculated as the absolute value of the
year’s earnings per share minus the previous year’s earnings per share, scaled by the share
price at the beginning of the fiscal year. EPSt is the earnings per share during period t (of a
given year), and EPSt-1 is the earnings per share during period t-1 (the previous year).
year fiscal theof beginning at the priceStock SurpriseEarnings 1−−= tt EPSEPS
According to Lang and Lundholm (1996), earnings surprise controls for the likely effect that
major events, such as a firm's introduction of a new product, have on forecasts. In these
circumstances, realized earnings are most apt to deviate from expected earnings, and it is
likely that analysts will not be able to make accurate forecasts.
Hope (2003) suggests that it is much more difficult to predict future earnings for firms with
negative earnings. We therefore use a control variable, loss, a dummy variable that has a
value of 1 if the company reported a loss and 0 otherwise. King et al. (1990) found that the
number of analysts following firms is likely to be related to variations in return. Fewer
analysts follow firms that experience significant fluctuations in profitability. In other words, a
negative relationship exists between the number of analysts and variations in profitability.
20
Thus, standard deviation of return on equity is the final control variable in our regressions,
and it is measured as the company’s return on equity over the previous three years.
Table 3 contains descriptive statistics for the sample. Within each country, we select all
publicly traded companies that were followed by analysts. We do not attempt to follow
individual analysts because we assume that the ability to predict values for a specific firm
improves over time and therefore might bias the results. From this population, we then
extracted those companies with at least one year of both IFRS reporting and non-IFRS
reporting. Persuaded by Byard et al.’s (2011) argument regarding the necessity of examining
the impact of IFRS on analysts’ forecasts over a longer time period, we chose the period of
1996-2009. However, it should be noted that because some companies existed during this
entire time period and others for only a couple of years, our sample is an unbalanced panel
data set. From our full sample, we obtained 17,449 valid observations of forecast accuracy
and 15,003 valid observations of forecast dispersion. We are missing observations of forecast
dispersion because those firms followed by only one analyst cannot display forecast
dispersion. Forecast accuracy is calculated as a mean of all analysts’ predictions for a specific
firm (the number of analysts ranges from 1 to 49). As Table 3 shows, the mean forecast error
for the full sample is approximately 6.3 percent, with the worst analyst performance occurring
in Germany and the best in the UK. The mean forecast dispersion is highest in Germany and
lowest in the UK.
-----------------------------------------------------------------------------------
Insert Table 3 about here
--------------------------------------------------------------------------------------
21
An inspection of the data on forecast accuracy and forecast dispersion reveals that these two
variables are highly skewed and therefore not normally distributed. An inspection of kurtosis
values suggests the same conclusion. For the full sample, the skewness is -41.122 for forecast
accuracy and -34.359 for forecast dispersion. These values are obtained because most of the
observed forecast errors and dispersions are small. The picture is similar for individual
countries. It is therefore more rational to focus on median values, which are provided in Table
4, which also contains significance tests to determine whether pre and post IFRS medians
differ from each other. We observe a significant decrease in forecast accuracy post IFRS in
the full sample and in Germany and a significant increase in Sweden and the UK. Forecast
dispersion exhibits a more consistent pattern, with significant decreases in Sweden and
France.
-----------------------------------------------------------------------------------
Insert Table 4 about here
--------------------------------------------------------------------------------------
3.3 Models and estimation techniques
We estimate the following two equations:
ititit
itititit
ititititQ
εββ
ββββ
βββαθ
+++
++++
+++=
n termInteractio9 Assets /TotalIntangible8
followed standard Accounting7ROE dev Std6surprise Earnings5sProfit/Los4
volumeTrading3ueMarket val2analysts ofNumber 1)accuracyForecast(
(1)
22
ititit
itititit
ititititQ
εββ
ββββ
βββαθ
+++
++++
+++=
n termInteractio9assets /TotalIntangible8
followed standard Accounting7ROE dev Std6surprise Earnings5sProfit/Los4
volumeTrading3ueMarket val2analysts ofNumber 1)dispersionForecast (
(2)
Equation 1 estimates the effect of our two test variables (accounting standard followed and
interaction term) and the six control variables for forecast accuracy. Equation 2 estimates the
effect of these same variables on forecast dispersion. We estimate both models for the entire
sample, as well as for individual countries.
Because of the skewness in our dependent variables, ordinary least squares (OLS) regression
models, fixed and random effects models, and other regression models yielding estimates that
predict the conditional mean of the dependent variable risk the obtainment of biased
estimation results. We therefore use a median regression model, namely, the quantile
regression estimation technique (Koenker and Baset, 1978), which, because it is a regression
model that does not produce estimates that predict the mean, is robust against this type of
problem. Another benefit of median regression models is their lack of sensitivity to outliers,
which allows for the use of all observations in estimations. The quantile regression model is a
technique for estimating the θth quantile (i.e., percentile) of a variable (in this case, the
dependent variables forecast accuracy and forecast dispersion), conditional on the values of
the predictor variables. This method allows us to estimate the effect of the variable accounting
standard followed in various percentiles of the distribution of the variables forecast accuracy
and forecast dispersion, not only at their respective means. In other words, quantile regression
enables us to estimate the effect of a changing accounting standard on the size of θth quantile
forecast errors and dispersions while mitigating the effects of skewness produced by the many
smaller forecast errors and dispersions. In this study, we estimate quantile regression models
23
predicting the 10th, 50th (i.e., the median) and 90th percentile for the full sample, as well as
for individual countries. Thus, when predicting the 10th percentile, we study the effect of
IFRS on only the largest forecast errors or highest dispersions. In addition to statistical
arguments, theoretical arguments justify the use of this procedure. Small forecast errors and
small forecast dispersions are likely to be random and independent of poor-quality financial
reporting. Hence, one could argue that whatever steps are taken to introduce improvements, it
is most likely not possible to eliminate small errors.
4. Results
Table 5 provides the correlations of all variables. The two dependent variables, forecast
accuracy and forecast dispersion, are significantly correlated, indicating a relationship
whereby an increase in forecast accuracy correlates with a decrease in forecast dispersion.
Therefore, when analysts reach greater consensus, their EPS forecasts become more accurate.
None of these variables is significantly correlated with accounting standard followed or the
interaction term. Accounting standard followed and the interaction term correlate negatively
with forecast accuracy, which is counter to the hypothesized relationship, in which the
introduction of IFRS would increase forecast accuracy, particularly in firms with a higher
degree of intangible assets. The table also shows that all six control variables significantly
correlate with forecast accuracy. The signs indicate that the number of analysts, market value,
and trading volume are associated with increased forecast accuracy, whereas unprofitable
firms, earnings surprise and variation in profitability appear to worsen forecast accuracy.
These correlations all occur in the expected direction, as observed earlier in Table 1. The table
provides similar results for forecast dispersion, with the exception of trading volume, which is
not significant. There appear to be no problems with multicollinearity; the highest correlation
among the independent variables is 0.35 (for market value and number of analysts). However,
24
as expected, a high positive correlation exists between accounting standard followed and the
interaction term. Nonetheless, all variance inflation factor (VIF) values are lower than 2; thus,
there is no reason to believe that this correlation is affecting the accuracy of the estimations.
------------------------------------------------------------
Insert Table 5 about here
-------------------------------------------------------------
The complete results of the regressions are presented in appendices 1-6. The strongest
predictors of forecast accuracy and forecast dispersion are the control variables profit/loss and
earnings surprise, which have the expected signs in all cases except two (earnings surprise for
Sweden and the Netherlands, in appendix 6). Overall, the models have much higher
explanatory power when predicting the 10th percentile (Pseudo R2 from 0.43-0.63 for
estimations of equation 1 and 0.25-0.44 for estimations of equation 2) than when predicting
the 50th percentile (Pseudo R2 from 0.17-0.27 for equation 1 and 0.05-0.14 for equation 2) or
the 90th percentile (Pseudo R2 from 0.03-0.05 for equation 1 and 0.02-0.03 for equation 2).
This result indicates that smaller errors and dispersions are more random than systematic and,
therefore, should be little affected by a change in accounting standard.
Table 6, which summarizes the results for the test variables from appendices 1-3, indicates
that IFRS have no measurable impact on forecast accuracy, either overall or for individual
countries, and that there are no measurable differences between firms with varying degrees of
intangible assets. None of the coefficients is significant and there is no consistency among the
signs of the coefficients. Therefore, in general, we fail to find evidence in support of H1 or
H5. Because we find no systematic difference between countries, the results support neither
25
H3a nor H3b. IFRS appear to have no impact on forecast accuracy, regardless of prior GAAP-
difference or legal enforcement.
------------------------------------------------------------
Insert Table 6 about here
-------------------------------------------------------------
Table 7, which summarizes the results for the test variables from appendices 4-6, shows a
slightly more consistent pattern regarding forecast dispersion. All coefficients of the variable
accounting standard followed are positive and many are significant, indicating that IFRS
appears to result in diminished forecast dispersion. In particular, IFRS appears to have
affected forecast dispersion in the 50th percentile; the only exception is the UK, for which
there is a positive but insignificant coefficient. In the 10th percentile and the 90th percentile,
the effect is not equally consistent, but there is a significant positive effect in France and for
the full sample at the 10th percentile level and a significant positive effect in the Netherlands
at the 90th percentile level. However, the interaction term exhibits no consistent pattern. The
signs are mixed, and only one coefficient is significant, although it has a sign that is the
opposite of that expected.
------------------------------------------------------------
Insert Table 7 about here
-------------------------------------------------------------
These results provide some support for H2 but no support for H6. Although IFRS appear to
have a measurable impact on forecast dispersion, the degree of intangible asset appears not to
26
affect this relationship. When examining cross-country differences, we note that in the UK,
forecast dispersion has not been affected by IFRS. The UK exhibits the smallest GAAP-
difference of all of the countries in the sample, which might explain why there is no effect on
forecast dispersion. At the same time, the UK has the strongest enforcement, which in theory,
should indicate a stronger effect. Another country with a low GAAP-difference is the
Netherlands, in which we detect an effect both in the 10th percentile and the 50th percentile,
which suggests that the impact of GAAP-difference is not so straightforward. The
Netherlands has the weakest enforcement in the sample but still displays positive significant
coefficients. The only other country that has two significant coefficients, in the 50th percentile
and in the 90th percentile, is France, which exhibits both a high GAAP-difference and strong
enforcement. That there is an effect in Sweden, a country with a large GAAP-difference but
weak enforcement, but not the UK, suggests that GAAP-difference is more important than
enforcement. Although the results for the Netherlands may appear to discredit this
interpretation, it is possible that GAAP-difference has a non-linear positive effect, suggesting
support for H4a. It is difficult to argue that enforcement has an effect based on our results,
indicating that H4b is likely false.
Broadly speaking, our results indicate that analysts have become more uniform in their
forecasts since the introduction of IFRS, suggesting that uncertainty among these
professionals has decreased. This decreased uncertainty appears not to be driven by IFRS
asset valuation methods’ better representation of firms’ underlying economic value because
the effect is not more pronounced in firms with higher degrees of intangible assets.
27
5. Robustness analysis
As an alternative procedure, we have also estimated our equations using OLS regressions.
Because contrary to quantile regression, OLS is sensitive to skewness and outliers, the sample
was winsorized. A total of 2.5 percent of each tail was altered during this process, which
should resolve the outlier problem. The sample is still highly skewed, so the risk of biased
estimators remains and the result should be interpreted with caution. The estimated models
are provided in appendices 7-8.
The result for forecast accuracy differs slightly from that obtained using quantile regression.
There is a small but significant positive effect in Sweden and France, whereas there is a
significant and negative effect in the UK. We find no significant effect for the sample as a
whole. Obviously, these effects are not consistent and, because of the remaining skewness of
the sample, this result might be viewed as being uncertain. However, it is possible to argue
that this result suggests that IFRS have increased forecast accuracy in Sweden and France
while decreasing it in the UK. The interaction term is significant and positive for Germany
but not for any other country or overall. The result for forecast dispersion is practically the
same as that obtained using quantile regression. We record a positive effect of IFRS in all
countries except the UK. Three countries (Sweden, France and Germany) exhibit significant
interaction terms, but these point in different directions.
The OLS estimations thus do little to challenge the overall pattern identified in the data.
Although a few more coefficients become significant, there is no consistent pattern, which
causes us to suspect that they are spurious.
28
6. Conclusions
In summary, our results demonstrate that IFRS have no impact on financial analysts’ forecast
accuracy but a more consistent impact on forecast dispersion, which has diminished in all
countries in the sample except the UK. These results appear not to be driven by the asset
valuation methods of IFRS, but the difference between IFRS and prior GAAP appears to have
an impact.
Prior research suggests that IAS/IFRS have a positive effect on analyst performance overall
(e.g., Ashbaugh and Pincus, 2001; Jiao et al., 2012) or at least if accounting standard
enforcement is strong (Byard et al., 2011; Horton et al., 2012; Preiato et al., 2010), prior
GAAP differ from IFRS (Beuselinck et al., 2010), or prior disclosure standards were of high
quality (Yang, 2010). Our study contributes to this literature because we combine a long time
period with a consistent estimation method that allows us to estimate the impact of IFRS on
both large and small errors without assuming the same impact for the entire distribution and
introduce a new proxy that allows us to determine whether it is the asset measurement
methods of IFRS that affect accounting quality. Our results differ slightly in that we find no
overall improvement in forecast accuracy regardless of prior GAAP-difference or
enforcement, whereas the impact of forecast dispersion is more broad and positive. The only
country in our sample that did not see a diminished forecast dispersion was the UK, a country
in which enforcement is strong and GAAP-difference is minimal. Because Sweden, a country
with weak enforcement of accounting standards but a significant GAAP-difference, displays
decreasing forecast dispersion, our results suggest, in line with Beuselinck et al. (2010), that
GAAP-difference is the crucial factor.
29
Because forecast dispersion is a measure of information asymmetry (Krishnaswami and
Subramaniam, 1999), we claim that some aspect of information asymmetry has likely
decreased under IFRS although forecast accuracy has not increased. This effect is an
indication that analysts have a more even playing field, suggesting that some previously
private or withheld information is now available to more analysts, which would explain why
forecast dispersion has decreased. This explanation would imply that the qualitative increase
produced by IFRS may therefore be more connected to increasing information harmonization
and comparability than with making firms’ financial reports more accurately represent
underlying economic value. Standard setters argue that fair value accounting provides more
relevant information for predictions of firm performance (Hitz, 2007). If this is the case,
perhaps analysts processed accounting numbers acquired from historical cost accounting to
obtain estimates of fair value under their local GAAP. With IFRS, this processing is no longer
as necessary, as it is conducted by firms themselves. This method is likely to result in less
forecast dispersion because all analysts will have access to the same fair value accounting
numbers, although on average, these estimates may not be superior to the average estimates
processed under national GAAP. If this interpretation is correct, the implication is that IFRS
accounting methods create a more level playing field for accounting users but without
necessarily producing higher predictive value. Another more speculative implication is that
fair value accounting (which is more pronounced in IFRS than in previous GAAP) is
preferred over historical cost accounting by financial analysts.
Soderstrom and Sun (2007) argue that there is a direct link between accounting standard and
accounting quality. If this link exists, transitioning to an accounting standard of higher quality
should increase accounting quality. Our results imply that it is fruitful to distinguish between
how accounting quality affects users’ accuracy and how it affects users’ consistency.
30
Although IFRS appears to affect consistency, we have little evidence to suggest that it affects
users’ accuracy. Therefore, IFRS can be viewed as a standard of higher quality than
previously used local accounting standards in Sweden, France, the Netherlands and Germany,
but only in the sense of consistency. This conclusion would appear to imply that there is a
need to develop additional measures of accounting quality that consider this distinction for
both practical and research applications because it is difficult to see how the standard
measures of accounting quality (earnings management, timely loss recognition and value
relevance) can capture this dimension.
A number of factors beyond the standards themselves could explain the limited effect of IFRS
on analyst performance. Our empirical design has limited power in isolating the effects of
IFRS from those of, for example, general macroeconomic changes or general developments in
financial markets. However, we do use control variables to mitigate those time periods in
which the job of prediction is especially troublesome. Another design limitation is the
assumption that financial analysts use financial reports as a primary source of information
when formulating predictions. Although this idea is supported by the literature (Block, 1999;
Roger and Grant, 1997) and our findings regarding forecast dispersion, more thorough
documentation is needed to demonstrate how analysts actually use financial reports for
developing forecasts and how different sources of information relate to one another. Such
documentation will aid us in gaining a better understanding of the effects of changing
accounting standards. A number of previous studies (Ball, 2006; Zeff, 2007) suggest that the
implementation of IFRS will not have a uniform impact in all countries and that
implementation will take time. Kvaal and Nobes (2012) find that national patterns still
prevail. Our design is likely better suited to distinguishing between immediate and uniform
effects.
31
Despite these limitations, we conclude that when evaluated from a decision usefulness
perspective, IFRS has limited impact on accounting quality in the examined countries;
however, this impact is connected more to the presentation of more consistent pictures for
predictions of firm performance than to the presentation of more accurate pictures.
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35
Table 1. GAAP difference and enforcement
GAAP‐difference Bae et al. 2008
Enforcement Preiato et al. 2010
Sweden 10 14Netherlands 4 12
France 12 21Germany 11 18
United Kingdom 1 24
36
Table 2. Independent variables Variable Explanation Predicted sign of the
dependent variables Number of analysts The number of analysts following a company. + Market value Market value is measured as the company’s
market value at the beginning of the fiscal year. +
Trading volume Trading volume refers to the company’s absolute daily trading volume during the first month of the fiscal year.
+
Profit/Loss Loss, a dummy variable that takes the value of 1 if the company reported a loss and 0 otherwise.
-
Earnings surprise The absolute value of the year’s earnings per share, minus the previous year’s earnings per share, scaled by the share price at the beginning of the fiscal year. EPSt is the earnings per share during period t (of a given year), and EPSt-1 is the earnings per share in period t-1 (the previous year).
-
Std ROE The company’s standard deviation return on equity over the previous three years.
-
Accounting standard followed
A dummy variable that takes the value of 1 if the company used IFRS for preparing last years’ financial reports and 0 otherwise.
+
Proportion of intangible assets
Reported value of intangible assets divided by reported total assets.
?
Interaction term Accounting standard followed multiplied by proportion of intangible assets.
+
37
Table 3. Sample statistics
Number of observations Mean Std Dev
Country Number
of firms Total
sample
Valid
Forecast
accuracy
Valid
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Sweden 259 1829 1829 1460 -0.0633 -0.0201 0.1748 0.0424
Netherlands 93 1064 1064 1043 -0.0562 -0.0170 0.3111 0.0622
France 635 3836 3836 3404 -0.0613 -0.0182 0.2298 0.0615
Germany 647 3797 3797 3186 -0.1139 -0.0390 0.5477 0.2323
UK 813 6923 6923 5910 -0.0380 -0.0125 0.1718 0.0568
All 2447 17449 17449 15003 -0.0634 -0.0205 0.3138 0.1189
38
Table 4. Medians and percentiles
Significant difference comparing medians pre and post IFRS * p < .05
25th percentile Median 75th percentile Median
Forecast accuracy
Median
Forecast dispersion Country
Forecast
accuracy
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Forecast
accuracy
Forecast
dispersion
Pre IFRS Post IFRS Pre IFRS Post IFRS
Sweden -0.0627 -0.0178 -0.0245 -0.0088 -0.0091 -0.0044 -0.0260* -0.0190* -0.0094* -0.0076*
Netherlands -0.0414 -0.0127 -0.0172 -0.0056 -0.0070 -0.0024 -0.0164 -0.0190 -0.0057 -0.0054
France -0.0462 -0.0152 -0.0177 -0.0068 -0.0066 -0.0033 -0.0179 -0.0174 -0.0074* -0.0061*
Germany -0.0724 -0.0190 -0.0257 -0.0074 -0.0088 -0.0034 -0.0234* -0.0282* -0.0076 -0.0072
UK -0.0263 -0.0082 -0.0101 -0.0036 -0.0032 -0.0015 -0.0105* -0.0093* -0.0036 -0.0033
All -0.0439 -0.0129 -0.0157 -0.0056 -0.0053 -0.0024 -0.0152* -0.0163* -0.0056 -0.0055
39
Table 5. Variable correlations
Variable 1 2 3 4 5 6 7 8 9 10 11 1. Forecast accuracy 1.0000 2. Forecast dispersion 0.8123* 1.0000 3. Number of analysts 0.0565* 0.0596* 1.0000 4. Market value 0.0211* 0.0270* 0.3496* 1.0000 5. Trading volume 0.0201* 0.0153 0.2902* 0.2974* 1.0000 6. Profit/loss -0.1875* -0.2036* -0.1522* -0.0464* -0.0426* 1.0000 7. Earnings surprise -0.8456* -0.6996* -0.0632* -0.0257* -0.0194 0.1829* 1.0000 8. Standard deviation of Roe -0.0234* -0.0264* -0.0629* 0.0791* -0.0178 0.1415* 0.0221* 1.0000 9. Accounting standard -0.0119 0.0159 -0.0281* 0.0189 0.0185 -0.0272* 0.0123 -0.0268* 1.0000 10. Intangible/Total -0.0193 -0.0128 -0.0240* -0.0293* -0.0371* 0.0055 0.0022 0.0437* 0.1917* 1.0000 11. Interaction term -0.0171 -0.0026 -0.0127 -0.0076 -0.0123 -0.0395* 0.0067 -0.0128 0.6531* 0.5590* 1.0000
* p < .05
40
Table 6. Quantile regression on dependent variable ‘forecast accuracy’ – results for the variable ‘accounting standard followed’ and the interaction variable between the proportion of intangible assets and accounting standard followed
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Panel A: Quantile 10 Standard 0.0137 -0.0213 0.0101 0.0040 0.0021 -0.0030 (0.0116) (0.0140) (0.0084) (0.0118) (0.0029) (0.0048) Interaction -0.0390 -0.0954 -0.0061 0.0591 0.0036 0.0113 Term (0.0450) (0.0628) (0.0327) (0.0551) (0.0103) (0.0192) Panel B: Quantile 50 Standard 0.0053 -0.0003 0.0032 0.0024 0.0005 0.0014 (0.0053) (0.0065) (0.0030) (0.0063) (0.0027) (0.0018) Interaction -0.0232 0.0029 -0.0051 -0.0078 0.0011 -0.0017 Term (0.0207) (0.0293) (0.0119) (0.0292) (0.0095) (0.0071) Panel C: Quantile 90 Standard 0.0014 0.0010 0.0008 0.0007 0.0000 0.0001 (0.0018) (0.0015) (0.0010) (0.0017) (0.0003) (0.0004) Interaction -0.0028 -0.0007 -0.0014 -0.0012 0.0005 0.0004 Term (0.0069) (0.0066) (0.0039) (0.0081) (0.0009) (0.0017) Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
41
Table 7. Quantile regression on dependent variable ‘forecast dispersion’ – results for the variable ‘accounting standard followed’ and the interaction variable between the proportion of intangible assets and accounting standard followed
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Panel A: Quantile 10 Standard 0.0088 0.0038 0.0093** 0.0099 0.0008 0.0044*** (0.0056) (0.0077) (0.0040) (0.0084) (0.0021) (0.0015) Interaction -0.0124 0.0043 -0.0114 0.0002 0.0076 -0.0005 Term (0.0213) (0.0349) (0.0149) (0.0403) (0.0075) (0.0059) Panel B: Quantile 50 Standard 0.0025** 0.0020* 0.0018** 0.0020* 0.0005 0.0007** (0.0011) (0.0011) (0.0008) (0.0012) (0.0004) (0.0004) Interaction -0.0075* -0.0049 -0.0022 0.0015 -0.0004 -0.0002 Term (0.0043) (0.0051) (0.0028) (0.0058) (0.0015) (0.0014) Panel C: Quantile 90 Standard 0.0001 0.0012** 0.0003 0.0002 0.0001 0.0001 (0.0004) (0.0006) (0.0003) (0.0004) (0.0001) (0.0001) Interaction -0.0013 -0.0008 -0.0012 0.0016 0.0001 0.0000 Term (0.0016) (0.0026) (0.0010) (0.0020) (0.0004) (0.0005) Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
42
Appendix 1 Quantile regression (10%) on dependent variable ‘forecast accuracy’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0012* 0.0006 0.0014*** 0.0006 0.0006*** 0.0005** analysts (0.0007) (0.0005) (0.0004) (0.0006) (0.0002) (0.0002) Market value 0.0000 0.0000 0.0000 ‐0.0000 0.0000 ‐0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000 ‐0.0000** 0.0000 ‐0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0665*** ‐0.0818*** ‐0.1204*** ‐0.1085*** ‐0.0972*** ‐0.1086*** (0.0123) (0.0167) (0.0087) (0.0122) (0.0034) (0.0053) Earning ‐0.9918*** ‐0.8924*** ‐0.9437*** ‐1.3495*** ‐0.9303*** ‐1.1088*** surprise (0.0182) (0.0122) (0.0220) (0.0086) (0.0022) (0.0044) Std roe ‐0.0014*** ‐0.0291* ‐0.0017 ‐0.0891*** ‐0.0058*** ‐0.0017*** (0.0003) (0.0158) (0.0018) (0.0093) (0.0013) (0.0003) Standard 0.0137 ‐0.0213 0.0101 0.0040 0.0021 ‐0.0030 (0.0116) (0.0140) (0.0084) (0.0118) (0.0029) (0.0048) Intangible/Total 0.0590** 0.0101 0.0199 ‐0.0523 ‐0.0014 0.0032 (0.0275) (0.0353) (0.0196) (0.0392) (0.0059) (0.0114) Interaction ‐0.0390 ‐0.0954 ‐0.0061 0.0591 0.0036 0.0113 term (0.0450) (0.0628) (0.0327) (0.0551) (0.0103) (0.0192) Constant ‐0.0448*** ‐0.0247*** ‐0.0480*** ‐0.0276*** ‐0.0149*** ‐0.0227*** (0.0079) (0.0094) (0.0059) (0.0099) (0.0018) (0.0032) N 1270 861 2789 2069 4850 11839 R2 0.5208 0.6167 0.4327 0.5831 0.6333 0.5474 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
43
Appendix 2 Quantile regression (50%) on dependent variable ‘forecast accuracy’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0000 0.0001 0.0002 0.0002 0.0003* 0.0001 analysts (0.0003) (0.0002) (0.0001) (0.0003) (0.0002) (0.0001) Market value 0.0000 0.0000 0.0000 ‐0.0000 0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume 0.0000 ‐0.0000 ‐0.0000 0.0000 ‐0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0466*** ‐0.0707*** ‐0.0607*** ‐0.0570*** ‐0.0325*** ‐0.0486*** (0.0056) (0.0078) (0.0032) (0.0064) (0.0031) (0.0020) Earning ‐0.3075*** ‐0.5297*** ‐0.3092*** ‐0.5584*** ‐0.3739*** ‐0.4424*** surprise (0.0084) (0.0057) (0.0080) (0.0046) (0.0021) (0.0016) Std roe ‐0.0005*** 0.0020 ‐0.0008 0.0021 ‐0.0009 ‐0.0003*** (0.0001) (0.0073) (0.0006) (0.0049) (0.0012) (0.0001) Standard 0.0053 ‐0.0003 0.0032 0.0024 0.0005 0.0014 (0.0053) (0.0065) (0.0030) (0.0063) (0.0027) (0.0018) Intangible/Total 0.0234* 0.0058 0.0069 0.0004 ‐0.0029 ‐0.0002 (0.0126) (0.0165) (0.0072) (0.0208) (0.0054) (0.0042) Interaction ‐0.0232 0.0029 ‐0.0051 ‐0.0078 0.0011 ‐0.0017 term (0.0207) (0.0293) (0.0119) (0.0292) (0.0095) (0.0071) Constant ‐0.0100*** ‐0.0093** ‐0.0115*** ‐0.0064 ‐0.0057*** ‐0.0056*** (0.0036) (0.0044) (0.0022) (0.0052) (0.0017) (0.0012) N 1270 861 2789 2069 4850 11839 R2 0.1834 0.2693 0.1684 0.2419 0.1878 0.2021 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
44
Appendix 3 Quantile regression (90%) on dependent variable ‘forecast accuracy’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0000 ‐0.0000 0.0000 0.0001 0.0000*** 0.0000 analysts (0.0001) (0.0001) (0.0000) (0.0001) (0.0000) (0.0000) Market value 0.0000 0.0000 ‐0.0000 ‐0.0000 0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000 0.0000 0.0000 ‐0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0208*** ‐0.0195*** ‐0.0155*** ‐0.0206*** ‐0.0024*** ‐0.0094*** (0.0019) (0.0018) (0.0011) (0.0018) (0.0003) (0.0005) Earning ‐0.0086*** ‐0.0413*** ‐0.0474*** ‐0.0682*** ‐0.0224*** ‐0.0318*** surprise (0.0028) (0.0013) (0.0026) (0.0013) (0.0002) (0.0004) Std roe ‐0.0000 ‐0.0024 ‐0.0001 ‐0.0011 ‐0.0002 ‐0.0001*** (0.0000) (0.0017) (0.0002) (0.0014) (0.0001) (0.0000) Standard 0.0014 0.0010 0.0008 0.0007 0.0000 0.0001 (0.0018) (0.0015) (0.0010) (0.0017) (0.0003) (0.0004) Intangible/Total 0.0048 ‐0.0014 ‐0.0002 0.0008 ‐0.0003 ‐0.0003 (0.0042) (0.0037) (0.0024) (0.0058) (0.0005) (0.0010) Interaction ‐0.0028 ‐0.0007 ‐0.0014 ‐0.0012 0.0005 0.0004 term (0.0069) (0.0066) (0.0039) (0.0081) (0.0009) (0.0017) Constant ‐0.0039*** ‐0.0015 ‐0.0018** ‐0.0020 ‐0.0010*** ‐0.0013*** (0.0012) (0.0010) (0.0007) (0.0015) (0.0002) (0.0003) N 1270 861 2789 2069 4850 11839 R2 0.0275 0.0471 0.0343 0.0453 0.0281 0.0266 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
45
Appendix 4 Quantile regression (10%) on dependent variable ‘forecast dispersion’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0001 0.0004 0.0004** 0.0002 0.0001 0.0001** analysts (0.0003) (0.0003) (0.0002) (0.0004) (0.0001) (0.0001) Market value 0.0000 0.0000 0.0000 ‐0.0000 0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000 ‐0.0000** ‐0.0000 ‐0.0000* 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0658*** ‐0.0183** ‐0.0582*** ‐0.0182** ‐0.0481*** ‐0.0464*** (0.0061) (0.0091) (0.0041) (0.0090) (0.0030) (0.0018) Earning ‐0.1827*** ‐0.2523*** ‐0.2349*** ‐0.2907*** ‐0.3114*** ‐0.2912*** surprise (0.0084) (0.0065) (0.0106) (0.0057) (0.0033) (0.0017) Std roe ‐0.0003** ‐0.0914*** ‐0.0010 ‐0.1292*** ‐0.0045*** ‐0.0004*** (0.0001) (0.0090) (0.0008) (0.0076) (0.0009) (0.0001) Standard 0.0088 0.0038 0.0093** 0.0099 0.0008 0.0044*** (0.0056) (0.0077) (0.0040) (0.0084) (0.0021) (0.0015) Intangible/Total 0.0108 0.0091 0.0116 ‐0.0063 ‐0.0043 0.0023 (0.0132) (0.0190) (0.0088) (0.0287) (0.0044) (0.0036) Interaction ‐0.0124 0.0043 ‐0.0114 0.0002 0.0076 ‐0.0005 term (0.0213) (0.0349) (0.0149) (0.0403) (0.0075) (0.0059) Constant ‐0.0178*** ‐0.0147*** ‐0.0207*** ‐0.0141* ‐0.0078*** ‐0.0138*** (0.0043) (0.0053) (0.0029) (0.0074) (0.0015) (0.0011) N 1039 819 2446 1671 3888 9863 R2 0.3000 0.4353 0.2457 0.4184 0.4285 0.3618 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
46
Appendix 5 Quantile regression (50%) on dependent variable ‘forecast dispersion’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of ‐0.0001 0.0001* 0.0001* ‐0.0001 ‐0.0000 ‐0.0000** analysts (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) Market value 0.0000*** 0.0000 0.0000 0.0000 0.0000*** 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000 ‐0.0000*** 0.0000 ‐0.0000*** 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0085*** ‐0.0062*** ‐0.0092*** ‐0.0031** ‐0.0072*** ‐0.0074*** (0.0012) (0.0013) (0.0008) (0.0013) (0.0006) (0.0004) Earning ‐0.0189*** ‐0.0461*** ‐0.0456*** ‐0.1074*** ‐0.0828*** ‐0.0741*** surprise (0.0017) (0.0009) (0.0020) (0.0008) (0.0006) (0.0004) Std roe ‐0.0001** ‐0.0169*** ‐0.0004** ‐0.0023** ‐0.0010*** ‐0.0002*** (0.0000) (0.0013) (0.0002) (0.0011) (0.0002) (0.0000) Standard 0.0025** 0.0020* 0.0018** 0.0020* 0.0005 0.0007** (0.0011) (0.0011) (0.0008) (0.0012) (0.0004) (0.0004) Intangible/Total 0.0092*** ‐0.0007 0.0035** ‐0.0027 0.0006 0.0005 (0.0027) (0.0028) (0.0017) (0.0041) (0.0009) (0.0008) Interaction ‐0.0075* ‐0.0049 ‐0.0022 0.0015 ‐0.0004 ‐0.0002 term (0.0043) (0.0051) (0.0028) (0.0058) (0.0015) (0.0014) Constant ‐0.0076*** ‐0.0040*** ‐0.0063*** ‐0.0032*** ‐0.0019*** ‐0.0029*** (0.0009) (0.0008) (0.0005) (0.0011) (0.0003) (0.0003) N 1039 819 2446 1671 3888 9863 R2 0.0501 0.1447 0.0707 0.1247 0.1185 0.0972 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
47
Appendix 6 Quantile regression (90%) on dependent variable ‘forecast dispersion’
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of ‐0.0002*** 0.0000 ‐0.0000** ‐0.0001*** ‐0.0000*** ‐0.0000*** analysts (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Market value 0.0000*** 0.0000* 0.0000*** 0.0000 0.0000*** 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000*** ‐0.0000 0.0000 ‐0.0000*** 0.0000*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0017*** ‐0.0019*** ‐0.0018*** ‐0.0010** ‐0.0005*** ‐0.0013*** (0.0005) (0.0007) (0.0003) (0.0004) (0.0001) (0.0001) Earning 0.0000 0.0009* ‐0.0065*** ‐0.0126*** ‐0.0088*** ‐0.0087*** surprise (0.0006) (0.0005) (0.0007) (0.0003) (0.0002) (0.0001) Std roe 0.0000 ‐0.0030*** ‐0.0001* ‐0.0001 0.0001** ‐0.0000*** (0.0000) (0.0007) (0.0001) (0.0004) (0.0000) (0.0000) Standard 0.0001 0.0012** 0.0003 0.0002 0.0001 0.0001 (0.0004) (0.0006) (0.0003) (0.0004) (0.0001) (0.0001) Intangible/Total 0.0027*** 0.0002 0.0012* ‐0.0007 ‐0.0000 0.0001 (0.0010) (0.0014) (0.0006) (0.0014) (0.0002) (0.0003) Interaction ‐0.0013 ‐0.0008 ‐0.0012 0.0016 0.0001 0.0000 term (0.0016) (0.0026) (0.0010) (0.0020) (0.0004) (0.0005) Constant ‐0.0017*** ‐0.0012*** ‐0.0016*** ‐0.0008** ‐0.0004*** ‐0.0006*** (0.0003) (0.0004) (0.0002) (0.0004) (0.0001) (0.0001) N 1039 819 2446 1671 3888 9863 R2 0.0305 0.0212 0.0160 0.0266 0.0234 0.0158 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
48
Appendix 7 OLS regression on dependent variable ‘forecast accuracy’ with 2.5% winsorizing
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0006** 0.0009*** 0.0008*** 0.0011*** 0.0014*** 0.0008*** analysts (0.0003) (0.0002) (0.0002) (0.0002) (0.0001) (0.0001) Market value 0.0000 0.0000* 0.0000 0.0000 0.0000* 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume 0.0000 ‐0.0000*** ‐0.0000 ‐0.0000 ‐0.0000*** 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0689*** ‐0.1091*** ‐0.0760*** ‐0.1191*** ‐0.0870*** ‐0.0994*** (0.0052) (0.0064) (0.0035) (0.0046) (0.0026) (0.0018) Earning ‐0.0985*** ‐0.0643*** ‐0.2236*** ‐0.0393*** ‐0.0344*** ‐0.0467*** surprise (0.0078) (0.0047) (0.0089) (0.0032) (0.0017) (0.0015) Std roe ‐0.0010*** ‐0.0092 ‐0.0019*** ‐0.0023 ‐0.0029*** ‐0.0008*** (0.0001) (0.0060) (0.0007) (0.0035) (0.0010) (0.0001) Standard 0.0171*** 0.0023 0.0082** ‐0.0037 ‐0.0051** ‐0.0009 (0.0049) (0.0054) (0.0034) (0.0044) (0.0023) (0.0016) Intangible/Total 0.0505*** 0.0223 0.0125 ‐0.0397*** 0.0077* 0.0111*** (0.0117) (0.0136) (0.0079) (0.0147) (0.0045) (0.0038) Interaction ‐0.0312 ‐0.0226 0.0006 0.0484** 0.0006 0.0058 term (0.0192) (0.0241) (0.0132) (0.0206) (0.0080) (0.0064) Constant ‐0.0372*** ‐0.0399*** ‐0.0316*** ‐0.0411*** ‐0.0279*** ‐0.0335*** (0.0033) (0.0036) (0.0024) (0.0037) (0.0014) (0.0011) N 1270 861 2789 2069 4850 11839 R2 0.360 0.434 0.385 0.371 0.314 0.325 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01
49
Appendix 8 OLS regression on dependent variable ‘forecast dispersion’ with 2.5% winsorizing
(1) (2) (3) (4) (5) (6) Sweden Netherlands France Germany United Kingdom All Number of 0.0000 0.0003*** 0.0001* 0.0002** 0.0002*** 0.0001*** analysts (0.0001) (0.0001) (0.0001) (0.0001) (0.0000) (0.0000) Market value 0.0000** 0.0000*** 0.0000*** 0.0000 0.0000*** 0.0000*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Trading volume ‐0.0000 ‐0.0000*** ‐0.0000*** ‐0.0000 ‐0.0000*** ‐0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Profit/loss ‐0.0210*** ‐0.0146*** ‐0.0210*** ‐0.0199*** ‐0.0219*** ‐0.0229*** (0.0019) (0.0022) (0.0013) (0.0015) (0.0011) (0.0006) Earning ‐0.0069*** ‐0.0150*** ‐0.0367*** ‐0.0096*** ‐0.0240*** ‐0.0146*** surprise (0.0026) (0.0016) (0.0033) (0.0010) (0.0012) (0.0006) Std roe ‐0.0001** ‐0.0200*** ‐0.0008*** ‐0.0032** ‐0.0013*** ‐0.0002*** (0.0000) (0.0022) (0.0002) (0.0013) (0.0003) (0.0000) Standard 0.0057*** 0.0055*** 0.0059*** 0.0034** 0.0004 0.0024*** (0.0017) (0.0019) (0.0012) (0.0014) (0.0008) (0.0005) Intangible/Total 0.0149*** 0.0078* 0.0085*** ‐0.0110** 0.0005 0.0028** (0.0041) (0.0046) (0.0028) (0.0048) (0.0016) (0.0013) Interaction ‐0.0121* ‐0.0049 ‐0.0079* 0.0178*** 0.0006 0.0009 term (0.0067) (0.0085) (0.0047) (0.0068) (0.0027) (0.0021) Constant ‐0.0145*** ‐0.0132*** ‐0.0122*** ‐0.0147*** ‐0.0081*** ‐0.0111*** (0.0013) (0.0013) (0.0009) (0.0012) (0.0005) (0.0004) N 1039 819 2446 1671 3888 9863 R2 0.182 0.295 0.219 0.228 0.247 0.206 Standard errors in parentheses * p < .1, ** p < .05, *** p < .01