Accounting Standard Complexity, Analyst Forecast Properties, and Auditor Industry
Specialization

Abstract
This paper examines the impact of international accounting standards (hereafter IFRS)
complexity on analyst forecast properties including forecast errors, forecast dispersion, andforecast revision. Because Industry Specialisation (ISP) is likely to moderate the effect ofcomplexity, it is introduced as a moderating variable in this analysis. The complexity of
individual IFRS standards is measured based on IFRS reconciliation statements capturing theadjustments on accounts affected by the changes in accounting standards. We analyze 322
companies listed on the Australian Securities Exchange (ASX). Our results suggest that all
IFRS standards are not equally complex. A few standards are relatively more complex. Thesestandards have a positive effect on analyst forecast errors, forecast dispersion, and forecast
revision. In addition, we find that city-level industry specialist auditors (ISP) can mitigate theadverse impact of IFRS complexity on analyst forecast errors but not on forecast dispersion
and revision. More interestingly, the mitigating effect of industry specialists is restricted toonly a group of companies exposed to a higher level of aggregate IFRS complexity. The
findings of our study contribute to the IFRS literature by identifying the most complex
accounting standards. Secondly, this study provides an ex-ante experiment of IFRS adoption
which can be used by the adopting countries to understand the impact of IFRS on the analyst
forecasts. Finally, our study helps regulators to understand the effects of the complexity ofaccounting standards on users of financial statements.
Keywords: IFRS; complexity; financial analyst forecast; auditor industry specialization

11. Introduction
This study investigates the impact of accounting complexity arising from IFRS adoption on
analyst forecast properties including forecast errors, dispersion, and revision. We conjecturethat analysts’ forecasting abilities are adversely affected by more complex accounting
standards. In addition, we investigate whether high-quality audit, proxied by auditor industry
specialization, moderates the relation between accounting complexity and analyst forecast
properties.
Prior research on the impact of IFRS adoption on analyst forecasting properties either
investigates the impact of IFRS using a dummy variable to differentiate post-IFRS period fromthe pre-IFRS (e.g., Tan et al., 2011; Xi and Yang., 2016) or tests the impact of the difference
in clauses between local standards and IFRS on forecast properties of analyst forecast
performance (Byard et al., 2011; Bae et al., 2008). The findings on the effect of IFRS on
analysts are largely inconclusive. Three studies investigate the impact of specific standards on
analysts’ forecast properties (Bugeja et al., 2015, Cotter et al., 2012, Matolcsy and Wyatt,2006), but all limit their focus on standards pertinent to intangibles or operating segments.
Later, Ahmed et al. (2013) conduct a meta-analysis of IFRS adoption effects on earnings
transparency, capital market and quality of analysts ‘earnings forecasts. They document thatanalysts’ forecast accuracy has increased in post-IFRS adoption. Our study differs from theextant studies in that we focus on the complexity of IFRS measured at both an aggregate leveland for individual standards. This allows identification of collective effects of complex
standards and the effects of individual standards on analyst forecasting abilities.
Specifically, this study uses the concept of accounting complexity to capture thechallenge that analysts face as a result of changing accounting standards.1 We calculate
complexity scores for a set of complex IFRS standards based on financial statements
reconciliation, which is prepared during the first year of IFRS adoption by companies inAustralia. The data required to compute complexity are hand-collected from annual reports.
Measuring complexity scores for each accounting standard separately enables us to discern

1

The extant accounting literature measures complexity as readability using a technique of syntax analysis (forexample see, Filzen and Peterson, 2015, Lehavy et al., 2011), the length of the reports by word counts (for e.g.,
Franco et al., 2015). In our study, we measure complexity based on individual standards.

2

how the complexity of standards affects analyst forecast properties by those standards
individually.
This study exploits the unique setting of Australia to examine the effects of thecomplexity of accounting standards. In 2005, Australia adopted all accounting standards whichwere issued by the International Accounting Standards Board (IASB). So, it is worthwhile tocheck how such a big change in the accounting reporting environment affected the accounting
information users such as financial analysts. Australia has a mature financial analyst industry
where analysts are expected to have reasonable financial literacy and use their financial skills
to analyze annual report information.
Using 322 sample firm observations from the IFRS adoption year, our empiricalanalyses show that complexity at an aggregate level does not explain the increase in forecast
error, dispersion, and revision. Analyses using complexity scores of the set of individual
standards show that analyst forecast errors are positively associated with accounting
complexity arising from two standards, namely AASB 2 Share-based Payment and AASB 132
Financial Instruments: Presentation. AASB 117 Leases is positively associated with forecast
dispersion and forecast revision increases with the increase in accounting complexity of two
standards, AASB 3 Business Combination, and AASB 117 Leases. Our examination on themoderating effect of high-quality audit on the relation between accounting complexity andforecast properties is conducted with high vs. low complexity subsamples. The results showthat analyst forecast error is lower in firms audited by city-level ISP compared to those audited
by non-ISP.2
This study contributes to IFRS and analyst forecast literature in several ways. First, ouranalysis using individual standards identify a few specific standards as more complex thanothers resulting in impaired analyst forecast performance. Additionally, this study shows themoderating effect of high-quality audit on the relation between accounting complexity andforecast properties, pinpointing the importance of auditor's monitoring effect when firms facechanges in accounting standards. Although prior studies demonstrate that high-quality auditors
can reduce analyst forecast errors and forecast dispersion (Behn et al., 2008), our study

2

However, we do not find any evidence on the moderating effect of national-level industry specialization(NATIONAL_ISP) on the association between forecast properties and aggregate accounting complexity in either
original six or new-six category of accounting standards.

3

advances their findings by showing that this effect of high-quality auditors on analyst forecast
performance is more pronounced in a context where firms face accounting complexity.
The paper is organized as follows. Section 2 reviews the literature and develops our
hypotheses. Empirical procedure and sample selection are described in Sections 3. Section 4reports the analysis of the impact of accounting complexity on analyst forecast properties.
Section 5 discusses the moderating effect of high-quality audit on the relation between
accounting complexity and forecast errors. Section 6 concludes.
2 Literature Review and Hypothesis Development
2.1 Accounting Complexity and Analyst Forecasting Error
Financial analysts are the most important, sophisticated, and visible users of financial
statements (Bae et al., 2008, Schipper, 1991, Tan et al., 2011). They provide earnings forecasts,
buy/sell recommendations and other information to brokers, money managers and institutional
investors (Lang and Lundholm, 1996). The extant IFRS and financial analyst forecast studies
can be broadly classified into two streams including (i) voluntary adoption of IFRS studies andanalyst forecast properties (Ashbaugh and Pincus, 2001, Bae et al., 2008, Ernstberger et al.,2008, Hodgdon et al., 2008); and (ii) mandatory adoption of IFRS and analyst forecast
properties (Byard et al., 2011; Jiao et al., 2012; Tan et al., 2011; Xi and Yang, 2016). All
voluntary IFRS adoption and analyst forecast studies provide consistent results that theanalysts’ consensus forecast accuracy significantly improves after firms adopting IFRS
voluntarily. However, studies of mandatory adoption of IFRS provide inconclusive results,
and, therefore, provide the motivation to explore this phenomenon further. For instance,
Horton et al. (2013), investigate whether improved analyst forecast accuracy can be attributedto (i) higher-quality information; (ii) greater comparability, or (iii) constraining managers’
opportunities to manipulate earnings. Based on a large sample covering all available companiesof all countries in The Institutional Brokers’ Estimate System (I/B/E/S), they find that forecast
accuracy increases due to both higher information quality and greater comparability of
information prepared on the basis of IFRS.
Tan et al. (2011) argue that if widespread mandatory IFRS adoption increases
timeliness, analyst following may also increase because of the increasing usefulness ofaccounting data. However, due to increasing earnings volatility, forecast accuracy may
decrease. Alternatively, the subjectivity involved in the fair value approach under IFRS may

4

result in earnings smoothing. This, in turn, may cause the analyst following to decrease becauseof the decreasing usefulness of accounting information, and forecast errors may also decrease.
On the other hand, Byard et al. (2011) contend that mandatory adopters may not provide
enough incentive for analysts to follow IFRS rigorously because firms may have already
optimized their financial reporting quality under the local standards, resulting in little change
in the analyst information environment. By examining 1168 EU IFRS mandatory adopter firms
and 250 voluntarily IFRS adopter firms, they find that simply making IFRS mandatory, on
average, does not change the analysts’ information environment (forecast errors and forecast
dispersion), but significantly improves the information environment for firms domiciled incountries with both strong enforcement regimes and significant differences between domestic
accounting standards and IFRS.3
Focusing on a single country, Cotter et al. (2012) examine the impact of IFRS
disclosure on analyst forecasts of 145 Australian listed firms from 2003 to 2007. They
document that analyst forecast accuracy improves in both the adoption year and post-adoption
years but dispersion does not decrease. They claim that improvement in forecast accuracy andunchanged dispersion can be attributed to additional efforts and attention which was given to
IFRS during the adoption period. In the same setting, Chalmers et al. (2012) investigate theassociation between analyst forecast accuracy and dispersion and the new methods ofintangible reporting under IFRS. They find that the association between intangibles and analyst
forecast error is reduced after the adoption of IFRS. IFRS no longer permits a firm's straightline amortization of intangible assets, instead prescribes a new impairment approach, thus
decreasing the forecast errors.
Taken together, it is evident that none of the prior studies comprehensively explores theimpact of changes in individual IFRS standards on analyst forecasting properties, although
Chalmers et al. (2012) make an important contribution with regard to intangibles. Our study
seeks to extend this line of investigation, by investigating the impact of each of the IFRS
standards that have been identified as ‘complex’.

3

Byard et al. (2011) examine the effect of mandatory adoption of IFRS on financial analysts' information
environment in 1168 EU IFRS adopter firms and 250 non-adopting control firms (voluntarily IFRS adopter firms).

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Analyst forecast literature reaches consensus that analyst forecast properties areaffected by (i) analysts’ abilities and analyst expertise (Ramnath et al., 2008); (ii) reporting
complexity (Chang et al., 2016; Plumlee, 2003); and (iii) economic complexity (Chang et al.,2016). Chang et al. (2016) investigate the impact of complexity due to ambiguous and unclear
standards regarding derivatives on analyst forecast properties. They categorized their entire
sample into different groups to disentangle reporting complexity from economic complexity. 4
Through empirical analysis, they find that reporting complexity, rather than economic
complexity, is significantly associated with analyst forecast errors. Plumlee (2003) investigatesand finds that reporting complexities arising from changes in tax laws are significantly
associated with forecast errors.5
Following this line of reasoning, our study investigates if analyst forecast errors
increase because of increased reporting complexity arising from IFRS adoption. We posit that
if certain IFRS standards are complex evidenced by the higher/significant levels of differences
between domestic GAAP and IFRS standards, an analyst may not be able to analyze IFRS
based financial statements or make forecast decisions without required sufficient information,
thereby results in less accurate forecasts. We develop our first hypothesis as follows:
H1: Accounting complexity arising from IFRS is negatively associated with analyst forecast
accuracy.
2.2 Accounting Complexity and Analyst forecast dispersion
Forecast dispersion (hereafter DISP), a measure of uncertainty embedded in earnings,
is one of the important analyst forecast properties. It is perceived by investors to be valuable
information because it indicates the uncertainty of future performance (Givoly and Lakonishok,1984). It is argued that better disclosures reduce information asymmetry (Brown and Hillegeist,
4

Because prior literature show that first derivative accounting standards such as Statement of Financial
Accounting Standards (SFAS) No 133/138 [from 2000-2003] were neither able to clear about the conditions ofderivatives contracts, nor, comparability issues of different contracts (Pollock, 2005). However, SFAS 149 andlater standards repair the problems of SFAS 138/139. However, investigation of the impact of economic
complexity is beyond the scope of our study. Moreover, discriminating reporting complexity from economic
complexity is challenging, if not impossible (Chang et al., 2016)). In addition, as economic factors are consideredwhen accounting standards are issued (Peterson, 2012). Our study is based on only reporting complexity reflected
in IFRS-AGAAP reconciliation statements, related to IFRS adoption.
5

Two changes are regarding tax rates; one change is for calculation of taxable income and three changes are
related to tax credits. Plumlee (2003) uses analyst effective tax rate (ETR) forecast as the dependent variable inher study.

6

2003a), and consequently improve analyst forecast consensus (Byard and Shaw, 2003).
Empirically, Lang and Lundholm (1996) provide evidence of lower dispersion amongindividual analysts due to informative disclosures.
However, IFRS forecast dispersion studies provide mixed results. For instance, on the one
hand, it is argued that due to inexperience, analysts may face difficulty in understanding andinterpreting information presented under a set of accounting standards that differ from theirdomestic GAAP. This may result in heterogeneity in earnings forecasts (Cuijpers and Buijink,2005). Cuijpers and Buijink (2005) investigate the impact of voluntary adoption of IAS or US
GAAP on information asymmetry for 133 non-financial firms in the EU. They find that forecast
dispersion among individual analysts increases for firms adopting IAS or US GAAP. However,they document an increase in analyst following for firms adopting IAS or US GAAP compared
to non-adopting firms. However, Cotter et al. (2012) examine financial analyst forecasting
properties of 145 Australian listed firms for the period 2003–2007 and find that forecast
dispersion remains unchanged in the IFRS adoption year.
Nevertheless, prior research shows that forecast dispersion may also decrease with the
adoption of IFRS, because of improvement in information environment through enhanced
disclosures required under IFRS and increasing comparability of financial reports (e.g., Bae etal., 2008; Horton et al., 2013). Studying a sample of 1,168 mandatory IFRS adopters fromtwenty European countries, Byard et al. (2011) find that analysts’ absolute forecast dispersion
decreases upon mandatory adoption of IFRS. However, they limit their findings to those thatcountries have both strong enforcement and significant differences between Domestic
Accounting Standards (DAS) and IFRS. For instance, though mentioned in earlier hypothesis
development, Chalmers et al. (2012) investigate the impact of IFRS on forecast properties
taking a sample of 3,328 observations in Australia covering pre- and post-IFRS adoption
periods. Similarly, they find that the impairment approach suggested by IFRS provides more
information compared to the former straight-line amortization approach under local standards(i.e., Australian Generally Accepted Accounting Principles, known as AGAAP), thereby
decreasing forecast dispersion.
Unlike the extant IFRS and analyst forecast studies, we hypothesize that due toaccounting complexity arising from IFRS especially in the first year of adoption, the learning
curve of analysts would be steeper and some analysts are likely able to learn the IFRS effectmore efficiently than others. This expectation leads to the development of the following
directional hypothesis:7H2: Accounting complexity arising from IFRS is positively associated with analyst forecast
dispersion.
2.3 Accounting Complexity and Forecast Revision
Analyst forecast revision has important implications for investors who revise theirbeliefs of earnings based on analyst forecast revision (Mendenhall, 1991) because investors bythemselves are unable to determine the persistence of earnings when earnings are announced(Freeman and Tse, 1989). Following the approach of Barth and Hutton (2004), we measureforecast revision at consensus level as the difference between the last mean forecast madebefore current year earnings announcement date and the first mean forecast made after last year
earnings announcement date.
Prior studies find that forecast revision can predict a firm’s future profitability. For instance,
Barth and Hutton (2004), in comparing hedge return to different strategies, investigate whether
forecast revision can reveal information about earnings persistence beyond that obtained from
accruals. Barth and Hutton (2004) document that a combined strategy of accruals and forecast
revision can generate a return significantly larger than either of the two individual strategies.
Clement and Tse (2003) investigate whether investors can extract the required informationfrom analysts' characteristics which are associated with forecast accuracy. In particular, theyfind that investor's response to forecast revisions are influenced by other forecast characteristics
such as timely forecasts, broker firms' size, and frequency of the forecasts other than forecast
accuracy. They concluded that investors’ response to forecast revision indicates that theforecast accuracy is not all that matters.
Prior research has been identified that attempts to investigate the impact of IFRS
adoption on the tendency of analyst forecast revision. We posit that analysts revise theirforecast in response to change in accounting standards from local GAAP to IFRS on thefollowing grounds. First, it takes time for analysts to adjust their earnings predictions based on
new accounting standards. This may be due to analysts’ lack of experience in comprehending
and interpreting accounting-related regulations. Plumlee (2003) predicts and finds thatcomplexity arising from changes in tax laws affects analyst forecast errors and forecast
revisions, suggesting that analysts do not consider complex information in forecast revision as
this information does not accurately support them in forecasting firms’ effective tax rates(ETR). Second, analysts’ previous knowledge of AGAAP and firms’ historical accounting
information forms the basis of their forecasts for contemporaneous and future performance.
8

During the year of IFRS adoption, analysts’ early forecasts are firstly formed on the basis of
their understanding of historical information. However, they may gradually realize thedeviations of their predictions from the actual performance that is prepared using IFRS andthus make forecast revisions. This realization may occur through their newly acquired
knowledge of IFRS, management guidance, and most importantly, firms’ disclosures ofquarterly financial results. The reconciliation adjustments in the first year of IFRS adoption areused to proxy for the differences in accounting treatments. The greater they are, the more likely
analysts are to conduct forecast revisions in the year of adoption. Taken together, our study
predicts a positive relationship between accounting complexity and forecast revision anddevelops the following directional hypothesis:
H3: Accounting complexity arising from IFRS is positively associated with analyst forecast
revision.
2.4 Accounting complexity, audit quality, and financial analyst forecast properties
We also examine the moderating role of audit quality on the relationship between
accounting standard complexity and financial analyst forecast properties. It is argued thatanalyst forecasts properties are also moderated by audit quality because financial information
is used by analysts as one of the primary resources for stock analysis and earnings forecast.
The high quality auditor provides assurance to accounting information quality (Stokes andWebster, 2010), and thus analysts’ forecast performance should be improved when they use
high quality accounting information assured by a quality auditor (e.g., Behn et al., 2008).
Following this line of argument, a stream of literature examines and finds supportive evidenceof the discernible effect of audit quality on the properties of analysts’ forecasts (Behn et al.,2008; Payne, 2008; Yi and Wilson, 2016; He et al., 2014). Behn et al. (2008) find that there isa positive association between high quality and analyst forecast accuracy. More specifically,
they show that forecast accuracy is higher and dispersion is lower for firms audited by industry
specialist audit firms.
He et al. (2014) extend Behn et al. (2008) to test the impact of high quality audits on
the information environment in which analysts operate. They find that higher audit quality
results in analysts placing more weight on public rather than private information. In addition,
both analysts’ common and private information tends to be more precise for the companies
audited by industry specialists. Yi and Wilson (2016) argue and find that analyst forecast error
is lower for the firms audited by industry specialists if the firm is given less coverage by
9

industry specialized analysts. This finding suggests a complementary effect of high quality
auditors in reducing analyst forecast errors, especially when analysts are less sophisticated.6
Given the theoretical argument on the importance of high quality auditor and theaforementioned empirical evidence, the main motivation is to examine whether high quality
auditors improve analyst forecast performance when analysts face greater reporting uncertainty
and difficulty due to changing accounting standards. We use industry specialization of auditor
following the approach of Krishnan et al. (2013), as a proxy for high quality auditor.7 Industry
specialist auditors have more experience and training than non-specialist auditor (Sun and Liu,2011), and they are familiar with accounting principles, specific industries', and specific firms'
transaction processes and therefore can make more effective professional judgments and be
more likely to detect accounting fraud (Tang and Peng, 2013). We argue that the adverse effectsof accounting standard complexity on analyst forecast performance can be ameliorated if thefirms are audited by ISP auditors as ISP auditors are distinguished because of theirdifferentiated audit quality. The following hypothesis is formulated:
H4: The negative impact of IFRS complexity on analyst forecast properties is moderated byhigh quality audits.
3 Empirical Procedures
3.1 Measurement of Variables
3.1.1 Measurement of Dependent variables
The dependent variables, in our study, are analyst forecast properties including analyst forecast
error (AFE), dispersion (DISP), and revision (REVISION). AFE is measured as the absolutevalue of analyst forecast error that is, the median forecast minus actual EPS and then is deflatedby the stock price at the end of last year.

6

Conversely, Payne (2008) shows that analysts' forecast errors are greater for the firms audited by an industry
specialist. They argue that this is due to focus on end-of-year forecasts which induce benchmark-beating
incentives for earnings manipulations. In addition, Yi and Wilson (2016) claim that end-of-year forecasts containnoisiness7
See (Balsam et al. 2003; Behn et al., 2008; Beasley and Petroni, 2001) for industry specialization used for a highquality audit.

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AFE=

Median Forecasts  Actual EPS
Share price

Forecast dispersion (DISP) is defined as the standard deviation of all earnings forecasts issued
by all analysts following the same firm.
DISP= STD ( Forecasts)
Share price

Forecast Revision (REVISION), following the approach of Barth and Hutton (2004), is
calculated as last consensus forecast minus first consensus forecasts and is then scaled by theshare price at the end of the last year.
REVISION=

LF  FF
Share price

Where the first forecast (FF) is the first one which is made after the last year’s earnings
announcement date; The last consensus forecast (LF) is calculated using all available forecasts
made before the current year’s earnings announcement; All forecast properties are calculated
based on forecasts issued between the last year’s earnings announcement date and the current
year’s earnings announcement date.8
3.1.2 Measurement of Accounting Complexity
There is no formal definition of “complexity” in extant accounting literature {Baudot,
2018 #1816}, however, prior research measured and examined complexity from different
perspectives. For instance, one perspective tries to measure complexity by looking into thevolume/length of the annual reports or its readability by using Gunning Fog Index (GFI) or
other lexical methods (Lehavy, 2011; Li, 2008). Another perspective examined information
complexity (e.g., Plumlee, 2003; Peterson, 2012). Plumlee (2003) measured complexity arisingfrom the changes in tax -laws and how it affects financial analysts. Peterson (2012) examined
the impact of complexity associated with revenue recognition and they found that revenue
restatement increases due to such complexity.
Recently, Baudot (2018) measured complexity based on comment letters submitted to the
Financial Accounting Standards Board (FASB). More specifically, they investigate the8

For example, Ansell Limited follows a July-June fiscal period and announces their earnings for 2004-05 on 17
August 2005 and their current year earnings (2005-06) on 24 August 2006. Forecasts which are issued after 17
August 2005 and before 24 August 2006 are considered in calculating forecast properties.

11accounting profession’s engagement in complexity taking a viewpoint of multiplicity,
diversity, and interrelatedness on comment letters submitted to FASB.9 By investigating 864
comment letters submitted by only Big 4 firms in the last 12 years, they found that firms mainly
oppose a change when they believe it may increase complexity. However, they conclude thatBig 4 audit firms do not hold universal opinion regarding the main causes of complexity. Taken
together, it is clear that there is no single measure of complexity. The present study measurescomplexity by using a quantitative proxy which is considered an objective measure by priorresearch (Baudot, 2018). In this study, a complexity score is measured for each standard foreach sample company. AASB 1047 requires an entity shall disclose an explanation of how thetransition to Australian equivalents to IFRS is being managed and a narrative interpretation of
the key differences in accounting policies that are expected to arise from the adoption of AIFRS in its financial reports (para 4.1a and b, AASB 1047). The above information is fairly
and comprehensively available in AGAAP-IFRS reconciliation statements. Another standard,
AASB 1 also requires that the first-time adopters of Australian equivalents to IFRS should
provide comprehensive reconciliation statements showing their financial performance andfinancial position under the two accounting systems, the old AGAAP and the new AIFRs(AASB 1, 2004a). We measure complexity based on the magnitude of the adjustments madein the reconciliation statements, prepared in the year of IFRS adoption. The main-focus of ourstudy is on individual accounting standards. Firstly, we identify six accounting standards thatare found to have a significant impact on financial statements in prior Australian accounting
studies (De George et al., 2013; Jermakowicz & Gornik-Tomaszewski, 2006; Jubb, 2005).
These standards includeAASB2 Share-based payments, AASB 3 Business Combinations,AASB 136 Impairment of Assets, AASB 138 Intangible Assets, AASB 112 Income Taxes, and
AASB 119 Employee Benefits. Although some other studies, for instance, Haswell (2008)
identified 57 defects in IFRS and their findings were heavily criticized by Bradbury (2008)
and Nobes (2008). Moreover, the latter two authors claim these defects as "may be personalassertions". These findings for Australia are largely consistent with findings for othercomparable jurisdictions such as New Zealand (for example Stent, Bradbury, & Hooks, 2010)
and Germany (e.g., Hung & Subramanyam, 2007).
In our data collection phase, some additional IFRS standards are identified as being
complex because many firms make substantial reconciliation adjustments that arise from these9

Baudot et al. (2018) take the concept of complexity, more specifically, multiplicity, diversity, andinterrelatedness from prior complexity research (e.g., Jacobs, 2011; Jacobs, 2013).

12

standards. These additional standards include AASB 116 Property, Plant and Equipment,
AASB 117 Leases, AASB 121The Effects of Changes in Foreign Exchange Rates, AASB 132
Financial Instruments: Presentation, AASB 139 Financial Instruments: Recognition andMeasurement, and AASB 140 Investment Property. We define those standards as new
standards. Recently, Barth et al. (2014) investigated the value relevance of reconciliation
adjustments for net income (NI) with the stock price. They consider the aggregate impact on
NI from 11 IFRS standards and find that the resulting net income adjustments are incrementally
value relevant for both financial and non-financial firms. Our study, therefore, extends the listof complex standards to include 9 of the 11 standards identified by Barth et al. (2014).10
For each standard, the differences in affected accounts (for instance, amortization,
share-based payments, etc.) from IFRS reconciliations are collected. These differences are thenmeasured as a percentage of either Total Revenue if the account is a statement ofcomprehensive income item or Total Assets if the account is a statement of financial position
related item. We classify those differences into four categories (i.e., ‘Material', ‘Moderate',‘Small' and ‘Zero') based on materiality thresholds used in auditing practice (Leung, Coram,
Cooper, & Richardson, 2015, pp. 41-420). That is, the difference is considered as Material if
it is 1% or more of either Total Revenue or Total Assets; as Moderate if it is in between 0.5%
and 1% of the above totals; as Small if it is less than 0.5% but greater than 0; and as Zero where
there is no difference as a result of the switch to IFRS. We use those categories for complexity
scoring (i.e., 6 is assigned for material, 4 is assigned for moderate, 2 is assigned to small and 0
is for no adjustments). For instance, Avexa Limited (Official Ticker: AVX) prepared areconciliation statement showing the impact of AASB 2 Share-based Payment (see AppendixB). It has shown that due to IFRS adoption, the company had an additional AU $61000 as an
expense adjustment, which is 9 percent of the Total Revenue of Avexa Limited. Based on theabove materiality thresholds, as it is more than 1 percent of turnover this is a material
adjustment, we assign 6 points for this adjustment. Another example using a statement offinancial position related item is that of BKM Management Limited (Official Ticker: BKM)
reporting on the impact of AASB 136 Impairment of Assets (see Appendix-B) in its
Barth et al. (2014)’s 11 standards are IAS 39IFRS 3, IAS 19, IFRS 2, IAS 2, IAS 12,IAS 16, IAS 17, IAS 37,
IAS 38 , other. The present study's list of complex standards covers all of the standards covered by Birth et al.
(2014) except IAS 2, IAS 37 and other (undefined). However, the present study found some other standards suchas IAS 121, IAS 32, and IAS 40 (categorized under "additional six standards"), and IAS 36 (listed under"originalsix") are complex which were not considered in Birth et al. (2014). Undoubtedly the present study will provide
better insights about the impact of reconciliation adjustments arising from both original and additional standards
on analyst forecast properties.
10

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reconciliation. Specifically, there is a reversal of amortization of AU$ 71,144 due to thechanges from the amortization approach under old AGAAP to the approach of impairment
testing under IFRS. To calculate complexity induced by AASB 136, we deflate this adjustment
by total assets, which returns a value of 8.77%, again resulting in a ‘material’ score of 6 points.
A high score indicates a high level of complexity because material adjustments have been madeon that account due to the adoption of IFRS.
When we consider all the original six accounting standards together, it represents thecomplexity scores assigned to individual standards which are aggregated by firms. For
instance, Insurance Australia Group Limited (IAG) shows that their financial statements as ofJune 30, 2006, are affected by AASB 2 Share-based Payment (percentage of total revenue is0.000457, which is given score 2), AASB 136 Impairment of Assets (total value of reversal ofamortization is 92, 000 which is 0.005369 percent to total assets; a score of 4 is assigned as it
is in between 0.5% to 1.0%), AASB 138 Intangibles Assets (adjustment amount is 24,000
which is 0.001401 percent to total assets, which can be categorized as small adjustment and ascore of 2 is assigned), AASB 112 Income Taxes (total value of adjustments is 0.019228 whichfalls under material adjustment category and it is given a score of 6), and finally, AASB 119
Employee Benefits (value of adjustment is 80023 which is 0.012195 percent to both total assets
and the total revenue; a score of 6 is given as this percentage falls under category 1). If we sum
up all the scores of IAG companies, it shows its total score is 20).
As a result, IAG Limited’s total complexity score of five complex standards scores is 20. Using
the same approach, we measure complexity as (i) aggregate composite complexity score oforiginal six complex standards; (ii) the composite complexity scores of original six complex
standards separately; (iii) aggregate composite complexity score of 12 standards including newsix standards that are identified during data collection phase; and (iv) the composite complexity
scores of 12 standards separately.
Empirical tests are then conducted with all four complexity measures. We have matched thecomplexity dataset with the analyst forecasting dataset to have a common sample.

14

3.2 Research Design
The first three hypotheses [H1-H3] are empirically tested using Model 1 below. The
model is based on empirical models of Bae et al. (2008), Barth and Hutton (2004), Byard et al.
(2011), and Horton Horton et al. (2013).
𝐹𝑂𝑅𝐸𝐶𝐴𝑆𝑇− 𝑃𝑅𝑂𝑃𝐸𝑅𝑇𝐼𝐸𝑆= 𝛽0 + β1 COMPLEXITY + β2 SIZE + β3 FOLLOW + β4 SURPRISE+ β5 HORIZON + β6 RETVOL + β7 NUMEST + β8 AGE + β9 EARNSD+ β10 ROA + β11 STOCKTURNOVER + YEAR DUMMIES+ INDUSTRY FIXED EFFECTS + ε … … … … … … … … … … … … … . . . … … (1)Where FORECAST_PROPERTIES are measured as analyst forecast errors (AFE), forecast
dispersion (DISP), and forecast revision (REVISION), respectively. COMPLEXITY, thevariable of interest, is measured using four different measurement methods: (i) a composite
complexity score of all original six complex standards (COMPLEXITY_6SD); (ii) the original
six complex standards individually (for instance, COMPLEXITY_AASB2 indicates thecomplexity score derived based on complexity arising from AASB 2 Share-based Payment;
COMPLEXITY_AASB136 indicates the complexity arising from AASB 136 Impairment of
Assets); (iii) a composite complexity score of a full set of twelve standards identified ascomplex (COMPLEXITY_12SD indicates the aggregate complexity scores from all IFRS
considered in this study); and (iv) the twelve standards individually. The control variable
definitions are available in Appendix-A.
Analyses on forecast error, dispersion and revision share the same set of complexity
and control variables, expect for forecast horizon (HORIZON). The forecast horizon is not usedin revision analysis following prior studies.
We expect the coefficients on COMPLEXITY variables to be positive for forecast error,
dispersion and revision analyses.11 With respect to the control variables for all cases, we expect
the coefficients on SIZE to be negative because forecast errors and dispersions are lower forlarger firms and fewer forecast revisions for larger firms (Lang and Lundholm, 1996). Prior
research by Lang and Lundholm (1996) find a positive association between SURPRISE and all
forecast properties. We expect negative coefficients for analyst following (FOLLOW) with
11

COMPLEXITY_IFRS means complexity for all standards considered in this study. For instance, it includes
Complexity_AASB2, Complexity_AASB3, etc.

15

respect to forecast errors but positive coefficients with forecast dispersion, because the greater
the analyst following for a firm, the smaller the forecast errors and forecast dispersions tend tobe (Bhushan, 1989), along with the forecast revisions being fewer. Following Brown (2001b),
this study controls HORIZON for both forecast error and dispersion analysis, because it isexpected that a forecast announced closer to the actual earnings announcement date is closer
to actual EPS; hence, it has lower forecast error and lower dispersion than those announced
earlier in the year. Return volatility (RETVOL) and earnings variability (EARNSD) are normallyused as proxies for uncertainty in a firm’s future performance. We expect the coefficients ofboth variables to be positive for all forecast properties, as firms with more volatile past earnings
and stock returns tend to have less disclosure, which increases information asymmetry among
analysts, thereby increasing forecast errors, dispersion, and forecast revisions (Lang &Lundholm, 1996). For the number of estimates (NUMEST) in regression models, we expectthat as the higher the number of forecasts issued by all analysts for a firm, the higher would be
the forecast errors, dispersion, and frequency of revisions (Jiao et al., 2012). Following
Matolcsy and Wyatt (2006), we expect AGE, the number of years the firm has been listed in
the ASX, to have positive coefficients for all three of our dependent variables, implying thatthe longer the firms are listed, the greater the tendency to have higher forecast errors, higher
dispersion, and more frequent revisions.
The profitability indicator variable (ROA) is included as analysts’ forecasts more
profitable firms have, on average, fewer forecast errors than loss-reporting firms (Lang andLundholm, 1996). Finally, we control stock turnover (STOCK TURNOVER) following Tan etal. (2011) and expect negative associations implying that firms having greater market liquidity
or turnover produce positive signals to market participants thereby decreasing forecast errors
and forecast dispersion.
To test whether ISP has moderating effects on the association between complexity andforecast properties, we follow two approaches. The first approach is to investigate whether
there is any moderating effect of ISP on the relation between accounting complexity andforecast properties (Model 2). We contend that high quality auditors can provide high quality
accounting information (Francis and Yu, 2009), that may increase the confidence of users ofaccounting information, in this case, financial analysts.12 We use both city-level industry
12

In Eq. (2), COMPLEXITY is replaced by COMPLEXITY_AASB132 and COMPLEXITY_AASB2 for AFE
analysis, COMPLEXITY_AASB138 and COMPLEXITY_AASB117 for forecast dispersion and
COMPLEXITY_AASB138 and COMPLEXITY_AASB117 for forecast revision. All other control variables are
similar to the ones used in Eq. (2)16specialists (CITY_ISP) and national-level ISP (NATIONAL_ISP) as proxies of high quality
audit.
𝐹𝑂𝑅𝐸𝐶𝐴𝑆𝑇− 𝑃𝑅𝑂𝑃𝐸𝑅𝑇𝐼𝐸𝑆= β0 + β1 COMPLEXITY + β2 ISP + β3 COMPLEXITY ∗ ISP + β4 SIZE
+ β5 FOLLOW + β6 SURPRISE + β7 HORIZON + β8 RETVOL + β9 NUMEST+ β10 AGE + β11 EARNSD + β12 ROA + β13 STOCKTURNOVER+ YEAR DUMMIES + INDUSTRY FIXED EFFECTS + ε. … … … . . … … … (2)Next, we conduct sub-sample analysis to test whether the effect of ISP (at both city-level andnational-level) using Model 3 on forecast properties differs between firms with high and low
complexity. To this end, we divide the whole sample into two groups based on the aggregate
complexity level using either six standards' aggregate score and the aggregate score of all 12
standards identified.
𝐹𝑂𝑅𝐸𝐶𝐴𝑆𝑇− 𝑃𝑅𝑂𝑃𝐸𝑅𝑇𝐼𝐸𝑆= β0 + β1 𝐶𝐼𝑇𝑌𝐼𝑆𝑃 + β2 SIZE + β3 FOLLOW + β4 SURPRISE + β5 HORIZON+ β6 RETVOL + β7 NUMEST + β8 AGE + β9 EARNSD + β10 ROA+ β11 STOCKTURNOVER + YEAR DUMMIES+ INDUSTRY FIXED EFFECTS + ε … … … … … … … … … … … … … . . … . (3)Where,
CITY_ISP = indicates city-level industry specialisation. Following Krishnan et al. (2013), we
use the audit market share to measure city level industry specialization. To add robustness to
the results, we also use national level ISP. We also measure industry specialist in both city andnational levels as joint specialist auditors (JOINT_ISP). To identify specialization, we follow
the following procedure. First, the location of audit firms is identified for all sample companies.
Then, the total audit fees of each audit firm in each industry and in each of the five cities (e.g.,
Adelaide, Brisbane, Perth, Melbourne, and Sydney) are calculated. The audit firm with thehighest audit revenue in a particular industry and a particular city is ranked as a city-level ISP.
This procedure is repeated for national-level ISP. Finally, joint ISP is identified by identifying
which auditors are specialists at both city and national levels.
Although a testable hypothesis for Model 3 we contend that for the sub-sample analysis
partitioned on the level of complexity, forecast error and forecast dispersion will be17significantly lower for firms with a higher complexity level compared to those with a lower
complexity level, conditional upon the firms being audited by an ISP. It is expected thecoefficient on CITY_ISP will be negative for all forecast properties, as our expectation is thathigh quality auditors (CITY_ISP) will increase financial reporting quality, mitigating the effectof complexity on analyst forecast errors, dispersion, and revision.

3.3 Data and Sample Selection
The sample comprises firms listed in the Australian Stock Exchange (ASX). Analyst forecast
data are collected from Institutional Brokers Estimate System (I/B/E/S). Actual EPSs and data
for measuring control variables are collected from COMPUSTAT Global database and
DataStream. Data used to measure complexity are manually collected from annual reports filed
and published on ASX listing web page. I/B/E/S gives analyst forecast data for 6915 forecasts
made for 442 firms in 2005. After matching with complexity dataset, 327 firms are kept. Then,
we eliminate firms with unavailable data. This results in a sample of 322 observations with therequired financial statement and complexity data. In Table 1, Panel A shows the sample
derivation and Panel B shows the whole sample breakdown industry-wise.
[TABLE 1ABOUT HERE]
To investigate the moderating effect of industry specialization, we identify how many
companies are located in the big 5 cities in Australia. We find that 287 out of the 322 firms areaudited by city level industry specialist auditors for forecast error analysis, 240 firms fordispersion analysis, and 214 firms for forecast revision analysis. Table 4 (Panel A) shows thedetailed descriptive used for moderating analysis. Panel B of Table 4 shows the correlation
statistics for variables used for moderating analysis. Next, we divide our whole sample into
two sub-samples as high complexity and low complexity groups (based on mean complexity(for both aggregate of six and aggregate of 12 standards). We follow the same classification
for both the aggregate of six complex standards (Panel A, Table 5) and for the aggregate of all
standards (Panel B, Table 5) considered in this study.

18

4. Empirical Results
4.1 Descriptive
In Table 2, Panel A presents univariate statistics for all the variables. The mean value of
forecast errors (AFE) is 0.048, suggesting that the difference between analysts' forecasts andcorresponding actual earnings is about 4.8% of the lagged stock price. The mean dispersion(DISP) is 0.023, which suggests that the average forecast dispersion is about 2.3% of the lagged
stock price. On the other hand, the mean value of forecast revision (REVISION) is 0.034 whichsuggests that the average forecast revision is 3.4% of the lagged stock price.
The means of the individual standards-based COMPLEXITY variables have noticeable
variations suggesting with AASB3 having the lowest and AASB112 having the highest means.
As expected, the combined means of the standards reflected in the aggregate variables,
COMPLEXITY_6SD and COMPLEXITY_12SD are much higher than that of its constituents,
the individual standards, at 9.335 and12.385, respectively.
Firm size, which is the logarithm of market capitalization (SIZE), has a mean of 5.889. The
mean of analyst following (FOLLOW) is 1.569, implying that on average; four analysts arefollowing a firm included in the final sample. The average earnings surprise (SURPRISE) is
0.065. The mean of the forecast HORIZON is 5.225, implying that the average number ofcalendar days between the forecast announcement date and the subsequent actual earnings
announcement date is 200. RETVOL measures the variations of weekly stock returns for a firm
at t-1. The mean of RETVOL is 0.077.
The mean of estimates (NUMEST) made by analysts is 2.213, which indicates that at least 9
forecasts have been made for the sample firms. The minimum and maximum value of NUMEST
is 0, and 4.663 respectively. It means some firms have only one forecast estimate in the sample
period, whereas the highest number of estimates is 106. The average listing period of the firm(AGE) is 2.181. On the other hand, EARNSD measures the standard deviation of firms actual
EPS over the last three years. The mean value of EARNSD is 0.124 while the median is 0.056.
In the regression analysis, this study uses STOCKTURNOVER, which shows the number ofshares traded in the current year divided by the average number of shares outstanding in thecurrent year. The mean value of STOCKTURNOVER is 0.003, implying that on average 1000
shares are traded during the sample period. However, the maximum number of shares tradedis 1010. Descriptive statistics are not reported for Year (Ye) and Industry Classification

19

(INDUSTRY FIXED EFFECTS) statistics for the variables are not tabulated in the interests ofparsimony.
Panel B presents the Pearson correlation matrices for the regression variables. We find apositive correlation between COMPLEXITY_AASB2, COMPLEXITY_AASB116 and analyst
forecast errors and dispersion. Similarly, we get positive associations between
COMPLEXITY_AASB138, COMPLEXITY_AASB139, and COMPLEXITY_AASB117 with both
forecast dispersion and forecast revision. All other complexity variables show correlations in
the opposite direction. However, forecast error is positively associated with both forecast
dispersion and forecast revision. For control variables, firm size (SIZE) is negatively correlatedwith all forecast properties but statistically significant with AFE and DISP only. SURPRISE ispositively and significantly correlated with all forecast properties. The profitability measure,
return on assets (ROA), is negatively and significantly correlated with both forecast error anddispersion but positively associated with forecast revision. The earnings variability measure(EARNSD) is positively associated with all forecast characteristics but statistically significant
only with respect to forecast error and forecast revision. Finally, the firms’ performance
uncertainty measure, RETVOL, is positively associated with all forecast properties but
statistically not significant.
4.2 Regression Results
Table 3 presents multivariate regression results for Model 1 that examines the effect ofcomplexity on forecast properties. Ordinary Least Square (OLS) regressions results are shown,in four columns for each analyst forecast property, respectively with (i) the composite
complexity score of all original six complex standards; (ii) the original six complex standards
separately; (iii) the composite complexity score of the full set of 12 standards identified ascomplex; and (iv) the 12 standards identified separately. In general, the results show no
discernible effect of complexity measured in aggregate as evidenced by the results for
COMPLEXITY_6SD and COMPLEXITY_12SD in columns 1, 3, 5, 7, 9, and 11. However,results for s of the individual complex standards do yield significant results – these have beenhighlighted in bold letters for ease of identification. This suggests the importance of
decomposing complexity into individual standards. Columns 1 and 3 show the impact ofaggregate complexity on AFE.

Measuring the complexity of multiple standards in theaggregate may, therefore, mask the effect of specific standards.

20

The results for individual complex standards are presented in columns 2, 4, 6, 8, 10,
and 12. Column 2, Table 3, shows the impact of individual standard complexity on AFE withthe original six standards being included in the model specification simultaneously.
COMPLEXITY_AASB2 (AASB 2 Share-based Payments) and COMPLEXITY_AASB132(AASB 132 Financial Instruments: Presentation) are positively and significantly associatedwith AFE (for AASB 2, coefficient 0.005, at least t-statistic 2.18, and significant at the better
than 5% level; while for AASB 132, coefficient 0.008, t-statistic 3.26, significant at the better
than 1% level). AASB 2 requires an entity to disclose how they determine the fair value of thegoods or services received, or the fair value of the equity instruments granted. Measuring thefair value of equity instruments is challenging, requiring subjective judgment which createsbias and information noise. For instance, complex stock option pricing models are often usedby firms to calculate fair values of equity instruments specified in the equity settled share-based
payment transactions (AASB, 2004d; para 46). However, even experienced accountants andfinancial analysts find it difficult to comprehend and evaluate the suitability of a particular
pricing mode (De George et al., 2013). In addition, entities need to explain any alternative
methods used, where the fair value method is impracticable. This will again increase thecomplexity for financial analysts.
Our study confirms that fair value measurements, along with a discretionary choice for
option pricing models for share valuation, increase AFE. This is consistent with prior researchfindings (Lihong and Riedl, 2014). Lihong and Riedl (2014) argue that the fair value method,which allows recognition of unrealized gains and losses, increases analyst forecast errors
because financial analysts do not eliminate those losses and gains in making their forecasts.
More specifically, they suggest that analysts face greater difficulty in forecasting statement ofcomprehensive income-based elements which have a low serial correlation.
Similar arguments apply for AASB 132 Financial Instruments: Presentation. This
standard requires firms to disclose descriptions of the financial instruments, their carrying
amount and an explanation of why fair value cannot be measured reliably (AASB, 2004b). In
addition, this standard requires firms to disclose assumptions used in valuation along withfinancial risk profiles. However, these disclosures involve complexity and require subjective
judgments due to the lack of active and liquid markets. The complexity and uncertainty inherent
in these disclosures may, therefore, also result in increased forecast errors.

21

For

forecast

dispersion

analysis(columns

5-8),

the

coefficientof

COMPLEXITY_AASB138, when we consider only the original six complex standards, ispositive and significant at p<0.10. Although the significance of this finding is lost when we
consider the new six standards identified as complex, it nevertheless suggests that complexity
arising from AASB 138 Intangibles Assets increases forecast dispersion. Significant changes
in intangible accounting standards were brought about by AASB 138 in Australia (e.g.,capitalization of development expenditure; need to demonstrate the technical feasibility ofdeveloping assets available for use or sale and the probability of generating future economic
benefits) (AASB, 2004c). Therefore, making decisions about the capitalization of expenditure
requires accountants' judgment and managerial discretion. When subjectivity is involved,
external information users may perceive greater levels of information asymmetry, resulting in
uncertainty and dispersion in predicting firms' future prospects. High uncertainty is innate tointangibles due to their abstract nature. Also, information on the likelihood of technologicalsuccess may only be observable by insiders, which accentuates information asymmetry.
Various feasible explanations can, therefore, be put forward to support this finding of asignificant and positive effect of complexity pertinent to intangible accounting standards on
analyst forecast dispersion.
The results in Column 8 show that when the additional six standards, under the new six
standards categories, are added in regression mode 1, COMPLEXITY_AASB117 relating to
AASB 117 Leases, is positively and significantly related to DISP (β= 0.007 with t= 2.14).
Adjustments required for leases in reconciliation statements, increase uncertainty about afirm’s future performance, which results in high analyst forecast dispersion.
Somewhatsurprisingly,

there

are

significant

negativecoefficientson

COMPLEXITY_AASB136 and COMPLEXITY_AASB116, suggesting that forecast dispersion
decreases among the analysts when there is complexity as proxied by a high reconciliation
adjustment as a result of these standards. The new IFRS-based accounting standard, AASB 116
Property, Plant and Equipment (PPE), brought significant change compared to previous
accounting standards. For instance, prior accounting standards relating to PPE (such as AASB
1015 Acquisitions of Assets, AASB 1021 Depreciation, and AASB 1041 Revaluation of NonCurrent Assets) were applicable to both tangible and intangible assets (AASB, 2004f). After
adopting IFRSs, one standard (AASB 116) concerns only tangible assets, while intangible
assets are now governed by a separate new standard (AASB 138 Intangible Assets). This new22

standard is more specialized and contains clear guidance, resulting in improved disclosure. In
addition, the new standard contains stricter requirements for entities to disclose the use of therevaluation model for individual assets as opposed to the cost model. These changes may
enhance the analyst information environment thereby reducing dispersion among analysts.
Lastly, for forecast revision analysis, results from the analysis of the original six
complex standards (Column 10) shows that none of the standard’s complexity is positively
associated with analysts’ forecast revision except COMPLEXITY_AASB136, which shows the
opposite direction. It may be due to the fact that analysts’ uncertainty is reduced as a result ofa change to the impairment approach promulgated in AASB 136, as opposed to the priorstraight-line amortization approach under AASB 1010 Recoverable Amount of Non-Current
Assets and AAS 10 Recoverable Amount of Non-Current Assets. This is consistent withliterature that the new impairment approach provides more useful information compared to theamortization method, thereby enhancing analyst forecast performance (Chalmers et al., 2012).
The extended analysis including the additional new six standards (column 12) confirmsthat COMPLEXITY_AASB117 is positively and significantly associated with analyst forecast
revision. Surprisingly, COMPLEXITY_AASB3 is identified in this analysis as positive andsignificant at the 5% level. Goodwill treatment was significantly changed after IFRS adoption
as AASB 3 requires entities to ensure the valuation of all identifiable assets, both tangible andintangible, at fair value, which is subject to the assumptions and judgments of preparers(AASB, 2004e). In addition, the determination of fair value is not always straightforward due
to unavailability of active markets for the net assets of a whole business (Barth & Landsman,1995). This limitation increases information asymmetry between firms and their analyst
following, thereby increasing the frequency of forecast revisions.
With respect to the control variables, SIZE is the only variable consistently significant
and showing a negative coefficient in all forecast properties, suggesting that analyst forecast
errors, forecast dispersion, and frequency of forecast revision are lower for larger firms whichis consistent with prior research (Brown, Richardson, & Schwager, 1987c; Lang & Lundholm,
1996). This is because analysts are more interested in larger firms. SURPRISE is positively andsignificantly associated with all forecast properties, except forecast revision. This indicates thatchanges to firms’ actual earnings from last year to the current year have a significant influenceon forecast errors and the standard deviation of firms’ forecasts (Lang & Lundholm, 1996). In
the case of forecast revision analysis, the control variable NUMEST has positive and significant
23

associations with forecast revision, implying that the greater the number of forecast estimations
for a firm, the greater the incidence of forecast revisions. Other control variables exhibit lessexplanatory power.
Overall, the results do not support an association between aggregate complexity arisingeither from the original six complex standards, or aggregate of original six and additional six
complex standards (i.e. 12 standards in total) and the properties of analysts’ forecasts.
However, the individual standard analyses using decomposed complexity scores reveal
insightful findings. These findings support the argument that aggregate scores are neither goodat capturing IFRS benefits/costs, nor capable of uncovering specific effects of individual
standards on information users. This is in line with critics of the use of dummy variables toinvestigate IFRS effects. Using a dummy variable approach (i.e., pre- and post-IFRS years arelabelled as 0 and 1), Tan et al. (2011) reach a similar conclusion that local analyst forecast
accuracy does not improve due simply to IFRS adoption. Cotter et al. (2012) report similar
results with respect to forecast dispersion.
[TABLE 2 ABOUT HERE]&[TABLE 3 ABOUT HERE]
4.3 Regression Results for the Moderating Effect of Audit Quality
To see the moderating effects of audit quality on the relation between accounting
complexity and analyst forecast properties, we estimate Model 2 by interacting complexity
with audit quality (proxied by ISP). However, we do not find any moderating effect of ISP on
the association between accounting complexity and analyst forecast properties using Model 2(results un-tabulated). Next, under the second approach, to have finer insights whether ISP'smoderating effect differs between high complexity group of companies and low complexity
group of companies. To do so, we partition sample observations into the highly complex andlow complex –sample groups. Regression analyses using Model 3 are conducted for each subsample. The aggregate complexity score of both six standards (together) and of 12 standards(together) are used in order to classify the sample into high and low complexity groups. A totalof 144 firms and 129 firms are classified as high complex firms under six and 12 standards

24respectively.13 In both cases, Model 3 is analyzed to regress forecast properties on both citylevel and national-level ISP and a set of control variables. The results are reported in Table 5.
Table 5 shows the sub-sample analysis. Panel A (Panel B) is based on aggregate scoresof the original six standards (12 standards together). For city-level ISP, Panel A reports thecoefficient of CITY_ISP, regarding forecast error (AFE), as significant at the 5% level andnegative (-0.017) for only the high complexity firms. In comparison, this effect is insignificant
for low complexity firms. The results suggest a decrease in analyst forecast errors (AFE) forfirms audited by city level ISPs, but only when firms face high levels of complexity during
IFRS adoption. The result is stronger when using the aggregate complexity score of the original
six standards than when using the aggregate complexity score of all 12 standards.
For forecast dispersion analysis, ISP shows no moderating effect for either highcomplexity or low complexity firms. However, the coefficient of CITY_ISP, for forecast
revision analysis in Panel B, is negative for low complexity firms, suggesting that the incidenceof analyst revisions is reduced where firms have lower levels of complexity and are audited bycity level ISPs. Nevertheless, no moderating effect of national level ISP is found on theassociation between accounting complexity and forecast properties (un-tabulated).
[TABLE 4 ABOUT HERE]&[TABLE 5 ABOUT HERE]
5. Additional Analyses
An alternative measure of audit quality
We re-estimate the models using Big Four (BIG4) instead of ISP as a proxy of audit quality to
see whether BIG4 and ISP are affecting the analyst forecasting abilities identically or not. Our
results (un-tabulated) show that there is no effect of Big 4 audit quality on the association
between aggregate complexity of six and twelve standards on analyst forecast properties, whichis consistent with our main analysis. A similar result is found when we interact Big4 withindividual standard complexity in our regression models.

13

Mean score for six standards together is 9.43 and 12.38 for an aggregate score of 12 standards.

25

Sub-sample analysis
In our main analysis, we partition our full sample into two samples (based on aggregate
complexity) to see the moderating effect of industry specialization (shown in Table 5). In thiscase, we replace by Big4 for city-level ISP (CITY_ISP) to see whether the audit quality (proxied
by Big4) reports better results on the association between complexity and analyst forecast
properties. We don’t find any surprising results except for sub-sample analysis based on theaggregate of the twelve standards (only for Revision Model) where the coefficient of Big 4(BIG4) is found negative, which suggests that analyst forecast revision decreases if the firm isaudited by big 4 audit firms. However, the results hold for firms which are exposed to lower
levels of complexity.
Complexity scoring alternatives

To validate our assignment of different scores to different levels of complexity
associated with different accounting standards (0, 2, 4, 6 for zero, small, moderate andmaterial level of complexity respectively), we re-run our regression by replacing with
0,1,2,3 for zero, small, moderate, and material level complexity. We find that our results(un-tabulated) are consistent those of the main analysis, which suggests that ourassignment of different scores for different levels complexities are not suffering from
scaling biases.
6. Conclusions
We examine the impact of complexity arising from IFRS adoption on financial analysts
forecast properties and also test whether this effect of complexity on analyst forecast property
varies with audit quality. While several prior studies (e.g., Byard et al. 2011; Horton et al. 2013)
examine the impact of mandatory adoption of IFRS on the information environment of firms(mainly analyst forecast errors/dispersion), this study contributes to the literature bydisentangling total IFRS impact into its components, individual IFRS impact on analyst
forecast properties including forecast errors, forecast dispersion and forecast revision.
We find that only two standards (AASB 2 Share-based Payment and AASB 132
Financial Instruments: Presentation) are positively and significantly associated with analyst
forecast errors, AASB 117 Leases is positively and significantly associated with analyst
forecast dispersion. AASB3 Business Combinations and AASB 117 Leases are found tocontribute to increased incidence of analyst forecast revisions in the IFRS adoption period. Our
26

study also finds that forecast errors decrease for high complex firms if they are audited by acity-level industry specialist audit firm, whereas no such evidence is found for either forecast
dispersion or forecast revision.
This study is subject to a few caveats, so readers should be cautious about interpretingand generalizing the results. First, our study does not cover all IFRS standards issued by IASB
and adopted by Australia because few Australian studies (e.g., De George et al., 2013, Jubb,
2005, Cotter et al., 2012, Pawsey, 2006) identified certain IFRS standards are relatively more
complex and require additional attention of accounting information users. However, this study
overcomes the limitations of those prior studies and provides a finer way of measuring
complexity and investigates the impact of the complexity on one of the important users ofaccounting information namely financial analysts. Similarly, our study, due to following theabove approach, may omit some IFRS standards that may be also complex. Lastly, therelatively small sample size due to a lack of analyst coverage on some companies in the sample.
Taken together, our study provides evidence of pitfall associated with first time adopters of
IFRS in the lens of analyst forecast performance. We believe that the results, of this study, canbe used as evidence by the countries considering adopting or harmonizing IFRS. Moreover, wenote that future research can extend our study by investigating the trade-off between thecomplexity of a certain set of IFRS and benefits of other sets of IFRS and how these trade-off
affects/benefits financial analysts.
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30

Appendix A: Variable Definitions
VARIABLE
AFE

DISP

REVISIONCOMPLEXITY_6SD
COMPLEXITY_12SD
COMPLEXITY_AASB2IFRS_DIFF
ROA
ISP
SIZE
AGE
SURPRISEFOLLOWNUMEST
RETVOLEARNSDHORIZON

STOCKTURNOVER
YEAR DUMMIESINDUSTRY FIXEDEFFECTS

DEFINITIONS
Analyst forecast error (AFE) is measured as the absolute value of the difference between
median consensus and actual EPS which is deflated by last year share market price. Firm's
actual EPS for the year ended June 30, 2006, is used for companies following fiscal period from
July-June and actual EPS of 2005 is used for companies which follow the calendar year (JanDec);
Dispersion is calculated as the standard deviation of forecasts made for a company. This
analysis considers all forecasts made between last year earnings announcement date and current
year earnings announcement date. More specifically, it is calculated as the standard deviationof the firm's EPS forecasts, scaled by share price which is at the end of the last fiscal period;
The difference of last forecast (LF) of EPS and first forecast (FF) of EPS. If there are multiple
forecasts on the same day in both forecasts (first and last Forecast), the average is used;
The extent of complexity based on complexity measurement for the original six complex
standards together;
The extent of complexity based on complexity measurement for the full set of 12 complex
standards together;
The extent of complexity based on complexity measurement for AASB 2 Share-based Payment;
the same definition applies to all other complexity variables measured based on other 11 otherstandards individually;
The extent of complexity measured based on equity difference (AGAAP and IFRS) only;
The ratio of net profit after tax to ending total assets;
Auditor industry specialization (ISP) is measured at city-level, national-level and both city andnational level (Joint ISP);
Natural logarithm of market capitalization for a firm (Chalmers et al. 2012; Matolcsy and Wyatt2006);Natural logarithm of the number of years that a firm has been listed with ASX at 2006
(Chalmers et al. 2012; Matolcsy and Wyatt 2006);
The absolute value of the difference between the current year’s earnings per share and lastyear’s earnings per share, divided by the price at the beginning of the fiscal year (Lang &Lundholm, 1996);
Natural logarithm of (1 + average number of analysts following a firm), it is calculated startingfrom last year earnings announcement (LYEA) date to current year earnings announcement(CYEA) date (Chalmers et al. 2012; Matolcsy and Wyatt 2006);
Natural logarithm of (1 + number of analysts’ forecasts is included in the consensus forecasts)(Payne, 2008; Cotter et al. 2012);
The standard deviation of weekly stock returns for a firm in the last year (Tan et al. 2011);
standard deviation of the firm’s reported earnings over the last three years (Chalmers et al.
2012; Matolcsy and Wyatt 2006);
The natural logarithm of the average number of calendar days between the forecast
announcement date and the corresponding actual earnings announcement date (Behn et al.,2008);
Number of shares traded in the current year divided by the firm's average number of shares
outstanding in the current year (Tan et al. 2011);
Indicator variable equal to 1 for firms with a June 30 year-end, otherwise equal to 0;
Industry classification for a firm in the year of 2006 and 2005;31

Appendix B: Disclosure of Reconciliation Statements
B-1: Reconciliation of Equity and Net Income (Net Loss) for the year ended 30 June 2005 (ASX:
AVX)
Reconciliation of Equity as Presented Under Previous AGAAP to that Under AIFRS.
30 June 2005$‘000
Total equity under AGAAP
Adjustments to accumulated losses (net of tax):
Recognition of share-based payment expense
Adjustments to accumulated losses (net of tax):
Recognition of share-based payment expense
Total equity under AIFRS

1 July 2004$’000

21,112-(61)
21,051-

61
21,112-(a) Reconciliation of Results as Presented Under AGAAP to that Under AIFRS

2005
$’000
13,536
61
13,597Net loss as reported under AGAAP
Share-based payment expense
Net loss under AIFRS

Note: Under AASB 2 Share Based Payments, the Company recognizes the fair value of options granted to
employees at grant date as an expense on a pro-rate basis over the vesting period in the income statement, with acorresponding adjustment to equity. Share-based payments costs were not recognized under previous AGAAP.

Note Previous
GAAP$
Revenue
Bad Debts
Audit Fees
Depreciation Expenses
Amortization
2b
Employee Costs
Model Agency Costs
Corporate and Administration Costs
Occupancy Costs
Mineral tenement Acquisition & Exploration Expenditure Written Off 2a
Impairment Loss on Goodwill
2b(Loss) before income tax expense
Income tax expense
Net (loss) attributable to members of the parent entity

2,072,096(8,453)(27,450)(11,189)(71,144)(595,518)(1,552,467)(332,490)(98,094)(624,709)

Adjustments A-IFRS
on
introductionof A-IFRS$$

71,144(5,909)(71,144)(5,909)2,072,096(8,453)(27,450)(11,189)(595,518)(1,552,467)(332,490)(98,094)(5,909)(71,144)(630,618)(624,709) (5,909)(630,618)

32

Table 1: Sample and Industry Distribution
Panel A: Sample Selection
Analyst data available in IBES (Firms)
Complexity database provides (firms)
Observation after matching
Less: Non-availability of horizon variables
Total observations used for analysis
The categorization of the companies based on the fiscal period following:
Following the July-June Fiscal period
Following the January-December period
Total firms in the analysis
Panel B: Distribution of firms across different industries [Ref. GICS industry
group (four digit)]
1.
Automobiles & Components
2.
Capital Goods
3.
Commercial & Professional Services
4.
Commercial Services & Supplies
5.
Consumer Durables & Apparel6.
Consumer Services
7.
Diversified Financials
8.
Energy9.
Food & Staples Retailing10.
Food, Beverage & Tobacco
11.
Health Care Equipment & Services
12.
Household & Personal Products
13.
Insurance
14.
Materials
15.
Media
16.
Miscellaneous
17.
Pharmaceuticals & Biotechnology
18.
Real Estate
19.
Retailing20.
Semiconductors & Semiconductor Equipment
21.
Software & Services
22.
Technology Hardware & Equipment
23.
Telecommunication Services
24.
Transportation
25.
Utilities
Total number of companies in the sample

Observations
443
1122
327
5
322
282
40
322
No. of firms
in the group
4
22
2
17
7
13
12
23
2
9
15
4
5
63
12
6
13
33
14
1
14
2
7
9
13
322

33

Table 2: Descriptive Statistics and Correlation Matrix
Panel A: Descriptive Statistics
Variables name

N

Mean

Max

1st
Percentile

99th
percent
ile

0
0
0
0
0
0
0
0
0
0
0
0
0
0

6
6
6
6
6
6
26
6
6
6
6
6
6
32

0
0
0
0
0
0
0
0
0
0
0
0
0
0

6
6
6
6
6
6
22
6
2
6
6
6
6
30

0.075

0.000

0.407

0.000

0.353

0.01
0.012

0.048
0.067

0.001
0

0.385
4.84

0.001
0

0.385
0.5

5.690
1.609
0.025
5.314
0.053
2.197
2.250
0.057
0.072
0.003

1.676
0.713
0.107
0.441
0.060
1.107
0.958
0.217
4.886
0.002

2.0794
0.693
0.000
3.466
0.016
0.693
0
0.002
-22.332
0.000

9.652
2.833
0.630
5.869
0.325
4.663
3.7842
1.621
20.0580.010

2.0794
0.693
0.000
3.466
0.016
0.693
0
0.002
-21.602
0.000

9.652
2.833
0.630
5.869
0.325
4.663
3.7842
1.621
20.058
0.010

Media
n

S. D

2
0
2
0
4
0
8
0
0
0
0
0
0
12

2.032
1.006
2.447
1.710
2.601
1.369
5.381
1.833
0.508
1.346
1.546
1.576
1.259
6.843

0.017

Min

Complexity Variables
COMPLEXITY_AASB2
322
2.087
COMPLEXITY_AASB3
322
0.217
COMPLEXITY_AASB136
322
2.478
COMPLEXITY_AASB138
322
0.733
COMPLEXITY_AASB112
322
3.267
COMPLEXITY_AASB119
322
0.553
COMPLEXITY_6SD
322
9.335
COMPLEXITY_AASB121
322
0.894
COMPLEXITY_AASB132
322
0.056
COMPLEXITY_AASB140
322
0.379
COMPLEXITY_AASB116
322
0.671
COMPLEXITY_AASB139
322
0.565
COMPLEXITY_AASB117
322
0.484
COMPLEXITY_12SD
322 12.385
Forecast Properties and Control Variables
AFE
322
0.046
DISP
271
0.023
REVISION
272
0.034
SIZE
322
5.922
FOLLOW
322
1.593
SURPRISE
322
0.063
HORIZON
322
5.225
RETVOL
322
0.077
NUMEST
322
2.248
AGE
322
2.189
EARNSD
322
0.126

ROA

322

0.905

STOCKTURNOVER
322
0.003
See Appendix A for variable definitions.

34

Table 2: CONTINUED
Panel B: Pearson Correlation Matrix
1

2

3

4

5

6

1

AFE

1.00

2

DISP

.367**

1.00

3

REVISION

0.04

0.07

1.00

4

COMPLEXITY_AASB2

0.09

0.02

-0.01

1.00

5

COMPLEXITY_AASB3

0.00

-0.04

-0.03

-0.05

1.00

-0.04

-0.07

0.08

1.00

0.11

0.00

0.07

0.08

6

COMPLEXITY_AASB136

-.203

7

COMPLEXITY_AASB138

-0.08

**

0.11
-0.02

0.00

-0.09

0.03

.186

-0.04

0.11

-0.01

0.09

.136*

-0.08

0.04

.296

**

-0.04

0.01

0.06

COMPLEXITY_AASB112

-.142

9

COMPLEXITY_AASB119-.110*COMPLEXITY_6SD

-.180

11

COMPLEXITY_AASB121

-0.07

**

.265

**

0.05

.595

8

9

10

11

12

13

14

15

16

17

18

**

**

0.10

.161**

1.00

0.07.149****.634.190**.468**.427**

1.00

0.07.242**.235**

1.00*

COMPLEXITY_AASB132

-0.01

-0.02

-0.01

-0.02

-0.02

-0.04

0.00

-0.05

.117

-0.03

0.04

1.00

13

COMPLEXITY_AASB140

0.11

-0.09

0.04

-.173**

-0.06

-0.07

-0.10

-0.03

-0.07

-.172**-.123*

-0.01

1.00

0.01

0.10

0.07

0.02

0.06

-0.03

-0.09

1.00

0.05

.112*

.161**

0.09

-0.04

-0.04

-0.01

.115*

1.00

0.10

.161

**

0.01

-0.04

0.02

0.07

.139*

1.00

.585

**

.342**.333**

.185

**

**

**

.295**

-0.05

*

14

COMPLEXITY_AASB116

0.02

0.01

-0.01

0.01

0.00

-.114

15

COMPLEXITY_AASB139

-0.02

0.02

0.09

0.06

-0.04

-0.07

16
17
18
19

COMPLEXITY_AASB117COMPLEXITY_12SD
SIZE
AGE

0.00

.156

-.140

*

-.272

**

-0.01

.200

-0.05
-.307

0.09
**

0.00

**

0.10

0.11

-.117

*

0.11

-.112

*

0.08

*

-.218

**

25

EARNSD

.161

-.148
-0.06
0.10

26

HORIZON-.123

27

STOCKTURNOVER

0.11

*

0.05

.211

.117
**

-0.03

0.04
0.04
.116

*

0.11
**

.190

**

.132

*

-0.03

0.05

-0.04
**

-0.07

0.02

-0.03

-0.03

.176

0.05

0.08

0.00

-0.04

-0.10

.428

**

0.04

-0.07
-.198

-.288

**

-0.02

-0.06

*

0.04

0.01

.117

-0.01

**

**

0.06

*

-.131*-.209**

RETVOL

-0.06

.453

**

-0.02

-.168**

24

0.07

.188

*

0.09

0.08

.604

ROA

NUMEST**.384

SURPRISE

23

-0.05

**

.213

-0.04

21

FOLLOW

-0.09

**

20

22

.380

**

20

21

22

23

24

25

26

27

1.00

12

*

19

1.00

**

8

10

-.204

**

7

0.10
*

0.00

-.124

-0.03

.133*

.135

*

.305

**

.115

*

.305

**

-0.01
0.02

.460

**

.295

**

.126

*

-.172
0.05

.331

**

.327

**
*

0.07

0.04

.220

**

0.02

**

0.05

.861

**

-0.05

-.118

-.127*

.126

0.08

-0.06

*

.142

0.06

*

.180

**

.325

**

.293

**

.437

**

.114

*

.171

**

0.04
0.02
0.00

0.01
0.07
-0.10

.276

**

0.04

.165

0.05

0.07

.154

1.00

.109

.117*

1.00
.117

*

1.00
**

-0.02

1.00

0.10

-.141*

0.09

**

0.03

-0.03

0.00

0.11

0.00

0.08

-0.09

-.234

0.08

0.06

0.07

0.02

-0.06

-0.04

0.06

.168**

.114

*

.210

**

.118

*

.150

**

.186

**

.174

**

-0.01

-0.05

0.07

**

.153

0.03
0.04
0.07
0.01

-0.04
-0.07
-0.08
0.10

.117

*

.160

**

-0.08
0.00

-0.06
0.03

0.10

.160

**

*

0.09

0.01

-0.01

0.04

-.125

0.10

-0.05

0.02

-0.03

0.03

0.10

.208**

.392

**

.357

**

.750

**

.708

**
*

-0.06

-0.08

-.134

0.06

.131

*

**

0.09

.144

**

.147**

0.07

.214

.138

.143**

1.00

-0.11

.162**.932**

0.04

*

**

-.172**

1.00

.227

**

.268**

0.06

**

**

-.288

.354**

-0.03

-.210
*

-.179
0.08

**

1.00

.186

.136
**

0.03

-.205

0.10

0.06

-0.11

-0.06

.267

.310**

-0.09

.109*

0.01

.350**

1.00

.251

1.00
**

0.05

1.00

0.08

-0.05

1.00

See Appendix A for variable definitions. **, and * represent statistical significance at the 1%, and 5%, respectively (two-tailed test).

35

Table 3: Multivariate Tests on The Association Between AFE, DISP, and Revision and Accounting Complexity arising from IFRS (H1, H2, And
H3)
𝐹𝑂𝑅𝐸𝐶𝐴𝑆𝑇− 𝑃𝑅𝑂𝑃𝐸𝑅𝑇𝐼𝐸𝑆= 𝛽0 + β1 COMPLEXITY + β2 SIZE + β3 FOLLOW + β4 SURPRISE + β5 HORIZON + β6 RETVOL + β7 NUMEST + β8 AGE + β9 EARNSD+ β10 ROA + β11 STOCKTURNOVER + YEAR DUMMIES + INDUSTRY FIXED EFFECTS + ε … … … … … … … … … … … … … . . . … … (1)Expected
Sign
INTERCEPT?

COMPLEXITY_AASB2+COMPLEXITY_AASB3

+

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

AFE

AFE

AFE

AFE

DISP

DISP

DISP

DISP

0.081
[1.08]

0.084[1.14]
0.005**[2.18]
0.005[1.40]
0.000[0.19]-0.001[-0.93]-0.001[-0.52]-0.002[-1.14]0.080[1.08]

0.084[1.12]
0.005**[2.27]
0.005[1.55]
0.000[0.33]-0.001[-0.45]-0.001[-0.45]-0.002[-0.80]0.085[1.11]

0.081[1.04]-0.002[-1.27]-0.000[-0.03]-0.002[-1.61]0.003*[1.78]
0.000[0.15]
0.001[0.70]

0.086[1.11]

0.099[1.27]-0.002[-1.05]0.001[0.61]-0.002*[-1.67]0.003[1.49]-0.000[-0.12]0.001[0.72]COMPLEXITY_AASB136 +COMPLEXITY_AASB138 +COMPLEXITY_AASB112 +COMPLEXITY_AASB119 +COMPLEXITY_6SD+

COMPLEXITY_AASB121 +COMPLEXITY_AASB132 +COMPLEXITY_AASB140 +-0.000[-0.18]0.000[0.44]-0.003[-1.43]0.008***[3.26]
0.001

(9)

(10)

(11)

(12)

REVISION REVISION REVISION REVISION-0.000[-0.01]

0.004[0.18]-0.002[-0.95]0.001[0.56]-0.003**[-2.30]0.001[0.79]
0.001[0.60]
0.001[0.35]-0.000[-0.01]0.029[1.27]-0.001[-0.50]0.004**[1.99]-0.003**[-2.12]0.001[0.57]
0.000[0.11]
0.001[0.44]-0.000[-0.52]-0.001[-0.45]-0.001[-0.88]-0.001-0.003[-1.27]0.002[0.80]-0.00336

[-0.33]-0.001[-0.52]0.000[0.19]COMPLEXITY_AASB139 +COMPLEXITY_AASB117 ++[-0.47]0.003**[-2.36]0.000[0.15]
0.007**[2.14][0.47]-0.001COMPLEXITY_AASB116 +

-

AGE

+

SURPRISE

+

ROA

-

FOLLOW-NUMEST+

RETVOL+EARNSD+HORIZON-

STOCKTURNOVER[-1.62]0.003[1.36]
0.010**[2.08]-0.000COMPLEXITY_12SD
SIZE[-1.23]-0.004[-0.20]-0.009** -0.009** -0.008** -0.009**[-2.13][-2.34][-2.11][-2.31]
0.005
0.005
0.005
0.006
[1.24][1.31][1.28][1.56]
0.303*** 0.309*** 0.302*** 0.313***[4.02][4.03][3.99][3.96]-0.000-0.000-0.000-0.000[-0.43][-0.20][-0.43][-0.15]-0.018-0.016-0.017-0.014[-0.97][-0.85][-0.92][-0.78]
0.003
0.006
0.003
0.005
[0.31][0.53][0.32][0.49]-0.065-0.088-0.062-0.095[-1.00][-1.37][-0.98][-1.43]
0.058
0.060
0.058
0.062
[1.52][1.64][1.53][1.62]-0.007-0.007-0.006-0.008[-0.50][-0.55][-0.48][-0.57]4.590** 4.154** 4.577** 4.301**[2.27][2.05][2.26][2.10]

0.010**[-2.23]0.002[1.14]
0.100*[1.92]-0.002[-1.45]0.003[0.34]
0.008[1.53]
0.076[1.28]
0.005[0.39]-0.012[-0.91]
0.922[0.67]

0.008**[-2.12]0.001[0.42]
0.088*[1.89]-0.002[-1.53]0.001[0.11]
0.006[1.15]
0.098[1.46]
0.003[0.26]-0.011[-0.81]
1.253[0.92]

0.000[0.22]
0.010** 0.009**[-2.24] [-2.46]
0.002
0.001
[1.10][0.33]
0.100* 0.081*[1.96][1.92]-0.002 -0.001[-1.45] [-1.30]
0.003-0.000[0.31] [-0.04]
0.008
0.007
[1.52][1.40]
0.074
0.072[1.26][1.15]
0.004
0.004[0.36][0.36]-0.013 -0.012[-0.91] [-0.87]
0.888
0.596
[0.64][0.45]-0.015***-0.014***-0.000[-0.05]-0.015***[-2.98]0.005[1.57]
0.151[1.62]
0.000[0.52]-0.009[-0.59]0.021**[2.38]
0.102[1.38]
0.026[0.86]
2.967[1.34][-2.86]0.004[1.04]
0.142[1.56]
0.000[0.55]-0.012[-0.76]0.019**[2.12]
0.118[1.47]
0.023[0.76]
3.124[1.43][-3.00]0.005[1.50]
0.153[1.64]
0.000[0.50]-0.010[-0.62]0.021**[2.38]
0.097[1.32]
0.025[0.85]
2.932[1.32]-0.016***[-3.52]0.003[0.93]
0.134[1.55]
0.001[1.06]-0.014[-0.93]0.022**[2.39]
0.071[0.91]
0.029[1.09]
1.717[0.84]37

YE
INDUSTRY FIXEDEFFECTSOBSERVATIONS

YES-0.002[-0.13]YES

0.001[0.10]
YES-0.002[-0.15]YES-0.001[-0.10]YES

0.027**[2.32]
YES

0.028**[2.42]
YES

0.026**[2.22]
YES

0.023**[1.98]
YES

322

271

271

271

271

272

272

272

272

-0.006[-0.52]-0.006[-0.52]-0.005[-0.47]-0.005[-0.44]

YES

YES

YES

322

322

322

R-SQUARED
0.43
0.45
0.43
0.48
0.39
0.41
0.39
0.45
0.47
0.49
0.47
0.49
2
ADJ.R
0.33
0.35
0.33
0.37
0.30
0.30
0.30
0.33
0.40
0.41
0.40
0.40
See Appendix A for variable definitions. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test).
Model (1) AFE and original six complex standards are considered together as stand-alone experimental variable Model (2) AFE and original six complex standards are consideredseparately, Model (3) AFE and full set of 12 complex standards are considered together as stand-alone experimental variable, Model (4) AFE and full set of 12 complex standards are
considered separately, Model (5) DISP and original six complex standards are considered together as stand-alone experimental variable Model (6) DISP and original six complex
standards are considered separately, Model (7) DISP and full set of 12 complex standards are considered together as stand-alone experimental variable, Model (8) DISP and full set of
12 complex standards are considered separately, (Model (9) RIVISION and original six complex standards are considered together as stand-alone experimental variable Model (10)RIVISION and original six complex standards are considered separately, Model (11) RIVISION and full set of 12 complex standards are considered together as stand-alone experimental
variable, Model (12) RIVISION and full set of 12 complex standards are considered separately.

38

Table 4: Descriptive statistics for Moderating Analysis
Panel A: Descriptive statistics
Variable
AFE
DISP
REVISION
BIG4*COMPLEXITY_6SD
COMPLEXITY_6SD
BIG4
CITY_ISPCOMPLEXITY_6SD*CITY_ISP
NATIONAL_ISPCOMPLEXITY_6SD*NATIONAL_ISP
JOINT_ISPCOMPLEXITY_12SD
COMPLEXITY_12SD*CITY_ISPCOMPLEXITY_12SD*NATIONAL_ISP
BIG4*COMPLEXITY_12SD
SIZE
FOLLOW
SURPRISE
HORIZONRETVOLNUMESTAGE
EARNSDROASTOCKTURNOVER
YE

N
Mean Median S.D.
287 0.046
0.017 0.080
240 0.024
0.011 0.050
214 0.039
0.015 0.0752
287 7.875
8 6.073287 9.603
10 5.171287 0.812
1 0.392
287 0.401
0 0.491
287 4.070
0 6.067
287 0.341
0 0.475
287 3.436
0 5.721287 0.254
0 0.436
287 12.516
12 6.764
287 5.463
0 8.089
287 3.436
0 5.721287 10.495
10 8.015
287 5.841
5.56 1.699
287 1.593
1.609 0.725
287 0.068
0.026 0.129
287 5.222
5.32 0.457
287 0.079
0.055 0.060
287 2.279
2.197 1.135
287 2.226
2.303 0.935
287 0.122
0.054 0.229
287 0.887
0.072 6.464
287 0.003
0.003 0.002
287 0.864
1 0.343

Min
0
0.001
0
0
0
0
0
0
0
0
0
0
0
0
0
1.838
0.693
0
3.401
0.015
0.693
0
0.002
-48.12
0
0

Max
0.517
0.385
0.50
26
26
1
1
26
1
26
1
32
32
26
32
10.03
2.89
0.927
5.875
0.355
4.71
3.807
1.708
24.731
0.01
1

1st
99th
Percentile Percentile
0
0.402
0.002
0.385
0
0.50
0
22
0
22
0
1
0
1
0
20
0
1
0
20
0
1
0
30
0
28
0
20
0
30
1.838
10.03
0.693
2.89
0
0.927
3.401
5.875
0.017
0.355
0.693
4.71
0
3.807
0.002
1.708
-22.332
24.731
0
0.01
0
1

39

Panel B: Pearson Correlation Analysis
1

2

3

4

5

6

7

8

9

1

AFE

1.000

2

DISP

.256**

1.000

3

COMPLEXITY_6SD-.141*-0.0111.000

4COMPLEXITY_12SD-0.1100.040.866**

1.000

5

Big4

-0.017-0.054

0.039

.127*

1.000

*

-0.073

.740

**

**

.625**

1.000-0.077.643**.792**.631**.922**

1.000

*

**

**

.300**

1.000

6

Big4*COMPLEXITY_6SD

-.121

7

BIG4*COMPLEXITY_12SD-0.103

.703

12

13

14

15

16

CITY_ISP-0.058-0.045

0.088

.136

9

NATIONAL_ISP-0.020-0.049

0.064

0.104

.347**.260**.270**.506**

1.000-0.074

.132

*

.164

**

.281

**

.279

**

.286

**

.714

**

.811**

1.000

.453

**

.443

**

.303

**

.571

**

.536

**

.822

**

.470**.646**

1.000

.398

**

.489

**

.299

**

.524

**

.570

**

.827

**

.470

**

.647

**

.957**

1.000

.388

**

.381

**

.290

**

.502

**

.487

**

.835

**

.750

**

.665**.637**

.388

**

.381

**

.290

**

.502

**

.487

**

.835

**

.750

**

.665

**

.637

**

1.000**

1.000

.250

**

.294

**

.229

**

.332

**

.191

**

.130

*

.180

**

.265

**

.302

**

.185**.185**

1.000.318**.269**.269**.753**
**

11
12
13
14

JOINT_ISPCOMPLEXITY_6SD*CITY_ISPCOMPLEXITY_12SD*CITY_ISPCOMPLEXITY_6SD*NATIONAL_ISPCOMPLEXITY_12SD*NATIONAL_ISP-0.038-0.073-0.071-0.049-0.037-0.038-0.060-0.049-0.060

**

-.202

**

15

SIZE

-.294

16

FOLLOW-.241**

0.011.319**

**

**

-.172

**

-0.005.177**

17

SURPRISE.498

18

LNHORISON-.144*.442.385**

.291

11

8

10

.339

10

**

.473
.473

**

.359

**

.413**.187**.196**.209**.291**.387**-0.073

0.001

-.154

**

-0.099-0.054-0.041-0.054-0.061-0.037-0.029-0.029-.239.164**

0.083.201**.175**

0.102

0.055

0.082

.124*

0.113

0.090

0.090

*

-0.083-0.080-0.090

-.125

*

-.125

.158**.259**.294**

**

**

.124

.183**

RETVOL

0.046

0.087

-0.049-0.085-0.054-0.092-0.115-0.085-.127

20

NUMEST-.170**

0.071.265**.344**.202**.335**.372**.167**.165**

*

0.101

.140

*

**

0.089

0.078

.182

.134*

.168**

0.115

0.107

.134*

.143*

24

1.000

-0.0981.000-0.106

-.213

**

0.052

-.315**

1.000.232**.232**.724**.931**-0.064.250**-.192**

1.000

*

.124

*

*

0.066

-0.053

0.066

-.192

**

0.112

1.000

.132*

.132*

.202**.225**.159**

0.054

0.112

.269**

0.062

1.000

*

0.078

-0.103-0.047

.155

**

0.098

0.109

0.033

1.000

.328**.153**-0.079-0.005.364**-0.0680.076-0.060-0.016

0.057

0.107

.133

.198**

0.069

0.070

.136*

.118*

*

-.140

*

0.040

0.051

0.015

0.026

0.039

0.058

0.057

0.077

0.022

0.036

0.028

0.028

.151

0.089

-0.018

0.068

0.104

0.033

0.091

-0.0290.006-0.036-0.0090.033-0.017-0.017.249**

.128*

23

*

0.008

-.143

22

1.000

EARNSD

STOCKTURNOVER

21.265**

AGE

ROA

20

0.095

22

24

.198

19

-.169**

21

23

.180

18

1.000

.217**

19

17

.129

40

1

Table 5: Multivariate Tests on Moderating Role of City-Level Industry Specialisation on
Forecast Properties Based on Sub-Sample
𝐹𝑂𝑅𝐸𝐶𝐴𝑆𝑇− 𝑃𝑅𝑂𝑃𝐸𝑅𝑇𝐼𝐸𝑆= β0 + β1 𝐶𝐼𝑇𝑌_𝐼𝑆𝑃 + β2 SIZE + β3 FOLLOW + β4 SURPRISE + β5 HORIZON+ β6 RETVOL + β7 NUMEST + β8 AGE + β9 EARNSD + β10 ROA+ β11 STOCKTURNOVER + YEAR DUMMIES + INDUSTRY FIXED EFFECTS + ε. (3)Panel A: When the sub-sample is determined based on the aggregate score of six complex standards
VARIABLES
AFE
AFE
DISP
DISP REVISION REVISIONINTERCEPT
CITY_ISPSIZEFOLLOW
SURPRISE
HORIZONRETVOLNUMESTAGE
EARNSDROASTOCKTURNOVERYE
INDUSTRY FIXEDEFFECTS

Observations
R-squared
Adj. R-squaredHigh
Complex

Low
ComplexHigh
Complex

Low
ComplexHigh
Complex

Low
Complex0.094[0.93]-0.017**[-2.14]-0.008[-1.59]-0.013[-0.68]0.317***[2.75]-0.013[-0.70]0.078[1.45]
0.010[1.09]
0.008*[1.96]-0.018[-1.31]-0.003**[-2.48]
2.523

0.093
[0.95]
0.012[0.83]-0.019***[-2.84]-0.006[-0.17]0.199**[2.37]-0.005[-0.32]-0.030[-0.29]0.005[0.23]
0.006[0.77]
0.156***[2.93]-0.001[-1.33]
4.875

0.078
[1.50]-0.003[-0.29]-0.015*[-1.68]-0.001[-0.06]0.188[1.02]-0.012[-0.80]0.182[1.33]
0.017[1.09]
0.003[0.98]-0.015[-0.56]-0.002[-1.33]-0.5970.185[1.00]-0.002[-0.23]-0.007[-1.64]-0.006[-0.60]0.041[1.32]-0.032[-1.01]-0.110[-0.73]0.013[1.47]
0.009[1.38]
0.036[1.14]-0.001*[-1.80]
1.509

0.035
[0.49]-0.008[-0.43]-0.026**[-2.08]-0.008[-0.31]0.315[1.11]
0.270[1.32]
0.037[1.46]
0.002[0.30]-0.031[-0.76]-0.001[-0.35]-2.2680.033[0.50]-0.014[-0.86]-0.010[-1.09]-0.008[-0.22]0.191[1.32]-0.230[-0.89]0.009[0.45]
0.010[1.02]
0.086[1.06]
0.000[0.54]
9.476*[1.11]-0.014[-0.94]Controlled[1.38][-0.28][1.07]-0.0100.011-0.016[-0.69][0.62][-0.94]Controlled Controlled Controlled[-0.50]0.035[1.31]
Controlled[1.77]-0.015[-0.54]Controlled

113
0.46
0.21

101
0.46
0.22

144
0.66
0.55

143
0.53
0.39

129
0.48
0.28

41

111
0.48
0.26

Panel B: When the sub-sample is determined based on the aggregate score of twelve standards

VARIABLES
INTERCEPT
CITY_ISP
SIZE
AGE
SURPRISEROAFOLLOWNUMEST
RETVOLEARNSDHORIZONSTOCK
TURNOVERYE
INDUSTRY FIXEDEFFECTS

Observations
R-squared
Adj. R-squared

AFE

AFE

DISP

DISP

REVISIONREVISION

High
Complex

Low
ComplexHigh
Complex

Low
ComplexHigh
Complex

Low
Complex

0.082[1.18]-0.016*[-1.88]-0.002[-0.27]0.002[0.55]
0.422***[6.46]-0.002*[-1.92]-0.021[-1.30]0.005[0.59]-0.107*[-1.76]0.003[0.16]-0.004[-0.28]0.835[1.06]
0.033[0.70]-0.045**[-2.26]0.058[1.57]
0.099[0.61]-0.003*[-1.82]0.186[1.27]-0.114[-1.47]-0.335[-0.85]0.922**[2.21]-0.158[-1.17]0.013[0.18]-0.005[-0.40]-0.009[-1.30]0[-0.19]0.334***[3.46]-0.001[-0.59]-0.001[-0.04]0.01[1.21]
0.088[1.06]-0.022[-0.98]0.003[0.23]

0.134[1.13]
0[-0.00]-0.009**[-2.18]0.007[1.54]
0.03[1.01]-0.001[-1.51]0[0.00]
0.011*[1.68]
0[-0.00]-0.009[-0.45]-0.022[-1.11]0.068[1.03]-0.005[-0.27]-0.015[-1.53]-0.003[-0.56]0.659***[4.74]
0.002[1.29]-0.02[-0.62]0.036**[2.21]-0.014[-0.11]0[0.01]-

0.062[1.31]-0.019*[-1.69]-0.014**[-2.23]0.009[1.49]
0.084[0.81]
0[1.12]-0.015[-0.73]0.017[1.45]-0.161[-0.77]-0.038[-0.91]--6.496-2.4451.124[-0.32][-0.75][0.86]
0.034
0
-0.01
[0.68][-0.02][-0.74]Controlled Controlled Controlled-7.399[-1.17]0.018[0.93]
Controlled

5.634[1.47]-0.015[-0.69]Controlled

103
0.64
0.45

111
0.44
0.21

1.968
[1.01]-0.034***[-2.97]
Controlled
126
0.72
0.61

161
0.4
0.23

117
0.59
0.42

123
0.47
0.27

See Appendix A for variable definitions. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels
respectively (two-tailed test).

42