A model of the effects of audit task complexity

Bonner, Sarah

doi:10.1016/0361-3682(94)90033-7

Cited by 284 publications

(278 citation statements)

References 59 publications

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“…Overall, several studies find that as complexity rises, analyst performance declines. This conclusion is consistent with a decline in judgment quality as task complexity increases (Payne 1976; Payne et al 1988;Bonner 1994). Yet, research that directly links broad measures of accounting complexity to analysts' performance is scarce.…”

Section: Analyst Performancesupporting

confidence: 67%

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The Effect of Accounting Reporting Complexity on Financial Analysts

Hoitash

Yezegel

2017

SSRN Journal

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We investigate the association between a new XBRL based measure of accounting reporting complexity (ARC) and analyst behavior. We find that analysts are less likely to cover firms with complex accounting. Further, higher ARC is associated with lower forecast accuracy, higher forecast dispersion, and lower informativeness of recommendation revisions and responsiveness to earnings announcements. This association is attenuated when analysts have longer tenure, greater firm-specific experience, and are focused on fewer industries. Investigating several complex accounts, we find that the complexity of derivatives, fair value, and pension accounts are each negatively associated with forecast accuracy, suggesting that understanding these complex accounts requires specialization. We propose a new measure of analysts' account-specific expertise and find that expertise with derivative and fair value accounts attenuates the negative effects of complexity in these accounts to a greater extent than general analyst experience. Overall, our findings suggest that analysts' expertise plays an important role in mitigating the adverse effects of ARC. JEL Classification: G24, G29, M41Keywords: XBRL, accounting complexity, financial analysts' performance, financial analysts' expertise Data Availability: Data are publicly available from sources identified in the paper. 1 The Effect of Accounting Reporting Complexity on Financial Analysts I. IntroductionRegulators and standard setters have long recognized that financial reporting has become overly complicated (SEC 2008; FRC 2009). As a result, the Securities and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB) undertook several initiatives to understand and simplify the financial reports (e.g. SEC 2008; FASB 2016). In this study, we examine how accounting reporting complexity (hereafter, ARC), measured as the count of accounting items disclosed in eXtensible Business Reporting Language (XBRL) 10-K filings, is associated with financial analysts' performance and coverage decisions. Since each accounting item is based on authoritative standards and regulations, understanding the financial reports of firms with higher ARC necessitates broader and more in-depth knowledge of accounting.While Hoitash and Hoitash (2017) find that more accounting disclosure complicates the work of preparers and auditors, it is unclear how disclosure volume will be associated with the work of sophisticated market participants such as financial analysts. Greater volume of accounting disclosures can help analysts understand past performance and generate more accurate forecasts.Indeed, several studies find that more disaggregated disclosures can lead to better analyst performance (Lang and Lundholm 1996;Chen et al. 2015). In contrast, more disclosure requires greater knowledge of accounting rules and regulations on part of financial analysts and a need to collect, analyze, and incorporate more information into their predictions. As a result, ARC may hurt overall analyst perfor...

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Section: Analyst Performancesupporting

confidence: 67%

“…First, past research suggests that more information contributes to task complexity (e.g., Steinmann 1976;Campbell 1988;Bonner 1994).…”

mentioning

confidence: 99%

The Effect of Accounting Reporting Complexity on Financial Analysts

Hoitash

Yezegel

2017

SSRN Journal

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“…Drawing parallels from general decision making literature which finds that task-related complexity influences decision makers' strategies, 18,19 information processing behaviors, 20 DSS use, 17 and decision outcomes and performance, 19,21 one may logically suppose that complexity of a time series will have similar effects in forecasting. However, the lack of a well-defined and validated approach to identifying series complexity has limited our understanding of the implications of complexity for design and use of FDSS, related research, and forecasting practice.…”

Section: Time Series Complexitymentioning

confidence: 99%

“…29 In low complexity domains, however, they arrive at correct decision strategies expeditiously and consistently. 18,19 While some studies have found such compensatory processes to result in time-savings without noticeable loss in decision accuracy, 30 they produce inconsistent results. 18,31 Such effects may lead forecasters to overlook useful cues or to classify cues as random variations.…”

Section: Time Series Complexitymentioning

confidence: 99%

“…There are, however, some indications that expertise and environmental factors can reverse these effects. Skill 19 and motivation 36 of decision makers can stimulate them in difficult situations, potentially improving outcomes. Similar contradictory effects are evident in forecasting where some studies find experts to be better at applying domain knowledge 1 while others find novice forecasters to be as accurate as experts.…”

Section: Time Series Complexitymentioning

confidence: 99%

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Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

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Abstract:Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.Decision Support Systems, Vol 83 (March 2016): pg. 70-82. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.2 development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity.

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Information Cues and Accounting Judgment

Simnett

2015

Wiley Encyclopedia of Management

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In a world of numerous sources of potentially relevant and reliable information, it is important for the accountant or the auditor to use their judgment as to what information to collect and report and for the auditor to identify the evidence that allows them to most efficiently and effectively attest the veracity of the reported information. This article first identifies general guidelines for identifying the reliability of information cues as a form of audit evidence. It then examines the potential biases that may arise when selecting information cues, followed by a review of the various strategies of the decision maker may use when selecting a set of information cues from a population in order to make a choice between competing alternatives. Factors affecting information cue selection are then categorized under decision‐maker cue, task and environmental characteristics. The article concludes with an examination of the research evidence involving information cues impacting accounting and auditing judgments.

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A model of the effects of audit task complexity

Cited by 284 publications

References 59 publications

The Effect of Accounting Reporting Complexity on Financial Analysts

The Effect of Accounting Reporting Complexity on Financial Analysts

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Information Cues and Accounting Judgment

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