Accounting Reporting Complexity Measured Behaviorally

Dirk Beerbaum, Maciej Piechocki, Julia M. Puaschunder

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

We propose a new measure of accounting reporting complexity (ARC) based on customized extensions XBRL elements in relation to the number of reporting tags (NRT), expressed as the relative Extension Rate (ER) as a behavioral economics solution to improve markets. Behavioral insights have recently gained attention in different scientific and applied fields. Thereby behavioral economists set out to improve market conditions to aid practitioners and consumers make wiser and more informed decisions that have a positive impact over time. XBRL extensions reduce comparability of financial disclosures and complicate financial analysis and investor decision making. We find that ER is negatively associated with market capitalization and profitability. ER is on average higher in industries perceived as complex. The preparation and disclosure of more accounting items deviating from the base taxonomy is more complex for consumers of financial and non-financial information. Increasing ER imply comparability among peers is less enabled. In comparison to commonly used measures of operating and linguistic complexity, the associations between ARC and these outcomes are more consistent, exhibit greater explanatory power, and have stronger economic significance. The ER resulting from IFRS-filers, i.e. companies which prepare their financial statements under International Financial Reporting Standard (IFRS) are on average significantly higher than US GAAP filers, i.e. companies which prepare their financial statements under United States General Accepted Accounting Principles (US GAAP). This article is based on the “transparency technology XBRL (eXtensible Business Reporting Language)” (Sunstein, 2013), which should make data more accessible as well as usable for private investors. Overall, the findings contribute to the emerging behavioral economics trend with a novel application in data science and accounting.
Original languageEnglish
Pages (from-to)35-47
JournalInternal Auditing & Risk Management
Volume4
Issue number56
DOIs
Publication statusPublished - Dec 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • accounting reporting complexity
  • behavioral economics
  • behavioral insights
  • customized extensions elements
  • financial reporting quality and inductive method
  • ifrs taxonomy
  • nudging
  • relative extension rates
  • XBRL

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