Measuring Accounting Reporting Complexity with customized extensions XBRL – A Behavioral Economics approach

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • BearingPoint
  • The New School for Social Research

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 positively 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 implyc omparability among peers is less enabled. Comparing with standard used indicators 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 statement under United States General Accepted Accounting Principles (US GAAP). In addition, the authors identify the nature and reasoning of extensions by conducting semi-structured interviews. 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.

Details

Original languageEnglish
Article number1
Pages (from-to)3
Number of pages41
JournalJournal of Applied Research in the Digital Economy
Volume1
Issue number1
Publication statusPublished - 18 Oct 2019
MoE publication typeA1 Journal article-refereed

ID: 37897567