Does XBRL help improve data processing efficiency?

Abstract

Purpose The US Securities and Exchange Commission (SEC) requires public companies to file structured data in eXtensible Business Reporting Language (XBRL). One of the key arguments behind the XBRL mandate is that the technical standard can help improve processing efficiency for data aggregators. This paper aims to empirically test the data processing efficiency hypothesis. Design/methodology/approach To test the data processing efficiency hypothesis, the authors adopt a two-sample research design by using data from Compustat: a pooled sample (N = 61,898) and a quasi-experimental sample (N = 564). The authors measure data processing efficiency as the time lag between the dates of 10-K filings on the SEC’s EDGAR system and the dates of related data finalized in the Compustat database. Findings The statistical results show that after controlling for potential effects of firm size, age, fiscal year and industry, XBRL has a non-significant impact on data efficiency. It suggests that the data processing efficiency benefit may have been overestimated. Originality/value This study provides some timely empirical evidence to the debate as to whether XBRL can improve data processing efficiency. The non-significant results suggest that it may be necessary to revisit the mandate of XBRL reporting in the USA and many other countries.

The influence of XBRL technology on the quality of financial reporting: exploring mediating variables in the Indian context

“…The sole purpose of using XBRL for financial reporting is to enhance the quality of publicly disseminated financial and non-financial information to various stakeholders (Zhou, 2019). Further, in other studies by Zhou (2019) and Rao and Guo (2022), it is noted that the prime concern behind the development and implementation of XBRL technology for financial reporting was to ensure transparency, reliability, accuracy, comparability and efficiency of annual financial reports communicated to end-users. In addition, there were studies (Valentinetti and Rea, 2011;Bovee et al, 2002 andTawiah andBorgi, 2022) that stressed that another important concern behind the adoption of XBRL technology for financial reporting was to eliminate the diversity and bring uniformity in financial reports of various companies in a capital market.…”

“…However, a study by Bartolacci et al (2021) revealed that there is a scarcity of studies focused on the consequences of the adoption of XBRL for financial reporting. In addition, Rao and Guo (2022) also opined that there is a need to empirically assess the expected benefits of XBRL in improving the quality of business reporting.…”

“…In addition, now the companies that have already adopted XBRL for their financial reporting are enjoying the benefits of enhanced business reporting, through which they are incurring fewer costs and saving more time because, through XBRL, multistakeholder business reports can be generated, which in turn reduces compliance costs (Baldwin et al, 2006;Rao and Guo, 2022). Thus, the following hypotheses have been framed: 3.3 Extensible business reporting language, enhanced business reporting and financial reporting quality XBRL is an interactive and interoperable business reporting language that is built based on the taxonomy of business and accounting rules, principles and GAAPs and indirectly helps reporting organizations customize their business reports as per the requirements of various stakeholders (Liu, 2013).…”

“…Further, XBRL as a tool certainly enhances the quality of financial reporting (Zhu and Wu, 2011;Boritz et al, 2012). Here, the quality of financial reporting means the ability of financial reports to meet the various information needs of stakeholders (Rao and Guo, 2022). The XBRL-based business reports are the most promising reports in which search-facilitating technology is backed by enhanced reading capabilities (Geiger et al, 2014).…”

A Methodology on Converting 10-K Filings into a Machine Learning Dataset and Its Applications

“…It was revealed that there are significant differences in terms of shared information among different companies. Data processing efficiency in financial reports was calculated by Rao and Guo [3] in their work. Therefore, they compared the EDGAR and COMPUSTAT databases.…”