Measuring Financial Statement Disaggregation Using XBRL

IF 2 4区管理学 Q2 BUSINESS, FINANCE

Journal of Information Systems Pub Date : 2023-10-01 DOI:10.2308/isys-2021-004

Joseph A. Johnston, Kenneth J. Reichelt, Pradeep Sapkota

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引用次数: 0

Abstract

ABSTRACT We develop a measure of disclosure quality using disaggregation of financial statement items from the Form 10-K XBRL filing. Our measure (ITEMS) extends Chen, Miao, and Shevlin’s (2015) DQ measure and is distinct from R. Hoitash and U. Hoitash’s (2018) ARC measure. Our measure provides a simple measure of disaggregation by counting the balance sheet and income statement line items, it does not depend on the data aggregators’ collection process and is readily available shortly after the Form 10-K is filed. We validate ITEMS by showing that firm fundamentals correlate to ITEMS in the predicted direction using OLS regression. We find that ITEMS explains consequences of disclosure quality: forecast error, forecast dispersion, bid-ask spread, and cost of equity capital. Further, ITEMS has explanatory power of disclosure quality consequences incremental to DQ and ARC, and it is distinct from ARC evident from different associations with disclosure quality consequences and reporting quality. Data Availability: Data are available from public sources identified in the text. JEL Classifications: M10; M40; M41.

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用XBRL衡量财务报表分解

我们利用10-K表格XBRL文件中的财务报表项目分类制定了披露质量的衡量标准。我们的测量(ITEMS)扩展了Chen、Miao和Shevlin(2015)的DQ测量，与R. Hoitash和U. Hoitash(2018)的ARC测量不同。我们的方法通过计算资产负债表和损益表的项目提供了一种简单的分类方法，它不依赖于数据聚合器的收集过程，并且在提交10-K表格后不久就可以使用。我们通过使用OLS回归显示公司基本面在预测方向上与ITEMS相关来验证ITEMS。我们发现ITEMS解释了披露质量的后果:预测误差、预测离散、买卖价差和权益资本成本。此外，项目对DQ和ARC的披露质量后果增量具有解释力，并且在与披露质量后果和报告质量的不同关联上与ARC明显不同。数据可用性:数据可从文本中确定的公共来源获得。JEL分类:M10;M40;M41。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Information Systems BUSINESS, FINANCE-

CiteScore

3.90

自引率

21.10%

发文量

期刊介绍： The Journal of Information Systems (JIS) is the academic journal of the Accounting Information Systems (AIS) Section of the American Accounting Association. Its goal is to support, promote, and advance Accounting Information Systems knowledge. The primary criterion for publication in JIS is contribution to the accounting information systems (AIS), accounting and auditing domains by the application or understanding of information technology theory and practice. AIS research draws upon and is informed by research and practice in management information systems, computer science, accounting, auditing as well as cognate disciplines including philosophy, psychology, and management science. JIS welcomes research that employs a wide variety of research methods including qualitative, field study, case study, behavioral, experimental, archival, analytical and markets-based.