Can the text features of regulatory inquiry letters predict companies’ financial restatements? Evidence from China

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS Kybernetes Pub Date : 2024-04-26 DOI:10.1108/k-12-2023-2605
Chao Zhang, Zenghao Cao, Zhimin Li, Weidong Zhu, Yong Wu
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Abstract

Purpose

Since the implementation of the regulatory inquiry system, research on its impact on information disclosure in the capital market has been increasing. This article focuses on a specific area of study using Chinese annual report inquiry letters as the basis. From a text mining perspective, we explore whether the textual information contained in these inquiry letters can help predict financial restatement behavior of the inquired companies.

Design/methodology/approach

Python was used to process the data, nonparametric tests were conducted for hypothesis testing and indicator selection, and six machine learning models were employed to predict financial restatements.

Findings

Some text feature indicators in the models that exhibit significant differences are useful for predicting financial restatements, particularly the proportion of formal positive words and stopwords, readability, total word count and certain textual topics. Securities regulatory authorities are increasingly focusing on the accounting and financial aspects of companies' annual reports.

Research limitations/implications

This study explores the textual information in annual report inquiry letters, which can provide insights for other scholars into research methods and content. Besides, it can assist with decision making for participants in the capital market.

Originality/value

We use information technology to study the textual information in annual report inquiry letters and apply it to forecast financial restatements, which enriches the research in the field of regulatory inquiries.

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监管问询函的文本特征能否预测公司的财务重述?来自中国的证据
目的自监管问询制度实施以来,有关其对资本市场信息披露影响的研究日益增多。本文以中国的年报问询函为基础,聚焦于一个特定的研究领域。从文本挖掘的角度,探讨这些问询函中包含的文本信息是否有助于预测被问询公司的财务重述行为。设计/方法/途径使用Python处理数据,进行非参数检验进行假设检验和指标选择,并采用6个机器学习模型预测财务重述。研究结果模型中一些表现出显著差异的文本特征指标对预测财务重述是有用的,特别是形式上的阳性词和停止词的比例、可读性、总字数和某些文本主题。证券监管部门越来越重视公司年报中的会计和财务方面的内容。研究局限/意义本研究探讨了年报问询函中的文本信息,可以为其他学者提供研究方法和内容方面的启示。原创性/价值我们利用信息技术研究年报问询函中的文本信息,并将其应用于预测财务重述,丰富了监管问询领域的研究。
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来源期刊
Kybernetes
Kybernetes 工程技术-计算机:控制论
CiteScore
4.90
自引率
16.00%
发文量
237
审稿时长
4.3 months
期刊介绍: Kybernetes is the official journal of the UNESCO recognized World Organisation of Systems and Cybernetics (WOSC), and The Cybernetics Society. The journal is an important forum for the exchange of knowledge and information among all those who are interested in cybernetics and systems thinking. It is devoted to improvement in the understanding of human, social, organizational, technological and sustainable aspects of society and their interdependencies. It encourages consideration of a range of theories, methodologies and approaches, and their transdisciplinary links. The spirit of the journal comes from Norbert Wiener''s understanding of cybernetics as "The Human Use of Human Beings." Hence, Kybernetes strives for examination and analysis, based on a systemic frame of reference, of burning issues of ecosystems, society, organizations, businesses and human behavior.
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