Dual Deterrent Effects of Randomized On‐Site Inspections on Voluntary Information Disclosure: Evidence From Voluntary Management Earnings Forecasts

ABSTRACT In the face of the dilemma between disclosure willingness and disclosure quality in voluntary information disclosure regulation, alongside prevalent violations in voluntary disclosure practices, it is necessary to further analyze whether random on‐site inspection is an effective approach of regulating voluntary disclosure. This paper examines the impact of the China Securities Regulatory Commission's (CSRC) randomized inspections on voluntary information disclosure, focusing on voluntary earnings forecasts. Using a sample of Chinese A‐share listed companies from 2016 to 2022, the study explores whether the “double random, one open” effectively resolves the conflicting issues between disclosure quality and willingness in the regulation of voluntary information disclosures. Our findings indicate that after being included in the CSRC's randomized inspection list, companies show a significant improvement in the quality of their voluntary earnings forecast disclosures, without a reduction in their willingness to disclose. Mechanism analysis reveals that randomized inspections enhance the quality of voluntary earnings forecasts through direct deterrence, by increasing the intensity of regulatory penalties, and indirect deterrence, by attracting market participants, media, and regulatory attention. Heterogeneity analysis suggests that such deterrence effect is more pronounced in regions with higher regulatory pressure and in cases where resampling selection is employed. Heterogeneity analysis based on firm characteristics indicates that the deterrent effect of randomized on‐site inspections is more pronounced in smaller firms and non‐state‐owned enterprises. Economic consequence analysis shows that enhancing the quality of voluntary earnings forecasts through randomized on‐site inspections can help reduce the risk of stock price crashes. Additionally, randomized inspections may lead to more conservative disclosures by listed companies, expanding the width of earnings forecasts. However, the reduction in the precision of management earnings forecasts would not result in negative economic consequences. In the context of inadequate regulation of voluntary information, this paper provides feasible solutions for further standardizing the regulation of voluntary disclosure.

Authors

Institutions

Publication Details

Journal
Managerial and Decision Economics
Published
2025-05-19
DOI
https://doi.org/10.1002/mde.4556
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dual Deterrent Effects of Randomized On‐Site Inspections on Voluntary Information Disclosure: Evidence From Voluntary Management Earnings Forecasts

Feng Gu, Boyuan Tian, Yixian Ma
Managerial and Decision Economics
Auditing, Earnings Management, Governance
article

Dual Deterrent Effects of Randomized On‐Site Inspections on Voluntary Information Disclosure: Evidence From Voluntary Management Earnings Forecasts

Feng Gu, Boyuan Tian, Yixian Ma
article en

Abstract

ABSTRACT In the face of the dilemma between disclosure willingness and disclosure quality in voluntary information disclosure regulation, alongside prevalent violations in voluntary disclosure practices, it is necessary to further analyze whether random on‐site inspection is an effective approach of regulating voluntary disclosure. This paper examines the impact of the China Securities Regulatory Commission's (CSRC) randomized inspections on voluntary information disclosure, focusing on voluntary earnings forecasts. Using a sample of Chinese A‐share listed companies from 2016 to 2022, the study explores whether the “double random, one open” effectively resolves the conflicting issues between disclosure quality and willingness in the regulation of voluntary information disclosures. Our findings indicate that after being included in the CSRC's randomized inspection list, companies show a significant improvement in the quality of their voluntary earnings forecast disclosures, without a reduction in their willingness to disclose. Mechanism analysis reveals that randomized inspections enhance the quality of voluntary earnings forecasts through direct deterrence, by increasing the intensity of regulatory penalties, and indirect deterrence, by attracting market participants, media, and regulatory attention. Heterogeneity analysis suggests that such deterrence effect is more pronounced in regions with higher regulatory pressure and in cases where resampling selection is employed. Heterogeneity analysis based on firm characteristics indicates that the deterrent effect of randomized on‐site inspections is more pronounced in smaller firms and non‐state‐owned enterprises. Economic consequence analysis shows that enhancing the quality of voluntary earnings forecasts through randomized on‐site inspections can help reduce the risk of stock price crashes. Additionally, randomized inspections may lead to more conservative disclosures by listed companies, expanding the width of earnings forecasts. However, the reduction in the precision of management earnings forecasts would not result in negative economic consequences. In the context of inadequate regulation of voluntary information, this paper provides feasible solutions for further standardizing the regulation of voluntary disclosure.

Managerial and Decision EconomicsVol. 46(7)
Nanjing Audit University (CN), Capital University of Economics and Business (CN)
National Social Science Fund of China
Openalex Percentile: Top 84%
Auditing, Earnings Management, Governance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.