Can Reform of Information Disclosure by an Exchange Restrain Corporate Fraud? Evidence from China*

Wang, Meng; Zhao, Wanlong; Zhang, Wei

doi:10.1111/ajfs.12364

Cited by 5 publications

(6 citation statements)

References 100 publications

(187 reference statements)

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“…IIPs represent a significant institutional innovation in the Chinese capital market, leveraging new technologies to strengthen communication between investors and companies. Previous studies have found that IIPs have governance benefits in monitoring management (Chau et al 2023), such as enhancing investors' demand for information and subsequently improving company disclosure (Wong et al 2023), reducing the likelihood of management fraud (Wang et al 2022), and actual earnings management (Li et al 2023). However, there is limited research on the impact of investor and management interaction on the accounting conservatism of companies.…”

Section: Introductionmentioning

confidence: 99%

“…2023), reducing the likelihood of management fraud (Wang et al . 2022), and actual earnings management (Li et al . 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Control variables.Drawing insights fromWang et al (2022), additional controls for other communication methods between listed companies and investors were incorporated into the regression model.…”

mentioning

confidence: 99%

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Do Interactions Between Investors and Management Result in More Conservative Accounting Information?*

Li,

Wang,

Zhao

2024

Asia-Pac J of Fin Stud

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Within the context of the investor–management interaction platforms “E‐Interaction” and “Interaction Easy,” this paper investigates the influence of investor and management interaction on accounting conservatism and explores the strategic response behavior of management. The findings indicate that both the intensity and depth of investor inquiries contribute to the enhancement of accounting conservatism. Moreover, compared to “template” responses, management's “targeted” responses to investors significantly increase the accounting conservatism of companies. Mechanism tests reveal that investor–management interaction, by enhancing investor monitoring capabilities and increasing the litigation and reputational risks faced by companies, subsequently enhances the accounting conservatism of companies. Further analysis shows that in cases where companies are audited by non‐Big Four auditor and have lower analyst coverage, indicating a weaker external information environment, investor–management interaction can yield better governance effects.

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Section: Introductionmentioning

confidence: 99%

“…2023), reducing the likelihood of management fraud (Wang et al . 2022), and actual earnings management (Li et al . 2023).…”

Section: Introductionmentioning

confidence: 99%

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Do Interactions Between Investors and Management Result in More Conservative Accounting Information?*

Li,

Wang,

Zhao

2024

Asia-Pac J of Fin Stud

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“…XGBoost integrated learning algorithm can more effectively predict financial fraud, which is currently an excellent classification algorithm. Therefore, in order to more accurately predict the existence of fraud in enterprises, a model based on deep learning combined with SHAP to prevent financial fraud in enterprises was studied, enabling regulators to conduct regulatory early warning for listed companies, creating a good ecological environment for the healthy growth of the capital market [4][5][6]. The research proposed combining ratio scaling with XGBoost model.…”

Section: Introductionmentioning

confidence: 99%

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Wang¹

2023

IJACSA

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Preventing financial fraud in listed companies is conducive to improving the healthy development of China's accounting industry and the securities market, is conducive to promoting the improvement of the internal control system of China's enterprises, and is conducive to promoting stability. Based on the combination of SHAP (Shapley Additive explanation), a prediction and identification model should be built to determine the possibility of financial fraud and the risk of fraud for the company. The research model has effectively improved the identification accuracy of financial fraud in listed companies, and the research model has effectively dealt with the gray sample problem that is common in the forecasting model through the LOF algorithm and the IF algorithm. When conducting comparative experiments on the models, the overall accuracy rate of the research model is over 85%, the recall rate is 78.5%, the precision rate is 42%, the AUC reaches 0.896, the discrimination degree KS reaches 0.652, and the model stability PSI is 0.088, compared with traditional financial fraud Forecasting models FS model and CS model has a higher predictive effect. In the empirical analysis, selecting a company's fraud cases in 2020 can effectively analyze the characteristic contribution in the fraud process and the focus on fraud risks. The established model can effectively monitor the company's finance and prevent fraud.

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“…At the same time, due to the development and changes of the social and economic environment, new transactions and events are constantly appearing, and the current financial reports are increasingly exposed to drawbacks and shortcomings, so they need to be constantly adjusted and improved. It can be predicted that today's financial report will change greatly in the near future [4] .…”

Section: Introductionmentioning

confidence: 99%

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

Li¹,

Yao²

2022

Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

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In order to reduce the financial risks existing in enterprises, this paper studies the risk identification of enterprise accounting information fraud with weighted key algorithm. Based on the study of fuzzy association rules, this study analyzes the financial risks of enterprises, puts forward an analysis model of enterprise financial risks based on fuzzy association rules mining algorithm, and makes a qualitative analysis of enterprise financial crises, so as to predict enterprise financial risks and provide reference for enterprises to avoid risks. Comparing the correlation algorithm with the linear regression model, the result shows that the prediction error rate of the linear regression model is higher than that of the correlation algorithm model. Class I misjudgment rate is 40% higher, class II misjudgment rate is flat, overall misjudgment rate is 20% higher, and accuracy linear regression is 20% lower than correlation algorithm. It can be seen that the misjudgment rate of the correlation algorithm model is low, the prediction accuracy is high, and the discrimination and prediction ability of the linear regression model is poor, which proves the effectiveness of the application of the correlation algorithm in this paper.

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Can Reform of Information Disclosure by an Exchange Restrain Corporate Fraud? Evidence from China*

Cited by 5 publications

References 100 publications

Do Interactions Between Investors and Management Result in More Conservative Accounting Information?*

Do Interactions Between Investors and Management Result in More Conservative Accounting Information?*

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

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