Redesign of the Model for Evaluating the Quality of Corporate Risk Disclosure Based on Hidden Markov Chain Model

Document Type : Research Paper

Authors

Department of Accounting, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran.

10.22103/jak.2024.23666.4071

Abstract

Objective: This study aims to develop a model for evaluating risk disclosure quality based on the likelihood of alignment between risk disclosure and the company’s actual risk status.
 
Method: The research model is designed and estimated using a Hidden Markov Chain model and the Markov Chain Monte Carlo algorithm. Data from 150 listed companies active on the Tehran Stock Exchange were used. The data from 2013 to 2019 were applied for training and estimating the model, while the data from 2020 to 2022 were used to assess the model’s predictive accuracy.
 
Results: Systematic risk indicators (beta), stock return volatility, the ratio of export revenue to total revenue, and operating cash flow volatility are influential factors affecting the likelihood of alignment between risk disclosure and firms’ actual risk status. Furthermore, liquidity ratio, operating leverage, financial leverage, and firm size have a negative relationship with risk disclosure quality when the company is in a low-risk state.
 
Conclusion: The proposed model can serve as an appropriate foundation for analyzing and predicting risk disclosure patterns and for improving the quality of information provided to financial statement users. The model also predicts the effect of risk indicators on the likelihood of increased risk disclosure in consecutive periods. This suggests that companies tend to increase their level of risk disclosure regardless of their actual risk conditions, which leads to lower-quality risk disclosure.

Keywords

Main Subjects

References

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  • Receive Date: 22 June 2024
  • Revise Date: 05 August 2024
  • Accept Date: 11 September 2024

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