Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement

The purpose of this research is Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement. In this regard, in order to predict the earning behavior of companies and to derive the exact parameters of the model from the Bayesian Markov Monte Carlo (MCMC) technique, which takes cross-sectional heterogeneity into account, an analysis was done by coding in Python. In this research, the earning signals extracted from the financial statements on a quarterly basis for a period of 5 years (2018-2022), for 104 companies admitted to the Tehran Stock Exchange, were collected and analyzed using a new measure of earning quality. Auxiliary variables of accounting comparability, financial leverage, operating cycle, and sales volatility were used to achieve more accurate results, and several statistical performance measures (R2, RMSE, and MSE) were used to evaluate the effectiveness of Bayesian-based forecasting models. The results showed that the proposed criterion of the present study derived from the Bayesian model for training and testing data is well able to predict the quality of earning. The evidence shows that the results of the proposed model are superior to the conventional accrual earning management model, which suggests an error rate of MSE=0.0188 and RMSE=0.1369, respectively. The results of the present research can be used to analyze the portfolio and predict the quality of future earnings of companies using historical data. It can also be used to study factors affecting investment performance.