“Bayesian Modeling of Corporate Financial Distress Risk in Emerging Markets with Evidence from Moroccan Firms” (2026), by Oumaima Abouzaid. Business and Management research paper. Original abstract below, unchanged. A free PDF is available.

Bayesian Modeling of Corporate Financial Distress Risk in Emerging Markets with Evidence from Moroccan Firms

Oumaima Abouzaid · October 27, 2026 · Business and Management

Abstract

Corporate financial distress prediction remains a central issue in financial risk management, particularly in emerging markets where economic volatility, information asymmetry, and limited financial transparency increase uncertainty in credit risk assessment. Traditional financial distress prediction models, including discriminant analysis and logistic regression, often fail to adequately capture uncertainty and parameter heterogeneity in such environments. This study proposes a Bayesian probabilistic framework for modeling Corporate Financial Distress Risk among Moroccan listed firms. The proposed approach employs a Bayesian hierarchical logistic model to estimate the posterior probability of corporate financial distress using key financial indicators, including liquidity ratios, leverage, profitability, and cash flow measures. By incorporating prior information and posterior updating, the model provides a flexible statistical structure for handling uncertainty and improving predictive inference. Parameter estimation is performed using Markov Chain Monte Carlo (MCMC) simulation to obtain posterior distributions and credible intervals for risk parameters. The empirical analysis uses panel data from firms listed on the Casablanca Stock Exchange over multiple years. Results are expected to demonstrate that Bayesian estimation improves predictive stability and provides richer uncertainty quantification compared with classical approaches. The findings contribute to applied financial statistics by introducing a robust probabilistic framework for financial distress risk prediction in emerging economies. This study also offers practical implications for investors, banks, regulators, and policymakers seeking more reliable credit risk assessment tools under uncertain market conditions.

Free PDF DOI: 10.21203/rs.3.rs-10950171/v1