Predicting financial distress of Zimbabwean banks

Abstract

Prediction of financial distress for lending institutions has been a major concern since the financial crisis of 2008. The motivation for empirical research in bank bankruptcy prediction is clear — the early detection of financial distress and the use of corrective measures are preferable to protection under bankruptcy law. If it is possible to recognize failing banks in advance, then appropriate action can be taken to reverse the process before it is too late. This study uses panel multi-state Markov (MSM) chains to build a predictive model for financial distress of banks in Zimbabwe. Microeconomic factors and the CAMELS ratings were used in the construction of the MSM model. Distress probabilities were calculated using hazard ratios found by MSM and then the Altman [Formula: see text]-Scores were generated for each bank as a means of validating the built MSM model. The scores generated were very similar to the current CAMELS ratings.

Risk Management in Africa's Financial Landscape: A Review

“…Several studies have explored credit risk management practices. Moyo, Gangopadhyay, Gumbo, Chikodza, and Jones (2020) conducted a comprehensive study on credit risk management in Zimbabwean banks, emphasizing the importance of credit scoring models and collateral evaluation. In contrast, Akujuobi, Anyanwu, and Eke (2021) examined credit risk management in Nigerian banks.…”