Predicting financial distress of Zimbabwean banks

Isabel Linda Moyo, Victor Gumbo, Eriyoti Chikodza and Brian Jones
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Isabel Linda Moyo: Department of Statistics and Operations Research, National University of Science Technology, P. O. Box AC 939, Ascot, Bulawayo, Zimbabwe
Victor Gumbo: #x2020;University of Botswana, Gaborone, Botswana
Eriyoti Chikodza: #x2021;Great Zimbabwe University, Masvingo, Zimbabwe
Brian Jones: Department of Statistics and Operations Research, National University of Science Technology, P. O. Box AC 939, Ascot, Bulawayo, Zimbabwe

International Journal of Financial Engineering (IJFE), 2020, vol. 07, issue 04, 1-26

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 Z-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.

Keywords: Multi-state Markov; CAMELS ratings; microeconomic factors; bank distress (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S2424786320500395

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