Skip to content
Research Article Open access CC BY 4.0

Bank Distress Prediction Model for Botswana

Hassan Kablay, Victor Gumbo

Asian Research Journal of Mathematics · pp. 47–59 · Published 31 Mar 2021

10.9734/arjom/2021/v17i230273

Abstract

"Financial distress" has many dierent meanings but generally it is said to be a state of unhealthy condition. Botswana's banking system comprises of commercial, development and savings banks. None of these types of banks has actually failed but rather some of them have experienced some form of distress. The Bank of Botswana uses the CAMELS ratings to measure distress. The CAMELS ratings is based on a score between 1 and 5, with 1 being the best score and indicates strong performance, while 5 is the poorest rating and it indicates a high probability of bank failure and the need for immediate action to rectify the situation. For this study, we consider 1-3 to be good scores (non-distressed) and a bank to be distressed if it has a score of 4-5. Utilising secondary data sources for the period 2015 to 2019, inclusive, the study evaluated the drivers of bank distress in Botswana. The data was sourced from the audited nancial statements and annual reports of the 11 banks involved in the study. Panel data logistic regression was used for analysis. The results of the study showed that Non-Performing Loans (NPL) ratio and Return on Equity (ROE) were the best predictors of bank distress.

CAMELS bank distress panel data logistic regression. *Corresponding author: logistic regression

Cited by 7

Financial distress and its determinants: evidence from commercial banks in Ethiopia

Hamse Abdiwahab Abdilahi, Obsa Teferi Erena, Alemayo Solomon Abebe · Cogent Economics & Finance · 2026

A macro–micro framework for predicting bank distress: empirical insights

S. Sethi, Mohinder Singh, A. Basantaray · Indian Economic Review · 2025

Analysis The Determinants Affecting Banking Performance

Reniati Karnasi, Afrizal Elgi · Dinasti International Journal of Economics, Finance & Accounting · 2025

Determinants of Banking Performance for Commercial Banks on Indonesia Stock Exchange

Afrizal Elgi, Reniati Karnasi · Dinasti International Journal of Economics, Finance & Accounting · 2024

Assessment of Banking Conditions on Financial Distress During the Period of COVID-19 in Indonesia

S. Purwanto, D. Perkasa, Ferryal Abadi · Wseas Transactions on Business and Economics · 2023

Comparison of Multiple Linear Regression and Neural Network Models in Bank Performance Prediction in Botswana

Hassan Kablay, Victor Gumbo · Journal of Mathematics and Statistics · 2021

Financial Performance of Banks in Botswana

Hassan Kablay, Victor Gumbo · 2021

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

7

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.