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PREDICTING NON-PERFORMING LOAN IN GHANA USING SARIMA MODEL
Keywords:
non-performing loans, financial stability, time series analyses, SARIMADOI:
https://doi.org/10.17654/0972361724050Abstract
Banks’ non-performing loans are an important indicator of financial stability, which assumes critical importance since they reflect asset quality, credit risk, and efficiency in the allocation of resources to productive sectors in an economy. This study used the seasonal autoregressive integrated moving average technique, which plays an important role in many practical fields, to examine the monthly non-performing loan data in Ghana from 2006 to 2022. The autocorrelation function, partial autocorrelation function, Akaike information criterion, and Bayesian information criterion analytical tools were used to assess the reliability of the models. The results of the study suggest that the SARIMA $(0, 1 , 2) \times (1, 0, 0)_{12}$ model was appropriate for modeling the non-performing loan in Ghana. This model had the lowest benchmark value compared with other competitive models. It was found that the forecasted values indicate relative stability of the monthly non-performing loan in the banking industry for the next few years, though at a high level. We recommend that policies to target minimizing monthly non-performing loans, hence financial stability. We further recommend that policymakers as well as banking sector regulators need to ensure good corporate governance and risk management practices to avoid loan defaults in the banking sector.
Received: March 13, 2024
Accepted: May 6, 2024
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