Time series analysis on non-performing loans of peoples’ own savings bank (2022-2023)

Author

Chiketah T Monalisa

Title

Time series analysis on non-performing loans of peoples’ own savings bank (2022-2023)

Abstract

Non-Performing loans threatens the stability and profitability of financial institutions. This research utilize a complete time series analysis on NPLs of POSB, which aims to recognize trends, patterns and potential causes of NPLs over the period 2022 to 2023. This research employs ARIMA model forecasting models and trend analysis. The adjusted R², Sigma volatility, Akaike information criterion and Bayesian information criterion analytical tools were used to evaluate the reliability of the model. The diagnostic analysis indicated that the ARIMA (3,2,1) model is the most applicable for forecasting NPLs at POSB as determined by the AIC. The researcher would recommend financial institutions like POSB to implement proactive measures to control NPL risk, enhance loan portfolio management and maintain financial health. The results of this research add to the established knowledge base in financial risk management and offer valuable insights in for policy makers, regulators and practitioners within POSB. Additionally, the methodology and insights displayed can be adapted and applied to similar studies in other financial institutions, adopting a more robust understanding of NPL trends so that they can decide on appropriate risk control strategy in the light of forecast made.

Date

June 2024

Publisher

BUSE

Keywords

AR

Non-Performing Loans

, Forecasting

ARIMA

Supervisor

MS P HLUPO