ACCESSING THE MODEL FINANCIAL INSTITUTION PERFORMANCE USING BAYESIAN TECHNIQUES
Abstract
This study explores the application of Bayesian statistical techniques to model the financial performance of Nigerian banks. Unlike traditional methods that rely on fixed parameters and large datasets, Bayesian models incorporate prior knowledge and dynamically update in response to new data, making them particularly suitable for uncertain and evolving financial environments. Using key performance indicators, such as return on assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), this study develops and validates Bayesian regression models to assess the impact of internal factors (e.g., Cost-to-Income Ratio, Non-Performing Loans) and external macroeconomic variables (e.g., Inflation, GDP). The findings reveal that operational efficiency and macroeconomic conditions significantly influence financial performance. Posterior distributions highlight performance trends and anomalies across banks and years, offering deeper insights than point estimates. Validation of the model through out-of-sample forecasts and credible interval coverage confirmed its robustness and predictive accuracy of the Bayesian framework. This approach provides financial analysts and policymakers with a powerful tool for adaptive decision-making, risk assessment, and strategic planning in emerging financial markets
Keywords:
Bayesian modeling, financial performance, Nigerian banks, Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), posterior distribution, MCMC, internal and external factors, predictive analysisDownloads
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Copyright (c) 2025 David Cecilia Bose, Alhaji Ismaila Sulaiman, Adenomon Monday Osagie , Chaku Shammah Emmanuel
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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