Machine learning-based business risk analysis for big data: a case study of Pakistan

Mohsin Nazir, Zunaira Butt, Aneeqa Sabah, Azeema Yaseen and Anca Jurcut

International Journal of Computational Economics and Econometrics, 2024, vol. 14, issue 1, 23-41

Abstract: In finance, machine learning helps the business by improving its abilities and flexibility to prevent risks, errors and to accept such challenges. This research analyses and forecasts the interest rate risk of Pakistan using machine learning models. It took a ten-year financial dataset of Pakistan investment bonds from the State Bank of Pakistan website. In this study, a framework was proposed and four different models were developed to forecast the interest rates: neural network, bootstrap aggregated regression trees, cascade-forward neural network, and radial basis neural network. Subsequently, these models were run under four different scenarios: forecasting with original, generated, LASSO extracted and weighted average features. In addition, the outcomes of these models were compared with four performance metrics: mean absolute percentage error, daily peak mean absolute percentage error, mean absolute error, and root mean square error. Overall, the results showed that radial basis neural network provided the best forecasting.

Keywords: machine learning; business risk analysis; interest rate risk; risk analysis; big data; forecasting models; Pakistan. (search for similar items in EconPapers)
Date: 2024
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