评估预测印度银行危机的机器学习技术

Sreenivasulu Puli, Nagaraju Thota, A. C. V. Subrahmanyam
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引用次数: 0

摘要

银行危机的历史普遍性及其对全球经济的深远影响凸显了决策者完善危机预测框架的必要性。在此背景下,本研究试图利用一系列人工智能和机器学习技术(AI-ML)来预测印度潜在的银行危机。这些技术包括逻辑回归、随机森林、天真贝叶斯、梯度提升、支持向量机、神经网络、K-近邻和决策树。首先,利用 2002 年至 2023 年的月度银行业数据构建了银行业脆弱性指数,划分了危机期和稳定期。随后,我们采用了一系列预警指标(EWIs),包括资产价格、宏观经济因素、外部影响和信贷相关变量,以预测危机时期。我们的研究结果表明,AI-ML 模型在预测银行业危机方面表现出合理的准确性。此外,先进的模型性能指标突出表明,神经网络和随机森林模型在危机预测方面尤为有效,超过了其他方法。值得注意的是,在 EWIs 中,与信贷、利率和流动性相关的变量在辨别印度银行系统脆弱性方面具有相对较高的信息价值。重要的是,本文介绍的方法框架可以推广到其他经济体的银行危机预测中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Machine Learning Techniques for Predicting Banking Crises in India
The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies.
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