Abstract
Emerging economies, while exhibiting higher growth rates compared to developed countries, are susceptible to external shocks, leading to financial fragility. Traditional analysis methods often fall short in accuracy and timeliness. This research introduces a novel approach utilizing Back-Propagation Neural Network (BPNN) to predict financial fragility in emerging markets, focusing on the BRICS countries. By considering twelve impactful factors and employing Principal Component Analysis (PCA), five key influencers are identified. The BPNN model is iteratively optimized to achieve superior quality. Historical data validation attests to the model’s effectiveness. The study identifies five critical factors influencing financial fragility: GDP growth rate, inflation rate, monetary policy, interest rates, and bank’s capital-asset ratio. Among these, GDP growth rate emerges as a significant determinant. Positive growth is correlated with financial stability, while a slowdown or negative growth signals elevated risks. Emerging markets are particularly vulnerable to global economic fluctuations due to their reliance on exports and foreign capital. Additionally, weaker financial systems amplify their susceptibility to shocks.The research underscores the importance of building robust financial sectors, replenishing funding buffers, and proactively managing distressed assets in emerging market economies. The proposed BPNN model provides a powerful tool for risk prediction, though it requires strong indicator data support. While computational intensity and interpretability remain challenges, the benefits of BPNNs outweigh these limitations. Effective communication and information exchange across countries and markets are crucial for maintaining stability in emerging market finance. This study contributes valuable insights into the prediction of financial fragility in emerging markets, offering a comprehensive framework for policymakers and financial practitioners to navigate the challenges and opportunities presented by these dynamic economies.
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Among the authors, XS and PY contributed equally as first authors. FY and ZQ responsible for the language translation of this research. SY and XW are all corresponding authors who contribute equally to the article. All authors read and approved the final manuscript.
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Sun, X., Yuan, P., Yao, F. et al. Financial Fragility in Emerging Markets: Examining the Innovative Applications of Machine Learning Design Methods. J Knowl Econ (2024). https://doi.org/10.1007/s13132-023-01731-w
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DOI: https://doi.org/10.1007/s13132-023-01731-w