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RETRACTED ARTICLE: Financial Fragility in Emerging Markets: Examining the Innovative Applications of Machine Learning Design Methods

Author

Listed:
  • Xiyan Sun

    (Yunnan Arts University, Art and Design)

  • Pei Yuan

    (Henan University of Urban Construction, School of Management)

  • Fengge Yao

    (Harbin University of Commerce, Finance of School)

  • Zenan Qin

    (Harbin University of Commerce, Finance of School)

  • Sijia Yang

    (Jiangxi Normal University Science and Technology College, Business School)

  • Xiaomei Wang

    (Harbin University of Commerce, School of Tourism and Culinary Arts)

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.

Suggested Citation

  • Xiyan Sun & Pei Yuan & Fengge Yao & Zenan Qin & Sijia Yang & Xiaomei Wang, 2025. "RETRACTED ARTICLE: Financial Fragility in Emerging Markets: Examining the Innovative Applications of Machine Learning Design Methods," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 5862-5883, June.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-023-01731-w
    DOI: 10.1007/s13132-023-01731-w
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    References listed on IDEAS

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    1. Chong, Byung-Uk & Kim, Heonsoo, 2019. "Capital structure volatility, financial vulnerability, and stock returns: Evidence from Korean firms," Finance Research Letters, Elsevier, vol. 30(C), pages 318-326.
    2. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    3. Ma, Chao & Wu, Jingyi & Sun, Heyuan & Zhou, Xin & Sun, Xiyan, 2023. "Enhancing user experience in digital payments: A hybrid approach using SEM and neural networks," Finance Research Letters, Elsevier, vol. 58(PB).
    4. Wenjie Yang & Yue Zhao & Dong Wang & Huihui Wu & Aijun Lin & Li He, 2020. "Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
    5. Michael B. Imerman & Frank J. Fabozzi, 2020. "Cashing in on innovation: a taxonomy of FinTech," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 167-177, May.
    6. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.
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