Title: Predictive modelling of financial crisis in the environmental protection industry using PCA-enhanced BP neural network and SVM approaches

Authors: Yupeng Li; Ke Sun

Addresses: School of Accountancy, Anyang Institute of Technology, Anyang, Henan, China ' UBS Business Solutions (China) Co., Ltd. (Wuxi Branch), Wuxi, Jiangsu, China

Abstract: Corporate financial crises are complex and unpredictable, making the establishment of effective early warning mechanisms essential for minimising potential risks. This study assesses the forecasting capabilities of Backpropagation Neural Networks (BPNNs) and Support Vector Machines (SVMs) in predicting financial crises within enterprises. Using data from 92 listed companies in the environmental protection industry, a comprehensive early warning indicator system was created, incorporating 16 financial indicators and 2 non-financial indicators. Principal Component Analysis (PCA) was employed to reduce the dimensionality of financial indicators, extracting six main components. MATLAB-based training and testing revealed that BPNN achieved a precision of 98.39% in the training set, outperforming SVM by 5.17% and a precision of 93.33% in the test set, surpassing SVM by 7.68%. These results highlight the superior performance of BPNN in early warning systems, offering a robust tool for enhancing financial risk management and decision-making.

Keywords: corporate financial crisis early warning; principal component analysis; support vector machine; environmental protection industry; back propagation neural network.

DOI: 10.1504/IJIPT.2026.153848

International Journal of Internet Protocol Technology, 2026 Vol.19 No.2, pp.53 - 64

Received: 22 Apr 2025
Accepted: 23 May 2025
Published online: 28 May 2026 *

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