Financial distress prediction using signatures: evidence from Chinese listed firms

Jiaqi Kuang (), Zihao Guo (), Jinghan Wang (), Yezhen Wang () and Kaiwen Zhang ()
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Jiaqi Kuang: Oxford Suzhou Centre For Advanced Research, Mathematical Modelling and Data Analytics Center
Zihao Guo: Shandong University, Zhongtai Securities Institute for Financial Studies
Jinghan Wang: Shandong University of Science and Technology, College of Mathematics and Systems Science
Yezhen Wang: Tsinghua University, Shenzhen International Graduate School
Kaiwen Zhang: University of Toronto, Department of Computer Science

Risk Management, 2026, vol. 28, issue 1, No 9, 27 pages

Abstract: Abstract In studies related to the prediction on corporate financial distress/risk, achieving efficient and accurate risk prediction for imbalanced datasets is an important topic. This paper explores methods for efficiently training imbalanced datasets while ensuring that the trained models perform well in real-world market predictions. It also compares the performance of different models (Logistic, DNN, CNN, LSTM, Transformer, XGBoost, SVM) in real market data. Unlike previous research, this paper introduces the mathematical theory of the Signature algorithm and verifies that this algorithm significantly improves prediction performance across all models. These conclusions will bring new perspectives to future research on the predictions of imbalanced datasets and the combined application of mathematical theories and deep learning.

Keywords: Signature; Deep learning; Financial distress; Neural network (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1057/s41283-025-00186-4

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