What question did this study set out to answer?

The aim is to develop a time-series framework for predicting corporate financial crises using Recurrent Neural Networks.

February 14, 2026Open Access

Time-Series Modeling for Corporate Financial Crisis Prediction: Evidence from Recurrent Neural Networks

Key Points

The aim is to develop a time-series framework for predicting corporate financial crises using Recurrent Neural Networks.
Constructed a time-series dataset from annual data of Chinese A-share listed companies (2019-2023).
Compared RNNs to other models like Random Forest, SVM, and BPNN using identical datasets.
Assessed model performance through metrics like Accuracy, Precision, Recall, F1 score, and AUC.
Performed statistical validation using McNemar tests and bootstrap confidence intervals.
RNNs significantly outperformed other models in multi-period predictions.
RF and BPNN showed strong performance in single-period tasks but less effectiveness over time.
Statistical validation confirmed the stability and systematic advantages of RNNs in prediction.

Abstract

Corporate financial distress typically emerges through a gradual accumulation process, rendering crisis prediction inherently dynamic and path-dependent. However, many existing studies continue to rely on static cross-sectional data or short-term observations, which limits their ability to capture the temporal evolution of financial risk. To address this issue, this study develops a time-series financial crisis early warning framework based on Recurrent Neural Networks (RNNs) and systematically evaluates the incremental value of temporal information in corporate distress prediction. Using annual data of Chinese A-share listed companies from 2019 to 2023, we construct both single-year cross-sectional datasets and a five-year multi-period time-series dataset under a unified experimental protocol. Within this dual-framework setting, RNNs are compared with Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN) using identical feature sets, training–testing splits, and evaluation criteria. Model performance is assessed through multiple metrics, including Accuracy, Precision, Recall, F1 score, and AUC, complemented by statistical validation using McNemar tests, loss-based comparisons, and bootstrap confidence intervals. The empirical results show that while RF and BPNN exhibit strong robustness in static, single-period prediction tasks, RNNs achieve consistently superior performance when multi-period temporal information is explicitly modeled. Statistical tests indicate that the observed performance advantages of RNNs are systematic and stable, though moderate under the current sample size. This study provides empirical evidence that incorporating temporal structures into financial crisis prediction can substantially enhance predictive effectiveness under constrained labeled data. The findings highlight the importance of time-series modeling for early warning applications and offer practical guidance for selecting appropriate predictive frameworks across different data structures.

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