Financial Crisis Prediction Model of Listed Companies Based on Financial Network Indicators

As the information technology industry rapidly advances, ensuring the accuracy and timeliness of financial crisis predictions for listed companies has become progressively more crucial. Traditional prediction models mainly rely on internal financial indicators, which fail to fully reflect the systemic risks of companies in complex market environments. To address this issue, the study proposes a financial crisis prediction model for information technology listed companies by integrating financial network indicators. The model constructs a financial network using company stock returns, extracts network indicators such as degree centrality, eigenvector centrality, betweenness centrality, and pagerank, and combines these indicators with traditional financial metrics. A Logistic model is introduced for optimization. The experimental results indicate that the fusion model achieves a prediction accuracy of 93.2%, marking a notable improvement over the 82.5% accuracy of the traditional Logistic model, and consistently surpassing 92% in several trials. In practical application validation, the model consistently maintains a prediction accuracy above 90% across different industry segments, with a lead time of 8.5 weeks and a response time of only 42 milliseconds. The false positive rate and under-reporting rate are reduced to 3.2% and 2.8%, respectively. The study provides a more precise technical solution for financial risk early warning in information technology companies and has significant practical implications for improving corporate risk management.