Financial Risk Prediction and Control Optimization of Listed Seed Companies Based on Machine Learning Algorithms: An Empirical Analysis Using Time-Series Data

Abstract

The seed industry is a core strategic sector for national food security. Due to high R&D investment, long operating cycles, and dual impacts from natural and market factors, listed seed companies exhibit distinct financial risk characteristics with temporal dynamics. This study takes 6 leading A-share listed seed companies as research samples, using time-series financial data from authoritative databases such as CSMAR and Wind covering Q1 2016 to Q3 2024. Integrating enterprise risk management (ERM) theory and anomaly detection theory, a financial risk evaluation index system is constructed, encompassing 6 dimensions: solvency, profitability, operational capacity, growth potential, cash flow capacity, and seed industry-specific indicators. After dimension reduction via factor analysis, three predictive models, logistic regression (LR), XGBoost, and LSTM time-series model, are established for empirical research on financial risk prediction, with their performance compared. The results show that the LSTM model achieves the optimal fit for time-series financial data of listed seed companies, with a test set AUC value of 0.889, significantly outperforming the traditional LR model (0.758) and XGBoost model (0.821). Incorporating industry-specific indicators such as R&D investment ratio and seed production cost rate improves the model’s prediction accuracy by 11.8%, verifying the importance of industry-specific indicators for risk prediction. Based on empirical findings, optimization strategies for financial risk control of listed seed companies are proposed from enterprise, industry, and regulatory perspectives, providing empirical reference and practical pathways for constructing intelligent financial risk early warning systems in the seed industry.