Shi Xiangting, Zhang Yakang, Yu Manning, Zhang Lihao
Industrial Engineering and Operations Research Department, Columbia University, New York, United States.
Department of Statistics, Amsterdam Avenue New York, Columbia University, New York, United States.
PeerJ Comput Sci. 2025 Jan 27;11:e2661. doi: 10.7717/peerj-cs.2661. eCollection 2025.
Risk management is a critical component of today's financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic models are inadequate to address the complex structures of the latest data. This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. A combination of quantitative and qualitative ratings derived from the analysis of financial texts results in high accuracy and stability compared with the HFRP model. Evaluating key findings, the quantity of training & testing loss decreased considerably and they have their final value as 0.0013 and 0.003, respectively. According to the hypothesis, the selected HFRP model demonstrates the values of the revenue, net income, and earnings per share (EPS), and are closely similar to the actual values. The model achieves substantial risk mitigation: credit risk lowered from 0.75 to 0.20, liquidity risk from 0.70 to 0.25, market risk from 0.65 to 0.30, while operational risk is at 0.80 to 0.35. By analyzing the results of the HFRP model, it can be stated that the proposal promotes improved financial stability and presents a reliable model for the contemporary financial markets, which in turn helps in making sound decisions and improve the assessment of risks.
风险管理是当今金融环境的关键组成部分，因为财务报表中包含的数据量巨大且复杂。借助传统方法（包括分析、技术和启发式模型）进行的业务情况、计划和进度风险评估，不足以应对最新数据的复杂结构。本研究提出了混合金融风险预测器（HFRP）模型，使用卷积神经网络（CNN）和长短时记忆（LSTM）网络来改进金融风险预测。与HFRP模型相比，通过对金融文本分析得出的定量和定性评级相结合，可实现更高的准确性和稳定性。评估关键发现，训练和测试损失的数量大幅下降，最终值分别为0.0013和0.003。根据假设，所选的HFRP模型展示了收入、净利润和每股收益（EPS）的值，并且与实际值非常相似。该模型实现了显著的风险缓解：信用风险从0.75降至0.20，流动性风险从0.70降至0.25，市场风险从0.65降至0.30，而操作风险从0.80降至0.35。通过分析HFRP模型的结果，可以说该提议促进了金融稳定性的提高，并为当代金融市场提供了一个可靠的模型，这反过来有助于做出明智的决策并改进风险评估。
