标题： 基于深度学习的风险控制多因素投资模型优化方法 显示英文标题

标题： Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control

摘要： 提出一种基于深度学习的多因子投资模型优化方法用于风险控制。通过构建基于长短期记忆网络（LSTM）的深度学习模型，并将其与多因子投资模型相结合，优化因子选择和权重确定，以提高模型对市场变化的适应性和稳健性。实证分析表明，LSTM模型在最大回撤、夏普比率和风险价值（VaR）等风险控制指标上显著优于基准模型，在不同市场环境中表现出强大的适应性和稳健性。此外，该模型应用于实际组合中，优化资产配置，显著提高了组合的表现，为投资者提供了更科学准确的投资决策依据，有效平衡了收益与风险。 显示英文摘要

摘要： Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retracement, Sharp ratio and value at risk (VaR), and shows strong adaptability and robustness in different market environments. Furthermore, the model is applied to the actual portfolio to optimize the asset allocation, which significantly improves the performance of the portfolio, provides investors with more scientific and accurate investment decision-making basis, and effectively balances the benefits and risks.