Accurate forecasting of stock prices not only guides investor behavior but also assesses financial risk and promotes balanced economic and social development. This paper uses a dynamic factor-enhanced model averaging method to forecast the daily closing price of the Shanghai Composite Index, maximizing the use of valid information by weighting the forecast values of different models. Firstly, the common factor is extracted from the smoothed original explanatory variables; then the dynamic factor augmented model selection method and the model averaging method based on different criteria are used to predict different lag orders of the common factor and the explanatory variables, and the effectiveness of the dynamic factor augmented censored group cross-validation model averaging method is verified using multiple predictor error indicators as well as the DM test. The experimental results show that the dynamic factor augmented censored group cross-validation model averaging method has better prediction results and is more robust.

准确预测股票价格不仅可以指导投资者行为，还可以评估金融风险，促进经济社会均衡发展。本文采用动态因子增强模型平均法对上证指数每日收盘价进行预测，通过对不同模型的预测值进行加权，最大限度地利用有效信息。首先，从平滑后的原始解释变量中提取公因子；然后采用动态因子增广模型选择法和基于不同准则的模型平均法来预测公因子和解释变量的不同滞后阶数，动态因子增广删失组交叉验证模型平均法的有效性为使用多个预测误差指标以及 DM 测试进行验证。实验结果表明，动态因子增广删失组交叉验证模型平均方法具有更好的预测效果，并且更加鲁棒。