Based on traditional macroeconomic variables, this paper mainly investigates the predictability of these variables for stock market return. The empirical results show the mean combination forecast model can achieve superior out-of-sample performance than the other forecasting models for forecasting the stock market returns. In addition, the performances of the mean combination forecast model are also robust during different forecasting windows, different market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains than other models considering different investors' risk aversion coefficients and trading costs. This paper tries to provide more evidence of combination forecast model to predict stock market returns.

本文在传统宏观经济变量的基础上，主要考察这些变量对股市收益的可预测性。实证结果表明，均值组合预测模型在预测股票市场收益方面比其他预测模型具有更好的样本外表现。此外，平均组合预测模型在不同的预测窗口、不同的市场条件和多步超前预测下的表现也很稳健。重要的是，考虑到不同投资者的风险厌恶系数和交易成本，平均组合预测始终比其他模型产生更高的 CER 收益。本文试图为组合预测模型预测股票市场收益提供更多的证据。