Stock market return predictability: A combination forecast perspective

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.

Automated summary

This paper investigates the predictability of stock market returns based on traditional macroeconomic variables. The empirical results suggest that the mean combination forecast model outperforms other models in forecasting stock market returns, and its performance remains robust across different forecasting windows, market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains compared to other models, considering different investors' risk aversion coefficients and trading costs.