We investigate the impact of China's economic policy uncertainty (EPU) on the time series variation of Chinese stock market expected returns. Using the news‐based measure of EPU, we find that EPU predicts negatively future stock market return at various horizons. This negative relation between economic policy uncertainty and expected future return remains significant as we control for a number of economic and market uncertainty variables or conduct out‐of‐sample tests. Our findings are consistent with behavioural asset pricing models, in which high uncertainty amplifies behavioural biases and generates speculative mis‐pricing under short‐sales constraint.
This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.
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