Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model
Wentao Gu, Yongwei Yang, and Zhenshan Liu
Department of Statistics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education park, Hangzhou 310018, China
Stock returns play an important role in the empirical study of asset pricing, and are often applied in portfolio allocation and performance evaluation. The effect of macroeconomic and financial variables on stock returns is a hot topic and many studies have utilized these variables in time series models to improve the forecasts of stock returns. This study imposes macroeconomic and financial variables as weighting factors on kernel density and establishes a new prediction model – the time-varying factor weighted density model. We apply this model to monthly price data of the Chinese stock index and employ the rolling window strategy for out-of-sample forecasting. The result shows that this method improves both statistical and economic measures of out-of-sample forecasting performance.
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