JACIII Vol.22 No.6 pp. 831-837
doi: 10.20965/jaciii.2018.p0831


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

Corresponding author

July 15, 2017
December 25, 2017
October 20, 2018
stock returns, time-varying factor weighted density, forecasting

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.

Cite this article as:
W. Gu, Y. Yang, and Z. Liu, “Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.6, pp. 831-837, 2018.
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