پیش بینی پذیری بازده سهام از منظر یک مدل آمیخته
Abstract
1- Introduction
2- Data and summary statistics
3- Econometric methodology
4- Out-of-sample forecasting results
5- Extension analysis
6- Robustness analysis
7- Conclusion
References
Abstract
We find that mixing existing forecasting models can significantly improve prediction performance of stock returns. Empirical results suggest that the stock return forecasting by three proposed mixed models are more significant both in statistical and economic terms than the corresponding models in Campbell and Thompson (2008), Wang et al. (2018) and Zhang et al. (2019). This improvement of predictability is also remarkable when we employ the multivariate information to predict stock return. The prediction performance of mixed models is robust to a series of robustness test. Particularly, the three proposed mixed models obtain superior out-of-sample forecasting performance of stock return for business cycles, rolling window predictions and different out-of-sample periods.
Introduction
Stock return prediction is of great significance to asset allocation, risk management and asset pricing. An influential research by Goyal and Welch (2008) indicates that it is difficult to find a predictor or a rational model to accurately forecast out-of-sample stock return. So far, numerous literatures have proposed predictors that could be used to predict stock returns, including interest rates (Ang and Bekaert, 2007; Fama and Schwert, 1977; Campbell, 1987), dividend ratios (Fama and French, 1988, 1989; Goyal and Welch, 2003; Lewellen, 2004), the consumption-wealth ratio (Lettau and Ludvigson, 2001), inflation (Campbell and Vuolteenaho, 2004), stock variances (Guo, 2006; Ludvigson and Ng, 2007), downside variance risk (Feunou et al. 2015; Kilic and Shaliastovich, 2018), the variance risk premium (Bollerslev et al. 2009; Bollerslev Bollerslev et al. 2014), economic policy uncertainty (Chen et al. 2017), investor sentiment (Huang et al. 2015), short interest index (Rapach et al. 2016), news-implied volatility (Manela and Moreira, 2017), technical indicators (Neely et al. 2014; Gao et al. 2018; Zhang et al. 2019), manager sentiment (Jiang et al. 2017), oil-related variables (Chiang and Hughen, 2017; Nonejad, 2018; Wang et al. 2019), and among others.