Abstract
This chapter considers whether the log dividend yield provides forecast power for stock returns. Using a five-year rolling window, we compare forecasts from the dividend yield model to those from the historical mean model across forecast magnitude, sign and investment metrics. Results show that in each case the dividend yield model provides superior forecasts. While the difference between, for example, RMSE and the success ratio is small, results support improved market timing and better investment performance. In explaining these results, we also consider three-year and seven-year rolling forecasts as well as recursive forecasts and note that these do not perform as well. Thus, it is the nature of time-variation within the forecast parameter that is important. Overall, these results support stock returns forecasting but stress the importance of time-variation in the forecast model to ensure forecast power.
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- 1.
In a recent paper, Black et al. (2014) noted that forecast results differ between recursive and different sized window rolling approaches.
- 2.
The rolling in-sample periods thus contain k observations, while the out-of-sample will consist of a series from k + 1 to the end of the sample, T. We refer below to this out-of-sample period as containing τ observations.
- 3.
The series used is the total market price index and the dividend yield index based on the last dividend announced divided by the latest price.
- 4.
- 5.
One issue that can arise within predictive regressions is the potential for persistence and endogeneity in any of the regressors to affect the estimates, often referred to as the Stambaugh (1999) bias. A recent set of papers has suggested a feasible quasi-GLS (FQGLS) t-test that is robust to both, as well as heteroscedasticity (Westerlund and Narayan 2012, 2015). A related procedure is also presented in Maio (2016). As the focus in this paper is on the forecasting ability of the model and not the point estimates and their significance, we refer the reader to this work but do not consider it further here.
- 6.
In a related but separate point, Henkel et al. (2011) argue that predictability rises during recessionary periods when risk premiums are high and prices low. Thus, providing a similar observation to the one made here.
- 7.
Obviously, a dividend growth predictability model would form the second part of the error correction system.
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McMillan, D.G. (2018). Forecasting Stock Returns—Historical Mean Vs. Dividend Yield: Rolling Regressions and Time-Variation. In: Predicting Stock Returns. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-319-69008-7_3
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