دانلود رایگان مقاله انگلیسی قابلیت پیش بینی بازده سهام بین المللی: نقش ایالات متحده - اشپرینگر 2017

Abstract

This study investigates the predictability of 11 industrialized stock returns with emphasis on the role of U.S. returns. Using monthly data spanning 1980:2–2014:12, we show that there exist multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the U.S. returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (J Finance LXVIII(4):1633–1662, 2013). Our results show that while the U.S. returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, its role based on the adaptive elastic net model is weak. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that U.S. stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the U.S. returns and those of other industrialized countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.

4 Conclusion

This study analyse the lead-lag relationship among 11 industrialized country (Australia, Canada, France, Germany, Italy, Japan, Netherlands, Sweden, Switzerland, United Kingdom and United States) stock returns with specific aim of identifying the predictive role of the U.S. return. We use the national economic variables (dividend yield and 3-month Treasury bill rates) as control variables. Our data is monthly data covering the 1980:2–2014 period for all countries except Sweden for which data is available only from 1982:3 to 2014:12. Although, the idea behind this study is based on Rapach et al. (2013), we contribute by accounting for structural breaks and nonlinearities that pose challenge to financial time series data since these properties invalidate results from full sample standard Granger causality tests. This we do by employing two different approaches: a subsample analysis that are based on the same set of models (OLS, adaptive elastic net and news-diffusion models) estimated in Rapach et al. (2013) and a bootstrap rolling window causality test. The rolling window approach does not only account for multiple structural breaks, it is capable of dating exactly the periods for which the U.S. returns has predictive power for the international returns and it is robust to small sample size.

To determine the suitability of these two approaches, we first conduct multiple structural breaks and linear dependency tests. We find the existence of multiple structural breaks and nonlinearities in the data. Given this outcome we proceed first with the subsample analysis using the same data set and sample range as in Rapach et al. (2013). The subsample results based on the pairwise Granger causality predictive regression and the News-diffusion model in general support the findings in Rapach et al. (2013): the lagged U.S. returns has predictive power over other countries returns and that information friction plays a key role in the impact of U.S. return shocks on other countries. However, in contrast to Rapach et al. (2013) we do not find much evidence of the U.S. returns predictive power when adaptive elastic net models. Also we obtain more robust estimates in almost all cases than they did.