Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction
Jiti Gao () and
Oliver Linton ()
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Tingting Cheng: Institute for Fiscal Studies
No CWP03/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary. We also establish the estimation theory and asymptotic properties for these models in the short horizon and long horizon case. To evaluate the effectiveness of these models, we investigate their capability of stock return prediction. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we fi nd that these models can always beat the historical mean model in terms of in-sample fi tting, and also for some cases in terms of the out-of-sample forecasting.
Keywords: Kernel estimator; locally stationary process; series estimator; stock return prediction (search for similar items in EconPapers)
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Working Paper: Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction (2017)
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