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- W2121362183 abstract "One of the most studied questions in economics and finance is whether equity returns or premiums can be predicted by empirical models. While many authors favor the historical mean or other simple parametric methods, this article focuses on nonlinear relationships. A straightforward bootstrap-test confirms that non- and semiparametric techniques help to obtain better forecasts. It is demonstrated how economic theory directly guides a model in an innovative way. The inclusion of prior knowledge enables for American data a further notable improvement in the prediction of excess stock returns of 35% compared to the fully nonparametric model, as measured by the more complex validated R 2 as" @default.
- W2121362183 created "2016-06-24" @default.
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- W2121362183 date "2011-01-01" @default.
- W2121362183 modified "2023-09-27" @default.
- W2121362183 title "Nonparametric prediction of stock returns guided by prior knowledge" @default.
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