Long-horizon stock return predictability test with a nonlinear nonparametric bootstrap method

다국어 초록 (Multilingual Abstract)

A nonparametric bootstrap procedure with an LSTAR modeling of the
valuation ratio is applied to the continuously compounded real stock return and the log
of the price-dividend process. The empirical distribution of the test statistics shows that
the evidence for a stock return predictability weakens when we take care of nonlinearity
dynamics in the regressor. We split the sample into two regimes and implement the
long-horizon predictability tests. Results show that the stock return is predictable in the
stationary regime, while the test statistic under the null of unpredictability is insignificant
in the non-stationary regime.

다국어 초록 (Multilingual Abstract)

A nonparametric bootstrap procedure with an LSTAR modeling of the
valuation ratio is applied to the continuously compounded real stock return and the log
of the price-dividend process. The empirical distribution of the test statistics shows that
the evidence for a stock return predictability weakens when we take care of nonlinearity
dynamics in the regressor. We split the sample into two regimes and implement the
long-horizon predictability tests. Results show that the stock return is predictable in the
stationary regime, while the test statistic under the null of unpredictability is insignificant
in the non-stationary regime.

참고문헌 (Reference)

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