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Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

机译：股息价格比率的结构突破：学习股票回报可预测性的含义

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摘要

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents' perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents' time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents' learning plays an important role in the dynamics of stock returns.
机译：在结构中断存在的情况下，Lettau和Van Nieuwerburgh（2008）隐立地认为，经济代理人对股息价格比的制度特定意义的看法是股票的政权。在本文中，我们挑战了这种假设，并采用了在估算经济代理人对股息价格比的平均值的持续增益（或恒定增益学习）的持续增益（或恒定增益学习）的最小方格。我们为美国和日本股市获得了比Lettau和Van Nieuwerburgh（2008）更好的股票回报预测。我们的经验结果表明，经济代理商的学习在股票回报的动态中发挥着重要作用。

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