Predictability of stock returns and asset allocation under structural breaks
结构断点下的股票收益可预测性与资产配置AI 解读 ↓
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结构断点下的股票收益可预测性与资产配置AI 解读 ↓
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This paper adopts a new approach that accounts for breaks to the parameters of return prediction models both in the historical estimation period and at future points. Empirically, we find evidence of multiple breaks in return prediction models based on the dividend yield or a short interest rate. Our analysis suggests that model instability is a very important source of investment risk for buy-and-hold investors with long horizons and that breaks can lead to a negative slope in the relationship between the investment horizon and the proportion of wealth that investors allocate to stocks. Once past and future breaks are considered, an investor with medium risk aversion reduces the allocation to stocks from close to 100% at short horizons to 10% at the five-year horizon. Welfare losses from ignoring breaks can amount to several hundred basis points per year for investors with long horizons.