Measuring the Predictable Variation in Stock and Bond Returns
指出，用回归模型预测长期股票和债券回报时，样本R²会随回报期增长而大幅上升，但这并不代表真实的可预测性，因为即使总体R²很小或为零，长期回归也可能产生高R²和显著的t值，因此不支持长期回报高度可预测的观点。
Recent studies show that when a regression model is used to forecast stock and bond returns, the sample |$R^2$| increases dramatically with the length of the return horizon. These studies argue, therefore, that long-horizon returns are highly predictable. This article presents evidence that suggests otherwise. Long-horizon regressions can easily yield large values of the sample |$R^2,$| even if the populations |$R^2$| is smaller or zero. Moreover, long-horizon regressions with a small or zero population |$R^2$| can produce t-ratios that might be interpreted as evidence of strong predictability. In general, the analysis provides little support for the view that long-horizon returns are highly predictable.