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AI 根据所提供的论文材料归类:摘要直接研究股票风险溢价预测,并提出监测预测关系状态变化和暂时性可预测区间的程序。

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关注这篇论文的作者
David I. Harvey
Robert Sollis
A.M. Robert Taylor

美国股票风险溢价可预测状态的实时检测

研究问题
如何实时识别样本末端出现的短暂可预测状态,并在解释变量持续性、内生性和一定异方差存在时控制误报?
方法与思路
作者在较短时期内序贯使用异方差稳健的预测回归 t 统计量,通过训练期信息设定检测规则,并识别历史和实时状态。
材料陈述的贡献
摘要称程序对所述持续性、内生性及部分异方差具有稳健性;美国月度股票溢价应用显示存在可实时发现的短暂可预测窗口。
阅读关注点
应核对稳健性的准确条件、训练期与监测期设计、误报控制是否覆盖整个监测过程,以及检测延迟和预测窗口选择。
阅读边界
仅据 Crossref 摘要,不能确认检测条件、具体误报率或经济收益;所称可预测窗口不等于样本外投资策略有效。

原文摘要

Summary We propose new real‐time monitoring procedures for the emergence of end‐of‐sample predictive regimes using sequential implementations of standard (heteroskedasticity‐robust) regression t ‐statistics for predictability applied over relatively short time periods. The procedures we develop can also be used for detecting historical regimes of temporary predictability. Our proposed methods are robust to both the degree of persistence and endogeneity of the regressors in the predictive regression and to certain forms of heteroskedasticity in the shocks. We discuss how the monitoring procedures can be designed such that their false positive rate can be set by the practitioner at the start of the monitoring period using detection rules based on information obtained from the data in a training period. We use these new monitoring procedures to investigate the presence of regime changes in the predictability of the US equity premium at the 1‐month horizon by traditional macroeconomic and financial variables, and by binary technical analysis indicators. Our results suggest that the 1‐month‐ahead equity premium has temporarily been predictable, displaying so‐called “pockets of predictability,” and that these episodes of predictability could have been detected in real time by practitioners using our proposed methodology.

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