探索行业特定溢价的周期性可预测性

Till Sänger
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引用次数: 0

摘要

本文通过Raftery等人(2010)的动态模型平均(DMA),结合Tsiakas等人(2020)在不同制度状态下表现良好的两个预测模型,简化并大幅改进了股票溢价和行业回报预测。这种方法允许在衰退和扩张期间进行具有统计意义和经济价值的股票溢价预测,而无需明确预测制度状态。将这种方法扩展到行业回报,DMA对行业特定动态的适应性允许回报预测优于所有考虑的替代方案,并在基于Kirby和Ostdiek(2012)的修正风险-回报时间策略中提供显著的经济价值,该策略基于跨越所有行业的等加权基准投资组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Cyclical Predictability of Sector-Specific Premia
This dissertation simplifies and substantially improves equity premium and sector return forecasts by combining two prediction models that perform well during different regime states as in Tsiakas et al. (2020) through Dynamic Model Averaging (DMA) of Raftery et al. (2010). This approach allows for statistically significant and economically valuable equity premium forecasts during both recessions and expansions, without needing to predict the regime state explicitly. Extending this approach to sector returns, the adaptability of DMA to sector-specific dynamics allows for return forecasts that outperform all considered alternatives and provide significant economic value in a modified Risk-to-Reward Timing strategy based on Kirby and Ostdiek (2012) over an equally weighted benchmark portfolio spanning all sectors.
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