Local Predictability in High Dimensions
16 Pages Posted: 31 Jan 2023
Date Written: January 30, 2023
We propose a dynamic subset forecast combination approach to investigate local predictability of aggregate stock returns in high dimensions. Our method accommodates a vast number of heterogeneous predictive signals and is designed to pick up structural changes. Based on more than 12,000 indicators, we find that text-based indicators have provided valuable signals for daily aggregate stock returns over the last two decades, a period where the predictive signals of economic indicators have weakened.
Keywords: Aggregate Stock Returns, Dynamic Subset Forecast Combination, Textual Data, Estimation Error
JEL Classification: G12; G17; C10
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