Local Predictability in High Dimensions

16 Pages Posted: 31 Jan 2023

See all articles by Philipp Adämmer

Philipp Adämmer

University of Greifswald

Sven Lehmann

University of Rostock - Department of Economics

Rainer Alexander Schüssler

University of Rostock - Department of Economics

Date Written: January 30, 2023

Abstract

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

Suggested Citation

Adämmer, Philipp and Lehmann, Sven and Schüssler, Rainer Alexander, Local Predictability in High Dimensions (January 30, 2023). Available at SSRN: https://ssrn.com/abstract=4342487 or http://dx.doi.org/10.2139/ssrn.4342487

Philipp Adämmer

University of Greifswald ( email )

Friedrich-Loeffler-Strasse 70
D-17487 Greifswald, 17489
Germany

Sven Lehmann

University of Rostock - Department of Economics ( email )

Ulmenstr. 69
Rostock, 18057
Germany

Rainer Alexander Schüssler (Contact Author)

University of Rostock - Department of Economics ( email )

Ulmenstr. 69
Rostock, 18057
Germany

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