Nowcasting Business Cycle Turning Points with Stock Networks and Machine Learning

53 Pages Posted: 25 Nov 2020

See all articles by Andres Azqueta-Gavaldon

Andres Azqueta-Gavaldon

Dominik Hirschbühl

European Commission - Joint Research Centre

Luca Onorante

Joint Research Centre, Italy

Lorena Saiz

European Central Bank (ECB); University of Oxford

Date Written: November, 2020

Abstract

We propose a granular framework that makes use of advanced statistical methods to approximate developments in economy-wide expected corporate earnings. In particular, we evaluate the dynamic network structure of stock returns in the United States as a proxy for the transmission of shocks through the economy and identify node positions (firms) whose connectedness provides a signal for economic growth. The nowcasting exercise, with both the in-sample and the out-of-sample consistent feature selection, highlights which firms are contemporaneously exposed to aggregate downturns and provides a more complete narrative than is usually provided by more aggregate data. The two-state model for predicting periods of negative growth can remarkably well predict future states by using information derived from the node-positions of manufacturing, transportation and financial (particularly insurance) firms. The three-states model, which identifies high, low and negative growth, successfully predicts economic regimes by making use of information from the financial, insurance, and retail sectors.

JEL Classification: C45, C51, D85, E32, N1

Suggested Citation

Azqueta-Gavaldon, Andres and Hirschbühl, Dominik and Onorante, Luca and Saiz, Lorena, Nowcasting Business Cycle Turning Points with Stock Networks and Machine Learning (November, 2020). ECB Working Paper No. 20202494, Available at SSRN: https://ssrn.com/abstract=3737432 or http://dx.doi.org/10.2139/ssrn.3737432

Dominik Hirschbühl

European Commission - Joint Research Centre

Via E. Fermi 2749
Ispra, 21027
Italy

Luca Onorante

Joint Research Centre, Italy

Via E. Fermi 1
I-21020 Ispra (VA)
United States

Lorena Saiz

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

No contact information is available for Andres Azqueta-Gavaldon

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
109
Abstract Views
392
rank
348,244
PlumX Metrics