Nowcasting business cycle turning points with stock networks and machine learning

Arbeitspapier

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.

Object Details

• ISBN: 978-92-899-4411-3
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ECB Working Paper ; No. 2494

Classification and Topics

• Classification: Wirtschaft ; Neural Networks and Related Topics ; Model Construction and Estimation ; Network Formation and Analysis: Theory ; Business Fluctuations; Cycles
• Subject: real-time ; turning point prediction ; Granger-causality networks ; early warningsignal

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Azqueta-Gavaldon, Andres ; Hirschbühl, Dominik ; Onorante, Luca ; Saiz, Lorena
• Event: Veröffentlichung
• (who): European Central Bank (ECB)
• (where): Frankfurt a. M.
• (when): 2020

Further information

• DOI: doi:10.2866/23967
• Handle: http://hdl.handle.net/10419/229108
• Last update: 10.03.2025, 11:43 AM CET