Forecasting financial cycles: can big data help?

Abstract

Financial cycles as a source of financial crisis and business cycles that was demonstrated during the financial crisis of 2008, so it is important to understand proper methods of measuring and forecasting them to unravel their true nature. We searched financial big data for the UK, USA, Japan and China for a period 2004Q1 to 2019Q1 to find important data corresponding to the research and determine their importance for the financial cycle studies. We use singular spectral analysis (SSA without financial big data) and multichannel singular spectral analysis (MSSA with financial big data) to identify significant deterministic cycles in the residential property prices, credits to private non-financial sector and credit share in the GDP. The forecast test results show on the data for the UK, USA, Japan and China that inclusion of the financial big data significantly (on the level from 30% to four times) improves forecast accuracy for financial cycle components. This is a first study on the importance of the link between financial cycles and financial big data. Policymakers, practitioners and financial cycles research should take into the account the importance of financial big data for the studies of financial cycles for a better understanding of their true nature and improving their forecast accuracy. First published online 22 June 2020

Keywords

• financial cycles
• big data
• forecast accuracy
• singular spectral analysis
• multichannel singular spectral analysis
• time series