Abstract
We use rank correlations as distance functions to establish the interconnectivity between stock returns, building weighted signed networks for the stocks of seven European countries, the US and Japan. We establish the theoretical relationship between the level of balance in a network and stock predictability, studying its evolution from 2005 to the third quarter of 2020. We find a clear balance–unbalance transition for six of the nine countries, following the August 2011 Black Monday in the US, when the Economic Policy Uncertainty index for this country reached its highest monthly level before the COVID-19 crisis. This sudden loss of balance is mainly caused by a reorganization of the market networks triggered by a group of low capitalization stocks belonging to the non-financial sector. After the transition, the stocks of companies in these groups become all negatively correlated between them and with most of the rest of the stocks in the market. The implied change in the network topology is directly related to a decrease in stock predictability, a finding with novel important implications for asset allocation and portfolio hedging strategies.
Highlights
Introduction to AgentBased Economics, 229–252 (Elsevier, 2017). 20
We discover that the observed balance-unbalance transition (BUT) are mainly triggered by a reorganization of the topology of the stock market networks
The detrended cumulative sums (DCS) increases for those periods in which the balance does not follow the general trend, i.e., when it does not decay with time
Summary
Introduction to AgentBased Economics, 229–252 (Elsevier, 2017). 20. Iori, G. & Mantegna, R. N. Empirical analyses of networks in finance. In Handbook of Computational Economics, vol 4, 637–685. M. et al The Physics of Financial Networks. E. Default cascades: When does risk diversification increase stability?. S., Puliga, M., Kaushik, R., Tasca, P. S. et al Complexity theory and financial regulation. R. et al Econophysics and sociophysics: Their milestones & challenges. Analysis of correlation based networks representing DAX 30 stock price returns.
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