Loss of structural balance in stock markets

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.

Automated summary

This study utilizes rank correlations as distance functions to establish weighted signed networks for stock returns, examining the relationship between network balance levels and stock predictability. It discovers a clear balance-unbalance transition for six countries after the 2011 Black Monday in the US, which is primarily caused by a reorganization of market networks triggered by low capitalization stocks in the non-financial sector. This shift leads to a decrease in stock predictability and has important implications for asset allocation and portfolio hedging strategies.