Recurrence measures and transitions in stock market dynamics

The financial markets are understood as complex dynamical systems whose dynamics is analysed mostly using nonstationary and brief data sets from stock markets. For such data sets, the most reliable method of analysis is the one based on recurrence plots and recurrence networks, constructed from the data sets over the period of study. In this study, we do a comprehensive analysis of the complexity of the underlying dynamics of 26 markets around the globe using recurrence based measures. We also examine trends during the transitions as revealed from these measures by the sliding window analysis along the time series during the Global Financial Crisis (GFC) of 2008 and compare that with changes during the most recent pandemic related lock down. We show that the measures derived from recurrence patterns can be used to capture the nature of transitions in stock market dynamics. Thus, our study indicates that the transition in the dynamics prior to GFC is due to increasing stochasticity as seen from the recurrence measures. We also find that the markets have not stabilised after the 2020 pandemic and may possibly approach a crisis in recent future. Further the markets that go together during GFC are responding differently during the pandemic indicating that the underlying causes and mechanisms can be different.