Nonlinear Dynamics of Stock Markets During Critical Periods

TLDR: In this article, the authors used the recurrence statistics to detect critical periods in advance for all the cases where there was a known bubble building up in the market and used this technique to identify changes in market dynamics and serve as a warning bell.

Abstract: Stock market crashes have always been a subject of intimate study in financial economics literature. Starting from [1] to [2], the reasons, nature and impact of stock market crashes have been analysed in a various ways by various authors ranging from econometric to behavioural and physics based models to explain the phenomena. Mostly, these recent works have shown an analogy between crashes and phase transition. Using a technique evolved from nonlinear dynamics and physics, it is possible to graphically represent the dynamic evolution of a system. This technique known as Recurrence Plot (RP) can detect critical phases in the system and changes in the same. Inspired by this several authors used this technique to try and detect bubbles and crashes including [28]. The present work extends the findings of the same work. Using the recurrence statistics, we show that it is possible to detect critical periods in advance for all the cases where there was a known bubble building up in the market. RP alone can not predict cashes but definitely, this tool may be used to identify changes in market dynamics and can serve as a warning bell.