Market states: A new understanding

TL;DR

This paper analyzes the clustering of correlation matrices from S&P 500 and Nikkei 225 markets over 2006-2019, identifying distinct market states and their transitions, which may signal impending critical events.

Contribution

It introduces a clustering approach to classify market states based on correlation structures and analyzes their transitions as potential precursors to market crises.

Findings

Identified 4 market states for S&P 500 and 6 for Nikkei 225.

Market transitions between states can indicate upcoming critical events.

Surrogate data analysis confirms fluctuations are due to white noise.

Abstract

We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of 2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructed from the sliding epochs. The correlation matrices can be classified into different clusters, named as market states based on the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states by optimizing the value of intracluster distances. The market shows transitions between these market states and the statistical properties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyze the same clustering technique on surrogate data constructed from average correlations of market states and the fluctuations arise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction of surrogate data whose average correlation equals the average of the real data.