A Complex Network Clustering and Phase Transition Models for Stock Price Dynamics before Crashes

Abstract

Researchers from multiple disciplines have tried to understand the mechanism of stock market crashes. Precursory patterns before crashes agree with various empirical studies published by econophysicists, namely the prolific work of Didier Sornette. We intend to add more empirical evidence of synchronization of trading and demonstrate the prospect of predicting stock market crashes by analyzing clusters’ dynamics in the period of bubble build-up leading to a crash. We apply the Potential-based Hierarchical Agglomerative (PHA) Method, the Backbone Extraction Method, and the Dot Matrix Plot on S&P500 companies daily returns. Our innovative approach is proposed in this paper, empirical results and discussion presented in another publication.