Open AccessA Complex Network Clustering and Phase Transition Models for Stock Price Dynamics before Crashes
Author(s) -
Jiajia Ren,
Rossitsa Yalamova
Publication year - 2022
Publication title -
international journal of economics and statistics
Language(s) - English
Resource type - Journals
ISSN - 2309-0685
DOI - 10.46300/9103.2022.10.1
Subject(s) - crash , computer science , stock market , econometrics , cluster analysis , stock (firearms) , stock price , empirical evidence , stock market crash , empirical research , operations research , economics , artificial intelligence , engineering , mathematics , statistics , geography , mechanical engineering , paleontology , philosophy , context (archaeology) , archaeology , epistemology , series (stratigraphy) , biology , programming language
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.