2026-09-19T11:28:40.272762300Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/38592016-12-09T22:30:30Zcom_10133_3346com_10133_1com_10133_296col_10133_3351col_10133_298
ULethbridge OPUS Repository
supervisor
Yalamova, Rossitsa
author
Ren, Jiajia
author
University of Lethbridge. Faculty of Management
2016-01-07T20:56:05Z
2016-01-07T20:56:05Z
2015
https://hdl.handle.net/10133/3859
Historically, stock market crashes have caused trillions of dollars in losses and have dramatically destroyed investors’ confidence in the stock market. Independent empirical studies have converged to prove the synchronization phenomenon as the trigger of stock market crashes (Tse, Liu, & Lau, 2010). As well, the Phase Transition Model explains the building-up mechanism and the critical point existing in stock market crash (Yalamova & McKelvey, 2011). In this study, we propose to add more empirical evidence to the current studies and provide an indicator to possibly predict the stock market crashes. We apply the Potential-based Hierarchical Agglomerative (PHA) Method, the Backbone Extraction Method, and the Dot Matrix Plot to extract and display the changing clusters’ structure dynamics from the market equilibrium state to a bubble building-up state by applying the Standard & Poor 500 (S&P 500) index constituents’ daily price correlation matrix.
en_CA
network methods
network theory
stock market crashes
synchronization phenomenon
triggers
Stock price dynamics before crashes : a complex network study on the U.S. stock market
Thesis
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URL
http://localhost:4000/bitstreams/b3aeb5d7-bf91-42c9-8390-6c83b696fb28/download
File
MD5
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application/pdf
REN_JIAJIA_MSC_2015.pdf
URL
http://localhost:4000/bitstreams/3232b1ef-7f6d-4df3-8969-fad1ad7751e0/download
File
MD5
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text/plain
REN_JIAJIA_MSC_2015.pdf.txt