{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/3859"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/3859","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"Stock price dynamics before crashes : a complex network study on the U.S. stock market","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Ren, Jiajia","University of Lethbridge. Faculty of Management"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-27T20:02:23Z","subjects":["network methods","network theory","stock market crashes","synchronization phenomenon","triggers"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/3859"],"render_values":[{"text":"hdl:10133/3859","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["network methods","network theory","stock market crashes","synchronization phenomenon","triggers"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/3859"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["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."]},{"key":"dc:title","label":"Title","values":["Stock price dynamics before crashes : a complex network study on the U.S. stock market"]}]}],"canonical_facts":{"dc:date.issued":["2015"],"dc:description.other":["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."],"dc:identifier":["hdl:10133/3859"],"dc:subject":["network methods","network theory","stock market crashes","synchronization phenomenon","triggers"],"dc:title":["Stock price dynamics before crashes : a complex network study on the U.S. stock market"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:23Z"}