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|Title:||Macroscopic & mesoscopic dynamics of financial markets||Authors:||Teh, Boon Kin||Keywords:||DRNTU::Science::Physics||Issue Date:||2018||Source:||Teh, B. K. (2018). Macroscopic & mesoscopic dynamics of financial markets. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||With hundreds trillion dollars of capital floating in the stock market, it is extremely important to understand market structures and dynamics of stock markets. In this thesis, we studied the macroscopic and mesoscopic dynamics of financial markets, from the econophysics (a marriage between physics and economics) point of view. When econophysicists study stock markets, they frequently borrow methods developed in other areas of physics. However, because of the nature of their problems, econophysicists sometimes also invent new methods. In this thesis, we have also contributed methodological innovations (MI), to contrast the phenomenological discoveries (PD) that we have also made. The first of these methodological innovations is (MI1) the method of partial hierarchical clustering (PHC), a supervised clustering method with the advantage of using multiple thresholds to determine clusters. Through the PHC results, we demonstrated (PD1) the existence of hierarchical structures in the Singapore Exchange and Hong Kong Stock Exchange: from market sectors, to country markets, and to global markets. Furthermore, we also investigated the dynamics of these hierarchical structures across market crashes. To do this, we (MI2) extend the complete-linkage hierarchical clustering algorithm, to obtain robust clusters of stocks with high intra-cluster homogeneity and high inter-cluster heterogeneity. By visualizing these robust clusters using (MI3) the fusion-fission diagram, we observed that (PD2) when approaching market crashes, the movements of stock prices become synchronized, causing most of stocks to merge into a giant cluster. Right after the crash, this giant cluster fragmented and thereafter mixed strongly. This discovery points us to the fusion-fission processes in the market, which we can exploit to forecast market crashes. We assume that the traders’ strategies form strategy clusters in the constantly changing strategy space, and the stock price movements are governed by the dynamics of these strategy clusters. Moreover, this dynamic can be described by a statistical physics fusion-fission model: the soup-of-groups model (SoG). We (MI4) derived a mean-field SoG forecasting equation and showed that some market crashes can be predicted. Specifically, by fitting the continuous returns of the component stocks of the Straits Times Index to the SoG forecasting equation, we (PD3) found episodes of heightened crash likelihoods close to the Chinese Correction (27 Feb 2007), beginning of the Subprime Crisis (17 Aug 2007), and the Asian Correction (9 Mar 2009), with early warning four to six months prior to the crashes.||URI:||http://hdl.handle.net/10356/73698||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SPMS Theses|
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