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    Title: 藉由小世界股票網路探索不同景氣區間的差異性
    Exploring economy-realated differences by small-world stock networks
    Authors: 邱建堯
    Chiu, Chien Yao
    Contributors: 蕭又新
    Shiau, Yuo Hsien
    邱建堯
    Chiu, Chien Yao
    Keywords: 小世界網路
    景氣對策信號
    最小生成樹
    分層樹
    small world network
    business indicators
    minimum spanning tree
    hierarchical tree
    Date: 2017
    Issue Date: 2017-02-08 16:42:57 (UTC+8)
    Abstract:   股票市場對投資者而言是以極大化自有資產為目的,因此如何辨別不同景氣區間對股市的影響為投資者感興趣的議題。傳統上,使用統計資料來幫助我們比較不同景氣區間之差異,然而股票市場之複雜、非線性及不可預測性也經常成為各統計資料失準的關鍵,因此,本篇論文以複雜網路作為分析股票市場之模型,並將各個股票表示成節點、股價變化之關聯性作為連結下,建立出複雜網路,藉此探討股市中的景氣差異。
      在本研究中,先利用國發會制定的景氣對策信號,來幫助我們選取四段景氣區間,接著將台積電作為網路核心建構個股的相關網路。並以最小生成樹(Minimum Spanning Tree) 將複雜的股票網路簡單化。同時我們計算出各股相關網路之全域網路參數(Global Network Parameters)及區域網路參數(Regional Network Parameters),以利我們討論兩段景氣好區間與兩段景氣差區間之差異。最後,我們將股市相關網路以分層樹(Hierarchical Tree)來表示,以了解網路分群的結果。
      結果顯示,我們建構的個股相關網路符合小世界網路特性,在全域網路參數中,景氣好相關網路之常規化平均特徵路徑(Normalization Average Characteristic Path Length)及景氣差相關網路中之平均群聚係數(Average Clustering Coefficient)、平均特徵路徑(Average Characteristic Path Length)、常規化平均特徵路徑(Normalization Average Characteristic Path Length)有顯著差異。
    在區域網路參數中,在景氣好相關網路中,被選為網路樞紐並有顯著差異之個股有台達化、宜進與華通,景氣差相關網路則有瑞利、日月光、矽品及萬企。在景氣好相關網路比較時,台積電的連結度與點效率皆具有顯著差異。
    Reference: [1] Barabasi, A.-L., Albert, R.,1999, Emergence of scaling in random networks, Science 286, 509–512.
    [2] Barabasi, A.-L., Albert, R. ,2002,Statistical mechanics of complex networks, Rev. Mod. Phys.,volume 74:47-97.
    [3]Erdős, P.,A. Rényi. ,1976,On the evolution of random graphs, Selected Papers of Alfréd Rényi, volume 2: 482-525.
    [4] Duncan J. Watts,Steven H. Strogatz,1998,Collective dynamics of small-world networks,Nature 393, 440–442.
    [5] Lixia Tian, Jinhui Wang, Chaogan Yan, Yong He,2010,Hemisphere and gender-related differences in small-world brain networks: A resting-state functional MRI study, NeuroImage volume 54:191-202.
    [6]M.E.J.Newman,2004, Scientific collaboration networks. II. shortest paths, weighted networks, and centrality, Phys. Rev. E., volume 64, 016132.
    [7] Mantegna , 1999,Hierarchical structure in financial markets, Euro. Phys. J.B volume 11:193-197.
    [8] Giovanni Bonanno, 2003, Topology of correlation-based minimal spanning trees in real and model markets, Phys. Rev.E volume 68,046130.
    [9]N. Vandewalle, F.Brisbois, X.Tordoir, 2001, Self-organized critical topology, Quantit. Finan.
    [10] Chi K. Tse,A network perspective of the stock market, 2010,Journal of Empirical Finance volume 17:659-667.
    [11] Adrian Cho,2009, Econophysics: Still Controversial After All These Years ,Science:Volume 325, Issue 5939, pp. 408.
    [12] Michael Schulz ,2002,Statistical Physics and Economics: Concepts, Tools, and Applications,Springer.
    [13] Bonanno,2001,High-frequency cross-correlation in a set of stocks,Quantit. Finan. Volume 1:96-104.
    [14] Coronnello,C,Tumminello,M,Lillo,F,Micciche,S,Mantegna, R.N.,2005,Sector Identification in a Set of Stock Return Time Series Traded at the London Stock Exchange, Acta Physica Polonica B., volume 36, p. 2653.
    [15]J. P. Onnela,A. Chakraborti,K. Kaski, J. Kertesz, A. Kanto,2003, Phys. Rev. E volume 68, 056110.
    [16] Jun-Hong Lin,2016,Age-related differences on the functional connectivity of insular cortex: An approach based upon small-world theory,Master Thesis of Graduate Institute of Applied Physics, College of Science NCCU.
    Description: 碩士
    國立政治大學
    應用物理研究所
    103755001
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0103755001
    Data Type: thesis
    Appears in Collections:[應用物理研究所 ] 學位論文

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