政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/31205

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/31205

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/31205

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/31205

Title:	探討群體決策模式對高頻率交易市場之影響－兼論模型應用於台灣股市之可行性
Authors:	江佳芳
Contributors:	李桐豪江佳芳
Keywords:	群聚行為經濟物理學穩定分配 EZ模型吉尼係數 ABM
Date:	2008
Issue Date:	2009-09-14 09:31:50 (UTC+8)
Abstract:	本文使用經濟物理學的方法建構模型，同時使用其數據分析的方式探討台灣股市特徵，並檢視模型模擬的市場與台灣股市相比較，探討模型應用於台灣股市之可行性，最後比較不同模型之間的差異性，以作為未來深入研究時修改模型的依據。模型建構的概念乃取自於Agent-based Model（ABM），為一以使用電腦技術模型自發性行動者（agent）在不同的交互作用下為系統帶來的變化，而模型根據不同的規則可以對系統帶來不同的影響，透過建構新模型、分析模型的複雜系統，我們可以發現系統的規則後進一步找出預測的方法。數據分析的方式則分別採用物理統計學和傳統財務理論使用的統計計量方法進行分析。物理統計的方法有Mantegna演算法和冪次現象（power law）的探討，而傳統的計量方法則是使共整合及Granger因果關係檢測探討價量關係。本文的結論分三大部分說明：（1）台灣股市的特徵：台灣加權股價指數的報酬率變化不符合常態分配，但存在冪次現象；而價量之間沒有共整合的關係，但有「價為量之因」的發現。（2）本文建構的系統能與台灣股市的分配和冪次現象相符合，但在價量關係的部分對台灣股市的解釋程度大幅下降；而本文設計的財富賦予制度能夠捕捉到市場投資人財富分配愈趨不均的現象。（3）在模型差異性的部分，本文的發現如下：1.加入交易限制的EZ模型股價波動度會小於原始EZ模型的股價波動度。2.民主EZ模型的股價報酬波動度會大於獨裁EZ模型股價報酬波動度。3.本研究所建構的模型，在模擬高頻率交易市場時，確實能夠捕捉到參與者財富分配不均之情況。4.在95%的信心水準下，我們無法說明民主EZ模型財富分配較不平均。
Reference:	中文參考文獻王碩濱(2006)，「以經濟物理學觀點分析台灣股市日內時間序列」，國立東華大學應用物理研究所碩士論文田瀅嫆(2006)，「厚尾分配下風險值與ETL探討--穩定分配與一般化誤差分配的應用」，銘傳大學財務金融系碩士論文任青松(2002)，「台灣股價指數與期貨指數之價量關聯性研究」，國立高雄第一科技大學財務管理系碩士論文苟成玲李英姿刘玉萍(2006)，｢金融市場研究的新視角｣，中國科技論文在線保羅∙奧莫羅德(2000)，「蝴蝶效應經濟學」，聯經保羅∙奧莫羅德(2008)，「敗部經濟學—99%失敗與1%成功的道理」，早安財經文化馬克∙布侃南(2003)，「連結—混沌、複雜之後，最具開創性的小世界理論」，天下文化陳焙焿(2007)，「台灣股價指數期貨報酬率與成交量關係之研究」，南華大學財務管理研究所碩士論文菲利浦∙鮑爾(2008)，「用物理學找到美麗新世界—洞悉事務如何環環相扣」，木馬文化華德羅普(1994)，「複雜—走在秩序與混沌邊緣」，天下文化英文參考文獻 Anderson, P.W., Arrow, K.J., and D. Pines (Eds.) (1988), “The Economy as an Evolving Complex System”, Addison-Wesley, New York Arthur, W.B. ( 1999), “Complexity and the Economy,” Science, Vol. 238, pp. 107–109 Bak, P. (1996), “How Nature Works: the Science of Self-organized Criticality”, Copernicus, New York, , USA Bouchaud J.P. and R. Cont (2000), “Herd Behavior and Aggregate Fluctuations in Financial Markets” Macroeconomic Dynamics, 4, 2000, 170–196. Clauset Aaron , Cosma Rohilla Shalizi, and M. E. J. Newman(2009),” Power-law distributions in empirical data”, http://arxiv.org/abs/0706.1062 Challet D. and Y.C. Zhang, “Emergence of Cooperation and Organization in an evolutionary Game”, Physica A 246 (1997), pp. 407–418. Challet D., and Yi‐Cheng Zhang, “ On the minority game: Analytical and numerical studies”, Physica A 256 (1998), pp. 514–532. Du Mouchel, and W.H. (1983), “Estimating the Stable Index α in Order to Measure Tail Thickness: A Critique”, The Annals of Statistics 11, 1019-1031. Eguiluz Victor M., and Martin G. Zimmermann(2000), “Transmission of Information and Herd Behavior: An Application to Financial Markets”, Physical Review Letters,Vol.85, No.26 Fama E. F., “The Behavior of Stock‐Market Prices”, Journal of Business, Vol. 38, 1965, pp. 34‐105. Farmer J.D. (2002), "Market force, ecology and evolution,", Industrial and Corporate Change, Oxford University Press, vol. 11(5), pages 895-953 G.J. Rodgers, and Dafang Zheng(2002), “A Herding Model with Preferential Attachment and Fragmentation” Physica A: Statistical Mechanics and its Applications Guptaa N., Hausera R., and Neil F. Johnson, “Using Artificial Market Models to Forecast Financial Time‐Series”, http://arXiv:physics/0506134v2. Johnson Neil F., Paul Jefferies, and Pak Ming Hui(2006),”Financial Market Complexity” Oxford University Press Mantegna, R. and H.E Stanley (1995), “Scaling behavior in the dynamics of an economic index”, Nature 376 Mantegna R.N. and H.E. Stanley(2000), “An Introduction to Econophysics: Correlations and Complexity in Finance”, Cambridge University Press, Cambridge. Ren F., S. Trimper, B. Zheng, and D.F. Zheng(2004), “A generalized dynamic herding model with feed-back interactions” Physica A 343 (2004) 653–661 R. d’Hulst and G.J. Rodgers(2000), “Democracy versus Dictatorship in Self-Organized Models of Financial Markets”, Physica A 280 554–565: Statistical Mechanics and its Applications R. d’Hulst and G.J. Rodgers (2001), “Transition from coherence to bistability in a model of financial markets.” The European Physical Journal B, Vol. 20, Issue 4, pp. 619-625. StauNer, D., and D. Sornette, (1999), “Self-organized percolation model for stock market Kuctuations”. Physica A 271, 496–506.
Description:	碩士國立政治大學金融研究所 96352021 97
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0096352021
Data Type:	thesis
Appears in Collections:	[Department of Money and Banking] Theses

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