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    題名: 股票群的隨機行走模型與內在結構 - 以1996-1999年美國股票S&P500為例之初步分析
    Random walk model and underlying structure - a primitive study of collections of US stocks over 1996-1999
    作者: 黃鈺峰
    Huang, Yu Feng
    貢獻者: 馬文忠
    Wen Jong Ma
    黃鈺峰
    Huang, Yu Feng
    關鍵詞: 相關矩陣
    隨機矩陣定理
    耦合隨機行走
    最小展開樹
    correlation matrix
    random matrix theorem
    coupled random walk
    minimum spanning tree
    日期: 2012
    上傳時間: 2013-07-11 17:54:17 (UTC+8)
    摘要: 我們從計算股價的相關矩陣,然後利用隨機矩陣定理的結果,了解到股票市場並非符合隨機過程的預測,進而得知股票對股票之間具有關聯性,然其長時距下股票價格對數報酬的變化會呈現隨機行走的模式,因此我們對其結果提出二種不同的耦合隨機行走模型,試圖闡釋股票市場間的關聯性可融合到耦合隨機行走模型之中,並藉由均方對數報酬(mean square log-return,MSLR)來探討此事情。
    最後,為了瞭解關聯性的關係,並利用其來了解股票市場內部結構的特性,因此我們利用股價的相關矩陣來建構最小展開樹進行分析,發現當時間尺度越大其圖形越密集,中心幾乎為「GE」這家公司,因此其股票市場具有一定的判斷指標。
    By means of calculating the correlation matrix of the price of stock and using the results of random matrix theorems,we learned that the stock market does not match the prediction of stochastic processes and the stock-stock is correlated。However,stock’s price log-return changes under long time scale will appear random walk model. Therefore,we propose two kinds of the different coupled random walk model,that try to explain the correlation between the stock markets can be integrated into the coupled random walk model,and using the mean square log-return( MSLR) to investigate this issue。
    Finally,to understand the relationship of correlation matrix and by using it to know the characteristics of the underlying structure of the stock market,we use the correlation matrix of the price to construct the minimum spanning tree for analysis。The results showed that when the time scale is greater, the graphics are more intensive,and the center is almost the same company,"GE", indicating that the stock market has a certain judgment index。
    參考文獻: [1] R. Engle, J. Econometrics 100, 53 (2001)
    [2] 以隨機行走模息來解讀股票間的關聯性 馬文忠/胡進錕 物理雙月刊2008.6
    [3] L.Laloux,P.Cizeau,J.-P.Bouchaud and M.Potters,Phys. Rev. Lett. 83, 1467 (1999).
    [4] V. Plerou et al., Phys. Rev. Lett. 83, 1471 (1999)
    [5] J. D. Noh, Phys. Rev. E 61, 5981 (2000).
    [6] W. J. Ma, C. K. Hu and R. E. Amritkar, Phys. Rev.E 70, 026101 (2004)
    [7] Ma,Wang,Chen and Hu Crossover behavior of stock returns and mean square
    displacements of particles governed by the Langevin equation(2013)
    [8] A. Einstein, Ann. Phys. (Leipzig) 17, 549 (1905).
    [9] 數學傳播第九卷第三期 漫談布朗運動-----李育嘉
    [10] L. Bachelier, Ann. Sci. Ecole Norm. Sup. s`erie 3,17, 21 (1900)
    [11] 財務統計 傅承德 中央研究院統計科學研究所
    [12] Cowles, A. (1933). Can stock market forecasters forecast?Econometrica,309-324.
    [13] Cowles, A. and John, H. E. (1937). Some a posteriori probabilities in stock market action. Econometrica, 5, 280-294
    [14] Working, H. (1934). A random-difference series for use in the analysis of time series. Journal of the American Statistical Association, 29, 11-24
    [15] Kendall, M. G. (1953). The analysis of economic time-series. Part 1. Prices. J ournal of the Royal Statistical Society, 96, 11-25
    [16] A first course in probability / Sheldon Ross
    [17] J. P. Bouchaud,M. Potters,Theory of Financial Risks second reprint
    [18] 論數學遊戲 張維忠 數學傳播 30卷4期, pp. 83-94
    [19] Van Den Heuvel, M., Mandl, R., & Pol, H. H. (2008). Normalized cut group clustering of resting-state FMRI data. PLoS One, 3(4), e2001.
    [20] Albert-László Barabási & Réka Albert (October 1999). "Emergence of scaling in random networks". Science 286 (5439): 509–512
    [21] R. C. Prim: Shortest connection networks and some generalizations. In: Bell System Technical Journal, 36 (1957), pp. 1389–1401
    [22] Joseph. B. Kruskal: On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. In: Proceedings of the American Mathematical Society, Vol 7, No. 1 (Feb, 1956), pp. 48–50
    [23] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Section 23.2: The algorithms of Kruskal and Prim, pp. 567–574.
    [24] R. N. Mantegna,Eur. Phys. J. B. 11, 193–197, (1999)
    [25] PEDRO J. BALLESTER, W. GRAHAM RICHARDS Vol. 28, No. 10 .Journal of Computational Chemistry
    描述: 碩士
    國立政治大學
    應用物理研究所
    100755001
    101
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G1007550011
    資料類型: thesis
    顯示於類別:[應用物理研究所 ] 學位論文

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