政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118273

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118273

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113325/144300 (79%)
造訪人次 : 51155702 線上人數 : 925

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 理學院 > 應用物理研究所 > 學位論文 > Item 140.119/118273

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/118273

題名:	股價變化之波動的若干流體動力訊號–跨天效應 Some hydrodynamic signatures in fluctuations of stock price changes–crossing day effect
作者:	吳培煜 Wu, Pei-Yu
貢獻者:	馬文忠 Ma, Wen-Jong 吳培煜 Wu, Pei-Yu
關鍵詞:	股票盤後交易動力參數跨天網路 Stock After trading Network Crossing-day Hydrodynamic
日期:	2018
上傳時間:	2018-07-03 17:31:37 (UTC+8)
摘要:	本論文探討 2004 年至 2009 年台灣市場(TWII)的股票群體報酬在不同時間尺度下，類比於多粒子系統的擴散常數 D、動力溫度 θ 及移動率 μ 等動力參數，並與 1996 年至 2001 年的美國、台灣、上海股市的數據進行比較，研究不同市場中的共同特性。我們密集計算個股報酬的高頻序列，其中序列時間點的間隔固定，計算報酬所採用的時間間隔時間 τ 則作為時間尺度變數。我們分析不同年份的台灣與美國市場、其交易日之間的跨天效應，發現跨天影響的重要性。我們檢視時間序列的交易數據，得到跨天的影響廣泛的存在於不同時間、市場的股票之中，藉由區分報酬數據是否跨天的情況、計算機率密度函數分佈的尺度參數的變化，從中確認這兩種市場中的共同統計性質，並發現跨天會使機率密度函數分佈呈現「不對稱」的特徵。我們發現跨天的效應會使數據中地使價格差持續為正(或持續為負)，在計算時間的統計分布中，透過盤後的等效交易時間tafter 可以有效量化跨天的效應。除了時間上的分析，本論文引入網路的概念把股票間的相關係數當作連結強度，分析股票網路在(時間序列長度)T 的維度空間中之結構，得到了股票網路中各節點(股票)的「不均質」特性，並透過一個網路模型的建構程序，對這「不均值」的特性進行定性上的研究。我們又引入時間的因素，同時考慮時間與空間上的相關性，並從中又發現跨天與跨天間有影響的訊號，此與盤後的等效交易時間tafter 的分析相互應，定量上確認了股票網路在時間上由跨天造成的「不均勻」的特性。 By the analogy of a system of many particles, we study the diffusion constant D, kinetic temperature θ and mobility μ obtained from the log-returns over different time scales for a collections of stocks in Taiwan market, over 2004- 2009. The values of these hydrodynamic parameters are compared with their counterparts for stocks in Taiwan, US and Shanghai markets from 1996 to 2001, to reveal their common properties. The sequences of returns of individual stocks are intensively evaluated, keeping the interval in between the time spots of the sequences fixed and treating the time τ over which the returns are calculated as the variable for time scales. We checked the time sequences of trading data and found that crossing-day effect is present in all points in time and market.By distinguishing between returns of crossing-day and those of non-crossing-day, the analysis both of scaling properties in probability functions and of the enhanced statistics of persistent time for up-trend (or down-trend) events as an effect caused by crossing-day contributions, show same features shared by Taiwan and US markets. The crossing-day effect makes the probability functions asymmetric. Such an efffect can be quantified by finding the effective after-market trading time tafter. In interpreting the cross correlation coefficient as the strength of the bond in between two stocks, a networks of stocks with continuous values in the bonds among the sites is introduced, which, in combination with model construction, helps to capture qualitatively the main features of stock-stock heterogeneity. In the analysis to include the temporal correlation as well as the stock-stock cross correlations, the crossing-day effect displays concrete signatures, which furtherly support the results revealed in the analysis of persistent time of up-trend events.
參考文獻:	[1] L. Laloux, P. Cizeau, J.-P. Bouchaud, M. Potters, Noise dressing of financial corre- lation. Phys. Rev. Lett. 83 (1999) 1467-1470. [2] V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, H.E. Stanley, Universal and nonuniversal properties of cross correlations in financial time series. Phys. Rev. Lett. 83 (1999) 1471-1474. [3] J. D. Noh, Model for correlations in stock markets. Phys. Rev. E 61, 5981 (2000). [4] W. J. Ma, C. K. Hu, R. E. Amritkar, Stochastic dynamical model for stock-stock correlations. Phys. Rev. E 70 (2004) 26101. [5] R. Engle, Financial econometrics - A new discipline with new methods. J. Econo- metrics 100 (2001) 53-56. [6] W. J. Ma, S. C. Wang, C. N. Chen, C. K. Hu, Crossover behavior of stock returns and mean square displacements of particles governed by the Langevin equation. EPL 102 (2013) 66003. [7] Yu Feng Huang, Random walk model and underlying structure. Master Thesis (National ChengChi University, 2013). [8] Steven Radelet, Jeffrey Sachs, The onset of the East Asian financial crisis. NBER Working Paper No. 6680 (1998). [9] John V Duca, Subprime Mortgage Crisis 2006-2010. Federal Reserve History (2014). https://www.federalreservehistory.org/essays/subprime_mortgage_crisis [10] Yuliya Demyanyk, Otto Van Hemert, Understanding the Subprime Mortgage Cri- sis.The Review of Financial Studies 24 (2011) 1848–1880. [11]Parameswaran Gopikrishnan, Vasiliki Plerou, Luis A. Nunes Amaral, Martin Meyer, and H. Eugene Stanley, Scaling of the distribution of fluctuations of financial market indices. Phys. Rev. E 60 (1999). [12] Shwu-Jane Shieh, Shlomo Havlin, H. Eugene Stanley, Statistical analysis of the overnight and daytime return. Phys. Rev. E 79 (2009) 056109. [13] M. G. Kendall, The analysis of economic time-series Part 1. Prices. Journal of the Royal Statistical Society 96 (1953) 11-25. [14] Louis Bachelier, Theorie de la speculation. Gauthier-Villars. Annales Scientifiques de l’École Normale Supérieure 3 (1900) 21–86 . [15] Albert Einstein, On the Motion of Small Particles Suspended in Liquids at Rest Required by the Molecular-Kinetic Theory of Heat. Physik 17 (1905) 549-560. [16] R. N. Mantegna, H. E. Stanley, Scaling behavior in the dynamics of an economic index. Nature 376 (1995) 46-49. [17] M. E. J, Newman: Networks an Introduction (Oxford University Press, 2010). [18] Zhichen Sun, Continuous Transportation Network Design Problem Based on Bilevel Programming Model. Procedia Engineering 137 (2016) 277–282. [19] Martin Gairing, Tobias Harks, and Max Klimm, Complexity and Approximation of the Continuous Network Design Problem. SIAM J. Optim. 27 (2017) 1554–1582. [20] Linghui Han, Huijun Sun, Chengjuan Zhu, Jianjun Wu, The Continuous Network Design Problem under the Traffic Network with Guidance System. 8th Interna- tional Conference on Traffic and Transportation Studies Changsha. (2012). arXiv: 1307.4258. [21] J. J. Chen, L.Tan, W. J. Ma and C. K. Hu (private communication 2015) . [22] 王碩濱. 以經濟物理學觀點分析臺灣股市日內時間序列 Analysis of Intraday time series in Taiwan stock market:Econophysics approach. Master Thesis (National Dong Hwa University, 2006). [23] Wang, Bo Yuan, Statistical and Dynamical Properties of Re-turns Using High Frequency 1-day Moving Averages For Collections of U.S Stocks Over 1996-1999. Master Thesis (National ChengChi University, 2013). [24] Yi-Fu Shih, Scaling of Event-Occurrence in Stock Market. Master Thesis (National Chengchi University, 2017).
描述:	碩士國立政治大學應用物理研究所 104755009
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G1047550092
資料類型:	thesis
DOI:	10.6814/THE.NCCU.AP.001.2018.B04
顯示於類別:	[應用物理研究所 ] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
009201.pdf	6124Kb	Adobe PDF2	337	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋