政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137063
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113451/144438 (79%)
Visitors : 51320419      Online Users : 870
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137063


    Title: 美國股票與公債市場戰略性投資輪轉策略-動態 Logit 模型的應用
    Tactical Rotation Strategy of Stock and Government Bond Markets in the United States: An Application of Dynamic Logit model
    Authors: 盧博廉
    Lu, Bo-Lian
    Contributors: 徐士勛
    Hsu, Shih-Hsun
    盧博廉
    Lu, Bo-Lian
    Keywords: 股票與公債輪轉
    戰略式投資策略
    股債熊牛市機率
    Logit 模型
    ROC 曲線
    Lasso Logistic Regression
    Date: 2021
    Issue Date: 2021-09-02 17:42:44 (UTC+8)
    Abstract: 本文研究美國股票與公債市場戰略式投資輪轉策略。首先,本文修 改 Pagan and Sossounov (2003) 提出的規則,將股票與公債認定成三 種不同的週期,分別為「月報酬方向」、「短週期趨勢」、「長週期 趨勢」。實證方面則是採用遞迴法,每期均會使用 ADF 檢定與 Lasso Logistic Regression 重新篩選一次變數,最後再使用 Logit 模型進行機 率估計。樣本外投資績效方面,本文發現三種模型均顯著優於大盤表 現,其中「短週期模型」所得到的績效表現最佳。另外,本文也發現 三種模型在不同期間選擇的變數均不盡相同,顯示相對於傳統方法, 採用遞迴選取變數法,不但可以看出三個模型所採用的變數均不相 同,並且每一個變數在不同時間下,對於股債項牛市機率也有不同的 預估能力。
    Reference: 何興強和周開國(2006),「牛、市週期和股市間的週期熊性」,《管理世界》,4,35-40。
    徐婉容(2020),「認定與預測台灣股市熊市」,《中央銀行季刊》,42(2),37-72。
    Ahmed, J., Straetmans, S. (2015), “Predicting Exchange Rate Cycles Utilizing Risk Factors,” Journal of Empirical Finance, 34, 112–130.
    Chen, Shiu-Sheng (2009), “Predicting the Bear Stock Market: Macroeconomic Variables as Leading Indicators,” Journal of Banking & Finance,33(2), 211-223.
    Clewell, D., Faulkner-Macdonagh, C., Giroux, D., Page, S., Shriver, C. (2017), “Macroeconomic Dashboards for Tactical Asset Allocation,” The Journal of Portfolio Management, 44(2), 50–61.
    Eugene F. Fama, Kenneth R. French (1993), “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics, 33(1),3-56.
    Gonzalez, L., Powell, J., Shi, J., Wilson, A. (2005), “Two Centuries of Bull and Bear Market Cycles,” International Review of Economics Finance,14(4), 469–486.
    Granger, C., Newbold, P., Econom, J. (1974), “Spurious Regressions in Econometrics,” Baltagi, Badi H. A Companion of Theoretical Econometrics, 557–61.
    Grauer, R., Hakansson, N., Shen, F. (1990), “Industry Rotation in the Us Stock Market: 1934–1986 Returns on Passive, Semi-Passive, and Active Strategies,” Journal of Banking Finance, 14(2-3), 513–538.
    Levis, M., Liodakis, M. (1999), “The Profitability of Style Rotation Strategies in the United Kingdom,” The Journal of Portfolio Management, 26(1),73-86.
    Lunde, A., Timmermann, A. (2004), “Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets,” Journal of Business & Economic Statistics, 22(3), 253-273.
    Maheu, J., McCurdy, T. (2000), “Identifying Bull and Bear Markets in Stock Returns,”Journal of Business & Economic Statistics, 18(1), 100-112.
    Nelson, C., Plosser, C. (1982), “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications,” Journal of Monetary Economics, 10(2), 139–162.
    Nyberg, H. (2013), “Predicting Bear and Bull Stock Markets With Dynamic Binary Time Series Models,” Journal of Banking Finance, 37(9), 3351–3363.
    Pagan, A., Sossounov, K. (2003), “A Simple Framework for Analysing Bull and Bear Markets,” Journal of Applied Econometrics, 18(1), 23–46.
    Said, S., Dickey, D. (1984), “Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order,” Biometrika, 71(3), 599–607.
    Shiller, R., Black, L., Jivraj, F. (2020), CAPE and the COVID-19 Pandemic Effect, Available at SSRN 3714737.
    Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society: Series B (Methodological), 58(1),267–288.39
    Wu, T., Chen, Y., Hastie, T., Sobel, E., Lange, K. (2009), “Genome-Wide Association Analysis by Lasso Penalized Logistic Regression,” Bioin-formatics, 25(6), 714–721.
    Description: 碩士
    國立政治大學
    經濟學系
    108258016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108258016
    Data Type: thesis
    DOI: 10.6814/NCCU202101344
    Appears in Collections:[Department of Economics] Theses

    Files in This Item:

    File Description SizeFormat
    801601.pdf9012KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    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 ©   - Feedback