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題名 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
作者 盧博廉
Lu, Bo-Lian
貢獻者 徐士勛
Hsu, Shih-Hsun
盧博廉
Lu, Bo-Lian
關鍵詞 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
日期 2021
上傳時間 2-Sep-2021 17:42:44 (UTC+8)
摘要 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。
參考文獻 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.
描述 碩士
國立政治大學
經濟學系
108258016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108258016
資料類型 thesis
dc.contributor.advisor 徐士勛zh_TW
dc.contributor.advisor Hsu, Shih-Hsunen_US
dc.contributor.author (Authors) 盧博廉zh_TW
dc.contributor.author (Authors) Lu, Bo-Lianen_US
dc.creator (作者) 盧博廉zh_TW
dc.creator (作者) Lu, Bo-Lianen_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-Sep-2021 17:42:44 (UTC+8)-
dc.date.available 2-Sep-2021 17:42:44 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2021 17:42:44 (UTC+8)-
dc.identifier (Other Identifiers) G0108258016en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137063-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 108258016zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.tableofcontents 1 前言 1
2 文獻回顧 3
2.1 金融資產熊牛市定義相關文獻 3
2.2 兩資產輪轉相關文獻 4
2.3 變數選擇 5
3 研究方法與模型 7
3.1 研究流程圖 7
3.2 兩資產的熊牛市認定基準 9
3.3 篩選變數 11
3.3.1 AugmentedDickey–Fuller(ADF)檢定 12
3.3.2 LassoLogisticRegression(LLR) 13
3.4 Logit模型 14
3.5 衡量模型結果與績效分析 16
4 資料與基本統計性質 19 4.1 股市、債市熊牛市認定 19
4.2 變數 22
5 實證結果 23
5.1 變數選擇結果 23
5.2 短週期樣本內預測結果 24
5.3 短週期樣本外預測結果 26
6 結論 32
7 參考文獻 38
A 附錄-變數詳細說明 41
B 附錄-長週期實證結果 43
B.1 長週期模型股債熊牛市 43
B.2 長週期實證結果 44
C 附錄-月報酬方向實證結果 50
C.1 月報酬方向模型股債熊牛市 50
C.2 月報酬實證結果 51		zh_TW
dc.format.extent 9228675 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108258016en_US
dc.subject (關鍵詞) 股票與公債輪轉zh_TW
dc.subject (關鍵詞) 戰略式投資策略zh_TW
dc.subject (關鍵詞) 股債熊牛市機率zh_TW
dc.subject (關鍵詞) Logit 模型zh_TW
dc.subject (關鍵詞) ROC 曲線zh_TW
dc.subject (關鍵詞) Lasso Logistic Regressionen_US
dc.title (題名) 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zh_TW
dc.title (題名) Tactical Rotation Strategy of Stock and Government Bond Markets in the United States: An Application of Dynamic Logit modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202101344en_US