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題名 結合重要經濟變數與因子結構下的台灣股市分類再探討
The regrouping of Taiwan stock market in combination with important economic variables and factor model作者 莊彥琳
Zhuang, Yan-Lin貢獻者 徐士勛
莊彥琳
Zhuang, Yan-Lin關鍵詞 股市分類
迭代模型
因子結構
SCAD
變數篩選日期 2019 上傳時間 2-Mar-2020 11:26:35 (UTC+8) 摘要 本研究主要參考Ando and Bai(2017) 所建構之全新架構，透過因子 分析和SCAD(Smoothly clipped absolute deviation) 懲罰項， 且搭配 PIC(Panel information criterion) 準則所建構出的迭代收 模型，將台灣股市中的股票進行重分組；而此過程中我們運用到機器學習之技術來決定股票樣本初始分組之最適情形。我們希望探討的問題包括台灣的股市報酬率之最適分組情形為何? 哪些產業之類股在報酬上有相同的趨勢? 又哪些類股在報酬上其實並不適合以產業型態來做分類? 哪些解釋變數對台灣股市是重要的變數?我們透過因子結構以探討台灣股市的共同因子，以及藉分組後的組內特定因子討論分組後組內的異質性；同時，我們透過SCAD 懲罰項，在迭代的過程中自動對台灣股市重要的解釋變數。透過這些研究，希望提供投資人在投資台灣股市時一些有別於傳統直接以產業為分類標籤之投資建議。實證結果中，我們首先發現台灣股市中的傳統類股在報酬上有較為相近 之趨勢，間接證實了傳統產業具有一定的共榮性；此外，生技以及建 類股亦有它們各自的報酬趨勢，且組內異質性低，故對於此兩類股而 以產業來將它們分組是恰當的。另外，電子工業類股的分組複雜而分散，且異質性高；深入分析後，我們發現電子工業類股在產品鏈中之 類在報酬表現上無特定趨勢，判斷可能原因為受到產業鏈上、中、下游之影響，而使得公司之間的股價報酬相互反映；唯有產品鏈以外的服務業，包括電子通路以及資訊服務，有其特定之報酬趨勢。最後，在解釋變數的篩選上，我們於公司面、市場面、總體經濟面以及匯率中選取 15 個解釋變數放入迭代之過程中。我們發現消費者物價指數對台股報酬率是最不重要之解釋變數；而貨幣供給、規模溢酬以及市價比溢酬為較重要之變數，對於台灣股市之影響則相對較為顯著。 參考文獻 王怡文. (2010). 總體經濟指標對股市及共同基金相關性之研究-以台灣股市為例.許溪南、王耀斌、洪銓. (2011). 台灣股票市場成分波動性之分解, 趨勢與影響因素. Web Journal of Chinese Management Review, 14-2.魏文欽、潘芝伶、蕭翊庭. (2013). 次貸風暴後對台灣股票市場影響之探討. International Journal of Lisrel, 6(2), 32–47.Alessi, L., Barigozzi, M., & Capasso, M. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), 1806–1813.Ando, T., & Bai, J. (2016). Panel data models with grouped factor structure under unknown group membership. Journal of Applied Econometrics, 31(1), 163–191.Ando, T., & Bai, J. (2017). Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. Journal of the American Statistical Association, 112(519), 1182–1198.Argüelles, M., Benavides, C., & Fernández, I. (2014). A new approach to the identification of regional clusters: hierarchical clustering on principal components. Applied Economics, 46(21), 2511–2519.Baca, S. P., Garbe, B. L., & Weiss, R. A. (2000). The rise of sector effects in major equity markets. Financial Analysts Journal, 56(5), 34–40.Bottou, L., & Bengio, Y. (1995). Convergence properties of the k-means algorithms. In Advances in neural information processing systems (pp. 585–592).Boyd, J. H., Hu, J., & Jagannathan, R. (2005). The stock market’s reaction to unemployment news: Why bad news is usually good for stocks. The Journal of Finance, 60(2), 649–672.Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of finance, 52(1), 57–82.39Cavaglia, S., Brightman, C., & Aked, M. (2000). The increasing importance of industry factors. Financial Analysts Journal, 56(5), 41–54.Chamberlain, G., & Rothschild, M. (1982). Arbitrage, factor structure, and meanvariance analysis on large asset markets.Diebold, F. X., Li, C., & Yue, V. Z. (2008). Global yield curve dynamics and interactions: a dynamic Nelson–Siegel approach. Journal of Econometrics, 146(2), 351–363.Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427–465.Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association, 96(456), 1348–1360.Filis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152 164.Forni, M., & Lippi, M. (2001). The generalized dynamic factor model: representation theory. Econometric theory, 17(6), 1113–1141.Hallin, M., & Liška, R. (2007). Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603–617.Hallin, M., & Liška, R. (2011). Dynamic factors in the presence of blocks. Journal of Econometrics, 163(1), 29–41.Heston, S. L., & Rouwenhorst, K. G. (1994). Does industrial structure explain the benefits of international diversification? Journal of Financial Economics, 36(1), 3–27.Humpe, A., & Macmillan, P. (2009). Can macroeconomic variables explain longterm stock market movements? A comparison of the US and Japan. Applied Financial Economics, 19(2), 111–119.Khan, S. S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern recognition letters, 25(11), 1293–1302.Kim, Y., Choi, H., & Oh, H.-S. (2008). Smoothly clipped absolute deviation on high dimensions. Journal of the American Statistical Association, 103(484), 1665–1673.Kuo, W., & Satchell, S. E. (2001). Global equity styles and industry effects: the pre-eminence of value relative to size. Journal of International Financial Markets, Institutions and Money, 11(1), 1–28.Lin, C.-C., & Ng, S. (2012). Estimation of panel data models with parameter heterogeneity when group membership is unknown. Journal of Econometric Methods, 1(1), 42–55.Lloyd, S. (1982). Least squares quantization in PCM. IEEE transactions on information theory, 28(2), 129–137.Meyers, S. L. (1973). A re‐examination of market and industry factors in stock price behavior. The Journal of Finance, 28(3), 695–705.Roll, R. (1992). Industrial structure and the comparative behavior of international stock market indices. The Journal of Finance, 47(1), 3–41.Sargent, T. J., & Sims, C. A. (1977). Business cycle modeling without pretending to have too much a priori economic theory. New methods in business cycle research, 1, 145–168.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.Wang, H., Li, R., & Tsai, C.-L. (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika, 94(3), 553–568.Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236–244.Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38(2), 894–942.Zhao, Y., Karypis, G., & Fayyad, U. (2005). Hierarchical clustering algorithms for document datasets. Data mining and knowledge discovery, 10(2), 141–168.Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology),67(2), 301–320. 描述 碩士
國立政治大學
經濟學系
106258029資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106258029 資料類型 thesis dc.contributor.advisor 徐士勛 zh_TW dc.contributor.author (Authors) 莊彥琳 zh_TW dc.contributor.author (Authors) Zhuang, Yan-Lin en_US dc.creator (作者) 莊彥琳 zh_TW dc.creator (作者) Zhuang, Yan-Lin en_US dc.date (日期) 2019 en_US dc.date.accessioned 2-Mar-2020 11:26:35 (UTC+8) - dc.date.available 2-Mar-2020 11:26:35 (UTC+8) - dc.date.issued (上傳時間) 2-Mar-2020 11:26:35 (UTC+8) - dc.identifier (Other Identifiers) G0106258029 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/128928 - 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) 106258029 zh_TW dc.description.abstract (摘要) 本研究主要參考Ando and Bai(2017) 所建構之全新架構，透過因子 分析和SCAD(Smoothly clipped absolute deviation) 懲罰項， 且搭配 PIC(Panel information criterion) 準則所建構出的迭代收 模型，將台灣股市中的股票進行重分組；而此過程中我們運用到機器學習之技術來決定股票樣本初始分組之最適情形。我們希望探討的問題包括台灣的股市報酬率之最適分組情形為何? 哪些產業之類股在報酬上有相同的趨勢? 又哪些類股在報酬上其實並不適合以產業型態來做分類? 哪些解釋變數對台灣股市是重要的變數?我們透過因子結構以探討台灣股市的共同因子，以及藉分組後的組內特定因子討論分組後組內的異質性；同時，我們透過SCAD 懲罰項，在迭代的過程中自動對台灣股市重要的解釋變數。透過這些研究，希望提供投資人在投資台灣股市時一些有別於傳統直接以產業為分類標籤之投資建議。實證結果中，我們首先發現台灣股市中的傳統類股在報酬上有較為相近 之趨勢，間接證實了傳統產業具有一定的共榮性；此外，生技以及建 類股亦有它們各自的報酬趨勢，且組內異質性低，故對於此兩類股而 以產業來將它們分組是恰當的。另外，電子工業類股的分組複雜而分散，且異質性高；深入分析後，我們發現電子工業類股在產品鏈中之 類在報酬表現上無特定趨勢，判斷可能原因為受到產業鏈上、中、下游之影響，而使得公司之間的股價報酬相互反映；唯有產品鏈以外的服務業，包括電子通路以及資訊服務，有其特定之報酬趨勢。最後，在解釋變數的篩選上，我們於公司面、市場面、總體經濟面以及匯率中選取 15 個解釋變數放入迭代之過程中。我們發現消費者物價指數對台股報酬率是最不重要之解釋變數；而貨幣供給、規模溢酬以及市價比溢酬為較重要之變數，對於台灣股市之影響則相對較為顯著。 zh_TW dc.description.tableofcontents 1 前言---42 文獻回顧---62.1 分群方法---62.2 因子分析(Factor Analysis)---72.3 壓縮方法(Shrinkage Method)---82.4 類股表現之分析---83 研究方法---93.1 PIC 準則(Panel information criterion)---113.2 變數初始值之選擇---123.3 變數選擇之迭代---143.4 迭代收斂以及最終結果之選擇---164 實證分析和應用---164.1 台灣各類股之分群分析---164.1.1 研究資料與敘述統計---164.1.2 實證結果---204.2 電子工業類股之分群分析---264.2.1 研究資料與敘述統計---264.2.2 實證結果---285 結論與建議---34参考文獻---39 zh_TW dc.format.extent 3850447 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106258029 en_US dc.subject (關鍵詞) 股市分類 zh_TW dc.subject (關鍵詞) 迭代模型 zh_TW dc.subject (關鍵詞) 因子結構 zh_TW dc.subject (關鍵詞) SCAD zh_TW dc.subject (關鍵詞) 變數篩選 zh_TW dc.title (題名) 結合重要經濟變數與因子結構下的台灣股市分類再探討 zh_TW dc.title (題名) The regrouping of Taiwan stock market in combination with important economic variables and factor model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 王怡文. (2010). 總體經濟指標對股市及共同基金相關性之研究-以台灣股市為例.許溪南、王耀斌、洪銓. (2011). 台灣股票市場成分波動性之分解, 趨勢與影響因素. Web Journal of Chinese Management Review, 14-2.魏文欽、潘芝伶、蕭翊庭. (2013). 次貸風暴後對台灣股票市場影響之探討. International Journal of Lisrel, 6(2), 32–47.Alessi, L., Barigozzi, M., & Capasso, M. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23-24), 1806–1813.Ando, T., & Bai, J. (2016). Panel data models with grouped factor structure under unknown group membership. Journal of Applied Econometrics, 31(1), 163–191.Ando, T., & Bai, J. (2017). Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. Journal of the American Statistical Association, 112(519), 1182–1198.Argüelles, M., Benavides, C., & Fernández, I. (2014). A new approach to the identification of regional clusters: hierarchical clustering on principal components. Applied Economics, 46(21), 2511–2519.Baca, S. P., Garbe, B. L., & Weiss, R. A. (2000). The rise of sector effects in major equity markets. Financial Analysts Journal, 56(5), 34–40.Bottou, L., & Bengio, Y. (1995). Convergence properties of the k-means algorithms. In Advances in neural information processing systems (pp. 585–592).Boyd, J. H., Hu, J., & Jagannathan, R. (2005). The stock market’s reaction to unemployment news: Why bad news is usually good for stocks. The Journal of Finance, 60(2), 649–672.Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of finance, 52(1), 57–82.39Cavaglia, S., Brightman, C., & Aked, M. (2000). The increasing importance of industry factors. Financial Analysts Journal, 56(5), 41–54.Chamberlain, G., & Rothschild, M. (1982). Arbitrage, factor structure, and meanvariance analysis on large asset markets.Diebold, F. X., Li, C., & Yue, V. Z. (2008). Global yield curve dynamics and interactions: a dynamic Nelson–Siegel approach. Journal of Econometrics, 146(2), 351–363.Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427–465.Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association, 96(456), 1348–1360.Filis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152 164.Forni, M., & Lippi, M. (2001). The generalized dynamic factor model: representation theory. Econometric theory, 17(6), 1113–1141.Hallin, M., & Liška, R. (2007). Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603–617.Hallin, M., & Liška, R. (2011). Dynamic factors in the presence of blocks. Journal of Econometrics, 163(1), 29–41.Heston, S. L., & Rouwenhorst, K. G. (1994). Does industrial structure explain the benefits of international diversification? Journal of Financial Economics, 36(1), 3–27.Humpe, A., & Macmillan, P. (2009). Can macroeconomic variables explain longterm stock market movements? A comparison of the US and Japan. Applied Financial Economics, 19(2), 111–119.Khan, S. S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern recognition letters, 25(11), 1293–1302.Kim, Y., Choi, H., & Oh, H.-S. (2008). Smoothly clipped absolute deviation on high dimensions. Journal of the American Statistical Association, 103(484), 1665–1673.Kuo, W., & Satchell, S. E. (2001). Global equity styles and industry effects: the pre-eminence of value relative to size. Journal of International Financial Markets, Institutions and Money, 11(1), 1–28.Lin, C.-C., & Ng, S. (2012). Estimation of panel data models with parameter heterogeneity when group membership is unknown. Journal of Econometric Methods, 1(1), 42–55.Lloyd, S. (1982). Least squares quantization in PCM. IEEE transactions on information theory, 28(2), 129–137.Meyers, S. L. (1973). A re‐examination of market and industry factors in stock price behavior. The Journal of Finance, 28(3), 695–705.Roll, R. (1992). Industrial structure and the comparative behavior of international stock market indices. The Journal of Finance, 47(1), 3–41.Sargent, T. J., & Sims, C. A. (1977). Business cycle modeling without pretending to have too much a priori economic theory. New methods in business cycle research, 1, 145–168.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.Wang, H., Li, R., & Tsai, C.-L. (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika, 94(3), 553–568.Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236–244.Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38(2), 894–942.Zhao, Y., Karypis, G., & Fayyad, U. (2005). Hierarchical clustering algorithms for document datasets. Data mining and knowledge discovery, 10(2), 141–168.Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology),67(2), 301–320. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202000201 en_US
