政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/54534
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113822/144841 (79%)
造訪人次 : 51867377      線上人數 : 338
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


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/54534


    題名: 測試主要要素模型對台灣股市報酬的預測能力
    Testing the forecasting performance of principal components analysis on Taiwan stock return rates
    作者: 林佳琪
    貢獻者: 郭維裕
    林佳琪
    關鍵詞: 主要要素模型
    預測
    股市報酬
    Principal Components Analysis
    Forecasting
    Stock Market
    日期: 2011
    上傳時間: 2012-10-30 11:18:48 (UTC+8)
    摘要: 本文的主要目的，是找出一個簡單且有效的方法，預測台灣的股市報酬。比較許多不同的研究後，我發現無論面對多重共線性亦或變動要素結構等問題，主要要素模型(Principal Components Analysis)都可以表現地比其他模型優異。因此，在此篇文章中，我結合了資產訂價理論(Asset Pricing Theory)與主要要素模型的概念，來預測台灣八大產業股票指數的報酬。分析結果顯示，雖然主要要素模型在本文中的預測表現不如預期，但是整體仍優於隨機漫步(Random Walk)的預測。這意味著，主要要素模型對台灣股市的預測，可以在某種程度上推翻效率市場假說(Efficient Market Hypothesis)。
    The original purpose of this paper is to find a useful and simple way to forecast the return rates of Taiwan stock market. Comparing different empirical studies, I found that no matter with problems of multicollinearity or changing factor structure, the Principal Components Analysis (PCA) can usually outperform other models. Therefore, I combined the concepts of Asset Pricing Theory (APT) and PCA, to predict the movements of eight industrial indexes return rates of Taiwan stock market. The analysis indicates that, although PCA forecasting results couldn’t be very impressive in Taiwan stock market, it still can perform better than Random Walk Regression. That means the forecasting results of PCA to Taiwan stock market can overthrow the Efficient Market Hypothesis (EMH), which represents the trends of stock return rates are unpredictable, to some extents.
    參考文獻: Anderson, T. W., An Introduction to Multivariate Statistical Analysis, 2nd edition, New York: John Wiley, 1984.
    Bai, J. and S. Ng, “Evaluation latent and Observed Factors in Macroeconomics and Finance.” Journal of Econometrics, v. 131 (2006), 507-537.
    Favero, Carlo A., Massimiliano Marcellino, and Francesca Neglia, “Principal Components at Work: The Empirical Analysis of Monetary Policy with Large Data Sets.” Journal of Applied Econometrics, v. 20 (2005), 603-620.
    Groen, Jan J. J. and George Kapetanios, “Revisiting useful approaches to data-rich macroeconomic forecasting.” Federal Reserve Bank of New York, Staff Reports, 2008, no. 327.
    Heij, Christiaan, Dick van Dijk, and Patrick J. F. Groenen, “Macroeconomic Forecasting with Matched Principal Components.” International Journal of Forecasting, v. 24 (2008), 87-100.
    Jolliffe, I. T., Principal Component Analysis, 2nd edition, Berlin: Springer, 2002.
    Kendall M., “The Analysis of Economic Time Series- Part 1: Pricies.” Journal of the Royal Statistical Society, v. 96 (1953), 11-25.
    Kosfeld, Reinhold and Jorgen Lauridsen, “Factor Analysis Regression.” Statistical Papers, v. 49 (2008), 653-67.
    Ross, Stephen A., “Return, Risk and Arbitrage.” University of Pennsylvania, Working Paper no. 17-73b., 1976.
    Schumacher, Christian, “Forecasting German GDP Using Alternative Factor Models Based on Large Datasets.” Journal of Forecasting, v. 26 (2007), 271-302.
    Stock, James H. and Mark W. Watson, “Forecasting Inflation.” Journal of Monetary Economics, v. 44 (1999), 293-335.
    Stock, James H. and Mark W. Watson, “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business and Economic Statistics, v. 20 (2002), 147-162.
    Stock, James H. and Mark W. Watson, “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association, v. 97 (2002), 1167-1179.
    Stock, James H. and Mark W. Watson, “an Empirical Comparison of Method for Forecasting Using Many Predictors.” Havard University, mimeo. , 2005.
    Stock, James H. and Mark W. Watson, “Forecasting with Many Predictors.” in Handbook of Economic Forecasting, Volume I, edited by G. Elliott, C.W.J. Granger and A. Timmermann, New York: North-Holland, 2006.
    描述: 碩士
    國立政治大學
    國際經營與貿易研究所
    100351005
    100
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0100351005
    資料類型: thesis
    顯示於類別:[國際經營與貿易學系 ] 學位論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML2314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