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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 30-Oct-2012 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
dc.contributor.advisor 郭維裕zh_TW
dc.contributor.author (Authors) 林佳琪zh_TW
dc.creator (作者) 林佳琪zh_TW
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 11:18:48 (UTC+8)-
dc.date.available 30-Oct-2012 11:18:48 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 11:18:48 (UTC+8)-
dc.identifier (Other Identifiers) G0100351005en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54534-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易研究所zh_TW
dc.description (描述) 100351005zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (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)。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 1.Introduction 3
     2.The Model and Estimation 6
     2.1 Basic Structure of PCA 6
     2.2 Introduction of APT 8
     2.3 Settings of Matched PCA Model 9
     3.Data and Forecasting Results 11
     3.1 Data 11
     3.2 Basic Forecasting 11
     3.3 Rolling Window Forecasting 17
     3.4 Comparison of the Results with Random Walk 22
     4.Conclusion 23
     References 25		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100351005en_US
dc.subject (關鍵詞) 主要要素模型zh_TW
dc.subject (關鍵詞) 預測zh_TW
dc.subject (關鍵詞) 股市報酬zh_TW
dc.subject (關鍵詞) Principal Components Analysisen_US
dc.subject (關鍵詞) Forecastingen_US
dc.subject (關鍵詞) Stock Marketen_US
dc.title (題名) 測試主要要素模型對台灣股市報酬的預測能力zh_TW
dc.title (題名) Testing the forecasting performance of principal components analysis on Taiwan stock return ratesen_US
dc.type (資料類型) thesisen
dc.relation.reference (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.		zh_TW