English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113451/144438 (79%)
Visitors : 51264959      Online Users : 833
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
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/94757


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/94757


    Title: 運用半參數平滑係數分量廻歸法探討產業與股市大盤間資訊傳遞速度
    Using Semiparametric Smooth Coefficient Quantile Regression Model to Analyze the Information Diffusion between Industries and Stock Markets
    Authors: 楊國偉
    Yang, Kuo-Wei
    Contributors: 黃台心
    楊國偉
    Yang, Kuo-Wei
    Keywords: 效率市場
    行為財務學
    半參數平滑係數分量廻歸模型
    資訊緩慢擴散
    Date: 2009
    Issue Date: 2016-05-09 11:46:56 (UTC+8)
    Abstract: 傳統財務理論認為市場具有效率，在投資者具有理性且追求最大效用的假設下，股價應能迅速且完全的反應所有資訊，但近年來許多學者研究發現一些違反傳統定價理論和效率市場的實證結果。為解釋上述傳統定價理論無法解釋的異常現象，以心理學對投資人決策過程的研究成果為基礎，重新檢視整體市場價格的行為財務學便獲得重視。
    本文使用半參數平滑係數分量廻歸模型，利用1988至2007最近20年的月資料，檢視G7各國在不同大盤表現分量上，不同產業股價超額報酬率，是否造成總體經濟指標對大盤未來超額報酬率的邊際影響有所不同？藉以了解各國在不同股市報酬率分量上的資訊傳遞速度與彼此間的差異。此外，利用半參數平滑係數分量廻歸模型，亦可觀察產業超額報酬率如何直接影響未來大盤超額報酬率，不但較傳統最小平方法（ordinary least squares, OLS）更富有彈性，也能觀察在不同分量上的變化情形。
    本文發現美國各產業超額報酬率，對未來大盤超額報酬率的直接或間接影響，在不同大盤表現分量上呈現很大差異，未來大盤超額報酬率皆明顯隨著產業超額報酬率的改變而變動；至於其他六國，亦有相似情況，顯示投資人無法即時解讀產業資訊對未來總體經濟的影響，導致產業資訊於產業與大盤間緩慢擴散。
    In this paper, we use semiparametric smooth coefficient quantile regression model to analyze the information diffusion between industries and stock markets. Under different quantile of stock market performances, we examine whether the returns of industry portfolios cause macroeconomic indicator to affect the future stock market performance marginally using data on monthly returns to G7 industry portfolios for the years between 1988 and 2007. We find that the returns of industry portfolios in USA affect the future stock market performance directly or indirectly which display much variously. Moreover, the other counties of G7 also have the same situation. Hence, these findings indicate that investors are unable to understand the influence of industry shocks on macroeconomic activities and information diffuses across investors in different markets only gradually.
    Reference: 英文參考文獻
    1. Ball, R. and P. Brown (1968), “An Empirical Evaluation of Accounting Income Numbers,” Journal of Accounting Research 6, 159-177.
    2. Balsara Nauzer J., Lin Zheng, Vidozzi Andrea, Vidozzi Luca (2006), “Explaining Momentum Profits with An Epidemic Diffusion Model,” Journal of Economics & Finance, Fall2006, Vol. 30 Issue 3, 407-422.
    3. Barberis, Nicholas, A. Shleifer and R. Vishny (1998), “A Model of Investor Sentiment,” Journal of Financial Economics 49, 307-343.
    4. Bernard, V. L. and J. K. Thomas (1989), “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research, Supplement 27, 1-48.
    5. Cai, Z. and X. Xu (2006), “Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models,” Wise Working Paper Series, WISEWP0603.
    6. Chou, S.Y., J.T. Liu, and C.J. Huang (2004), “Health insurance and savings over the life cycle—A semiparametric smooth coefficient estimation,” Journal of Applied Econometrics, 19, 295-322.
    7. Cohen, L. and Frazzni, A. (2006), “Economic Links and Predictable Returns,” Yale University Working Paper <http://www.econ.yale.edu/~af227/pdf/cofraz.pdf>.
    8. Daniel, Kent, D. Hirshleifer and A. Subrahmanyam (1998), “Investor Psychology and Security Market Under-and Overreactions,” Journal of Finance 53, 1839-1886.
    9. Ding, David K.; McInish, Thomas H. and Wongchoti, Udomsak (2008), “Behavioral Explanations of Trading Volume and Short-Horizon Price Patterns: An Investigation of Seven Asia-Pacific Markets,” Pacific-Basin Finance Journal, Jun2008, Vol. 16 Issue 3, 183-203.
    10. Fama, E., and K. French (1992), “The Cross-Section of Expected Stock Returns,” Journal of Finance 47, 427-65.
    11. Fama, E., and K. French (1993), “Common Risk Factors In the Returns of Bonds and Stocks,” Journal of Finance 33, 3-56.
    12. Fan, J. and W. Zhang (2008), “Statistical methods with varying coefficient models,” Statistics and Its Interface, 1, 179-195.
    13. Fan, Y. and T. Huang (2005), “Profile likelihood inferences on semiparametric varying-coefficient partially linear models,” Bernoulli, 11, 1031–1057.
    14. Goetzmann, W and M. Massa (2005), “Dispersion of Option and Stock Returns,” Journal of Financial Markets 8, 325-350.
    15. Harris, M and A. Raviv (1993), “Differences of Option Make a Horse Race,” Review of Financial Studies 6, 473-525.
    16. Hartog, J., P. Pereira, and J. Vieira (2001), “Changing Returns to Education in Portugal during the 1980s and Early 1990s: OLS and Quantile Regression Estimators,” Applied Economics 33, 1021-1037.
    17. Hirshleifer, D. and Teoh, S.H. (2003), “Limited Attention, Information Disclosure, and Financial Reporting,” Journal of Accounting and Economics 36, 337-386.
    18. Hong, H. Torous, W. and Valkanov, R. (2007), “Do Industries Lead Stock Markets?” Journal of Financial Economics 83, 367-396.
    19. Hong, H. and Stein, J. (1999), “A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets,” Journal of Finance, Vol.54(6), 2143-2184.
    20. Hou, K. (2007), “Industry Information Diffusion and the Lead-Lag Effect in Stock Returns,” Review of Financial Studies, Vol.20(4), 1113-1138.
    21. Hou, K., Lin Peng, and Wei Xiong (2006), “A Tale of Two Anomalies: the Implications of Investor Attention for Price and Earnings Momentum,” Working Paper, Ohio State University.
    22. Hou, K and Tobias J. Moskowitz (2005), “Market Frictions, Price Delay, and the Cross-Section of Expected Returns,” Review of Financial Studies 18, 981–1020.
    23. Huang, Cliff J., Tsu-Tan Fu and Yung-Lieh Yang (2007), “Quantile Regression of the Production Profile,” Working Paper.
    24. Jegadeesh, Narasimhan, and Sheridan Titman (1993), “Returns to Buying Winners and Selling Losers: Implication for Stock Market Efficiency,” Journal of Finance 48, 65-91.
    25. Kahneman, D. (1973), “Attention and Effort,” Englewood Cliffs, NJ: Prentice Hall.
    26. Koenker, R. (2005), “Quantile Regression,” Cambridge; New York: Cambridge University Press.
    27. Koenker, R. and G., Bassett (1978), “Regression Quantiles,” Econometrica 46, 33–50.
    28. Kuan, C. M. (2007), “An Introduction to Quantile Regression,” Institute of Economics Academia Sinica.
    29. Li, Qi., Cliff J. Huang, Dong Li and Tsu-Tan Fu (2002), “Semiparametric Smooth Coefficient Models,” Journal of Business and Economic Statistics 20, 412-422.
    30. Liu, T. K. (2006), “Information Technology, Knowledge Capital and Productivity with Spillovers,” Graduate Institution of Industrial Economics, National Central University, An unpublished doctoral dissertation.
    31. Menzly, L. and Ozbas, O. (2006), “Cross-Industry Momentum,” Unpublished Working Paper, <http://www.afajof.org:443/pdfs/2005program/UPDF/ P600_Asset_Pricing.pdf>.
    32. Miller, E. (1977), “Risk, Uncertainty and Divergence of Option,” Journal of Finance 32, 1151-1168.
    33. Park, Beum-Jo(2002), “On the Quantile Regression Based Tests for Asymmetry in Stock Return Volatility,” Asian Economic Journal, Vol. 16(2), 175-192.
    34. Peng, L. and Xiong, W. (2006), “Investor Attention, Overconfidence and Category Learning,” Journal of Financial Economics 80, 563-602.
    35. Sekeris, E., and A. Bolmatis (2007), “Information Diffusion Based Explanations of Asset Princing Anomalies,” FRB of Boston Quantitative Analysis Unit Working Paper QAU07-6.
    36. Shleifer, A. (2000), “Inefficient Markets: An Introduction to Behavioral Finance,” Journal of Institutional and Theoretical Economics 158, 369-374.
    37. Solow, R. M. (1987), “We’d Better Watch Out,” New York Times (July 12), Book Review, 36.
    38. Zhang, W., S. Y. Lee, and X. Song (2002), “Local polynomial fitting in semivarying coefficient models,” Journal of Multivariate Analysis, 82. 166–188.

    中文參考文獻
    1. 王名韡（2008），「由產業是否領先大盤探討台股市場的資訊傳遞速度」，淡江大學經濟學系研究所碩士論文。
    2. 周賓凰、張宇志與林美珍（2007），「投資人情緒與股票報酬互動關係」，證券市場發展季刊，Vol.19（2），153-190。
    3. 陳建良（2007），「台灣公私部門工資差異的擬真分解-分量迴歸分析」，經濟論文，35：4，473-520。
    4. 陳建良、管中閔（2006），「台灣工資函數與工資性別歧視的分量迴歸分析」，中央研究院經濟研究所經濟論文，34，435-468。
    5. 莊家彰（2003），「分量迴歸在報酬率和成交量關係的應用-以台灣股匯市為例」，國立台北商業技術學院學報，第六期，121-139。
    6. 曾雅茹（2004），「股票報酬與財務比率、總體經濟因素之關聯性：分量迴歸法之研究」，中國文化大學經濟學研究所碩士論文。
    Description: 碩士
    國立政治大學
    金融研究所
    94352009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094352009
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

    Files in This Item:

    There are no files associated with this item.



    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