English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113822/144841 (79%)
Visitors : 51778433      Online Users : 608
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
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/135938
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135938


    Title: 最佳資產配置法與多因子模型探討:以台灣市場為例
    Optimal Asset Allocation Strategy and Multi-Factor Models: The Case of Taiwan Stock Market
    Authors: 張芷涵
    Chang, Chih-Han
    Contributors: 林靖庭
    張芷涵
    Chang, Chih-Han
    Keywords: 投資組合策略
    1/N法
    多因子投資
    Carhart四因子模型
    資料勘誤
    Portfolio strategies
    1/N rule
    Multi-factor investing
    Carhart’s four-factor model
    Data-snooping bias
    Date: 2021
    Issue Date: 2021-07-01 17:53:59 (UTC+8)
    Abstract: 將多種投資組合策略及不考慮歷史資訊的1/N法應用於台灣股票市場,並同時修正資料勘誤的問題,欲檢視各投資組合策略績效是否優於1/N法績效,且以多因子模型建構的投資組合觀察台灣股票市場是否適合多因子投資。

    實證結果發現在修正資料勘誤的問題之後,對於含有較多高市值藍籌股的投資組合,確實有優於1/N法績效的投資組合策略,特別是Carhart (1997)的四因子模型績效最優,這代表台灣股票市場具有市場風險溢酬效應、規模溢酬效應、淨值市價比效應及動能效應。根據實證結果,投資人可根據這些資訊來決定投資組合配置策略以獲得投資報酬。此外,在構建投資組合策略時,也強調資料勘誤修正的重要性,避免影響投資組合績效結果而產生誤差。
    Applying the naïve portfolio strategy and various optimal portfolio strategies into the Taiwan’s stock market and conducting a series of tests to correct the data-snooping bias simultaneously, we examine the performance of portfolio strategies relative to the naïve 1/N rule and observe that whether multi-factor investing is useful for in Taiwan’ stock market. We find that for the portfolio containing more blue-chip stocks, there are indeed some portfolio strategies are better than the 1/n rule after controlling for the data-snooping, especially the Carhart’s (1997) four-factor model, which suggests that Taiwan’s stock market might capture the market risk effect, size effect, value effect, and momentum effect. According to the result, investors can follow such information to decide the investment decisions and earn the returns on the investments. Moreover, we also suggest the importance of the data-snooping bias, which would influence the performance outcomes, should be controlled by investors when constructing portfolio strategies.
    Reference: Barry, C. B., 1974. Portfolio analysis under uncertain means, variances and covariances. Journal of Finance, 29(2): 515-522.
    Bender, J., Briand, R., Melas, D., and Subramanian, R. A., 2013. Foundations of Factor Investing. Retrieved from MSCI report, https://www.msci.com/www/research-paper/foundations-of-factor-investing/016381488.
    Bertsimas, D., Gupta, V., and Paschalidis, I. Ch., 2012. Inverse optimization: A new perspective on the Black-Litterman model. Operations Research, 60(6): 1389-1403.
    Broadie, M., 1993. Computing efficient frontiers using estimated parameters. Annals of Operations Research, 45: 21-58.
    Brock, W., Lakonishok, J., and LeBaron, B., 1992. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5): 1731-1764.
    Carhart, M. M., 1997. On persistence in mutual fund performance. Journal of Finance, 52(1): 57-82.
    Choueifaty, Y., and Coignard, Y., 2008. Toward maximum diversification. Journal of Portfolio Management, 35(1): 40-51.
    DeMiguel, V., Garlappi, L., Nogales, F. J., and Uppal, R., 2009a. A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management Science, 55(5): 798-812.
    DeMiguel, V., Garlappi, L., and Uppal, R., 2009b. Optimal versus naïve diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22(5): 1915-1953.
    DeMiguel, V., Nogales, F. J., and Uppal, R., 2014. Stock return serial dependence and out-of-sample portfolio performance. Review of Financial Studies, 27(4): 1031-1073.
    Fama, E. F., and French, K. R., 1993. Common risk factors in the returns on bonds and stocks. Journal of Financial Economics, 33(1): 3-53.
    Fleming, J., Kirby, C., and Ostdiek, B., 2001. The economic value of volatility timing. Journal of Finance, 56(1): 329-352.
    Fleming, J., Kirby, C., and Ostdiek, B., 2003. The economic value of volatility timing using “realized” volatility. Journal of Financial Economics, 67(3): 473-509.
    Frost, P. A., and Savarino, J. E., 1988. For better performance: Constrain portfolio weights. Journal of Portfolio Management, 15(1): 29-34.
    Grinold, R. C., and Kahn, R. N., 1999. Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Selecting Superior Returns and Controlling Risk. McGraw-Hill Library of Investment and Finance.
    Guo, D., Boyle, P. P., and Weng, C., and Wirjanto, T. S., 2019. When does the 1/N rule work? Available at SSRN: https://ssrn.com/abstract=3111531.
    Hansen, P. R., 2005. A test for superior predictive ability. Journal of Business and Economic Statistics, 23(4); 365-380.
    Hsu, P. H., and Kuan, C. M, 2005. Reexamining the profitability of technical analysis with data snooping checks. Journal of Financial Econometrics, 3(4): 606-628.
    Hsu, P. H., Hsu, Y. C., and Kuan, C. M., 2010. Testing the predictive ability of technical anal- ysis using a new stepwise test without data snooping bias. Journal of Empirical Finance, 17(3): 471-484.
    Hsu, P. H., Kuan, C. M., and Yen, M. F., 2014. A generalized stepwise procedure with improved power for multiple inequalities testing. Journal of Financial Econometrics, 12(4): 730-755.
    Idzorek, T. M., 2007. A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels. Forecasting Expected Returns in the Financial Markets, 17-38.
    James, W., and Stein, C., 1961. Estimation with quadratic loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1: 361-379.
    Jorion, P., 1985. International portfolio diversification with estimation risk. Journal of Business, 58(3): 259-278.
    Jorion, P., 1986. Bayes-Stein estimation for portfolio analysis. Journal of Financial and Quantitative Analysis, 21(3): 279-292.
    Klein, R. W., and Bawa, V. S., 1976. The effect of estimation risk on optimal portfolio choice. Journal of Financial Economics, 3(3): 215-231.
    Kirby, C., and Ostdiek, B., 2012. It’s all in the timing: simple active portfolio strategies that outperform naive diversification. Journal of Financial and Quantitative Analysis, 47(2): 437-467.
    Lakonishok, J., and Smidt, S., 1988. Are seasonal anomalies real? A ninety-year perspective. Review of Financial Studies, 1(4): 403-425.
    Leamer, E. E., 1983. Let’s take the con out of econometrics. American Economic Review, 73(1): 31-43.
    Lintner, J., 1965. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1): 13-37.
    Liu, C. H., 2016. An Empirical test of factors model in Taiwan Stock Market. Working paper, Department of international business, National Chengchi University.
    Lo, A. W., and MacKinlay, A. C., 1990. Data-snooping biases in tests of financial asset pricing models. Review of Financial Studies, 3(3): 431-467.
    Maillard, S., Roncalli, T., and Teiletche, J., 2010. On the properties of equally-weighted risk contributions portfolios. Journal of Portfolio Management, 36(4): 60-70.
    Markowitz, H., 1952. Portfolio selection. Journal of Finance, 7(1): 77-91.
    Merton, R. C., 1980. On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4): 323-361.
    Michaud, R. O., 1989. The Markowitz optimization enigma: Is ‘Optimized’ optimal? Financial Analysts Journal, 45(1): 31-42.
    Politis, D. N., and Romano, J. P., 1994. The stationary bootstrap. Journal of the American Statistical Association, 89(428): 1303-1313.
    Romano, J. P., and Wolf, M., 2005. Stepwise multiple testing as formalized data snooping. Econometrica, 73(4): 1237-1282.
    Romano, J. P., and Wolf, M., 2007. Control of generalized error rates in multiple testing. Annals of Statistics, 35(4): 1378-1408.
    Sharpe, W. F., 1964. Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3): 425-442.
    Shyu, S. D., Jeng, Y., Ton, W. H., and Lee, K. J., 2006. Taiwan multi-factor model construction: equity market neutral strategies application. Managerial Finance, 32(11): 915-947.
    Stein, C., 1955. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1: 197-206.
    Tsai, C. H., 2019. The Study on the Relation Between Efficiency and Stock Return: An Application of Fama and French Multifactor Model. Working paper, Department of finance, National Taiwan University.
    Wei, L., Kolari, J. W., and Huang, J. Z., 2012. A new asset pricing model based on the zero-beta: Theory and evidence. SSRN No. 2022384.
    White, H., 2000. A reality check for data snooping. Econometrica, 68(5): 1097-1126.
    Description: 碩士
    國立政治大學
    金融學系
    108352008
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108352008
    Data Type: thesis
    DOI: 10.6814/NCCU202100523
    Appears in Collections:[金融學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    200801.pdf1121KbAdobe PDF20View/Open


    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