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題名 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作者 張芷涵
Chang, Chih-Han貢獻者 林靖庭
張芷涵
Chang, Chih-Han關鍵詞 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日期 2021 上傳時間 1-Jul-2021 17:53:59 (UTC+8) 摘要 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.參考文獻 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. 描述 碩士
國立政治大學
金融學系
108352008資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352008 資料類型 thesis dc.contributor.advisor 林靖庭 zh_TW dc.contributor.author (Authors) 張芷涵 zh_TW dc.contributor.author (Authors) Chang, Chih-Han en_US dc.creator (作者) 張芷涵 zh_TW dc.creator (作者) Chang, Chih-Han en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Jul-2021 17:53:59 (UTC+8) - dc.date.available 1-Jul-2021 17:53:59 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2021 17:53:59 (UTC+8) - dc.identifier (Other Identifiers) G0108352008 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/135938 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 108352008 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents Content iiiList of Tables iv1. Introduction 12. Literature Review 53. Data and Sample 94. Methodology 114.1. The 1/N rule 134.2. Portfolio strategies 134.2.1 Asset pricing model 134.2.2. Variance models 154.2.3. Reward-to-risk timing strategies (RRT) 174.2.4. Traditional Mean-Variance approach 184.2.5. Bayes approach 194.3. Test methodologies 204.3.1. Transaction cost 214.3.2. Performance measures 214.3.3. Tests for data-snooping bias 225. Empirical analysis 285.1. The descriptive statistics of the monthly excess returns of portfolio strategies for each dataset 285.2. The performance of portfolio strategies for each dataset under individual tests 315.3. The performance of portfolio strategies for each dataset with controlling for the data-snooping bias 335.4 The discussion of outperforming portfolio strategies 346. Conclusion 35Reference: 38 zh_TW dc.format.extent 1148802 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352008 en_US dc.subject (關鍵詞) 投資組合策略 zh_TW dc.subject (關鍵詞) 1/N法 zh_TW dc.subject (關鍵詞) 多因子投資 zh_TW dc.subject (關鍵詞) Carhart四因子模型 zh_TW dc.subject (關鍵詞) 資料勘誤 zh_TW dc.subject (關鍵詞) Portfolio strategies en_US dc.subject (關鍵詞) 1/N rule en_US dc.subject (關鍵詞) Multi-factor investing en_US dc.subject (關鍵詞) Carhart’s four-factor model en_US dc.subject (關鍵詞) Data-snooping bias en_US dc.title (題名) 最佳資產配置法與多因子模型探討：以台灣市場為例 zh_TW dc.title (題名) Optimal Asset Allocation Strategy and Multi-Factor Models: The Case of Taiwan Stock Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100523 en_US
