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題名 強化S&P 500報酬預測： 結合PCR、PLS及反轉法於SOP框架中
Enhancing S&P 500 Return Prediction: Integrating PCR, PLS, and Reversion into the SOP Framework
作者 廖睿辰
Liao, Rui-Chen
貢獻者 林靖庭<br>羅秉政
Lin, Ching-Ting<br>Luo, Bing-Zheng
廖睿辰
Liao, Rui-Chen
關鍵詞 股市
分部法
預測
主成份分析
偏最小平方法
stock market
sum-of-the-parts
prediction
principal component analysis
partial least squares
日期 2025
上傳時間 1-Jul-2025 15:17:34 (UTC+8)
摘要 本研究使用S&P 500指數由1991年1月至2024年3月之價格資料，以及該期間的數個總體經濟數據，透過 SOP (Sum-of-the-part) 方法來建構股市報酬預測模型。研究發現reversion加上PLS的第一模型組合 (REPLS1) 在預測效能上表現最佳，遠超越過去傳統SOP方法的預測表現，而在Markowitz optimal weight的交易策略當中則是單純PLS模型表現最好，在使用7個主成分時Sharpe ratio達2.77 、確定等值 (certainty equivalent) 達48.67，同時發現MOP (momentum-of-predictability) 預測限制方法可以廣泛的改善所有的模型組合表現。總體而言，本研究的結果表明，過去的傳統SOP模型在近年的股市報酬預測表現已不如以往，甚至不論是在預測準確度於策略Sharpe ratio上皆略遜於基準模型 (歷史平均法)，不過在經過本研究中多個方法增強模型預測能力後發現SOP法仍能夠為較複雜的模型增加預測的效能，因此仍建議在預測股市報酬時採用 SOP 法的框架，除此之外，在本研究中發現REPLS1所有主成分模型組合於兩個子期間 (2016年~2019年、2020年~2024年) 皆可保持高水準的預測效果，不同於其他模型組合，在疫情與後疫情期間預測能力明顯減弱。
This study utilizes the price data of the S&P 500 Index from January 1991 to March 2024, along with several macroeconomic indicators during the same period, to construct a stock return prediction model using the Sum-of-the-Parts (SOP) method. The findings reveal that the first model combination of reversion and PLS (REPLS1) demonstrates the best predictive performance, significantly surpassing traditional SOP methods. In trading strategies based on Markowitz optimal weight, the pure PLS model performed the best, achieving a Sharpe ratio of 2.56 and a certainty equivalent of 48.67 when using eight principal components. Additionally, the Momentum-of-Predictability (MOP) restriction method was found to broadly enhance the performance of all model combinations. Overall, the results indicate that traditional SOP models have underperformed in recent years in terms of both predictive accuracy and strategy Sharpe ratio, even when compared to benchmark models such as historical averages. However, after enhancing the predictive capabilities of the SOP framework with the methods proposed in this study, SOP still proves to be beneficial for improving the performance of more complex models. Furthermore, it was found that the REPLS1 model combination with all principal components maintained high predictive performance in two subperiods (2016–2019 and 2020–2024), unlike other model combinations whose performance significantly declined during the pandemic and post-pandemic periods.
參考文獻 Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651–707. Brandt, M. W., Kang, Q., & Santa-Clara, P. (2004). On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach. Journal of Financial Economics, 72(2), 217–257. Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18(2), 373–399. Campbell, J. Y., Giglio, S., Polk, C., & Turley, R. (2006). Efficient tests of stock return predictability. Journal of Financial Economics, 81(1), 27–60. Clark, T. E., & McCracken, M. W. (2001). Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics, 105(1), 85–110. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3–25. Fama, E. F., & Schwert, G. W. (1977). Asset returns and inflation. Journal of Financial Economics, 5(2), 115–146. Ferreira, M. A., & Santa-Clara, P. (2011). Forecasting stock market returns: The sum of the parts is more than the whole. Journal of Financial Economics, 100(3), 514–537. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509–548. Kothari, S. P., Shanken, J., & Sloan, R. G. (1997). Book-to-market, dividend yield, and expected market returns: A time-series analysis. Journal of Financial Economics, 44(2), 169–203. Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209–235. Ludvigson, S. C., & Ng, S. (2007). The empirical risk-return relation: A factor analysis approach. Journal of Financial Economics, 83(1), 171–222. Liu, C., Zhang, X., Nguyen, T. T., Liu, J., Wu, T., Lee, E., & Tu, X. M. (2022). Partial least squares regression and principal component analysis: Similarity and differences between two popular variable reduction approaches. General Psychiatry, 35(1), e100662. McCracken, M. W. (2007). Asymptotic tests of predictive ability. Journal of Econometrics, 138(1), 291–311. McCracken, M. W. (2007). Asymptotics for out-of-sample tests of Granger causality. Journal of Econometrics, 140(2), 719–752. Meese, R. A., Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1–2), 3–24. Pontiff, J., & Schall, L. D. (1998). Book-to-market ratios as predictors of market returns. Journal of Financial Economics, 49(2), 141–160. Stambaugh, R. F. (1999). Predictive regressions. Journal of Financial Economics, 54(3), 375–421. Valkanov, R. (2003). Long-horizon regressions: Theoretical results and applications. Journal of Financial Economics, 68(1), 201–232. Wang, Y., Liu, L., Ma, F., & Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45, 141–156.
描述 碩士
國立政治大學
金融學系
112352021
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112352021
資料類型 thesis
dc.contributor.advisor 林靖庭<br>羅秉政zh_TW
dc.contributor.advisor Lin, Ching-Ting<br>Luo, Bing-Zhengen_US
dc.contributor.author (Authors) 廖睿辰zh_TW
dc.contributor.author (Authors) Liao, Rui-Chenen_US
dc.creator (作者) 廖睿辰zh_TW
dc.creator (作者) Liao, Rui-Chenen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Jul-2025 15:17:34 (UTC+8)-
dc.date.available 1-Jul-2025 15:17:34 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2025 15:17:34 (UTC+8)-
dc.identifier (Other Identifiers) G0112352021en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157836-
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) 112352021zh_TW
dc.description.abstract (摘要) 本研究使用S&P 500指數由1991年1月至2024年3月之價格資料，以及該期間的數個總體經濟數據，透過 SOP (Sum-of-the-part) 方法來建構股市報酬預測模型。研究發現reversion加上PLS的第一模型組合 (REPLS1) 在預測效能上表現最佳，遠超越過去傳統SOP方法的預測表現，而在Markowitz optimal weight的交易策略當中則是單純PLS模型表現最好，在使用7個主成分時Sharpe ratio達2.77 、確定等值 (certainty equivalent) 達48.67，同時發現MOP (momentum-of-predictability) 預測限制方法可以廣泛的改善所有的模型組合表現。總體而言，本研究的結果表明，過去的傳統SOP模型在近年的股市報酬預測表現已不如以往，甚至不論是在預測準確度於策略Sharpe ratio上皆略遜於基準模型 (歷史平均法)，不過在經過本研究中多個方法增強模型預測能力後發現SOP法仍能夠為較複雜的模型增加預測的效能，因此仍建議在預測股市報酬時採用 SOP 法的框架，除此之外，在本研究中發現REPLS1所有主成分模型組合於兩個子期間 (2016年~2019年、2020年~2024年) 皆可保持高水準的預測效果，不同於其他模型組合，在疫情與後疫情期間預測能力明顯減弱。zh_TW
dc.description.abstract (摘要) This study utilizes the price data of the S&P 500 Index from January 1991 to March 2024, along with several macroeconomic indicators during the same period, to construct a stock return prediction model using the Sum-of-the-Parts (SOP) method. The findings reveal that the first model combination of reversion and PLS (REPLS1) demonstrates the best predictive performance, significantly surpassing traditional SOP methods. In trading strategies based on Markowitz optimal weight, the pure PLS model performed the best, achieving a Sharpe ratio of 2.56 and a certainty equivalent of 48.67 when using eight principal components. Additionally, the Momentum-of-Predictability (MOP) restriction method was found to broadly enhance the performance of all model combinations. Overall, the results indicate that traditional SOP models have underperformed in recent years in terms of both predictive accuracy and strategy Sharpe ratio, even when compared to benchmark models such as historical averages. However, after enhancing the predictive capabilities of the SOP framework with the methods proposed in this study, SOP still proves to be beneficial for improving the performance of more complex models. Furthermore, it was found that the REPLS1 model combination with all principal components maintained high predictive performance in two subperiods (2016–2019 and 2020–2024), unlike other model combinations whose performance significantly declined during the pandemic and post-pandemic periods.en_US
dc.description.tableofcontents 中文摘要 2 ABSTRACT 3 目錄 4 第 壹章 研究背景與動機 6 第 貳章 資料來源與研究方法 9 第一節 資料描述 9 第二節 模型說明 11 2.2.1 原始SOP模型 11 2.2.2 本研究模型: PCA、PLS 13 2.2.3 本研究模型: REPCA1、REPCA2、REPLS1、REPLS2 14 2.2.4 動量限制法 MOP 15 第 參章 實證分析 16 第一節 原始SOP模型 16 第二節 三階段模型 17 3.2.1 第一階段模型 17 3.2.2 第二階段模型 19 3.2.3 第三階段模型 21 3.2.4 SOP方法在複雜模型下之效能 22 3.2.5 recursive與rolling之建模方式 24 第三節 交易策略評估 25 3.3.1 馬可維茲 (Markowitz) 投資組合交易策略說明 25 3.3.2 過往交易策略績效比較 27 3.3.3 確定等值 (CE) 27 第四節 子期間分析 28 第 肆章 結論與未來展望 31 第一節 結論 31 第二節 未來展望 32 參考文獻 33 附錄 34zh_TW
dc.format.extent 3453453 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112352021en_US
dc.subject (關鍵詞) 股市zh_TW
dc.subject (關鍵詞) 分部法zh_TW
dc.subject (關鍵詞) 預測zh_TW
dc.subject (關鍵詞) 主成份分析zh_TW
dc.subject (關鍵詞) 偏最小平方法zh_TW
dc.subject (關鍵詞) stock marketen_US
dc.subject (關鍵詞) sum-of-the-partsen_US
dc.subject (關鍵詞) predictionen_US
dc.subject (關鍵詞) principal component analysisen_US
dc.subject (關鍵詞) partial least squaresen_US
dc.title (題名) 強化S&P 500報酬預測： 結合PCR、PLS及反轉法於SOP框架中zh_TW
dc.title (題名) Enhancing S&P 500 Return Prediction: Integrating PCR, PLS, and Reversion into the SOP Frameworken_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651–707. Brandt, M. W., Kang, Q., & Santa-Clara, P. (2004). On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach. Journal of Financial Economics, 72(2), 217–257. Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18(2), 373–399. Campbell, J. Y., Giglio, S., Polk, C., & Turley, R. (2006). Efficient tests of stock return predictability. Journal of Financial Economics, 81(1), 27–60. Clark, T. E., & McCracken, M. W. (2001). Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics, 105(1), 85–110. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3–25. Fama, E. F., & Schwert, G. W. (1977). Asset returns and inflation. Journal of Financial Economics, 5(2), 115–146. Ferreira, M. A., & Santa-Clara, P. (2011). Forecasting stock market returns: The sum of the parts is more than the whole. Journal of Financial Economics, 100(3), 514–537. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 509–548. Kothari, S. P., Shanken, J., & Sloan, R. G. (1997). Book-to-market, dividend yield, and expected market returns: A time-series analysis. Journal of Financial Economics, 44(2), 169–203. Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209–235. Ludvigson, S. C., & Ng, S. (2007). The empirical risk-return relation: A factor analysis approach. Journal of Financial Economics, 83(1), 171–222. Liu, C., Zhang, X., Nguyen, T. T., Liu, J., Wu, T., Lee, E., & Tu, X. M. (2022). Partial least squares regression and principal component analysis: Similarity and differences between two popular variable reduction approaches. General Psychiatry, 35(1), e100662. McCracken, M. W. (2007). Asymptotic tests of predictive ability. Journal of Econometrics, 138(1), 291–311. McCracken, M. W. (2007). Asymptotics for out-of-sample tests of Granger causality. Journal of Econometrics, 140(2), 719–752. Meese, R. A., Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1–2), 3–24. Pontiff, J., & Schall, L. D. (1998). Book-to-market ratios as predictors of market returns. Journal of Financial Economics, 49(2), 141–160. Stambaugh, R. F. (1999). Predictive regressions. Journal of Financial Economics, 54(3), 375–421. Valkanov, R. (2003). Long-horizon regressions: Theoretical results and applications. Journal of Financial Economics, 68(1), 201–232. Wang, Y., Liu, L., Ma, F., & Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45, 141–156.zh_TW