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題名 台灣股市中市場資訊交互預測新視角
A New Perspective on the Cross-Predictability of Market Information in Taiwan Stock Market
作者 林祥恩
Lin, Hsiang-En
貢獻者 羅秉政
Kendro Vincent
林祥恩
Lin, Hsiang-En
關鍵詞 交互預測
主投資組合分析
市場資訊
異常投資組合
Cross-Predictability
Principal Portfolio Analysis
Market Information
Anomaly Portfolio
日期 2024
上傳時間 4-Feb-2025 16:20:00 (UTC+8)
摘要 本研究運用主投資組合分析方法，探討以市場資訊建構之異常投資組合的交互預測能力及其對投資績效的影響。實證結果顯示，融入交互預測的投資組合在 Sharpe Ratio 上顯著超越基準投資組合。其中，主非對稱投資組合的 Sharpe Ratio 高達 0.63，且因子迴歸估計中具有顯著的 Fama-French 五因子模型 alpha 值 1.20%，表明其具備提升風險調整後報酬的能力。此外，分析顯示預期報酬率的排序未必完全反映實際表現，操作策略應綜合考量不同的主投資組合，以提升穩健性與潛在報酬。本研究為異常投資組合交互預測的應用提供了實證支持，並為投資策略優化提供參考。
This study utilizes Principal Portfolio Analysis to investigate the cross-predictability of anomaly portfolios constructed from market information and its implications for investment performance. Empirical results reveal that portfolios integrating cross-predictability significantly outperform the benchmark portfolio in terms of Sharpe Ratio. Notably, the principal alpha portfolio achieves a Sharpe Ratio of 0.63, with a statistically significant Fama-French 5 factor alpha of 1.20%, highlighting its superior capability in enhancing risk-adjusted returns. Furthermore, the findings indicate that the ranking of expected returns may not always align with actual performance, suggesting that investment strategies should integrate multiple principal portfolios to improve robustness and potential profitability. This study provides empirical evidence for the utility of cross-predictability in anomaly portfolios and offers practical insights for optimizing investment strategies.
參考文獻 Bui, D. G., Kong, D.-R., Lin, C.-Y., and Lin, T.-C. (2023). Momentum in machine learning: Evidence from the Taiwan stock market. Pacific-Basin Finance Journal, 82:102178. Cakici, N., Fieberg, C., Metko, D., and Zaremba, A. (2024). Do anomalies really predict market returns? New data and new evidence. Review of Finance, 28(1):1–44. Dong, X., Li, Y., Rapach, D. E., and Zhou, G. (2022). Anomalies and the expected market return. The Journal of Finance, 77(1):639–681. Du, D., Huang, Z., and Liao, B.-S. (2009). Why is there no momentum in the Taiwan stock market? Journal of Economics and Business, 61(2):140–152. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1):1–22. George, T. J. and Hwang, C.-Y. (2004). The 52-week high and momentum investing. The Journal of Finance, 59(5):2145–2176. Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5):2223–2273. Kelly, B., Malamud, S., and Pedersen, L. H. (2023). Principal portfolios. The Journal of Finance, 78(1):347–387. Lin, C., Ko, K.-C., and Yang, N.-T. (2022). Does the momentum gap explain momentum in Taiwan? Pacific-Basin Finance Journal, 72:101732. Yan, J. and Yu, J. (2023). Cross-stock momentum and factor momentum. Journal of Financial Economics, 150(2):103716.
描述 碩士
國立政治大學
金融學系
112352009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112352009
資料類型 thesis
dc.contributor.advisor 羅秉政zh_TW
dc.contributor.advisor Kendro Vincenten_US
dc.contributor.author (Authors) 林祥恩zh_TW
dc.contributor.author (Authors) Lin, Hsiang-Enen_US
dc.creator (作者) 林祥恩zh_TW
dc.creator (作者) Lin, Hsiang-Enen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-Feb-2025 16:20:00 (UTC+8)-
dc.date.available 4-Feb-2025 16:20:00 (UTC+8)-
dc.date.issued (上傳時間) 4-Feb-2025 16:20:00 (UTC+8)-
dc.identifier (Other Identifiers) G0112352009en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155529-
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) 112352009zh_TW
dc.description.abstract (摘要) 本研究運用主投資組合分析方法，探討以市場資訊建構之異常投資組合的交互預測能力及其對投資績效的影響。實證結果顯示，融入交互預測的投資組合在 Sharpe Ratio 上顯著超越基準投資組合。其中，主非對稱投資組合的 Sharpe Ratio 高達 0.63，且因子迴歸估計中具有顯著的 Fama-French 五因子模型 alpha 值 1.20%，表明其具備提升風險調整後報酬的能力。此外，分析顯示預期報酬率的排序未必完全反映實際表現，操作策略應綜合考量不同的主投資組合，以提升穩健性與潛在報酬。本研究為異常投資組合交互預測的應用提供了實證支持，並為投資策略優化提供參考。zh_TW
dc.description.abstract (摘要) This study utilizes Principal Portfolio Analysis to investigate the cross-predictability of anomaly portfolios constructed from market information and its implications for investment performance. Empirical results reveal that portfolios integrating cross-predictability significantly outperform the benchmark portfolio in terms of Sharpe Ratio. Notably, the principal alpha portfolio achieves a Sharpe Ratio of 0.63, with a statistically significant Fama-French 5 factor alpha of 1.20%, highlighting its superior capability in enhancing risk-adjusted returns. Furthermore, the findings indicate that the ranking of expected returns may not always align with actual performance, suggesting that investment strategies should integrate multiple principal portfolios to improve robustness and potential profitability. This study provides empirical evidence for the utility of cross-predictability in anomaly portfolios and offers practical insights for optimizing investment strategies.en_US
dc.description.tableofcontents 第一章 緒論 11 第一節 研究背景與動機 11 第二節 研究目的 12 第二章 文獻探討 13 第三章 研究方法 16 第一節 資料來源 16 第二節 異常投資組合建構與股票特徵介紹 16 第三節 主投資組合建構方法 20 第四節 主投資組合績效評估 27 第四章 實證結果 29 第一節 異常投資組合概述統計量 29 第二節 主投資組合績效 35 第三節 異常投資組合間的交互預測能力 54 第四節 不同訊號的分析 57 第五章 結論 63 參考文獻 65 附錄 A — 訊號設定為異常投資組合的上個月報酬率 66 附錄 B — 訊號設定為異常投資組合截至上個月結束的五個月累積報酬率 82 附錄 C — 其他因子迴歸 98 第一節 其他因子迴歸模型 98 第二節 其他因子迴歸估計 99zh_TW
dc.format.extent 27281375 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112352009en_US
dc.subject (關鍵詞) 交互預測zh_TW
dc.subject (關鍵詞) 主投資組合分析zh_TW
dc.subject (關鍵詞) 市場資訊zh_TW
dc.subject (關鍵詞) 異常投資組合zh_TW
dc.subject (關鍵詞) Cross-Predictabilityen_US
dc.subject (關鍵詞) Principal Portfolio Analysisen_US
dc.subject (關鍵詞) Market Informationen_US
dc.subject (關鍵詞) Anomaly Portfolioen_US
dc.title (題名) 台灣股市中市場資訊交互預測新視角zh_TW
dc.title (題名) A New Perspective on the Cross-Predictability of Market Information in Taiwan Stock Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bui, D. G., Kong, D.-R., Lin, C.-Y., and Lin, T.-C. (2023). Momentum in machine learning: Evidence from the Taiwan stock market. Pacific-Basin Finance Journal, 82:102178. Cakici, N., Fieberg, C., Metko, D., and Zaremba, A. (2024). Do anomalies really predict market returns? New data and new evidence. Review of Finance, 28(1):1–44. Dong, X., Li, Y., Rapach, D. E., and Zhou, G. (2022). Anomalies and the expected market return. The Journal of Finance, 77(1):639–681. Du, D., Huang, Z., and Liao, B.-S. (2009). Why is there no momentum in the Taiwan stock market? Journal of Economics and Business, 61(2):140–152. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1):1–22. George, T. J. and Hwang, C.-Y. (2004). The 52-week high and momentum investing. The Journal of Finance, 59(5):2145–2176. Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5):2223–2273. Kelly, B., Malamud, S., and Pedersen, L. H. (2023). Principal portfolios. The Journal of Finance, 78(1):347–387. Lin, C., Ko, K.-C., and Yang, N.-T. (2022). Does the momentum gap explain momentum in Taiwan? Pacific-Basin Finance Journal, 72:101732. Yan, J. and Yu, J. (2023). Cross-stock momentum and factor momentum. Journal of Financial Economics, 150(2):103716.zh_TW