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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/155529
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155529


    Title: 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
    Authors: 林祥恩
    Lin, Hsiang-En
    Contributors: 羅秉政
    Kendro Vincent
    林祥恩
    Lin, Hsiang-En
    Keywords: 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
    Date: 2024
    Issue Date: 2025-02-04 16:20:00 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Description: 碩士
    國立政治大學
    金融學系
    112352009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112352009
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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