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題名 藉由機器學習強化基本面、股市動能與市場情緒的動態因子模型：以台灣股票市場為例
Machine Learning-Enhanced Dynamic Factor Models for Taiwanese Stock Markets: Integrating Fundamentals, Momentum, and Sentiment Factors作者 簡祥育
Chien, Hsiang-Yu貢獻者 江彌修
Chiang, Mi-Hsiu
簡祥育
Chien, Hsiang-Yu關鍵詞 動態因子模型
主成分分析
機器學習
資產定價
Dynamic Factor Model
Principal Component Analysis
Machine Learning
Asset Pricing日期 2025 上傳時間 1-七月-2025 15:15:47 (UTC+8) 摘要 本研究以台灣股票市場為資料，藉由納入多種資產特徵，建構不同於 Fama and French (2018) 架構的動態因子模型。本文採用 Kelly et al. (2019) 所提出之 Instrumented Principal Component Analysis (IPCA) 方法，評估不同特徵組合與模型在統計解釋力與風險補償能力上的表現。研究結果顯示，相較於傳統的 Fama and French (2018) 六因子模型與主成分分析 (PCA) 方法，IPCA 模型於樣本外展現出更佳的表現。此外，IPCA 能夠從大量資產特徵中提取出具有財務意涵的潛在因子，並兼顧基本面、股價動能、投資人情緒及籌碼面資訊。另一方面，分析結果亦顯示模型所擷取之因子與產業結構具有關聯，提升對於因子財務詮釋的能力。此外，透過採用拔靴法進行特徵重要性檢定的結果顯示，模型所納入之多項特徵在統計上呈現顯著，進一步支持這些特徵於資產超額報酬解釋上的有效性。
This study utilizes data from the Taiwan stock market to construct a dynamic factor model that differs from the Fama and French (2018) framework by incorporating a wide range of asset characteristics. This study employs the Instrumented Principal Component Analysis (IPCA) method proposed by Kelly et al. (2019) to evaluate the model's performance related to both statistical explanatory power and risk compensation ability under several combinations of characteristics and models. The empirical results show that the IPCA model outperforms both the traditional Fama and French six-factor model and the Principal Component Analysis (PCA) approach in out-of-sample performance. Moreover, IPCA is able to extract latent factors with financial interpretations from high-dimensional characteristic data, capturing information combining fundamentals, price momentum, investor sentiment, and ownership structure. In addition, the analysis reveals a strong relationship between the extracted factors and industry structure, enhancing the model’s interpretability in terms of financial explanation. Finally, the bootstrap-based feature significance tests validate that many of the incorporated characteristics are statistically significant, further supporting their effectiveness in explaining cross-sectional variation in excess returns.參考文獻 周賓凰、張宇志、林美珍 (2019)。投資人情緒與股票報酬互動關係。證券市場發展季刊：行為財務學特別專刊，19（2），153-190。 蔡佩蓉、王元章、張眾卓 (2009)。投資人情緒、公司特徵與台灣股票報酬之研究。經濟研究，45（2），273-322。 Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375–410. Ait-Sahalia, Y., Jacod, J., & Xiu, D. (2020). Inference on risk premia in Continuous-Time asset Pricing models. SSRN Electronic Journal. Asness, C. S., Frazzini, A., & Pedersen, L. H. (2019). Quality minus junk. Review of Accounting Studies, 24(1), 34–112. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271–299. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680. Barber, B. M., Lee, Y.-T., Liu, Y.-J., & Odean, T. (2009). Just how much do individual investors lose by trading? The Review of Financial Studies, 22(2), 609–632. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1–27. Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. Chamberlain, G., & Rothschild, M. (1983). Arbitrage, factor structure, and Mean-Variance analysis on large asset markets. Econometrica, 51(5), 1281. Chordia, T., Roll, R., & Subrahmanyam, A. (2000). Commonality in liquidity. Journal of Financial Economics, 56(1), 3–28. Cochrane, J. H. (2011). Presidential address: Discount rates. The Journal of Finance, 66(4), 1047–1108. Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607–636. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The Journal of Finance, 51(1), 55–84. Fama, E. F., & French, K. R. (2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252. Feng, G., Giglio, S., & Xiu, D. (2020). Taming the Factor Zoo: A test of new factors. The Journal of Finance, 75(3), 1327–1370. Giglio, S., & Xiu, D. (2021). Asset Pricing with Omitted Factors. Journal of Political Economy, 129(7), 1947–1990. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222(1), 429–450. Han, Y., He, A., Rapach, D. E., & Zhou, G. (2024). Cross-sectional expected returns: New Fama–MacBeth regressions in the era of machine learning. Review of Finance, 28(6), 1807–1831. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881–898. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91. Kelly, B. T., Pruitt, S., & Su, Y. (2017). Instrumented Principal component analysis. SSRN Electronic Journal. Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524. Kelly, B., Palhares, D., & Pruitt, S. (2023). Modeling corporate bond returns. The Journal of Finance, 78(4), 1967–2008. Lee, C. M. C., & Swaminathan, B. (2000). Price momentum and Trading Volume. The Journal of Finance, 55(5), 2017–2069. Lettau, M., & Pelger, M. (2020). Estimating latent asset-pricing factors. Journal of Econometrics, 218(1), 1–31. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13. Pástor, Ľ., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3), 642–685. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. Sharpe, W. F. (1964). CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK. The Journal of Finance, 19(3), 425–442. 描述 碩士
國立政治大學
金融學系
111352011資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352011 資料類型 thesis dc.contributor.advisor 江彌修 zh_TW dc.contributor.advisor Chiang, Mi-Hsiu en_US dc.contributor.author (作者) 簡祥育 zh_TW dc.contributor.author (作者) Chien, Hsiang-Yu en_US dc.creator (作者) 簡祥育 zh_TW dc.creator (作者) Chien, Hsiang-Yu en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-七月-2025 15:15:47 (UTC+8) - dc.date.available 1-七月-2025 15:15:47 (UTC+8) - dc.date.issued (上傳時間) 1-七月-2025 15:15:47 (UTC+8) - dc.identifier (其他 識別碼) G0111352011 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157827 - 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) 111352011 zh_TW dc.description.abstract (摘要) 本研究以台灣股票市場為資料，藉由納入多種資產特徵，建構不同於 Fama and French (2018) 架構的動態因子模型。本文採用 Kelly et al. (2019) 所提出之 Instrumented Principal Component Analysis (IPCA) 方法，評估不同特徵組合與模型在統計解釋力與風險補償能力上的表現。研究結果顯示，相較於傳統的 Fama and French (2018) 六因子模型與主成分分析 (PCA) 方法，IPCA 模型於樣本外展現出更佳的表現。此外，IPCA 能夠從大量資產特徵中提取出具有財務意涵的潛在因子，並兼顧基本面、股價動能、投資人情緒及籌碼面資訊。另一方面，分析結果亦顯示模型所擷取之因子與產業結構具有關聯，提升對於因子財務詮釋的能力。此外，透過採用拔靴法進行特徵重要性檢定的結果顯示，模型所納入之多項特徵在統計上呈現顯著，進一步支持這些特徵於資產超額報酬解釋上的有效性。 zh_TW dc.description.abstract (摘要) This study utilizes data from the Taiwan stock market to construct a dynamic factor model that differs from the Fama and French (2018) framework by incorporating a wide range of asset characteristics. This study employs the Instrumented Principal Component Analysis (IPCA) method proposed by Kelly et al. (2019) to evaluate the model's performance related to both statistical explanatory power and risk compensation ability under several combinations of characteristics and models. The empirical results show that the IPCA model outperforms both the traditional Fama and French six-factor model and the Principal Component Analysis (PCA) approach in out-of-sample performance. Moreover, IPCA is able to extract latent factors with financial interpretations from high-dimensional characteristic data, capturing information combining fundamentals, price momentum, investor sentiment, and ownership structure. In addition, the analysis reveals a strong relationship between the extracted factors and industry structure, enhancing the model’s interpretability in terms of financial explanation. Finally, the bootstrap-based feature significance tests validate that many of the incorporated characteristics are statistically significant, further supporting their effectiveness in explaining cross-sectional variation in excess returns. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 2 第二章 文獻回顧 3 第一節 傳統定價模型與因子投組 3 第二節 特徵選取與隱性因子 4 第三節 機器學習與動態因子 5 第四節 流動性與投資人情緒 6 第三章 研究方法 8 第一節 理論模型 8 第二節 模型效能評估 10 第四章 實證結果 13 第一節 資料敘述 13 第二節 樣本內比較 25 第三節 樣本外比較 37 第四節 統計因子與財務意涵 45 第五節 樣本外獲利能力 56 第六節 特徵重要性 60 第七節 穩健性測試 61 第五章 結論 64 參考文獻 65 附錄 68 zh_TW dc.format.extent 3268249 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352011 en_US dc.subject (關鍵詞) 動態因子模型 zh_TW dc.subject (關鍵詞) 主成分分析 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 資產定價 zh_TW dc.subject (關鍵詞) Dynamic Factor Model en_US dc.subject (關鍵詞) Principal Component Analysis en_US dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Asset Pricing en_US dc.title (題名) 藉由機器學習強化基本面、股市動能與市場情緒的動態因子模型：以台灣股票市場為例 zh_TW dc.title (題名) Machine Learning-Enhanced Dynamic Factor Models for Taiwanese Stock Markets: Integrating Fundamentals, Momentum, and Sentiment Factors en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 周賓凰、張宇志、林美珍 (2019)。投資人情緒與股票報酬互動關係。證券市場發展季刊：行為財務學特別專刊，19（2），153-190。 蔡佩蓉、王元章、張眾卓 (2009)。投資人情緒、公司特徵與台灣股票報酬之研究。經濟研究，45（2），273-322。 Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375–410. Ait-Sahalia, Y., Jacod, J., & Xiu, D. (2020). Inference on risk premia in Continuous-Time asset Pricing models. SSRN Electronic Journal. Asness, C. S., Frazzini, A., & Pedersen, L. H. (2019). Quality minus junk. Review of Accounting Studies, 24(1), 34–112. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271–299. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680. Barber, B. M., Lee, Y.-T., Liu, Y.-J., & Odean, T. (2009). Just how much do individual investors lose by trading? The Review of Financial Studies, 22(2), 609–632. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1–27. Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. Chamberlain, G., & Rothschild, M. (1983). Arbitrage, factor structure, and Mean-Variance analysis on large asset markets. Econometrica, 51(5), 1281. Chordia, T., Roll, R., & Subrahmanyam, A. (2000). Commonality in liquidity. Journal of Financial Economics, 56(1), 3–28. Cochrane, J. H. (2011). Presidential address: Discount rates. The Journal of Finance, 66(4), 1047–1108. Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607–636. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The Journal of Finance, 51(1), 55–84. Fama, E. F., & French, K. R. (2018). Choosing factors. Journal of Financial Economics, 128(2), 234–252. Feng, G., Giglio, S., & Xiu, D. (2020). Taming the Factor Zoo: A test of new factors. The Journal of Finance, 75(3), 1327–1370. Giglio, S., & Xiu, D. (2021). Asset Pricing with Omitted Factors. Journal of Political Economy, 129(7), 1947–1990. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222(1), 429–450. Han, Y., He, A., Rapach, D. E., & Zhou, G. (2024). Cross-sectional expected returns: New Fama–MacBeth regressions in the era of machine learning. Review of Finance, 28(6), 1807–1831. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of Finance, 45(3), 881–898. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91. Kelly, B. T., Pruitt, S., & Su, Y. (2017). Instrumented Principal component analysis. SSRN Electronic Journal. Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524. Kelly, B., Palhares, D., & Pruitt, S. (2023). Modeling corporate bond returns. The Journal of Finance, 78(4), 1967–2008. Lee, C. M. C., & Swaminathan, B. (2000). Price momentum and Trading Volume. The Journal of Finance, 55(5), 2017–2069. Lettau, M., & Pelger, M. (2020). Estimating latent asset-pricing factors. Journal of Econometrics, 218(1), 1–31. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13. Pástor, Ľ., & Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3), 642–685. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. Sharpe, W. F. (1964). CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK. The Journal of Finance, 19(3), 425–442. zh_TW
