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題名 巴菲特阿爾法及波克夏海瑟威股票投資組合
Buffett’s Alpha and Berkshire Hathaway Stock Portfolios
作者 吳柄德
Wu, Ping-Te
貢獻者 許永明
Shiu, Yung-Ming
吳柄德
Wu, Ping-Te
關鍵詞 巴菲特阿爾法
波克夏海瑟威
Fama-French 模型
AQR 因子模型
投資組合
現代投資組合理論
均值變異優化
異質變異
自相關
Buffett’s Alpha
Berkshire Hathaway
Fama-French models
AQR multifactor model
stock portfolios
Modern Portfolio Theory
mean–variance optimization
heteroskedasticity
autocorrelation
日期 2026
上傳時間 2-Feb-2026 12:15:10 (UTC+8)
摘要 本研究旨在以量化方法探討波克夏海瑟威和其股票投資組合之超額報酬來源，並重新檢視「巴菲特阿爾法」是否可由現代因子模型所解釋。過去文獻多認為巴菲特的優異績效來自價值投資與企業品質選擇，但單因子CAPM 或傳統 Fama-French 模型不足以完全捕捉其報酬結構。本研究參照〈Buffett’s Alpha〉之方法框架，採用 Fama-French 四因子模型、五因子模型與AQR多因子模型迴歸分析。 接著計算波克夏投資組合各成分股之年化平均報酬與共變異矩陣，依現代投資組合理論建立均值–變異優化模型，建構最適化投資組合，並比較該最適投資組合與波克夏實際投資策略於報酬與風險上的差異。 主要實證結果包括（1）自相關問題於日頻較常見，尤其為科技與能源類股，異質變異性則普遍存在於高波動產業。(2)引入AQR模型後，包含品質與低β策略等風險因子後，巴菲特的阿爾法隱含值顯著下降，印證文獻所述巴菲特績效主要來自「安全的高品質價值股」以及低成本槓桿來源，而非傳統意義的選股阿爾法。（3）依現代投資組合理論推導的最適投資組合呈現更高的夏普比率，但其持股分散特性明顯高於波克夏實際風險，反映巴菲特的「集中式價值投資」與現代投資組合理論之「量化分散投資策略」差異。 本研究整合因子模型、投資組合資料與最適化分析，提供學術界與實務界對巴菲特阿爾法來源更全面的理解，亦對於因子模型在個股層級的跨產業適用性、資料頻率效果與殘差特性提出經驗性的證據。研究結果顯示，巴菲特阿爾法可由多因子模型大幅解釋，但無法完全消失，意味著其投資方法兼具可量化的風險暴露與難以形式化的企業分析能力。
This study aims to quantitatively explore the sources of excess returns for Berkshire and its stock portfolios, and to re-examine whether "Buffett Alpha" can be explained by modern factor models. In the past, most literature believed that Buffett's excellent performance came from value investing and corporate quality choices, but the single-factor CAPM or traditional Fama-French model was not enough to fully capture his return structure. This study refers to the methodological framework of "Buffett's Alpha" and uses Fama-French four-factor model, five-factor model, and AQR multifactor model regression analysis. Then, the annualized average return and covariance matrix of each constituent stock of Berkshire's portfolio are calculated, and a mean-variance optimization model is established according to modern portfolio theory to construct an optimized portfolio, and the differences in return and risk between the optimal portfolio and Berkshire's actual investment strategy are compared. The main empirical results include (1) autocorrelation problems are more common in daily frequency, especially in technology and energy stocks, while heterogeneous variability is prevalent in high-volatility industries. (2) After the introduction of the AQR model, Buffett's alpha implied value decreased significantly after including style factors such as quality and low-β strategy, confirming that Buffett's performance in the literature mainly comes from "safe high-quality value stocks" and low-cost leverage sources, rather than the traditional stock selection alpha. (3) The optimal portfolio derived from modern portfolio theory shows a higher Sharpe ratio, but its shareholding diversification characteristics are significantly higher than Berkshire's actual style, reflecting the difference between Buffett's "concentrated value investment" and the "quantitative diversification strategy" of modern portfolio theory. This study integrates factor models, portfolio data, and optimization analysis to provide academic and practical communities with a more comprehensive understanding of Buffett's alpha sources, and also provides empirical evidence for the cross-industry applicability of factor models, data frequency effects, and residual characteristics at the individual stock level. The research results show that Buffett's alpha can be largely explained by multi-factor models, but it cannot completely disappear, meaning that its investment method combines quantifiable style exposure with hard-to-formalize corporate analysis capabilities.
參考文獻 Asness, C.S., Frazzini, A. & Pedersen, L.H. Quality minus junk. Rev Account Stud 24, 34–112 (2019). https://doi.org/10.1007/s11142-018-9470-2 Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929–985. https://doi.org/10.1111/jofi.12021 Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3–18. https://doi.org/10.1016/0304-405X(81)90018-0 Basu, S. (1977). Investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis. The Journal of Finance, 32(3), 663–682. https://doi.org/10.2307/2326304 Brown, S. J. (2020). The efficient market hypothesis, the Financial Analysts Journal, and the professional status of investment management. Financial Analysts Journal, 76(2), 5–14. https://doi.org/10.1080/0015198X.2020.1734375 Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x 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. https://doi.org/10.1016/0304-405X(93)90023-5 Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international stock returns. Journal of Financial Economics, 105(3), 457–472. https://doi.org/10.1016/j.jfineco.2012.05.011 Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010 Frazzini, A., Kabiller, D., & Pedersen, L. H. (2018). Buffett’s alpha. Financial Analysts Journal, 74(4), 35–55. https://DOI.ORG/10.2469/faj.v74.n4.3 Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1–25. https://doi.org/10.1016/j.jfineco.2013.10.005 Goetzmann, W. N. (2020). The Financial Analysts Journal and investment management. Financial Analysts Journal, 76(3), 5–21. https://doi.org/10.1080/0015198X.2020.1766287 Goetzmann, W. N. (2023). Harry Markowitz in memoriam. Financial Analysts Journal, 79(4), 5–7. https://DOI.ORG/10.1080/0015198X.2023.2251861 Kaplan, P. D., & Idzorek, T. M. (2024). The importance of joining lifecycle models with mean-variance optimization. Financial Analysts Journal, 80(4), 11–17. https://DOI.ORG/10.1080/0015198X.2024.2382672 Kritzman, M., Page, S., & Turkington, D. (2010). In defense of optimization: The fallacy of 1/N. Financial Analysts Journal, 66(2), 31–39. https://doi.org/10.2469/faj.v66.n2.6 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), 13–37. https://doi.org/10.2307/1924119 Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. John Wiley & Sons. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610 Page, S., & Panariello, R. A. (2018). When diversification fails. Financial Analysts Journal, 74(3), 19–32. https://doi.org/10.2469/faj.v74.n3.3 Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.2307/2977928 White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934
描述 碩士
國立政治大學
國際金融碩士學位學程
113ZB1032
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0113ZB1032
資料類型 thesis
dc.contributor.advisor 許永明zh_TW
dc.contributor.advisor Shiu, Yung-Mingen_US
dc.contributor.author (Authors) 吳柄德zh_TW
dc.contributor.author (Authors) Wu, Ping-Teen_US
dc.creator (作者) 吳柄德zh_TW
dc.creator (作者) Wu, Ping-Teen_US
dc.date (日期) 2026en_US
dc.date.accessioned 2-Feb-2026 12:15:10 (UTC+8)-
dc.date.available 2-Feb-2026 12:15:10 (UTC+8)-
dc.date.issued (上傳時間) 2-Feb-2026 12:15:10 (UTC+8)-
dc.identifier (Other Identifiers) G0113ZB1032en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/161376-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際金融碩士學位學程zh_TW
dc.description (描述) 113ZB1032zh_TW
dc.description.abstract (摘要) 本研究旨在以量化方法探討波克夏海瑟威和其股票投資組合之超額報酬來源，並重新檢視「巴菲特阿爾法」是否可由現代因子模型所解釋。過去文獻多認為巴菲特的優異績效來自價值投資與企業品質選擇，但單因子CAPM 或傳統 Fama-French 模型不足以完全捕捉其報酬結構。本研究參照〈Buffett’s Alpha〉之方法框架，採用 Fama-French 四因子模型、五因子模型與AQR多因子模型迴歸分析。 接著計算波克夏投資組合各成分股之年化平均報酬與共變異矩陣，依現代投資組合理論建立均值–變異優化模型，建構最適化投資組合，並比較該最適投資組合與波克夏實際投資策略於報酬與風險上的差異。 主要實證結果包括（1）自相關問題於日頻較常見，尤其為科技與能源類股，異質變異性則普遍存在於高波動產業。(2)引入AQR模型後，包含品質與低β策略等風險因子後，巴菲特的阿爾法隱含值顯著下降，印證文獻所述巴菲特績效主要來自「安全的高品質價值股」以及低成本槓桿來源，而非傳統意義的選股阿爾法。（3）依現代投資組合理論推導的最適投資組合呈現更高的夏普比率，但其持股分散特性明顯高於波克夏實際風險，反映巴菲特的「集中式價值投資」與現代投資組合理論之「量化分散投資策略」差異。 本研究整合因子模型、投資組合資料與最適化分析，提供學術界與實務界對巴菲特阿爾法來源更全面的理解，亦對於因子模型在個股層級的跨產業適用性、資料頻率效果與殘差特性提出經驗性的證據。研究結果顯示，巴菲特阿爾法可由多因子模型大幅解釋，但無法完全消失，意味著其投資方法兼具可量化的風險暴露與難以形式化的企業分析能力。zh_TW
dc.description.abstract (摘要) This study aims to quantitatively explore the sources of excess returns for Berkshire and its stock portfolios, and to re-examine whether "Buffett Alpha" can be explained by modern factor models. In the past, most literature believed that Buffett's excellent performance came from value investing and corporate quality choices, but the single-factor CAPM or traditional Fama-French model was not enough to fully capture his return structure. This study refers to the methodological framework of "Buffett's Alpha" and uses Fama-French four-factor model, five-factor model, and AQR multifactor model regression analysis. Then, the annualized average return and covariance matrix of each constituent stock of Berkshire's portfolio are calculated, and a mean-variance optimization model is established according to modern portfolio theory to construct an optimized portfolio, and the differences in return and risk between the optimal portfolio and Berkshire's actual investment strategy are compared. The main empirical results include (1) autocorrelation problems are more common in daily frequency, especially in technology and energy stocks, while heterogeneous variability is prevalent in high-volatility industries. (2) After the introduction of the AQR model, Buffett's alpha implied value decreased significantly after including style factors such as quality and low-β strategy, confirming that Buffett's performance in the literature mainly comes from "safe high-quality value stocks" and low-cost leverage sources, rather than the traditional stock selection alpha. (3) The optimal portfolio derived from modern portfolio theory shows a higher Sharpe ratio, but its shareholding diversification characteristics are significantly higher than Berkshire's actual style, reflecting the difference between Buffett's "concentrated value investment" and the "quantitative diversification strategy" of modern portfolio theory. This study integrates factor models, portfolio data, and optimization analysis to provide academic and practical communities with a more comprehensive understanding of Buffett's alpha sources, and also provides empirical evidence for the cross-industry applicability of factor models, data frequency effects, and residual characteristics at the individual stock level. The research results show that Buffett's alpha can be largely explained by multi-factor models, but it cannot completely disappear, meaning that its investment method combines quantifiable style exposure with hard-to-formalize corporate analysis capabilities.en_US
dc.description.tableofcontents 第一章 緒論 7 第一節 研究背景動機 7 第二節 研究目的與問題 8 第三節 研究重要性與貢獻 9 第二章 文獻探討 10 第一節 傳統CAPM與其限制 10 第二節 Fama-French多因子及AQR模型 10 第三節 現代投資組合理論 12 第四節 波克夏投資策略與分散化理論 12 第三章 研究方法 13 第一節 資料樣本來源、期間及頻率 13 第二節 模型設定 16 第三節 估計方法 17 第四節 投資組合優化模擬 18 第四章 實證結果與分析 19 第一節 Fama-French四因子模型 19 第二節 Fama-French五因子模型 25 第三節 AQR因子模型 31 第四節 FF-4、FF-5及AQR迴歸因子係數結果及分析 38 第五節 波克夏股票投資組合資料與最適化分析 45 第五章 結論 53 第一節 結論 53 第二節 建議 53 第三節 研究限制 54 第四節 未來研究方向 54 附錄 55 參考文獻 81zh_TW
dc.format.extent 6244279 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0113ZB1032en_US
dc.subject (關鍵詞) 巴菲特阿爾法zh_TW
dc.subject (關鍵詞) 波克夏海瑟威zh_TW
dc.subject (關鍵詞) Fama-French 模型zh_TW
dc.subject (關鍵詞) AQR 因子模型zh_TW
dc.subject (關鍵詞) 投資組合zh_TW
dc.subject (關鍵詞) 現代投資組合理論zh_TW
dc.subject (關鍵詞) 均值變異優化zh_TW
dc.subject (關鍵詞) 異質變異zh_TW
dc.subject (關鍵詞) 自相關zh_TW
dc.subject (關鍵詞) Buffett’s Alphaen_US
dc.subject (關鍵詞) Berkshire Hathawayen_US
dc.subject (關鍵詞) Fama-French modelsen_US
dc.subject (關鍵詞) AQR multifactor modelen_US
dc.subject (關鍵詞) stock portfoliosen_US
dc.subject (關鍵詞) Modern Portfolio Theoryen_US
dc.subject (關鍵詞) mean–variance optimizationen_US
dc.subject (關鍵詞) heteroskedasticityen_US
dc.subject (關鍵詞) autocorrelationen_US
dc.title (題名) 巴菲特阿爾法及波克夏海瑟威股票投資組合zh_TW
dc.title (題名) Buffett’s Alpha and Berkshire Hathaway Stock Portfoliosen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Asness, C.S., Frazzini, A. & Pedersen, L.H. Quality minus junk. Rev Account Stud 24, 34–112 (2019). https://doi.org/10.1007/s11142-018-9470-2 Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929–985. https://doi.org/10.1111/jofi.12021 Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3–18. https://doi.org/10.1016/0304-405X(81)90018-0 Basu, S. (1977). Investment performance of common stocks in relation to their price-earnings ratios: A test of the efficient market hypothesis. The Journal of Finance, 32(3), 663–682. https://doi.org/10.2307/2326304 Brown, S. J. (2020). The efficient market hypothesis, the Financial Analysts Journal, and the professional status of investment management. Financial Analysts Journal, 76(2), 5–14. https://doi.org/10.1080/0015198X.2020.1734375 Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x 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. https://doi.org/10.1016/0304-405X(93)90023-5 Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international stock returns. Journal of Financial Economics, 105(3), 457–472. https://doi.org/10.1016/j.jfineco.2012.05.011 Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010 Frazzini, A., Kabiller, D., & Pedersen, L. H. (2018). Buffett’s alpha. Financial Analysts Journal, 74(4), 35–55. https://DOI.ORG/10.2469/faj.v74.n4.3 Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1–25. https://doi.org/10.1016/j.jfineco.2013.10.005 Goetzmann, W. N. (2020). The Financial Analysts Journal and investment management. Financial Analysts Journal, 76(3), 5–21. https://doi.org/10.1080/0015198X.2020.1766287 Goetzmann, W. N. (2023). Harry Markowitz in memoriam. Financial Analysts Journal, 79(4), 5–7. https://DOI.ORG/10.1080/0015198X.2023.2251861 Kaplan, P. D., & Idzorek, T. M. (2024). The importance of joining lifecycle models with mean-variance optimization. Financial Analysts Journal, 80(4), 11–17. https://DOI.ORG/10.1080/0015198X.2024.2382672 Kritzman, M., Page, S., & Turkington, D. (2010). In defense of optimization: The fallacy of 1/N. Financial Analysts Journal, 66(2), 31–39. https://doi.org/10.2469/faj.v66.n2.6 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), 13–37. https://doi.org/10.2307/1924119 Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. John Wiley & Sons. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610 Page, S., & Panariello, R. A. (2018). When diversification fails. Financial Analysts Journal, 74(3), 19–32. https://doi.org/10.2469/faj.v74.n3.3 Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.2307/2977928 White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934zh_TW