本研究旨在探討混合資產配置策略（Hybrid Asset Allocation, HAA）於台灣ETF市場中的應用表現，並比較其在不同投資情境下相對於均權策略（Equal Weight, EW）與買進持有0050之風險與報酬表現。研究期間涵蓋2019年7月至2025年7月，建構包含進攻、防禦與金絲雀資產的多元資產池，並採用13612U動能濾網。絕對動能門檻進行策略判斷。根據Keller（2023）架構，本研究設計三種HAA策略版本：HAA-G10T5（分散式配置）、HAA-Top2（集中式動能配置）與HAA-Simple（簡化配置），以歷史回測分析其在單筆投資與定期定額（Dollar-Cost Averaging, DCA）下的績效表現。
實證結果顯示，在未考量交易成本情境下，HAA系列策略於風險調整後報酬（Sharpe Ratio、RDD值）與最大回撤控制方面整體優於EW與買進持有到期0050，顯示其動態風險調整機制具穩健性。然在納入交易成本後，各策略報酬水準略為下降，仍維持穩定表現與較高實務可行性；HAA-G10T5則持續展現優異之風險抑制效果。於定期定額投資情境下，HAA系列策略的最大回撤均控制於–25.8%以內，明顯優於0050之–32.79%，顯示其在長期分批投入下仍具資本保全能力。
整體而言，HAA策略雖未在報酬率上全面超越市場基準，但在風險調整後績效與報酬穩定性方面表現優異，且其兼具動態調整與防禦切換特性，顯示該模型於台灣ETF市場具實務應用潛力，可作為長期資產配置與風險管理之參考架構。

This study investigates the empirical performance of the Hybrid Asset Allocation (HAA) framework in the Taiwan ETF market from July 2019 to July 2025. Building on Keller (2023), the HAA model combines momentum-based selection, dynamic risk switching, and a novel dual-canary signal (utilizing BND and VWO) to enhance portfolio stability. Three strategy variants—HAA-G10T5 (diversified), HAA-Top2 (concentrated), and HAA-Simple (simplified)—are constructed and compared with an equal-weight (EW) portfolio and a buy-and-hold strategy in the market index ETF 0050.
Empirical findings show that the HAA strategies successfully preserve competitive returns (statistically comparable to the benchmark) while simultaneously delivering significantly lower volatility and smaller maximum drawdowns compared to the buy-and-hold strategy in the 0050 ETF. This superior risk management translates directly into higher Sharpe ratios.
Overall, the results confirm that the HAA framework is feasible and resilient in the Taiwan ETF market, offering balanced performance and practical applicability for long-term portfolio management.

中文文獻
林秀純. (2004). 美國共同基金動能策略之研究 [碩士論文, 國立成功大學]. https://hdl.handle.net/11296/bn3r36
張士傑與黃雅文. (2010). 保證收益基金之最適跨期資產選擇. 證券市場發展季刊, 22(1), 73–104. https://doi.org/10.6529/RSFM.2010.22(1).3
高碩廷. (2019). 動能投資策略在台灣股票市場之應用 [碩士論文, 國立中央大學]. 臺灣博碩士論文知識加值系統. https://hdl.handle.net/11296/s3e9fw
李國政. (2019). 台股動能投資策略與績效分析 [碩士論文, 國立高雄科技大學]. 臺灣博碩士論文知識加值系統. https://hdl.handle.net/11296/qf8yj3
李彥儒. (2019). 台灣證券市場之動能交易策略分析 [碩士論文, 東吳大學]. 臺灣博碩士論文知識加值系統. https://hdl.handle.net/11296/375wez
曾湘淇. (2020). 動能與雙動能投資策略之研究 [碩士論文, 國立高雄科技大學]. 臺灣博碩士論文知識加值系統. https://hdl.handle.net/11296/5n6g3v
林佳儒（2024）。應用大膽資產配置( BAA )策略於中國、香港及台灣金融市場。﹝碩士論文。國立成功大學﹞臺灣博碩士論文知識加值系統。https://hdl.handle.net/11296/8dfgsa
陳晉漢（2025）。日本金融市場中混合資產配置策略之實證分析。﹝碩士論文。國立成功大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/a87kp2

英文文獻
Antonacci, G. (2012). Risk premia harvesting through dual momentum. SSRN. https://doi.org/10.2139/ssrn.2042750
Antonacci, G. (2013). Absolute momentum: a simple rule-based strategy and universal trend-following overlay. SSRN. https://doi.org/10.2139/ssrn.2244633
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. Journal of Finance, 68(3), 929–985. https://doi.org/10.1111/jofi.12021
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. Journal of Financial Economics, 116(1), 111–120. https://doi.org/10.1016/j.jfineco.2014.11.010
Black, F., & Litterman, R. (1991). Global asset allocation. Financial Analysts Journal, 47(5), 28–43. https://doi.org/10.2469/faj.v71.n5.4
Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28–43. https://doi.org/10.2469/faj.v48.n5.28
Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). Determinants of portfolio performance. Financial Analysts Journal, 42(4), 39–44. https://doi.org/10.2469/faj.v42.n4.39
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681–1713. https://doi.org/10.1111/j.1540-6261.1996.tb05222.x
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122(2), 221–247. https://doi.org/10.1016/j.jfineco.2016.01.002
Faber, M. T. (2007). A quantitative approach to tactical asset allocation. The Journal of Wealth Management, 10(4), 69–79. https://ssrn.com/abstract=962461
Ibbotson, R. G., & Kaplan, P. D. (2000). Does asset allocation policy explain 40, 90, or 100 percent of performance? Financial Analysts Journal, 56(1), 26–33. https://doi.org/10.2469/faj.v56.n1.2327
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. https://doi.org/10.1111/j.1540-6261.1993.tb04702.x
Jegadeesh, N., & Titman, S. (2001). Profitability of momentum strategies: an evaluation of alternative explanations. The Journal of Finance, 56(2), 699–720. https://doi.org/10.1111/0022-1082.00342
Keller, W. J. (2022). Relative and absolute momentum in times of rising/low yields: Bold Asset Allocation (BAA). SSRN. https://doi.org/10.2139/ssrn.4166845
Keller, W. J., & Keuning, J. W. (2016). Protective asset allocation (PAA). SSRN. https://doi.org/10.2139/ssrn.2759734
Keller, W. J., & Keuning, J. W. (2017). Vigilant asset allocation (VAA). SSRN. https://doi.org/10.2139/ssrn.3002624
Keller, W. J., & Keuning, J. W. (2018). Breadth momentum and the canary universe: defensive asset allocation (DAA). SSRN. https://doi.org/10.2139/ssrn.3212862
Keller, W. J., & Keuning, J. W. (2023). Dual and canary momentum with rising yields/inflation: hybrid asset allocation (HAA). SSRN. https://doi.org/10.2139/ssrn.4379392
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228–250. https://doi.org/10.1016/j.jfineco.2011.11.003
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
Sias, R. W. (2004). Institutional herding. The Review of Financial Studies, 17(1), 165–206. https://doi.org/10.1093/rfs/hhg035