近年來因資訊科技軟硬體的發展，過往傳統的投資觀念逐漸被科技的進步所顛覆，在投資市場崛起一股量化投資 (Quantitative Investment)的新投資觀念，透過大數據 (Big Data)的概念加上AI演算法的導入，成功搭上了這一波的金融科技 (FinTech)浪潮，「智能理財」或「理財機器人」的新投資概念就因此油然而生，機器人理財顧問 (Robo-Advisor)更成為國內外金融業近一年來最火紅的話題之一。科技在金融交易上被廣泛的運用，讓金融市場的行情變化速度加快，加速了各家金融交易團隊在交易策略上的開發及運用，而已經有幾十年歷史的資產配置 (Assest Allocation)的觀念仍然在金融快速變化的環境中屹立不搖。在大型法人交易機構，有較多的投資規範，例如：法規、市場胃納量及基金持股比例…等限制，無法過於頻繁的交易及標的轉換，故法人交易機構，皆透過資產配置 (Assest Allocation)的觀念來進行部位的建立及資金的配置規劃，以達到其想要的投資報酬績效。
本研究之研究樣本採取美國最大ETF發行商iShare所推薦的核心配置型ETF作為研究標的，投資期間標的選擇以動能策略 (Momentum Strategy)為基礎，再把投資標以資產配置之股債比例60/40法則來作為 Black-Litterman的市場權重設定，並透過 Black-Litterman資產配置模組來重新計算資產配置權重，最後分別比較三種績效指標的績效穩定度。本研究發現透過 Black-Litterman資產配置模組可以讓投資組合績效更為穩定，降低了每單位報酬所承擔的風險。最後研究者提出心目中對「保守」、「穩健」、「積極」…等三種資產配置策略。

　　In recent years, the popularization of IT software and hardware has gradually defied traditional conceptions of investment and aroused a new idea of Quantitative Investment in investment markets. With the idea of Big Data and the introduction of the AI algorithms, Quantitative Investment successfully rode on the FinTech trend, leading to new investment concepts like Robo-advisor. The widely use of technology in Financial transaction has speeded up the market change and made each financial trading team accelerate the development and the adoption of Trading Strategies. However, the decade-long concept of Asset Allocation still remains steady in the fast-changing financial environment. Large Institutional investors have more investment restrictions such as regulations, market capacity, the fund shareholding proportion, and so on. Owing to this, both the corporates and institutions work on the make position and the allocation of funds throughout the concept of Asset Allocation to reach the ideal performance.
The study took iShares Core Allocation ETF, which is recommended by the largest ETF issuer in the US, as the target sample. It chose the target on the basis of Momentum Strategy during the investment period, setting the market weight according to the 60/40 stock-and-bond weight rule, recalculating the weight of Asset Allocation with the approach of the Black-Litterman model, and then evaluate the performance stability with 3 Key Performance Indicators. The research found that the Black-Litterman model could create more stable performance, reduce the risk that each unit of expected return might take. In the end of the research are three Asset Allocation Strategies, showing the conservative type, stable type or the aggressive type of investment approaches.

中文期刊/書籍/論文
1. 劉宗聖及歐宏杰 (2005) ETF 指數股票型基金，高寶出版社。
2. 劉宗聖、黃昭棠、張明珠、張勝原、賴盈良、呂彥慧及黃漢昌 (2017) 機器人崛起：AI人工智慧理財新標竿，經濟日報出版社。
3. 康睿瑾 (2008)投資組合的最適持有期間研究，國立暨南國際大學財金所碩士論文。
4. 蔡姁靜 (2005)考慮隨機利率與跳躍事件下的動態資產配置，國立暨南國際大學財金所碩士論文。
5. 洪慶昇 (2003)不同風險預測模式之投資組合績效比較－以國際資產配置為例，樹德科技大學金融保險研究所碩士論文。
6. 黃俞翔 (2012)結合交易點預測之動態投資組合管理系統，國立中央大學資工所碩士論文。
7. 許筌鈞 (2011)利用動能策略建構 Black-Litterman模型之投資組合有效性分析—以台灣50指數為例，亞洲大學財金所碩士論文。
8. 陳柏青 (2008) Black-Litterman模型於投資型保單的資產配置-以國泰創世紀保單為例，銘傳大學EMBA論文。
9. 林煜恩 (2005)台灣共同基金聰明錢效果與動能投資策略，國立東華大學企管系碩士論文。
10. 游奕琪 (1999)台灣股市產業與價格動能策略關聯性之實證研究，政治大學財管所碩士論文。
11. 蔡尹詠 (2007)台灣機構投資人動能交易與績效之研究，國立高雄應用科技大學金融資訊研究所碩士論文。
12. 劉志諒 (2001)股市動能投資策略報酬來源之研究，國立中興大學企管所碩士論文。
13. 李香怡 (2001)信用評等與動能策略之分析，國立高雄應用科技大學商務經營研究所碩士論文。
14. 陳建甫 (2011)動能投資策略於ETF 50成分股的績效分析，國立高雄第一科技大學財務管理研究所碩士論文。
15. 蓋瑞‧安東納奇、 黃嘉斌 (2016)雙動能投資：高報酬低風險策略，寰宇出版社。

英文期刊/書籍/論文
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4. Jensen, M. C. (1968),“The Performance of Mutual Funds in the Period 1945-64”, Journal of Finance, Vol. 23, pp. 389-416.
5. Markowitz (1952).Portfolio Selection.The Journal of Finance, Vol. 7, No. 1. (Mar., 1952), pp. 77-91.
6. 45.Markowitz, H. M., “Portfolio Selection,” Journal of Finance, 1952. Vol.7, pp77-91.
7. 46.Markowitz, H. M., “Portfolio Selection,” Basil Blackwell, Cambridge, Massachusetts, 1991.
8. Brocato, J., & Steed, S. (1998). Optimal asset allocation over the business cycle. Financial Review, 33 (3), 129-148.
9. Brinson, G. P., Hood, L. R. & Beebower, G. L., “Determinants of Portfolio Performance,” Financial Analysts Journal, July/August 1986. Vol.42, Iss.4, pp. 33-44.
10. Michaud, R. (1989), “The Markowitz Optimization Enigma: Is ‘Optimized’ Optimal?” Financial Analysts Journal, January/February, Vol. 45, No. 1, pp. 32-42.
11. Idzorek, T. (2004),”A Step-By-Step Guide to the Black-Litterman Model.”
12. Sharpe, W. F., 1964, Capital asset prices: a theory of market equilibrium under conditions of risk, Journal of Finance, 19 (3), pp. 425-442.
13. Kooli, M. and Selam, M., 2010, Revisiting the Black-Litterman model: the case of hedge funds, Journal of Derivatives and Hedge Funds, 16 (1), pp. 70-81.
14. Jagadeesh, N., and Titman, S., 1993, “Returns to Buying Winners and Selling Losers: Implication for Market Efficiency,” Journal of finance 48, 65-91.
15. Louis K.C.Chen, Narasimhan Jegadeesh, and Josef Lakonishok, 1996 ,Momentum strategies, Journal of Finance 51,p 1681-1713.
16. Tobias J.Moskowitz and Mark Grinblatt, 1999 , Do Industries explain momentum, Journal of Finance 54,p 1249-1290.
17. Rouwenhorst K. Geert, 1998, “International momentum strategies,” Journal of Finance, vol. 53, pp. 267-284.
18. Rouwenhorst, K. Geert, 1999, “Local Return Factors and Turnover in Emerging Stock Markets,” Journal of Finance, vol. 54, no. 4, pp. 1439-1464.
19. Cooper, M.J., Gutierrez, R.C., and Hameed, A., 2004, “Market States and Momentum,” Journal of Finance, vol. 59, pp.1345-1365.
20. Daniel, K., Hirshleifer, D., and Subrahmanyam, A., (1998). Investor psychology and security market under- and overreactions, Journal of Finance 53, 1839-1885.
21. Grundy, B., and Martin, S., (1998). Understanding the nature of the risks and the source of the rewards to momentum investing, Review of Financial Studies 14, 29-78.