本研究嘗試使用隨機森林與XGBoost兩種機器學習分類模型於預測資產價格走勢，作為量化投資人觀點之依據，並結合Black-Litterman模型建構投資組合。本研究採用之基礎資產為台灣上市ETF，特徵因子選取價量相關的技術指標與台灣特有的籌碼面資料，來預測資產價格漲跌的方向及幅度，後將預測結果轉換為Black-Litterman模型的投資人觀點進行資產配置，並比較兩種機器學習方法在不同目標函數、不同限制條件與不同風險趨避係數下，所建立相應的投資組合其績效表現之優劣。實證結果顯示：（1）兩種機器學習投資組合在測試期間內，以各績效指標衡量，絕大多數優於本研究之基準投資組合；（2）以XGBoost建構之投資組合，其績效表現皆優於以隨機森林建構之投資組合；（3）以極大化效用函數形成之投資組合，其績效表現皆優於極大化Sharpe Ratio投資組合；（4）風險趨避係數（λ）大致上與報酬呈現反向關係，而與風險指標如波動度與MDD則呈現正向關係。其中，使用XGBoost並以極大化效用函數所得之投資組合，為本研究績效最佳的投資組合。

We attempt to use two machine learning classification models, random forest and XGBoost, to capture the trend of asset prices, as a basis for quantifying investors' views, and combine with the Black-Litterman model to construct portfolios. The underlying assets used in our study are Taiwan-listed ETFs, selected features in machine learnings are price-volume-related technical indicators and Taiwan-unique institutional investors Factor to predict the trends and fluctuations of asset prices, and then convert the predicted results into investor's views of Black-Litterman model to conduct the asset allocation process. Next, we analyze and compare the performance of the corresponding portfolios established by two machine learning algorithms under different objective functions, different constraints and different risk aversion coefficients. During the test period, We find that：(1) measured by various performance evaluation indicators, portfolios formed by two machine learning algorithms outperform the benchmark portfolios in our study, (2) performance of the portfolios constructed by XGBoost outperform the portfolios constructed by random forest, (3) performance of the portfolios formed by maximizing utility function outperform the maximized Sharpe Ratio portfolios, (4) The risk aversion coefficient（λ）is approximately inversely related to returns, while it is positively related to risk indicators such as volatility and MDD. Lastly, the portfolio generated from XGBoost by maximizing the utility function gains the best performance among all portfolios in our study.

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