本研究透過結合基本面分析以及集成學習的方式，在上市個股當中建立最適的投資組合，首先藉由兩個基本面的因子：本益比（PE Ratio）與股價淨值比（PB Ratio）篩選個股，接著，藉由包含支持向量迴歸模型（SVR）及多層感知器（MLP）的集成學習模型預測個股的數日後報酬，藉此挖掘出有潛力的個股進行投資組合的配置，並藉由歷史回測分析其績效。實驗結果顯示，在基本面篩選下的投資組合績效高於大盤指數績效，而加入集成學習模型後可以進一步提升其報酬績效。

The purpose of this study is trying to combine fundamental analysis and ensemble learning model to construct an optimal portfolio with listed stocks. First, use two fundamental factor, price-to-equity ratio and price-to-book ratio, to select potential stocks, and then predict the future return of each stock through ensemble learning model which including SVR and MLP to construct an optimal portfolio. Finally, analysis the performance according history stock data. The result shows that the portfolio constructed by only fundamental analysis outperforms Taiwan Capitalization Weighted Stock Index (TAIEX), and the portfolio constructed by fundamental analysis and ensemble learning outperforms the portfolio constructed by only fundamental analysis.

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