本研究融合因子投資、因子擇時、Black-Litterman資產配置模型等市場主流投資想法，以台灣上市股票作為資產池，建構投資策略，目標是建構穩健的投資組合，動態篩選有效因子，將有效因子融入權重優化過程，使得最終的資產配置權重能同時反映個股的優劣以及個股間相關性，動態配置資產。
本研究之目的及研究成果，以下分述之 :
•探討機器學習結合因子擇時模型之有效性
樣本外期間，因子擇時模型準確率約為55%，當因子本身對於下期報酬有顯著影響力時，準確率更高。動量因子在樣本期間對於下期報酬不具影響力，然而其因子擇時模型則有60%以上的準確率，代表模型可以預測動量因子的有效性，具有擇時能力。
•確認以橫斷面因子模型作為Black-Litterman之量化投資人觀點的可行性
以Long-Short五分位數投資組合策略，分析分析有效合成因子之有效性，策略績效表現顯示，經因子擇時模型之有效合成因子其策略勝率高達74%，夏普比率為1.31。
•研究結合因子擇時、量化投資人觀點、Black-Litterman權重配置而形成的投資策略之績效表現。
考慮交易稅負，極大化夏普比率形成的投資組合，夏普比率為0.8，高於未經因子擇時模型之投資組合的夏普比率約1.78倍，統計上顯著異於大盤報酬，同時有較低的最大回撤比率。

In this study, we take the stocks listed on TSE as assets pool and construct a robust portfolio strategy with novel investment ideas, including factor investing, factor timing and Black-Litterman model. With this strategy, we can dynamically detect the efficient factors and composite these factors into single index to identify future performance of a stock. Also, by combining this index and portfolio optimizer, the weight dynamically changes due to this index and the correlations structure between stocks.
The purposes and results of the study are listed below :
•Show the efficacy of machine learning factor timing model.
The averaging accuracy of factor timing models is about 0.55. The result also shows the fact that accuracy of factor model is positive correlative with degree of a factor’s efficiency.
•Check the feasibility of quantitative investors’ view of Black-Litterman derived from cross-sectional factor model.
We analyze efficacy of the efficient composite factor through quintile portfolio. The win rate of long-short strategy is 0.74, higher than benchmark. The Sharpe ratio is around 1.31 and beats the benchmark.
•Show the performance of portfolio strategy
The Sharpe ratio of maximum Sharpe ratios strategy hits 0.8, approximately 1.78 times that of benchmark. Also, the mean return of this strategy statistically significantly differs from TAIEX.

中文摘要 I
ABSTRACT II
表次 IV
圖次 V
第一章 緒論 1
第一節 研究背景及動機 1
第二節 研究目的 2
第三節 研究架構 2
第二章 文獻探討 4
第一節 現代投資組合理論 4
第二節 資產定價模型 4
第三節 多因子模型 5
第四節 因子擇時模型 5
第三章 研究方法 7
第一節 研究流程 7
第二節 資料說明 10
第三節 隨機森林因子擇時模型 12
第四節 橫斷面因子模型 22
第五節 BLACK-LITTERMAN模型 24
第六節 投資組合績效評估 34
第四章 實證結果 36
第一節 隨機森林因子擇時模型實證結果 36
第二節 量化投資人觀點之預期報酬分析 40
第三節 投資組合績效回測 41
第五章 結論及建議 45
第一節 結論 45
第二節 建議及後續研究 45
參考文獻 47
附錄一、因子定義及計算方式 49
附錄二、輸入變數敘述統計表-因子投組橫斷面特徵 53
附錄三、輸入變數敘述統計表-因子投組風險集中度及總體經濟及市場風險曝險 61
附錄四、投資組合權重比較圖表 69

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