台灣Smart Beta ETF近年受到投資者歡迎，在2021年時的自然成長率高達75%，高居亞洲首位，遠高於亞洲及太平洋地區第二的澳洲的20.2%。高成長率使得規模也超越中國，成為亞洲及太平洋地區第三大市場，僅次於日本及澳洲。而台灣Smart Beta ETF占總ETF比重也達到9.3%。
本研究以S&P500歷史成分股為股票池，建立多因子擇時投資組合。首先參照Barra USE4估算要素並合成風格因子，再進一步求出因子報酬與風險預測矩陣。用擴展窗口法(the expanding window approach)訓練XGBoost模型以總體經濟指標等特徵來預測未來風格因子報酬，並將其作為Black-Litterman模型中的投資人觀點，以每個月一次的頻率做動態調整，進行2013年至2021年的績效回測並對各個參數做敏感度分析。經由實證發現（1）多因子擇時投資組合的年化報酬、年化波動率、夏普比率都比S&P500指數來得更好，不過月周轉率高達55%；（2）Barra USE4風險預測模型能有效降低投資組合的年化波動率；（3）XGBoost模型能有效預測未來風格因子報酬的相對關係。

Smart Beta ETFs are becoming more and more popular among investors in Taiwan. The natural growth rate is 75% in 2021, which is the highest in the Asia-Pacific. Meanwhile, the rate in Taiwan is 75%; much higher than second-placer Australia's 20.2%. Taiwan also surpassed China’s growth rate, becoming the third largest market in the Asia-Pacific. This is just behind Japan and Australia. Smart Beta ETFs in Taiwan also account for 9.3% of total ETF market shares.
The forecast results are used as the investor's view in the Black-Litterman model, and the portfolio is dynamically adjusted once a month. Below is the observed sensitivity analysis of the parameters and portfolio performance from 2013 to 2021: (1) the annualized return, annualized volatility, and Sharpe ratio of the multi-factor portfolio outperform the S&P500 index, but the monthly turnover rate is as high as 55%; (2) the Barra USE4 risk prediction model can effectively reduce the annualized portfolio volatility; and (3) the XGBoost model can effectively predict the relative relationship among the future style factor returns.

論文審定書 i
摘要 ii
ABSTRACT iii
Content iv
List of Figures vii
List of Tables viii
1. Introduction 1
1-1. General Background Information and Research Motivation 1
1-2. Research Purpose 3
1-3. Research Framework 4
2. Literature review 6
2-1. Capital Asset Pricing Model 6
2-2. Multifactor Model 7
2-3. Factor Timing 8
2-3-1. Factor Valuation Timing 9
2-3-2. Factor Momentum Timing 9
2-3-3. Factor Volatility Timing 10
2-3-4. Market Sentiment Timing 10
2-3-5. Economic Cycle Timing 10
2-4. Machine Learning 11
2-4-1. General Linear Model 12
2-4-2. Nonlinear Model 13
2-4-3. Deep Learning 14
2-4-4. Machine Learning in Asset Pricing 14
3. Research Methodology 16
3-1. Research Process 16
3-2. Research Data 20
3-2-1. Usage Data and Period Range 20
3-2-2. Building Factor and Sorting Data 21
3-3. BARRA USE4 Model 25
3-3-1. Calculating Factor Returns 25
3-3-2. Modeling Covariance Matrices 27
3-3-3. Eigenfactor Risk Adjustment 28
3-4. XGBoost Model 31
3-5. Black-Litterman Model 35
4. Empirical Results 46
4-1. Information Gain Ratio 46
4-2. Investment Portfolio 48
4-3. Sensitivity Analysis 52
5. Conclusions and Suggestions 61
5-1. Conclusions 61
5-2. Suggestions 64
References 66
Appendix A　Tables and Figures 69
Appendix B　Style Factor Characteristic Importance Table 76

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