本研究目的為利用因子擇時模型建構增值型指數基金於台灣市場，文獻參考Miller, Li, Zhou和Giamouridis (2015) 提出的利用分類和回歸樹演算法來建構因子擇時模型。分類和回歸樹演算法是資料探勘中的一種經典演算法，提供了容易閱讀及理解的圖形樹輸出。我們利用此因子擇時模型過濾出在特定風險條件下之有效因子，並將多個有效因子標準化後合成複合因子。接下來，我們藉由排序複合因子篩選出表現良好及表現不佳的股票來建構在台灣市場的增值型指數基金。
本研究樣本訓練期間為2000年1月至2009年12月，回測期間為2010年1月至2017年3月，樣本所採用的股票為台灣上市公司。
由實證結果可以發現在台灣市場採用因子擇時模型的多空策略其績效比沒有使用因子擇時模型的年化報酬提高了3.5％，顯示出了因子擇時效果。另外使用排序法增值的增值型指數基金績效表現良好，資訊比率為1.2。

In this study, we aim to enhance index fund using factor timing model in the Tai-wan market. Based on Miller, Li, Zhou and Giamouridis (2015), we use the Classifica-tion and Regression Tree Method (CART) to construct factor timing model. CART is an important type of classification algorithm in data mining and provides output in a graphical tree form that is easy to read and understand. Next, we filter effective fun-damental factors using a factor timing model. In addition, we use a standardized ap-proach to composite factors. Finally, we construct an enhanced index portfolio using the composite factors.
The training data period is from January 2000 to December 2009 and the back-testing period is from January 2010 to March 2017. The sample stocks are the listed companies in Taiwan. The empirical results show that the performance of the long-short strategy with the factor timing model gives an annual rate of return of 3.5% better than that without the factor timing model. The enhanced index fund using the ranking method has good performance with an information ratio that is 1.2.

論文審定書 i
摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES viii
1. INTRODUCTION 1
1.1 Background Information 1
1.2 Research Purpose 2
1.3 Research Framework 3
2. LITERATURE REVIEW 4
2.1 Modern Portfolio Theory 4
2.2 Multi-Factor Model 5
2.3 Classification and Regression Tree 6
2.4 Factor Timing Model 8
3. METHODOLOGY 9
3.1 Analytical Procedures 9
3.2 Developing Input Data 11
3.3 Information Coefficient 16
3.4 Factor Timing Model Using CART 18
3.5 Portfolio Construction 18
3.5.1 Determine the stock pool 19
3.5.2 Composite effective factors 19
3.5.3 Transforming composite factor into factor loadings 21
3.6 Turnover Rate 25
3.7 Performance Analysis 26
3.7.1 Sharpe Ratio 26
3.7.2 Information Ratio 26
4. EMPIRICAL RESULTS 28
4.1 Data 28
4.2 Factor Timing Model 29
4.3 Comparison of the Model 31
4.3.1 Comparison of the using of factor timing 31
4.3.2 The factor timing model compare with Chueh (2017) method 34
4.4 Enhanced Index Fund Construction 37
4.4.1 The Triangle Method 38
4.4.2 The Ranking Method 40
5. CONCLUSION AND SUGGESTIONS 44
5.1 CONCLUSION 44
5.2 SUGGESTIONS 46
6. REFFERENCES 48

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