投資組合管理可簡單區分為兩大類的分析方法，分別是量化投資以及質化分析，台灣過去的主動式基金往往是使用傳統的質化分析方法，被動式基金由於計算過程的繁複，大多使用量化分析的方式，而這兩大類分析方式的結合卻是比較少見的。本篇論文的主要目的就是希望透過修改過去純量化選股模型的分析流程，提供量化選股模型一個可以同時考慮到基金經理人主觀判斷的接口，藉以結合兩種分析方式的優點。
我們藉由區分不同市場趨勢的方式，檢測在該趨勢下能夠有效預測股票報酬的要素因子組合，來改善原始一般化模型要素因子結構變異性過大的缺點，並提高模型的穩定度，同時透過不同要素因子組合的替換，保持模型的彈性以及對市場的適應性。
另外，我們透過相同的增值指數策略來建構投資組合，回測模型的績效表現，比較進階模型與原始一般化模型的分析結果對於投資組合的績效有何差異。
實證結果發現，進階模型的增值指數投資組合的資訊比率能夠達到0.72，相比之下，原始一般化模型只有0.41，且進階模型具有更加穩定的要素因子組成結構並能夠表現出更加穩定的績效成長。

While quantitative investment management has been extensively investigated and many models built in order to provide investment suggestions through quantitative analysis, the combination of quantitative and qualitative analysis is relatively unexplored. The objective of this study is to construct a quantitative stock selection model based on the standard model built by Hsu et al. (2011) which could improve the stability of descriptor and factor structures and the combinability of quantitative and qualitative analysis. The research focuses on the structure of effective factors and descriptors when faced with different types of market trends.
Furthermore, we test the performance of a Market Trend-Based Alpha Model (MTB alpha model) and compare with the standard alpha model. The strategy of portfolio construction is a TAIEX enhanced index fund.
We find the enhanced index portfolio constructed by the MTB model produces an information ratio of 0.72, which is much higher than the standard model ratio of 0.41. This finding suggests that a MTB model could not only improve performance but also make the descriptor and factor structures more stable and much more easily for managers’ adjusting.

論文審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
I. INTRODUCTION 1
1.1 Background information 1
1.2 Research purpose 6
1.3 Research topics and contributions 7
1.4 An overview of the paper 8
II. LITERATURE REVIEW 10
2.1 Modern portfolio theory 10
2.2 Multi-factor model 11
2.3 Information analysis 14
2.4 Optimal alpha model 16
III. METHODOLOGY 18
3.1 Analytical procedures 18
3.2 Research of descriptors 21
3.3 Setting a market variable to distinguish situations 23
3.4 Testing the descriptor effectiveness 25
3.5 Selecting descriptors under different situations 28
3.6 Computing optimal descriptor and factor weights 30
3.7 Combining the weekly and monthly alpha score 33
3.8 Smoothing Approach 34
3.9 Portfolio construction 36
IV. EMPIRICAL STUDY 41
4.1 Data 41
4.2 Sample 42
4.3 Empirical result 44
V. CONCLUSION 70
REPERENCES 74

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