本研究目的為討論高收益債券的因子投資，並透過增強投資組合提供給投資者相較被動投資表現更好的高收益債投資組合產品。
因為資訊揭露較不透明、價格更難估計且有限壽命等諸多因素，皆使處理債券資訊更為繁雜，而這也使得因子投資在債券上的研究相較股票市場研究慢。近來隨著眾多資產管理公司推出債券 ETF 商品並受到投資者愛戴，學術界與業界越佳關注固定收益投資組合的分析，而這也成為本研究探討高收益債券因子投資的動機。
我們首先藉由 Barra Risk Model Handbook (2007) 建構純因子投資組合並將市場風險做歸因，接著用五分位數因子投資組合及訊息係數衡量因子是否有效，研究發現較有效的五個因子，分別為 Size、Value、Carry、Liquidity 與 Duration 皆能帶來相較指數更高的風險調整後報酬與較小的風險回撤，其中又以Value 因子增值指數的 alpha 表現最佳，高達 0.85%，而其在 Tracking error 上相較其他因子增值也無較明顯，因此 Value 具有 0.97 Information ratio 的好表現。
最後，我們也建構全因子與僅採用較佳因子的不同多因子投資組合，研究結果顯示當加入了因子擇時的策略後，採用較佳因子搭配估值與積極評分的動能擇時策略可以得到 1.36 的 Sharpe ratio，而這也是本研究表現最好的風險調整後報酬 ，並且此策略也將單純動能擇時有最大風險回撤 19.74% 的問題改善至19.65%。

This study aims to discuss factor investing in high yield bonds and provide investors with enhanced portfolio products that perform better in high yield bonds than passive investments.
Factor investing in bonds has been slower than equity market research because of the complexity of handling bond information due to several factors, including less transparent information disclosure, more difficult price estimation, and limited life. With the recent launch of bond ETFs by many asset management firms and their popularity among investors, academics and the industry are increasingly interested in the analysis of fixed income portfolios, which is the motivation for this study to examine factor investing in high yield bonds.
We first construct a pure factor portfolio using the Barra Risk Model Handbook (2007) and attribute the market risk. Then, we measured the effectiveness of the factors using quintile factor portfolios and information coefficients. The five more effective factors are size, value, carry, liquidity and duration, which can generate higher risk-adjusted returns and lower drawdown than the index. Among them, value has the best alpha performance with 0.85%, and its tracking error is not significantly higher than other factors, so value has a good performance with a 0.97 information ratio.
Finally, we construct different multi-factor portfolios with all-factor and partial factor screening enhanced portfolios. The result of this study shows that using partial factor screening enhanced portfolio with valuation and aggressive-scoring momentum timing achieves a Sharpe ratio of 1.36, which is the best risk-adjusted return in this study. In addition, this strategy also improves the maximum drawdown of 19.74% to 19.65% for momentum alone.

論文審定書 i
摘要 ii
Abstract iii
Contents v
List of Figures viii
List of Tables ix
Chapter 1 Introduction 1
1-1 Background 1
1-2 Motivation for Research 4
1-3 Research Purpose 5
Chapter 2 Literature Review 7
2-1 Modern Portfolio Theory 7
2-2 Multi-Factor Model 8
2-3 The Factors of Fixed-Income Securities 9
2-4 Factor Timing Model 12
Chapter 3 Data and Methodology 14
3-1 Empirical Process 14
3-2 Data Description 16
3-3 Definition of Bond Factors 19
3-3-1 Size 19
3-3-2 Value 19
3-3-4 Momentum 20
3-3-5 Volatility 21
3-3-6 Liquidity 21
3-3-7 Interest-Rate Risk Parameters: Duration and Convexity 22
3-4 Establishing Factor Exposure 22
3-4-1 Missing Value Process 22
3-4-2 Standardizing Descriptors 23
3-4-3 Compressing Outliers 23
3-5 Multi-factor Model 24
3-6 Quantile Portfolio and Information Coefficient 28
3-7 Factor Enhanced Model 30
3-8 Factor Timing Method 32
3-8-1 Momentum timing 32
3-8-2 Value timing: 33
3-8-3 Combined strategy 34
3-8-4 Combining the results of the factor timing weights into the model 35
Chapter 4 Empirical Result 37
4-1 Empirical Results of the Factor Model 37
4-1-1 Multicollinearity Test and Gram–Schmidt process 37
4-1-1. Factor Result 38
4-1-2 Explanatory Power of the Model 43
4-2 Single-Factor Model 44
4-2-1 Quantile Portfolio and Information Coefficient Analysis 44
4-2-2 Index Tracking and Single-Factor Enhancing 51
4-3 Multi-Factor Model and Factor Timing 54
4-3-1 Multi-Factor Model 54
4-3-2 Factor Timing 55
Chapter 5 Conclusions and Suggestions 63
5-1 Conclusions 63
5-2 Suggestions 64
References 67

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