本研究為探討因子投資如何應用於投資等級債券市場。目前已有許多股票因子投資的相關文獻及實務應用，但債券因子的發展卻相對緩慢，因為其資訊不透明的特性且債券條款更為複雜，導致債券的估值也較不精確。目前公司債投資仍以總體、產業及信用分析為主，但債券因子投資具有極大的發展價值及潛力，亦值得深耕研究。
欲瞭解哪些因子可以捕捉共同因素的風險或獲取超額報酬，應以結構式模型分解報酬之收益來源。本文採用Barra Risk Model Handbook (2007)純因子模型架構，除了沿用Jong and Fabozzi (2020)和Houweling and Zundert (2017)之因子，同時還納入槓桿、凸性及VaR因子來建構因子模型。在得知各因子所能獲取的因子報酬後，本文採用五分位數因子投資組合及訊息係數衡量因子是否有效，並挑選有效因子建構單因子及多因子之增值投資組合，最後比較其績效和給予建議。
研究結果發現槓桿、凸性和VaR因子能夠有效地作為區分債券報酬的指標，經過投資組合之因子曝險調整後，能獲得更高的風險調整後報酬以及正的資訊比率。然而，由純因子模型的結果得知單位曝險於VaR因子的投資組合並無法帶來超額報酬，因此推論VaR增值投組的超額報酬可能源自於對其他因子的曝險。此外，使用多因子構成的指數增值投資組合也有優於標竿投資組合的夏普比率，並且能有效地降低投資組合之最大回撤率。

This research is to explore how factor investing can be applied to the portfolio management of investment-grade bonds. There are currently many relevant reviews and practical applications of stock factor investing. However, the development of bond factors is still limited due to opaque and more complicated trading conditions of the bond market. At present, strategies for corporate bond investment are still dominated by macroeconomic, industry, and credit analyses, but bond factor investment has great development value and potential.
In order to understand what factors can capture common factor risks or obtain excess returns, a structural model should continue to be studied to decompose sources of returns. This study adopts the pure factor model as described in the Barra Risk Model Handbook (2007) and combines Jong and Fabozzi (2020) and Houweling and Zundert (2017) factors. In addition, this study also incorporates leverage, convexity, and value-at-risk factors to construct a factor model. After exploring factor returns, this study uses the quintile portfolio and the information coefficient to measure whether the factors are practical and selects influential factors to construct single-factor and multi-factor enhanced portfolios. Finally, it compares their performances and provides suggestions.
The results of this study show that leverage, convexity, and VaR factors can be used to effectively distinguish bond returns. Through adjusting weights of investment portfolios in this study in accordance with their factor exposures, higher risk-adjusted returns are obtained. However, the results of our pure factor model show that excess returns on the VaR factor portfolio cannot be obtained by unit exposures. Therefore, our results may mean that excess returns on the enhanced portfolios may derive from other factor exposures. In addition, the enhanced index portfolio can be used to effectively reduce the portfolio's maximum drawdown.

摘要 ii
ABSTRACT iii
List of Tables vi
List of Figures viii
1. Introduction 1
1.1 Background 1
1.2 Motivation for Research 3
1.3 Research Purpose 6
2. Literature Review 7
2.1. Term Structure of Interest Rates 7
2.2. Factor Model and Asset Pricing 10
2.3. Corporate Bond Anomalies and Factors 12
2.4. Factor Timing Model 15
3. Data and Methodology 17
3.1 Empirical Process 17
3.2 Data Description 19
3.3 Nelson-Siegel Estimate Methodology 21
3.4 Corporate Bond Factors Definition 22
3.5 Establish Factor Exposure 29
3.6 Multi-Factor Model 29
3.7 Quintile Portfolio Analysis 32
3.8 Information Coefficient and Weighted Factor Scores 32
3.9 Factor Enhanced Method 34
4. Empirical Research 37
4.1 Empirical Results of the Factor Model 37
4.2 Single-Factor Portfolio 42
4.3 Multi-Factor Portfolio 60
5. Conclusion and Suggestions 65
5.1 Conclusion 65
5.2 Suggestion 66
Appendix 69
References 71

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