多因子模型主要被用來做為解釋資產報酬及風險之工具，而模型操作的概念為利用共同因子去分析因子和資產報酬間之關係，以進一步對資產報酬做分析以及預測；在此操作概念下，共同因子的質量操控多因子模型的效果。長久以來，在多因子模型領域裡有許多研究著重於找尋新的共同因子。Miller (2006) 提出綜合多因子模型概念， 以基本面多因子模型為基礎模型，在此之上再建立統計因子模型，使得在綜合多因子模型裡得以包含基本面共同因子以及統計面共同因子，以增加多因子模型之解釋能力。本研究以Miller (2006)所提出之架構為研究基礎，建立完整的綜合多因子模型，並且利用此綜合因子模型找出隱藏在台灣股票市場裡的共同因子。透過本研究建構之綜合多因子模型，我們找出在台灣股票市場中存在代表台灣全體市場的市場因子，以及由電子相關產業所組成的泛電子產業因子。

A Multifactor model is used to explain asset return and risk and its explanatory power depends on common factors that the model uses. Researchers strive to find reasonable factors to enhance multifactor model’s efficiency. However, there are still some unknown factors to be discovered. Miller (2006) presents a general concept and structure of hybrid factor model. The study follows the idea of Miller (2006) and aims to build a complete flow of constructing hybrid factor model that is based on fundamental factor model and statistical factor models. We also apply the hybrid factor model to the Taiwan stock market.

We assume that a fundamental factor model is already developed and therefore this study focuses on building the second stage, statistical factor model. Principal Component Analysis is used to form statistical factor and spectral decomposition is used to prepare data for principal component analysis. Those methods are applied to stocks on the Taiwan Stock Exchange in the period of January 1, 2000 to December 31, 2009. This study presents a complete construction flow of hybrid factor models and further confirms that a hybrid factor model is able to find missing factors in a developing market such as Taiwan’s stock market. The study also discovers that the missing factors might be market factor and extensive electronic industry factor.

ABSTRACT i
I. INTRODUCTION 1
1. Introduction and Background 1
2. Motivation and Purpose of the Study 3
3. Summary of the Study 4
II. LITERATURE REVIEW 5
1. Modern Portfolio Theory 5
2. Multi-Factor Model 6
2- 1. Macroeconomic Factor Model 7
2- 2. Fundamental Factor Model 8
2- 3. Statistical factor model 9
3. Hybrid Factor Model 10
4. Spectral Decomposition 11
III. METHODOLOGY 13
1. Analytical Framework 13
2. Analytical Flow 16
2- 1. Standardize Asset Residual Returns 17
2- 2. Estimate Covariance Matrix 18
2- 3. Estimate Statistical Factor Exposures by PCA 20
2- 4. Estimate Statistical Factor Return 22
2- 5. Estimate Hybrid Factor Model Covariance 23
2- 6. Model Evaluation by the Bias Test 24
IV. EMPIRICAL STUDY 26
1. Data 26
2. Sample 27
3. Empirical Result 28
3- 1. Sample Result 29
3- 2. Explanatory Power of Principal Components 31
3- 3. Statistical Factors Evaluation 32
3- 4. Correlation between Fundamental Factor and Statistical Factor 34
3- 5. Identify Statistical Factors by Corresponding Eigenvectors 40
3- 6. Application of Hybrid Factor model in Constructing Portfolio 42
V. CONCLUSION 47
1. Summary and Findings 47
2. Conclusions 48
3. Recommendations 49
APPENDIX A 51
APPENDIX B 52
APPENDIX C 55
APPENDIX D 61
APPENDIX E 67
REFERENCES 73

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