本研究參考Barra模型利用横斷面分析法，以實務為導向建構台灣股市的多因子模型。主要目的為二個，第一是找出能預測股票下期報酬之因子;第二則是利用顯著之因子建構多因子模型，並模擬市場中立策略以進行績效回測。研究發現，不論採取任何的投資策略，本研究之績效回測結果皆優於大盤。

The objective of this study is to discover the sources of securities return in forecasting stock return from different sides of potential factors including fundamental and market information. We test currency sensitivity, earnings variability, earnings yield, growth, leverage, trading activity, momentum, size, value, volatility, capital spending discipline, free cash flow, efficiency, solvency, earnings quality, corporate finance policy and technical 17 factors basing on different factor dimensions in this study. We construct a Taiwan multi-factor model by using the most significant factors for universal stocks according to 0HMSCI Barra’s Multiple-Factor Modeling process, and then apply market neutral investment to build portfolios for performance back-testing.

As a result, the most significant top five factors in forecasting are respectively “Volatility2,” “Earnings Quality1,” “Trading1,” “Volatility1” and “Growth1” factors. In addition, we find the most useless bottom four factors in forecasting are respectively “Size1,” “Earning Yield1,” “Value1,” and “Capital Spending1.” No matter which strategies we adopt to build the portfolio, the Sharpe ratios of back-testing performance are all higher than the Benchmark, and all bring stable and consistent performance. It actually proves that this model is robust.

I. INTRODUCTION 1
1. Introduction and Background 1
2. Pros & Cons of Quantitative Management 2
3. Multi-Factor Model and Factor Category 4
4. Market Neutral Strategy 5
5. The Objective of This Study 5
II. LITERATURE REVIEW 7
1. Modern Portfolio Theory 7
2. Multi-Factor Model 10
3. The Mathematics of Multi-Factor Model 12
4. Composite Factors by Principal Component Analysis 15
III. METHODOLOGY 17
1. Analytical Framework 17
2. Step One: Compute and Determine Descriptors 20
3. Step Two: Industry Allocation 26
4. Step Three: Handling The Outliers 20
5. Step Four: Descriptors Standardization 21
6. Step Five: Test and Select The Descriptors 21
7. Step Six: Develop Composite Risk Factors by PCA 22
8. Step Seven: Test and Select Significant Factors 26
9. Step Eight : Estimate Factor Return and MFM 27
10. Step Nine : Handle Heteroscedasticity and Multicollinearity problems 28
IV. EMPIRICAL STUDY 31
1. Data 31
2. Sample 31
3. Empirical Result 34
V. CONCLUSION 61
REFERENCES 67
APPENDIX A : Descriptor Definitions 71
APPENDIX B : Descriptor Significance Test 90
APPENDIX C : Principal Component Result 94
APPENDIX D : Detailed Performance Analysis 101

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