電子類股交易值佔大盤交易值約60%，近年來更不斷成長，即見其市場重要性，故本研究以電子類指指數為標竿指數，以多因子模型與非流動性指標為依歸，用以區別股性與客觀的評分排名，最後透過二次規劃求解最適權重，建構目標追蹤誤差3%的電子類股增值型指數基金。
本研究以主成分分析法綜合多因子模型與流動性效果，實證結果發現多因子模型在樣本期間2004/1到2009/12的平均解釋能力為52.4%，其中以累積因子溢酬測試因子穩定性發現穩定因子如下: 槓桿因子、價值因子、價值成長比因子、盈餘品質因子。以截面回歸 (Cross-regression)測試非流動性指標的有效性，實證發現子期間顯著解釋與報酬預期為正向關係。最後，透過主成分分析，結合多因子模型與非流動性指標效果，在適當的限制下輔以二次規劃求解，本研究得出在樣本期間內，電子類股增值型指數基金其資訊比率0.69、追蹤誤差3%。進一步支持多因子模型與非流動性指標的增值效果。

In Taiwan, the trading value of electronics related stocks makes up over 60% of Taiwan stock market and has grown gradually to the recent high of 70.03% in Dec. 2009. The high correlation between the TAIEX and TAIEX Electronic Index raises our interest to build a fund aiming to outperform TAIEX Electronic Index performance with similar risk as index by constructing an enhanced fund. We are keen to investigate if active management gain higher return than passive one according to our empirical study. This paper presents a combination effect of multi-factor model in the electronic sector and illiquidity, that expected returns are increasing in illiquidity. The major outcome is that we construct single industry Multi-Factor Model (MFM) and test for its prediction ability. The other is we form a proxy for illiquidity and incorporate it into the multi-factor model using Principal Component Analysis (PCA). The objective of this study is to discover mispriced stocks and make adjustments to build an enhanced fund, targeting 3% tracking error.
As a result, the most stable factors based on cumulative return in forecasting electronic sector are Leverage, Value3, ValueToGrowth, EarningQulity respectively. The average explanatory power of electronic multi-factor model (ELE-MFM) is around 52.4% over the sample from 2004/1 to 2009/12. For illiquidity measure, we run cross-regression of stock return on illiquidity and other stock characteristics from the period of 2000/1 to 2009/12. What we find is sub-period is the significant evidence for the work of illiquidity. With the PCA combination of electronic multi-factor model and illiquidity measure into scores coming from the first principal component, we rank stocks through it. With the appropriate constraint rules added into our quadratic programming, the portfolio using the techniques combining multi-factor model and liquidity measures shows IR 0.69, TE 3% and Alpha 2.04% in our sample period. The work of the electronic Multi-Factor Model (MFM) and the illiquidity measure showing satisfactory result support enhanced skills.

I.Introduction 6
1.1 Introduction and Background 6
1.2 Active and Passive management 9
1.3 The objective of this study 10
1.4 Research structure 11
II.Literature review 12
2.1 Modern portfolio theory 12
2.2 Illiquidity and stock return 13
2.3 The method of enhancement 14
III.Methodology 16
3.1 Data description 16
3.2 The stocks included 18
3.3 Benchmark 18
3.4 Multi-factor risk model (MFM) 19
3.5 Illiquidity 24
3.5 PCA Scores 27
3.7 Optimized weight 29
3.8 Performance analysis 30
IV.Empirical results 32
4.1 MFM ranking 32
4.2 Illiquidity ranking 39
4.3 Portfolio performance 44
4.4 Sensitivity analysis 49
V.Conclusion and Suggestions 55
5.1 Conclusion 55
5.2 Suggestion 57
REFERENCES 58
APPENDIX 61

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