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.

Many enhanced index funds are based on a quantitative model to control active risk and to acquire active return. In this thesis we first construct a multiple-factor model (MFM) and then use statistical methods to evaluate the significance and stability of factor explanatory power. Significant and stable factors are utilized to fine tune weights of T50 index fund portfolio by an intuitive weight allocation model to achieve the effect of return enhancement. Empirical studies show that the multiple-factor model can explain the excess stock return effectively; the average R-Square of multiple-factor model reaches 49%. After analyzing the sensitivity of parameter of enhanced index weight allocation, the study finds that the original weight retention rate has linear relationship with active return and active risk of the T50 index fund. Adjusting the retention rate allows us to control the active return and active risk of T50 index fund. Furthermore, adjusting the original weight retention rate according to the Adj-R2 of multiple-risk factor model can effectively improve the stability of active return. The study finds also that the expected rates of return which are calculated by multiple-risk factor model could not differentiate among future performance of the first your guarantee portfolios. Thus, the study adjusts the range of weight allocation to T50 constituent stocks with higher and lower expected return rates. The result shows that this adjustment increased the IR of the enhanced index funds.