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.

none

本研究的目的為建立一套適用於台灣股市的主動式量化投資策略。本研究利用多因子 Alpha 模型為分析架構，試圖掌握多維度的股價影響因子，以資訊係數（Information Coefficient）、T-test of ICs、成功率（Success rate）以及 Quintile 累積報酬做因子有效性的檢定，篩選出穩定且有效解釋股價報酬的月頻率因子，再組合因子形成Alpha 股票評分，Alpha 可拆解成三部分，包括市場波動度、因子預測下一期報酬的能力以及因子的獲利能力。本論文以此評分做為股票投資權重的依據，建構一個以台灣中型 100 指數為標竿指數的投資組合。實證結果發現，此主動式量化投資策略能夠有效擊敗標竿指數，獲得平均每個月 3.7%的超額報酬。 　　本研究並嘗試以設定原始權重保留率的方法，控制追蹤誤差以降低週轉率與交易成本，實證結果發現，此方法可有效降低追蹤誤差，但隨著保留率提升，資訊比率（Information Ratio）與投資組合的超額報酬將降低。 / The objective of this study is to build an investment process of active quantitative stock selection model. In this study, we use the Alpha Multi-factor model to find a multitude of factors which are significantly relative to the stock return. The tests we conduct to select the factors that end up in the final multi-factor model are monthly Information Coefficient, T-test of ICs, success rate and quintile cumulative return. Then we examine how to optimally combine correlated factors and calculate the Alpha score for each stock for each period. Alpha is Volatility times IC times Score. Volatility is the cross-sectional volatility of the residual return. IC is the predictive power of the model. And Score are the cross-sectional scores for each stock. We utilize a simple method to construct the portfolio that uses the Alpha score to adjust the weight of component stocks in the benchmark. The empirical result reveals that this investment process successfully outperform the Taiwan Mid-Cap 100 Index benchmark. Moreover, this study tries to decrease the turnover rate and transaction costs by controlling the tracking error. We set the original weight retention rate of the benchmark to control the tracking error. The empirical result reveals that the method works. But as the retention rate rises, the Information ratio and the excess return drops.