In recent years, the economic exchanges between China and Taiwan have become more frequent, hence the Chinese financial market is the main target that we should research and participate in actively. This study refers to Barra Multi-Factor Modeling process to construct a China Multi-Factor Model. We then apply MFM to establish a Shanghai Stock Exchange 50 enhanced index fund. The first objective of this study is to discover significant factors which can explain excess return of securities. The second is to identify significant factors to forecast stock returns and show the alpha effect in an Enhanced Index Fund via a new weight allocating model developed by this study. The result shows that the eight significant factors are Earning Quality, Efficiency, Growth, Momentum, Size, Trading Activity, Value, and Volatility. The performance of Enhanced Index Fund is better than that of the benchmark. Information ratio is 0.86, and turnover rate is 213%, which is acceptable.

The objective of this study is to build a complete process of quantitative stockselection model construction that combines a Multi-Factor Model and informationanalysis. Based on the quantitative stock selection model, we construct anenhanced index fund that uses the Taiwan 50 index as its benchmark. Stock prices change for a multitude of reasons, and these reasons may changeover time. In this study, we use a Multi-Factor Model and information analysis to find the relationship between stock price behavior and a factor‟s condition. Wecan use this relationship as a basis for stock selection. Moreover, the purpose of this study is to construct an enhanced index fund,hence we need to control the tracking error. We use an intuitive portfolio construction method, the original weight retention rate of the benchmark, to control tracking error. In addition, the turnover rate of a portfolio is also a significant problem as it may cause the profit of a portfolio to decreasesignificantly. In this study, we use the smoothing alpha score method to control the turnover rate of our portfolio.

While quantitative investment management has been extensively investigated and many models built in order to provide investment suggestions through quantitative analysis, the combination of quantitative and qualitative analysis is relatively unexplored. The objective of this study is to construct a quantitative stock selection model based on the standard model built by Hsu et al. (2011) which could improve the stability of descriptor and factor structures and the combinability of quantitative and qualitative analysis. The research focuses on the structure of effective factors and descriptors when faced with different types of market trends. Furthermore, we test the performance of a Market Trend-Based Alpha Model (MTB alpha model) and compare with the standard alpha model. The strategy of portfolio construction is a TAIEX enhanced index fund. We find the enhanced index portfolio constructed by the MTB model produces an information ratio of 0.72, which is much higher than the standard model ratio of 0.41. This finding suggests that a MTB model could not only improve performance but also make the descriptor and factor structures more stable and much more easily for managers¡¦ adjusting.

The objective of this study is to build the platform, and the user can choose one model of the three models (1) base multi-factor alpha model (2) sector-specific alpha model (3) market trend-based multi-factor alpha model. The user can choose one target index of the four indexes (1) Electronic (2) Finance (3) Non-Finance Non-Electronics (4) TAIEX. The platform also combined the score of sector-specific model and market model, which we called hybrid model. The platform provides (1) elasticity of equity management (2) completeness of investment strategy (3) inclusiveness of alpha models and target indexes. The user can select a suit model and allocate the model and the target index, and quickly back-testing and evaluate performance. The contributions of this study are that help asset management companies quickly design investment strategies and back-testing, or product many different equity portfolio funds, and evaluate performance for stabled performance.

We construct the multi-factor model using fundamental cross-sectional approach in the thesis. We adopt the principal of BARRA¡¦E3 for constructing our multi-factor model. In our study period, we finally obtain 34 significant explanatory factors including 7 risk indices and 27 industry factors. In particular, the industry factors are an important risk source of the stock returns. The explanatory power of the multi-factor model is 43.18% on average and it ranges from 12.89% to 82.35%. The study results can be considered satisfactory. Moreover, based on the multi-factor model, we construct the Taiwan Weighted Stock Index enhanced index fund by the tracking error minimization method in our study. Enhanced Index Fund was built to make use of both passive management and active management to construct a portfolio which has the similar characteristics but higher returns compared to benchmark index. Hence, we want to track the Taiwan Weighted Stock Index while producing at least 2% outperformance over the Taiwan Weighted Stock Index. Our empirical period is from January 2000 to December 2005 and the simulated period is from January 2006 to December 2007. The performance of our constructed Taiwan Weighted Stock Index enhanced index fund in the simulated period is better than the benchmark and the tracking error is 1.36%. We are satisfied with the study results.

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.

This study constructs a quantitative stock selection model across multiple sectors with the application of the Bayesian method. It employees factors from the Taiwan stock market which could explain stock returns. Under this structure, each sector that has different significant factors is allowed to be imported into sub models. The factors are calculated into alpha scores and used to do stock selection. Therefore, the demonstration of both intra and inter-sector alpha scores into sector-specific integration alpha scores is an important concept in this study. Furthermore, an enhanced index fund is built based on the model and related to the benchmark to illustrate the power of this model. Once the contents of a portfolio are decided, this model could provide stock selection criterion based on the predictive power of stock return. Finally, the results demonstrate that this model is practical and flexible for local stock portfolio analysis.

"Enhanced index fund" is an investment strategy, combining active and passive management elements, for index tracking and return enhancing through disciplined market timing, stock selection and leverage activities. Though enhanced index funds have been well developed globally, there is only one enhanced index fund in Taiwan - "Polaris/P-Shares Taiwan Dividend+ ETF". Taiwan's stock market falls between weak form and semi-strong form efficiency. With the growth of Taiwan's mutual fund industry size, the enhanced index funds have very good chance to become the main investment instruments of institutional investors, index investors, and pensions. This study attempts to build enhanced index funds, then analyzes the performance and checks the feasibility of launching such products in Taiwan. In this study, we select "TSEC Taiwan 50 index (TW50)" as the benchmark index. The innovative "Multi-section Adjustment Model" divides the original weights of constituent stocks into two sections. Each section is adjusted through parameters. The "multi-factor model section" is responsible for the delivery of enhanced return, while the "cash dividend yield section" is used to provide excess cash dividend yield. The investment target is set for less than 1.5 percent tracking error, at least 1 percent tracking difference, and higher cash dividend yield than the benchmark. Building methodology can be divided into "fixed parameter model" and "floating parameters model" according to its update frequency. Empirical studies show that: (1) The enhanced index fund built from the "fixed parameter model" not only exhibits risk slightly lower than the benchmark, but also enjoys higher return. (2) In the short-term, the performance of the enhanced index fund built from "floating parameters model" is difficult to predict; in the long-term, however, the risk is lower and the return is higher than TW50. The cumulative return from the "fixed parameter model" is higher than the "floating parameters model" by about 2 percent. (3) The effectiveness of the parameters used to control the optimal weight distribution is decreasing over time, so it is necessary to update parameters regularly. (4) Raising "enhancement multiplier" will cause higher tracking error, but also bring higher tracking difference. This result proves that "multi-factor model section" works nicely and has its contribution. (5) As the "section allotment" and/or "fixed rate" getting lower, there will be more and more weights distributed to the cash dividend yield level, resulting in higher cash dividend yield. It means the "cash dividend yield section" has its merit as well. (6) Regular parameter updates to the "floating parameters model" helps to reduce the tracking error and, at the same time, maintain positive tracking difference. Considering the perpetual life of real world funds, "floating parameters model" should be a better building methodology. "Multi-Section Adjustment Model" has following advantages: (1) Its concept is intuitive and easy to use. (2) Sections can be customized based on investment objectives. (3) It is easy to analyze the impacts and trade-off among the parameters.

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.