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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.

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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.

Background: Ever since Sharpe, Treynor and Jensen advanced the methods of fund performance evaluation in the 60’s it has been a popular field of study in academia. As the intricacies of fund performance was untangled it became clear that paying for active management doesn’t yield higher cost adjusted returns. An Index investment strategy is the most sensible approach and it’s the associated cost which separate index vehicles. Exchange traded funds have risen as a competitor to the conventional index mutual fund but the research evaluating these is very scarce. The research conducted comparing the costs of the two vehicles do not take into account implicit transaction costs that in turn depend on specific market microstructure designs and could affect the cost relationship. The problem: Do liquidity and market structural disparities between markets affect the cost relationship between exchange traded funds and index mutual funds, through the implicit transaction cost? Objective of the research: The objective of this paper is to examine whether structural differences between markets affect implicit transaction costs to the extent that the cost relationship between index funds and exchange traded funds differ from earlier findings. Method: The need to generalize the findings prompted a quantitative approach to the research. Comparative examination will be done on the microstructure and liquidity of two different markets. The transaction costs will then be measured with statistical means and incorporated in a cost comparison model. Result and conclusion: There are architectural and liquidity differences between the two sample markets allowing for systematic differences in transaction cost, which were found but were not a significant contributor to the tracking error cost of the index mutual funds. The Swedish ETF do not get more profitable as the investment sum increases. A finding which contradicts earlier findings and is likely a consequence of the Swedish tax-laws for capital gains as well as the higher levels of management fees for ETFs. ETFs might still be a worthwhile investment since they possess unique qualitative benefits.

iiABSTRACTBehavioral finance has been a popular research subject for a while and therefore the understanding of human behavior when it comes to private financial investments has increased. When comparing human behavior to the financial theories one can conclude thatthe assumption of perfect and efficient markets with fully informed and rational investors is not realistic. This study has therefore looked at the investor rationality when choosing which index fund to invest in. Index funds are to a large degree used asa savings tool for either pensions or other specific purposes. It was therefore interesting to look at the behavior of Swedish investors buyingthe Swedishindex funds available in Sweden with a quantitative analysis of the relationship between flow and other features of index funds. The dependent variable reflecting rationality was the fund flow and the independent variables were return, tracking error, size, fee and risk. No previous studies have been made on the investor rationality regarding index funds in the Swedish market, although similar studies havebeen done on the American S&P 500 investors. 17 index funds were included in this study, which is the whole population of index funds following Swedish indices available in Sweden at the point of time when this study was conducted. From this population funds that had been available for more than 3 years was chosen since we wanted to look at the behavior based on a longer time span than one year. In the end, 17index funds with 51observations was included in the study.Five hypotheses were created and tested of whichtwowereaccepted. From the regression model we found that return and standard deviation (SD) weresignificant andhadpositiverelationshipswith the fund flow. This implies that Swedish investors are rational to some degree but not fully rational since they are not taking any of the other variables into account which a rational investor ought to consider. It is therefore useful information for both investors and fund companies to see which factors weight in the most and how rational the behavior is. Conclusions from this study is that Swedish investors are subject to the index fund rationality paradox to some degree and the rational choice theory applies to some extent. One has to fully consider the outcomes of an action and base the decisionon utility maximization that the outcome will give one. To act fully rational is hard even for the most aware investor and even harder for an ordinary investor with gaps in knowledge and limited resources to information.

Abstract   Title: High-Risk Funds vs. Mutual- Index Funds A study of macro -variables influence on different funds choice   Level: Final assignment for Bachelor Degree in Business Administration   Author: Moses Yokie & Bo Lemar   Supervisor: Ogi Chun & Cheick Wagué   Date: 2011-05-25   Aim: The purpose with this thesis is to compare two different types of mutual-index fond and a high-risk fund in relation to the macro variables. The purpose also includes an investigation about if an investor will receives a higher return on high-risk fund than on mutual-index fund in a 10 years period. Method: A quantitative method has been use in this study, where the information has been received from Morningstar. Microsoft Excel has been used to process the collected data in order to calculate the expected return and the risk measures. The result is presented in graphs and tables on the empirical capital, in order to analyse and compare it with the theories and the selected macro- variables to see if there will be any correlation.   Conclusion: This research shows that there is no possibility that the macro-variable factors can benefit an investment on high-risk fund or on mutual-index fund in the short run. On the other hand there is a correlation between the high-risk fund and mutual-index fund with the macro-variables factors in the long run.   Suggestion for future research: Base on the results on this thesis. It would be interesting to do a quantitative investigation on more fund category with other macro-variable factors that are not included in this study to compare if there will be any correlation between them.   Key words: High-risk fund, BNP, Mutual-index, Arbitrage, Macro-variable, Inflation. / Syfte: Syftet med denna uppsats är att jämföra två olika aktieindex och högriskfonder med avseende på makrovariabler. Syftet innefattar också att undersöka om en investerare kan få bättre avkastning på högriskfonder än aktieindexfonder på 10 år period. Metod: En kvantitativ metod har använts i uppsatsen, där data inhämtats från Morningstar. Det insamlade material har sedan bearbetats i Microsoft Excel för att beräkna fonderna avkastning och Sharpekvoten. Resultatet har redovisats i grafer och tabeller i empirikapitlet, för att sedan analyseras och jämföras med de teorierna som används. Resultatet har jämförts med de valda makrovariablerna för att hitta korrelationer. Slutsats: Det har inte gått att påvisa några möjligheter att utnyttja makrovariabler för att skapa kortsiktiga vinster i högriskfonder eller aktieindexfonder. Däremot finns det långsiktiga samband mellan de valda investeringarna och de valda makrovariablerna Förslag till fortsatta studier: Det kan vara intressant att gör en kvantitativ undersökning på fler former av fondkategorier för att få ytterligare information om tillämpbarhet. Dessutom att undersöka samband med andra makrovariabler som inte innefattas i denna undersökning.

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.

Generally, investment in commodity ETFs cannot produce similar performance as well as spot goods. Evidence shows that ¡§rolling¡¨ futures positions experience ¡§contango and the effects on contango will harm ETFs¡¨ value. This study shows that two ETFs, USO and UNG, underperform the spot substantially because of rolling in the crude oil and natural gas market, respectively. In this study we employ four energy sector futures market data from the Thomson Reuters to investigate the impact of rolling positions on the relation between commodity index funds and in contango/backwardation. This paper finds that increasing trading in commodity index fund made futures market more contango in the WTI crude oil, natural gas and heating oil markets. This study termed the strategy as the Backwardation Sensitive Trading (BST) . Moreover, this research designs an investment strategy based on variation of backwardation. That is to examine whether BST can make a successful arbitrage: increase holding when the market is more contango and decrease holding when the market is more backwardation. Our strategy performs better than USO and UNG, and those performances perform lower tracking error on oil and natural gas over 2006 to 2010.