This study aims to find an effective linear combination of factors in different economic cycle periods and then construct two factor timing multi-factor alpha models, one each for the expansion and contraction periods. Then, we wish to examine a further two effects, namely calendar effect and cross effect. The calendar periods are divided into the first half year and the second half year. The cross effect is the combination of the economic cycle and the calendar effect. In addition, this study puts different loadings in core and satellite descriptors, which means we wish to examine which descriptors are more important when we rebalance our portfolio weekly. The empirical results show that the Value factor is effective in expansion and the first half year, and the Size and Earnings Quality factors are effective in contraction and the second half year. Moreover, the Price Momentum and Trading Activity factors are effective most of the time. We find that the optimal weight for core descriptors is 0.3 and for satellite descriptors is 0.7. Finally, the information ratios of our models are superior to the Standard alpha model by Hsu et al. (2011) and the Market Trend-based alpha model by Wang (2011). Taking the AMCross as an example, when the tracking error is below 3%, the IR is 1.40, the active return is 3.09%, the tracking error is 2.20%, the turnover rate is 207% and the transaction costs are 1.2%.

The objective of this study is to discover the non-linear effect of market sentiment to characteristic factor returns. We run ¡¥Quantile Regression¡¦ to help us extract useful information and design an effective strategy. Based on the quantitative investment method, using the platform of Multi-Factor Model (MFM), we attempt to construct a portfolio and enhance portfolio performance. If the market-sentiment variable increases performance, we could conclude that some characteristic factors in a high sentiment period will perform better or worse in the next period. What is the market or investor sentiment? It is still a problem in the finance field. There is no co-definition or consensus so far. We do our best to collect the indirect data, such as transaction data, price and volume data, and some indicators in other studies, VIX, put/call ratio and so on. Then, we test the proxy variables independently, and obtain some interesting results. The market turnover, the ratio of margin lending on funds/ margin lending on securities, and the growth rate of consumer confidence index have significant effects on some of the characteristic factors. This holds that some market sentiment variables could influence stocks with certain characteristics, and the factor timing approach could improve portfolio performance under examination by information ratio.

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.

The objective of this study is to discover the sources of securities return in forecasting stock return from different sides of potential factors including fundamental and market information. We test currency sensitivity, earnings variability, earnings yield, growth, leverage, trading activity, momentum, size, value, volatility, capital spending discipline, free cash flow, efficiency, solvency, earnings quality, corporate finance policy and technical 17 factors basing on different factor dimensions in this study. We construct a Taiwan multi-factor model by using the most significant factors for universal stocks according to 0HMSCI Barra¡¦s Multiple-Factor Modeling process, and then apply market neutral investment to build portfolios for performance back-testing. As a result, the most significant top five factors in forecasting are respectively ¡§Volatility2,¡¨ ¡§Earnings Quality1,¡¨ ¡§Trading1,¡¨ ¡§Volatility1¡¨ and ¡§Growth1¡¨ factors. In addition, we find the most useless bottom four factors in forecasting are respectively ¡§Size1,¡¨ ¡§Earning Yield1,¡¨ ¡§Value1,¡¨ and ¡§Capital Spending1.¡¨ No matter which strategies we adopt to build the portfolio, the Sharpe ratios of back-testing performance are all higher than the Benchmark, and all bring stable and consistent performance. It actually proves that this model is robust.

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.

The estimation results of Brazilian risk premia are not robust in the literature. For instance, among the 133 market risk premium estimates reported on the literature, 41 are positives, 18 are negatives and the remainder are not significant. In this study, we investigate the grounds for this lack of consensus. First of all, we analyze the sensitivity of the US risk premia estimation to two relevant constraints present in the Brazilian market: the small number of assets (137 eligible stocks) and the short time-series sample available for estimation (14 years). We conclude that the second constrain, small T, has greater impact on the results. Following, we evaluate the two potential causes of problems for the risk premia estimation with small T: i) small sample bias on betas; ii) divergence between ex-post and ex-ante risk premia. Through Monte Carlo simulations, we conclude that for the T available for Brazil, the betas estimates are no longer a problem. However, it is necessary to wait until 2041 to be able to estimate ex-ante risk premia with Brazilian data. / Os resultados das estimações de prêmios de risco brasileiros não são robustos na literatura. Por exemplo, dentre 133 estimativas de prêmio de risco de mercado documentadas, 41 são positivas, 18 negativas e o restante não é significante. No presente trabalho, investigamos os motivos da falta de consenso. Primeiramente, analisamos a sensibilidade da estimação dos prêmios de risco norte-americanos a duas restrições presentes no mercado brasileiro: o baixo número de ativos (137 ações elegíveis) e a pequena quantidade de meses disponíveis para estimação (14 anos). Concluímos que a segunda restrição, T pequeno, tem maior impacto sobre os resultados. Em seguida, avaliamos as duas potenciais causas de problemas para a estimação de prêmios de risco em amostras com T pequeno: i) viés de pequenas amostras nas estimativas dos betas; e ii) divergência entre prêmio de risco ex-post e ex-ante. Através de exercícios de Monte Carlo, concluímos que para o T disponível no Brasil, a estimativa dos betas já não é mais um problema. No entanto, ainda precisamos esperar até 2041 para conseguirmos estimar corretamente os prêmios ex-ante com os dados brasileiros.

This project focuses on risk analysis of corporate bond portfolios. We separate the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called basis which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition, we also introduce a Fama-French multi-factor model to analyze factor significance to the corporate bond portfolio.

This project focuses on risk analysis of corporate bond portfolios. We divide the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) which is determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called `basis' which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition we also introduce a Fama-French multi-factor model to analyze the factor significance to the corporate bond portfolio.

Investment decisions grounded in personal values and societal norms has seen a growth in the last decades, to a point where large institutional investors are abstaining from certain industries that share a specific characteristic altogether. The affiliation with sinful industries that promote human vice is not viewed as socially responsible in the eyes of the public, a reason why socially responsible investment funds that screen out these companies has experienced an increase in popularity. This study sets out to investigate the performance of American sin stocks in an attempt to increase the awareness of how these shunned industries has performed. While the existing literature provides evidence which proves sin stocks outperforms the market, we will provide further evidence concentrating on a mix of industries previously not focused on. Additionally we will extend the observation period beyond what has been done in the past. In this study, the definition of sin incorporates the industries of alcohol, defense, gambling and tobacco, and investigates the performance of a survivorship-free sample of 159 companies between July 1973 and June 2012. As performance measure, the four factor model is employed to capture any abnormal performance in relation to the market with three additional risk factors. In addition, we set out to investigate the performance of the different industries individually, to find if there is any that acts as a driver of the performance. Further, we look into the persistency of the performance over time. We find that the sample outperforms the market with 5.8% annually, and where the tobacco industry stands out with the highest abnormal return, the other industries grouped together still produce significant outperformance. The sinful index examined in this degree project has shown persistent performance, with no obvious trends of growth or decline. Unlike what has been found in previous research, the sample has shown a substantial difference in performance depending on the weighting scheme applied, not only individually for the industries, but also collectively.