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%.

This project describes the underlying principles of Modern Portfolio Theory, the Capital Asset Pricing Model (CAPM), and multi-factor models in detail, explores the process of constructing optimal portfolios using the Modern Portfolio Theory, estimates the expected return and covariance matrix of assets using CAPM and multi-factor models, and finally, applies these models in real markets to analyze our portfolios and compare their performances.

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.

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.

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.

Most software applications rely on the use of user-name and passwords to authenticate end users. This form of authentication, although used ubiquitously, is widely considered unreliable due to the users inability to keep them secret; passwords being prone to dictionary or rainbow-table attacks; as well as the ease with which social engineering techniques can obtain passwords. This can be mitigated by combining a variety of diferent authentication mechanisms, for example biometric authentication such as fingerprint recognition or physical tokens such as smart cards. The resulting multifactor authentication is typically stronger than any of the techniques used individually. However, it may still be expensive or prohibited to implement and more dificult to deploy due to additional accessories cost, e.g, finger print reader. Multi-modal biometric systems are those which utilise or are capable of utilising, more than one physiological or behavioural characteristic for enrolment, verification, or identification. So, in this research we present a multi-factor authentication scheme that is based on the user's own hardware environment, e.g. laptop with fingerprint reader, thus avoiding the need of deploying tokens and readily available biometrics, e.g., user keystrokes. The aim is to improve the reliability of the authentication using a multi-factor approach without incurring additional cost or making the deployment of the solution overly complex. The presented approach in this research uses unique sequential hardware information available from the user's environment to profile user behaviour. This approach improves upon password mechanisms by introducing a novel Hardware Authentication and User Profiling (HAUP) in form of Multi-Factor Authentication MFA that can be easily integrated into the traditional authentication methods. In addition, this approach observes the advantage of the correlation between user behaviour and hardware environment as an implicit veri_cation identity procedure to discriminate username and password usage, in particular hardware environment by specific pattern. So, the proposed approach uses hardware information to profile the user's environment when user-name and password are typed as part of the log-in process. These Hardware Manufacture Serial Part Numbers (HMSPNs) profiles are then correlated with the users behaviour, e.g., key-stroke behaviour that allows the system to profile user's behaviour dependent on their environment. As a result of this approach, the access control system can determine a particular level of trust for each user and base access control decisions on it in order to reduce potential identity fraud.