Quantitative analysis is one branch of portfolio management. The advantages of quantitative analysis are fast and objective. It has developed significantly in recent years because of the improvements in computer technology. This thesis applies the structure of a multi-factor model (MFM) to undertake quantitative analysis. Singapore has one of the most prosperous financial markets in Southeast Asia. The Singapore Stock Exchange (SGX) and Financial Times and the London Stock Exchange (FTSE) are now in cooperation, which has added vitality to this market. It has great influence in global financial markets, and this is why we select its security market to be our target in MFM. The model refers the multi-factor processes of Jeng and Tsai (2011) . For backtesting, we adopt an enhanced strategy as testimony. We transmit information from the MFM to the enhanced strategy. Then we create the stock weightings to constitute the enhanced portfolio. This model includes 68 significant descriptors, 14 composite factors and 7 industry factors. The Singapore MFM shows 43% adjusted R-Square in the sample period. The enhanced portfolio we suggested has an information ratio of 76.80% with a tracking error of 4.02% and 1.53% for monthly turnover rate.

This study constructs a cross-market risk model based upon local multi-factor risk models of Taiwan, China and Singapore equity markets. This model allows each local market to adopt different local factors rather than force all local markets to use one parsimonious set of factors. We employ the world, country, industry, and global risk factors to build a structural model which could explain the relationship between local factors across market by further decomposing local factor returns. Therefore, this model could provide both in-depth and broad coverage analysis of international equity portfolios. Furthermore, we build a simple portfolio and its corresponding benchmark to illustrate the usage of our model. Once the contents of a portfolio are decided, this model could provide not only the risk estimation and decomposition in advance but also the performance attribution compared with the benchmark after the portfolio is realized. The analytical viewpoint could also easily change with different numeraire perspectives. The result demonstrates that this model is practical and flexible for international equity portfolio analysis.

This Thesis attempts to construct a Taiwan equity multi-factor model using fundamental cross-sectional approach step by step. It is found that the model involves 28 explanatory factors (including 20 industry factors) and its explanatory power is 58.6% on average. The results of the estimations can be considered very satisfactory. Moreover, based on MFM, this study simulates applications of equity market neutral strategies through quantitative techniques over the period Jan.2003 ¡V Dec.2003. The results verified that the three major characteristics of equity market neutral portfolio performance are: 1) providing absolute return; 2) lack of correlation to the equity benchmark; and 3) low volatility due to hedged portfolio structures.

This research combines relational database framework and quantitative equity portfolio models based on the Barra Risk Model Handbook standard steps to design a database and computer platform for multi-factor risk management tasks. The multi-factor model facilitates fast search and efficient selection of descriptors with explanatory power for future stock returns. The design of database is divided into three steps. First, descriptors are calculated and daily-update modules constructed. This study finds 48 key descriptors which play important roles in explaining stock returns of Taiwan. Second, entity relational model is applied to sort out linkages between pieces of important information in the factor model. Lastly, database auto-run procedures are setup to update the latest raw data on a monthly basis. Model parameter update and portfolio rebalancing is hence made seamless to meet practical operation demand for such a platform. The development of the Multi-factor risk model is divided into five main steps. (1) Finding significant descriptors. (2) Forming common factors from descriptors. (3) Developing a multi-factor return model. (4) Developing a multi-factor risk model. (5) Running performance analysis and back-testing. The empirical results show that the average adjusted R-squared of the MFM model is 0.5 during the period of 1998/04~2005/11. For combining descriptors into common factors, we run factor analysis. The multi-collinearity problem existing in the descriptors is well taken care of by such procedures. We use the exponentially weighted averaging method to compute the factor returns and forecast stock ranking. A half-life of 24 months appears to deliver the best performance in Taiwan stock market.

One thing that everyone seems to be worried about when it comes to his or her computer is security. If your computer is not secure then private information could be stolen. Many people now use passwords to protect themselves though they are discovering that using multi-factor authentication is much more secure. It allows you to use multiple different proofs of who you are. Biometrics is one of the ways to prove identity. Using it, you could log into a system with just a fingerprint, which is something that is very difficult to steal. We present a suite of software tools that allows you to log into a network using multi-factor authentication. This thesis describes our design of a multi-factor authentication solution, the problems we encountered realizing this design, and Microsoft's own biometric system.

abstract: This paper quantitatively analyses the relation between the return of private seasoned equity offerings and variables of market and firm characteristics in China Ashare market. A multiple-factor linear regression model is constructed to estimate this relation and the result canhelp investors to determine the future return of private placement stocks. In this paper, I first review past theories about private placement stocks, including how the large shareholder participation, the discount of private offerings, the firm characteristics, and the investment on firm value will affect the return of private offerings. According to the past literature, I propose four main factors that may affect the return of private placement. They are the large shareholders participation in private placement; the discount that private placement could offer; the characteristics of the companies that offer a private placement and the intrinsic value of such companies. I adopt statistic and correlational analysis to test the impact of each factor. Then, according to this single-factor analysis, I set up a multiple-factor linear regression model on private seasoned equity offerings return in Chapter Four. In the last two chapters, I apply this quantitative model to other fields. I use this model to testify current financial products of private placement and develop investmen strategies on stocks with private seasoned equity offerings in secondary market. My quantitative strategy is useful according to the result of setback test. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2017

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.

Multi-factor authentication (MFA) offers a wide range of methods and techniques available today. The security benefits of using MFA are almost indisputable, however, users are reluctant to adopt the technology. While many new MFA solutions are being proposed, there is a lack of consideration for user sentiment in the early stages of development. In an attempt to balance security and usability, this report investigates the feasibility of a new authentication mechanism that uses browser fingerprinting, graphical passwords, and honeytokens. This was evaluated by conducting a limited literature review, producing a prototype, interviews with test users, and security experts, as well as ensuring feasibility through a requirements checklist. The results of this research provides evidence that this mechanism is feasible, and appealing to end users. However, more investigation is required in order to ensure the mechanism's viability in a real-world deployment.

Harry Markowitz work in the 50’s spring-boarded modernportfolio theory. It gives investors quantitative tools to compose and assessasset portfolios in a systematic fashion. The main idea of the Mean-Varianceframework is that composing an optimal portfolio is equivalent to solving aquadratic optimization problem.In this project we employ the Maximally Predictable Portfolio (MPP) frameworkproposed by Lo and MacKinlay, as an alternative to Markowitz’s approach, inorder to construct investment portfolios. One of the benefits of using theformer method is that it accounts for forecasting estimation errors. Ourinvestment strategy is to buy and hold these portfolios during a time periodand assess their performance. We show that it is indeed possible to constructportfolios with high rate of return and coefficient of determination based onhistorical data. However, despite their many promising features, the success ofMPP portfolios is short lived. Based on our assessment we conclude thatinvesting in the stock market solely on the basis of the optimization resultsis not a lucrative strategy / Modern portföljteori har sitt ursprung i Harry Markowitz arbete på 50-talet. Teorin ger investerare kvantitativa verktyg för att sammansätta och utvärdera tillgångsportföljer på ett systematiskt sätt. Huvudsakligen går Markowitz idé ut på att komponera en investeringsportfölj genom att lösa ett kvadratiskt optimeringsproblem. Det här examensprojektet har utgångspunkt i Maximally Predictable Portfolio-ramverket, utvecklat av Lo och MacKinley som ett alternativ till Markowitz problemformulering, i syfte att välja ut investeringsportföljer. En av fördelarna med att använda den förra metoden är att den tar hänsyn till uppskattningsfelen från prognostisering av framtida avkastning. Vår investeringsstrategi är att köpa och behålla dessa portföljer under en tidsperiod och bedöma deras prestanda. Resultaten visar att det mha. MPP-optimering är möjligt att konstruera portföljer med hög avkastning och förklaringsvärde baserat på historisk data. Trots sina många lovande funktioner är framgången med MPP-portföljer kortlivad. Baserat på vår bedömning drar vi slutsatsen att investeringar på aktiemarknaden uteslutande på grundval av optimeringsresultatet inte är en lukrativ strategi.

This dissertation contains three essays on the non-pecuniary preferences pertaining to financial asset characteristics and their implications for asset pricing. The first essay considers the pricing implications of screens adopted by socially responsible investors. A model including such investors reconciles the empirically observed risk-adjusted sin-stock abnormal return with a systematic “boycott risk premium” which has a substantial financial impact that is, however, not limited to the targeted firms. The boycott effect cannot be displaced by litigation risk, a neglect effect, and liquidity considerations, or by industry momentum and concentration. The boycott risk factor is valuable in explaining cross-sectional differences in mean returns across industries and its premium varies directly with the relative wealth of socially responsible investors and with the business cycle. The second essay generalizes Fama (1996)’s concept of Multi-Factor Efficiency without being limited by additional random state variables that must affect future investment opportunities. Incorporating non-pecuniary preferences into a representative investor’s utility function generates multi-factor pricing implications. A representative investor chooses among expected returns, variances, and levels of characteristics according to their taste, which gives rise to an N-fund separation theorem with static characteristics. If a portfolio is built to maximize the exposure to the asset characteristics, the covariance between asset returns and this portfolio returns will be identical to the underlying characteristics. Such identity makes obsolete any attempts to distinguish between characteristics and risk exposures as the driving forces behind the cross-sectional variation in stock returns. The third essay develops a procedure for deriving systematic factors from characteristics, based on maximizing each factor’s exposure to a characteristic subject to a given level of factor variance. The resulting characteristic-mimicking portfolios (CMP) price mean asset returns identically as the original characteristics, irrespective of the underlying model. Accordingly, differences in the performance of mimicking factors and characteristics in explaining mean returns should be interpreted as an artifact of arbitrary procedural choices for generating mimicking factors. Factors and characteristics may be distinguished usefully only by determining if CMPs have significant explanatory power for the time series of returns. / Thesis / Doctor of Philosophy (PhD)