I investigate the effect of shareholder rights and information asymmetry on option-related repurchase activity. Prior research shows that the dilution effect of the exercise of the employee stock options on earnings per share (EPS) decreases the value of stock options. Thus, managers tend to use stock repurchases rather than dividends to return cash to shareholders (the dividend substitution effect). I document that the executive stock option incentives to repurchase stock as a substitute for dividends are stronger when firms have weak shareholder rights and the level of information asymmetry positively influences managerial stock option incentives to repurchase stock. Furthermore, prior research indicates that information asymmetry is positively associated with stock repurchases. I also provide evidence indicating that the relationship between information asymmetry and stock repurchases is stronger when firms have weaker shareholder rights. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

I examine the presence of earnings management at pre-IPO and lockup periods. Motivated by significant post-lockup insider sales documented in prior research, I investigate whether insiders (managers and venture capitalists) inflate earnings around the lockup period in order to increase share price and maximize personal wealth from selling shares at lockup expiration. I also compare levels of earnings management in the pre-IPO and lockup periods with those in the post-lockup period. Prior research also documents that auditor quality mitigates earnings management behavior. I explore the impact of auditor quality in the unique setting of IPO lockups. ... Cross-sectional analysis reveals that my sample IPO firms also utilize real-activities manipulation, but only in the early pre-IPO period. The results are robust with respect to alternative abnormal accruals and real-activities measures. I also find that IPO firms that hire prestigious auditors experience less earnings management in the lockup period than firms with lower-quality auditors, after controlling for the monitoring role of venture capitalist and underwriter reputation. / by Lizhong Hao. / Thesis (Ph.D.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Na literatura vários estudos discutem uma previsão de aumento da demanda por soja no mercado mundial, como também enfatizam o aumento da safra brasileira para os próximos anos. Apesar destas previsões otimistas, insuficientes ou inadequadas, condições de infraestrutura em capacidade de armazenagem, de transportes e de portos no Brasil têm sido levadas em consideração na elaboração do Planejamento de Longo Prazo pelas empresas que comercializam soja. Esta pesquisa faz um levantamento dos principais atributos que compõem a decisão de armazenagem de soja a granel com o objetivo de construir um modelo que auxilie o processo de tomada de decisão sobre investimento em projeto de armazenagem de soja em grão, sob a visão de uma trading. A abordagem multicritério se faz necessária devido à complexidade envolvida na cadeia de soja e na escolha de um projeto propriamente dito. A seleção do método de análise multicritério foi fundamentada e direcionada através de uma análise bibliométrica sobre o assunto. O método utilizado é baseado na Teoria de Valor Multi-Atributo e foi construído no software V.I.S.A. para eleger um projeto dentre as alternativas disponíveis. Os pesos dos critérios representam as preferências de um grupo coletadas a partir da aplicação de questionários a dez profissionais do setor. Os valores dos pesos resultaram da comparação paritária proposta pelo método Processo Analítica Hierárquico (AHP) e agregados pelo método de Agregação Individual de Prioridades. A árvore da decisão obtida foi composta pelos critérios: localização e transporte, estudo de mercado, infraestruturas e aspectos regionais, aspectos de engenharia, análise de viabilidade econômica e capacidade e eficiência operacional. Os resultados foram complementados por um exemplo hipotético elaborado para validar o modelo construído através de análises de sensibilidade e robustez. Conclui-se que o modelo se mostra coerente em relação ao perfil das alternativas criadas e recomenda-se sua utilização por tomadores de decisão de uma trading como referência para compor a sua própria árvore de decisão, e as preferências obtidas podem auxiliar processos de tomadas de decisão reais. / Several studies in literature discuss an increasing demand for soybean worldwide and increasing supply of Brazilian grain harvest for the next few years. Despite these optimistic forecasts, insufficient or inadequate infrastructure in terms of storage capacity, transportation system and ports elevation in Brazil have been taken into account in Long Term Planning of agribusiness companies. This research is a survey of the main attributes in decision-making process of bulk warehouse for soybeans in order to build a model of this specific process under the vision of a trading company. A multicriteria approach is necessary due to the complexity involved in the soy supply chain and project selection itself. The multicriteria decision method (MCDM) selection was based and directed through a bibliometric analysis about the MCDM topic. The method used is based on the Theory of Multi-Attribute Value and was built in software VISA to choose a project among the available alternatives. The weights of each criteria were compound of a group decision that was obtained from the questionnaires of eleven professionals. The responses of the weights were based on pair-wise comparison proposed by the Analytic Hierarchy Process method, known as AHP and aggregated by Aggregation of Individual Priorities method. The decision tree of this study was composed by the criteria: \"location and transportation\", \"market research\", \"infrastructure and regional aspects\", \"engineering aspects\", \"economic viability analysis\" and \"capacity and operational efficiency.\" Besides a hypothetical example were designed to validate the model using sensitivity and robustness analysis. It is concluded that the model is consistent over the profile among created alternatives and this study is recommended as reference for decision makers of a trading company to build its own tree decision. And preferences used in this study may help in real world of decision-making processes.

Chiu Chun Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 112-114). / Abstracts in English and Chinese. / Abstract --- p.i / 論文摘要 --- p.ii / Acknowledgment --- p.iii / List of Figures n --- p.i / List of Tables n --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.4 / Chapter 3 --- Review of the Minimax Model --- p.7 / Chapter 4 --- Portfolio Optimization with Shorting --- p.13 / Chapter 4.1 --- Formulation of Minimax Model with Shorting --- p.13 / Chapter 4.2 --- A Simple Optimal Investment Strategy --- p.14 / Chapter 4.2.1 --- All Assets Are Risk --- p.14 / Chapter 4.2.2 --- Some Assets Are Riskless --- p.31 / Chapter 4.3 --- Tracing Out the Efficient Frontier --- p.34 / Chapter 4.3.1 --- No Riskless Assets Are Involved --- p.34 / Chapter 4.3.2 --- Riskless Assets Are Involved --- p.43 / Chapter 4.4 --- Chapter Summary --- p.44 / Chapter 5 --- Portfolio Optimization with Investment Limit --- p.50 / Chapter 5.1 --- Formulation of Minimax Model with Investment Limit --- p.51 / Chapter 5.2 --- Optimal Solution to POI(λ) --- p.52 / Chapter 5.2.1 --- All Assets Are Risky --- p.52 / Chapter 5.2.2 --- Some Assets Are Riskless --- p.67 / Chapter 5.3 --- Chapter Summary --- p.71 / Chapter 6 --- Numerical Analysis --- p.72 / Chapter 6.1 --- Data Analysis --- p.72 / Chapter 6.2 --- Experiment Description and Discussion --- p.75 / Chapter 6.2.1 --- Short-Selling is Allowed --- p.75 / Chapter 6.2.2 --- Comparison Between the Cases With Short-Selling and Without Short-Selling --- p.77 / Chapter 6.3 --- Chapter Summary --- p.79 / Chapter 7 --- Conclusion --- p.39 / Chapter A --- List of Companies Included in Numerical Analysis --- p.82 / Chapter B --- Graphical Result of Section 6.21 --- p.84 / Chapter C --- Graphical Result of Section 6.22 --- p.93 / Bibliography --- p.112

Yung Yan Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 144-147). / Chapter Chapter 1: --- Introduction --- p.1 / Chapter 1.1 --- Review on Portfolio Theory --- p.3 / Chapter - 1.1.1 --- Expected Return and Risk of a Security --- p.3 / Chapter -1.1.2 --- Expected Return and Risk of a Portfolio --- p.4 / Chapter -1.1.3 --- The Feasible Set --- p.5 / Chapter - 1.1.4 --- Assumptions on the Investor --- p.6 / Chapter -1.1.5 --- Efficient Portfolios --- p.6 / Chapter -1.1.5.1 --- Bounds on the Return and Risk of a portfolio --- p.6 / Chapter -1.1.5.2 --- Concavity of the Efficient Set --- p.8 / Chapter -1.1.6 --- The Market Model --- p.9 / Chapter -1.1.7 --- Risk-free Asset --- p.11 / Chapter - 1.1.8 --- Portfolio involving Risk-free Asset --- p.12 / Chapter -1.1.9 --- The Sharpe Ratio --- p.14 / Chapter 1.2 --- Review on Some Trading Models --- p.19 / Chapter -1.2.1 --- Buy and Hold Model --- p.19 / Chapter -1.2.2 --- Trading Model with Prediction Criteria --- p.20 / Chapter -1.2.2.1 --- Two School of Theories --- p.20 / Chapter - 1.2.2.2 --- Prediction of the stock price movement --- p.20 / Chapter -1.2.2.3 --- The Use of Neural Network in Prediction --- p.21 / Chapter -1.2.2.4 --- Single Step and Multi-step Prediction --- p.23 / Chapter - 1.2.2.5 --- Trading Model based on Prediction Criteria --- p.25 / Chapter - 1.2.2.6 --- For More Accurate Prediction --- p.25 / Chapter -1.2.3 --- Weigend's Model --- p.26 / Chapter - 1.2.3.1 --- Introduction --- p.26 / Chapter -1.2.3.2 --- The Model Setup --- p.26 / Chapter -1.2.3.3 --- The Objective Functions --- p.27 / Chapter - 1.2.3.4 --- The Gradient Ascending Algorithm --- p.27 / Chapter -1.2.3.5 --- The Gradient of the Sharpe Ratio --- p.27 / Chapter - 1.2.3.6 --- The Training Procedure --- p.28 / Chapter - 1.2.3.7 --- Some Properties of the Sharpe Ratio Training --- p.28 / Chapter -1.2.4 --- Bengio's Model --- p.29 / Chapter -1.2.4.1. --- Overview --- p.29 / Chapter -1.2.4.2. --- The Trading System --- p.29 / Chapter - 1.2.4.3 --- The Objective Function: the Portfolio Return --- p.31 / Chapter - 1.2.4.4. --- The Training Process --- p.32 / Chapter - 1.2.4.5 --- Computer Simulation --- p.34 / Chapter - 1.2.4.6 --- Discussion --- p.36 / Chapter Chapter 2: --- The Naive Sharpe Ratio Model --- p.38 / Chapter - 2.1 --- Introduction --- p.39 / Chapter - 2.2 --- Definition of the Naive Sharpe Ratio --- p.39 / Chapter - 2.3 --- Gradient of Naive Sharpe Ratio with respect to the portfolio weighting: --- p.40 / Chapter - 2.4 --- The Training Process --- p.40 / Chapter - 2.5 --- Analysis of the Gradient --- p.41 / Chapter -2.6 --- Compare with Bengio's and Weigend's Model --- p.42 / Chapter -2.7. --- Computer Simulations --- p.43 / Chapter -2.7.1 --- Experiment 1: How the Sharpe Ratio is Maximized --- p.43 / Chapter -2.7.1.1 --- Experiment 11 --- p.44 / Chapter -2.7.1.2 --- Experiment 12 --- p.45 / Chapter -2.7.1.3 --- Experiment 13 --- p.46 / Chapter -2.7.2 --- Experiment 2: Reducing the Unique Risk --- p.49 / Chapter -2.7.3 --- Experiment 3: Apply to the Stock Market --- p.52 / Chapter -2.8 --- Redefining the Naive Sharpe ratio with down-side risk --- p.56 / Chapter -2.8.1 --- Definitions --- p.56 / Chapter -2.8.2 --- Gradient of the Downside Nai've Sharpe Ratio --- p.57 / Chapter -2.8.3 --- Analysis of the gradient of the new Sharpe ratio --- p.57 / Chapter -2.8.4 --- Experiment: Compared with Symmetric Risk --- p.59 / Chapter -2.8.4.1 --- Experimental Setup --- p.59 / Chapter -2.8.4.2 --- Experimental Result --- p.60 / Chapter -2.8.4.3 --- Discussion --- p.62 / Chapter - 2.9 --- Further Discussion --- p.63 / Chapter Chapter 3: --- The Total Sharpe Ratio Model --- p.64 / Chapter - 3.1 --- Introduction --- p.65 / Chapter -3.2 --- Defining risk of portfolio in terms of component securities' risk --- p.65 / Chapter -3.2.1. --- Return for Each Security and the Whole Portfolio at Each Time Step --- p.65 / Chapter -3.3.2. --- Covariance of the Individual Securities' Returns --- p.66 / Chapter -3.2.3. --- Define the Sharpe Ratio and the Objective Function --- p.66 / Chapter -3.2.3.1. --- The Excess Return --- p.66 / Chapter -3.2.3.2. --- The Risk --- p.67 / Chapter -3.2.3.3. --- The Sharpe Ratio at Time t --- p.67 / Chapter -3.2.3.4. --- The Objective Function: the total Sharpe ratio --- p.67 / Chapter -3.2.3.5. --- The Training Process --- p.68 / Chapter -3.3 --- Calculating the Gradient of the Total Sharpe Ratio --- p.69 / Chapter -3.4. --- Analysis of the Total Sharpe Ratio Gradient --- p.70 / Chapter -3.4.1 --- The Gradient Vector of the Sharpe Ratio at a Particular Time Step --- p.70 / Chapter -3.4.2 --- The Gradient Vector of the Risk --- p.70 / Chapter - 3.5 --- Computer Simulation: --- p.72 / Chapter -3.5.1 --- Apply to the Stock Market1 --- p.72 / Chapter -3.5.1.1 --- Objective --- p.72 / Chapter - 3.5.1.2 --- Experimental Setup --- p.72 / Chapter -3.5.1.3 --- The Experimental Result --- p.73 / Chapter -3.5.2 --- Apply to the Stock Market2 --- p.78 / Chapter -3.5.2.1 --- Objective --- p.78 / Chapter -3.5.2.2 --- Experimental Setup --- p.78 / Chapter -3.5.2.3 --- The Experimental Result --- p.79 / Chapter -3.6 --- Defining the Total Sharpe Ratio in terms of Downside Risk --- p.84 / Chapter - 3.6.1. --- Introduction --- p.84 / Chapter -3.6.2. --- Covariance of the individual securities' returns --- p.84 / Chapter -3.6.3. --- Define the Downside Risk Sharpe ratio and the objective function --- p.85 / Chapter -3.6.3.1. --- The Excess Return --- p.85 / Chapter -3.6.3.2. --- The Downside Risk --- p.85 / Chapter -3.6.3.3. --- The Sharpe ratio at time T --- p.85 / Chapter -3.6.3.4. --- The Objective function --- p.85 / Chapter -3.6.4. --- The Training Process --- p.85 / Chapter -3.7 --- Total Sharpe Ratio involving Transaction Cost --- p.86 / Chapter -3.7.1 --- Introduction --- p.86 / Chapter -3.7.2 --- Return for each stock and the whole portfolio at each time step --- p.86 / Chapter -3.7.3 --- Linear Approximation of the Portfolio's return --- p.88 / Chapter -3.7.4 --- Covariance of the individual securities' returns --- p.89 / Chapter -3.7.5 --- Define the Sharpe ratio and the objective function --- p.90 / Chapter -3.7.5.1 --- The Excess Return --- p.90 / Chapter -3.7.5.2 --- The Risk --- p.90 / Chapter -3.7.5.3 --- The Sharpe Ratio at time T --- p.90 / Chapter -3.7.5.4 --- The Objective Function --- p.90 / Chapter -3.7.6 --- Calculation of the gradient of the Total Sharpe ratio --- p.91 / Chapter -3.7.7. --- Analysis of the Total Sharpe Ratio Gradient --- p.94 / Chapter -3.7.7.1 --- The Gradient Vector of the Sharpe Ratio at a Particular Time Step --- p.94 / Chapter -3.7.7.2 --- The Gradient Vector of the Risk --- p.94 / Chapter -3.7.8 --- Experiment 1: Compare with Buy and Hold Method --- p.96 / Chapter -3.7.8.1 --- Experiment 11 --- p.96 / Chapter -3.7.8.2. --- Experiment 12 --- p.102 / Chapter -3.7.9 --- Experiment 2: Compared with Naive Sharpe Ratio --- p.108 / Chapter -3.7.9.1 --- Objective --- p.108 / Chapter -3.7.9.2. --- Experimental Setup --- p.108 / Chapter -3.7.9.3. --- The Experimental Result --- p.109 / Chapter - 3.7.10 --- Experiment 3: Compared with other models --- p.113 / Chapter - 3.7.10.1 --- Experiment 31 --- p.113 / Chapter - 3.7.10.2. --- Experiment 32 --- p.117 / Chapter -3.7.11 --- Experiment 4: Apply to the Stock Market --- p.121 / Chapter -3.7.11.1 --- Objective --- p.121 / Chapter - 3.7.11.2. --- Experimental Setup --- p.121 / Chapter -3.7.11.3. --- The Experimental Result --- p.121 / Chapter Chapter 4: --- Conclusion --- p.126 / Appendix A --- p.130 / Appendix B --- p.139 / Appendix C --- p.141 / Appendix D --- p.142 / Reference --- p.144

by Wan-Lung Ng. / Thesis submitted in: November 1997. / On added t.p.: January 19, 1998. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 114-119). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Organization Outline --- p.5 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Modern Portfolio Theory --- p.7 / Chapter 2.1.1 --- Mean-Variance Model --- p.9 / Chapter 2.1.2 --- Setting-up the relationship between the portfolio and its component securities --- p.11 / Chapter 2.1.3 --- Identifying the efficient frontier --- p.12 / Chapter 2.1.4 --- Selecting the best compromised portfolio --- p.13 / Chapter 2.2 --- Stochastic Optimal Control --- p.17 / Chapter 2.2.1 --- Dynamic Programming --- p.18 / Chapter 2.2.2 --- Dynamic Programming Decomposition --- p.21 / Chapter 3 --- Multiple Period Portfolio Analysis --- p.23 / Chapter 3.1 --- Maximization of Multi-period Consumptions --- p.24 / Chapter 3.2 --- Maximization of Utility of Terminal Wealth --- p.29 / Chapter 3.3 --- Maximization of Expected Average Compounded Return --- p.33 / Chapter 3.4 --- Minimization of Time to Reach Target --- p.35 / Chapter 3.5 --- Goal-Seeking Investment Model --- p.37 / Chapter 4 --- Multi-period Mean-Variance Analysis with a Riskless Asset --- p.40 / Chapter 4.1 --- Motivation --- p.40 / Chapter 4.2 --- Dynamic Mean-Variance Analysis Formulation --- p.43 / Chapter 4.3 --- Auxiliary Problem Formulation --- p.45 / Chapter 4.4 --- Efficient Frontier in Multi-period Portfolio Selection --- p.53 / Chapter 4.5 --- Obseravtions --- p.58 / Chapter 4.6 --- Solution Algorithm for Problem E (w) --- p.62 / Chapter 4.7 --- Illstrative Examples --- p.63 / Chapter 4.8 --- Verification with Single-period Efficient Frontier --- p.72 / Chapter 4.9 --- Generalization to Cases with Nonlinear Utility Function of E (xT) and Var (xT) --- p.75 / Chapter 5 --- Dynamic Portfolio Selection without Risk-less Assets --- p.84 / Chapter 5.1 --- Construction of Auxiliuary Problem --- p.88 / Chapter 5.2 --- Analytical Solution for Efficient Frontier --- p.89 / Chapter 5.3 --- Reduction to Investment Situations with One Risk-free Asset --- p.101 / Chapter 5.4 --- "Multi-period Portfolio Selection via Maximizing Utility function U(E {xT),Var (xT))" --- p.103 / Chapter 6 --- Conclusions and Recommendations --- p.108 / Chapter 6.1 --- Summaries and Achievements --- p.108 / Chapter 6.2 --- Future Studies --- p.110 / Chapter 6.2.1 --- Constrained Investment Situations --- p.110 / Chapter 6.2.2 --- Including Higher Moments --- p.111

Li, Ming Hin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 42-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Inference for Fractional Cointegration --- p.5 / Chapter 2.1 --- Concept of Fractional Cointegration --- p.5 / Chapter 2.1.1 --- Fractional Integration --- p.5 / Chapter 2.1.2 --- Fractional Cointegration --- p.8 / Chapter 2.2 --- Fractional Cointegration Modeling --- p.9 / Chapter 2.2.1 --- Engle-Granger's Methodology --- p.9 / Chapter 2.2.2 --- Johansen's Methodology --- p.10 / Chapter 2.2.2.1 --- Maximum Likelihood Estimators --- p.12 / Chapter 2.2.2.2 --- Cofractional Rank Test --- p.16 / Chapter 3 --- Pairs Trading Strategy --- p.19 / Chapter 3.1 --- Statistical Arbitrage --- p.19 / Chapter 3.2 --- Fractional Cointegration Pairs Trading --- p.20 / Chapter 3.2.1 --- Trading Procedures --- p.22 / Chapter 4 --- Empirical Study --- p.27 / Chapter 4.1 --- Backgrounds --- p.27 / Chapter 4.2 --- Settings --- p.28 / Chapter 4.3 --- Empirical Results --- p.29 / Chapter 5 --- Conclusions and Further Research --- p.39 / Bibliography --- p.42

O Franchising apresentou um crescimento expressivo no Brasil nas últimas duas décadas. O principal marco regulatório se deu com o advento da Lei 8.955 de 1994. Tal crescimento é expresso tanto em número de franquias instaladas, quanto na diversidade dos segmentos das mesmas. Em função disso, uma questão recorrente é justamente saber à qual franquia o pretenso franqueado deve aderir dada uma gama de opções colocada à sua disposição pelo mercado. Com o objetivo de auxiliar a solução desse problema, o presente trabalho propõe uma sistemática baseada no uso de método multicritério e simulação. Primeiramente são identificados tanto na literatura, quanto em coleta de informações provenientes de trabalhos de campo, os critérios balizadores da escolha de franquias. Posteriormente e com base nestes critérios, realizam-se análises de cunho econômico financeiro onde geram-se como produtos dois rankings: Um proveniente do uso de método de decisão multicritério e outro proveniente de avaliação rentabilidade-risco realizada através do uso de simulação. Assim, considerando semelhanças e diferenças entre esses dois rankings, um pretenso franqueado poderia, seguindo esta proposta de sistemática estruturada, optar pela adesão à franquia mais atraente. Essa é a principal contribuição de cunho prático. Já a principal contribuição de cunho acadêmico é suprir lacunas existentes na literatura, principalmente pelo fato de tratar o assunto franchising conjugado com o uso de métodos estatísticos e matemáticos. / In the last two decades franchising has grown significantly in Brazil. The main regulation mark occurred with the advent of 8.955/1994 Franchise Law. Such growth is expressed both in the number of franchises installed as well in the diversity of their segments. Accordingly, a recurring issue is the question to which franchise the prospective franchisee must choose considering a range of options at its disposal in the market. With the objective of helping to solve this problem, the present work proposes a system based on the use of multicriteria method and simulation. Firstly, the criteria for choosing franchises are identified both in the literature and in the collection of information from work fields. Subsequently, based on these criteria, economic and financial analysis is carried out where two rankings are generated as products: One deriving from the use of a multi-criteria decision method and the other one from a profitability-risk valuation carried out through the use of simulation. Thus, considering similarities and differences between these two rankings, a prospective franchisee could, following this proposal of structured system choose the most attractive franchise. This is the main practical contribution since the main benefit of academic nature is to fill gaps in the literature, mainly to deal with the franchising subject in conjunction with the use of statistical and mathematical methods.

As we know, the deterministic LQ problems are well-posed if the state weighting matrix and the control weighting matrix are nonnegative and positive definite in the cost function, respectively. Some practical problems, however, often include indefinite weighting matrices in their cost functions such as mean-variance portfolio selection problem. This inspires us to further study the indefinite LQ problems in detail. / In this thesis, we study indefinite stochastic linear-quadratic (LQ) control with jumps and present some financial applications of this new development. / The results of the above LQ control problems are employed to deal with a mean-variance portfolio selection model in an incomplete financial market. An optimal analytical investment strategy is directly derived and the expression of its risk is explicitly presented. In addition, a mean-variance portfolio selection model in a financial market where shorting is not allowed is investigated in detail via the stochastic LQ problem with nonnegative controls. In particular, the explicit expression of the efficient frontier enables an investor to better understand the relation between the expected terminal wealth and the risk in a stock market with no-shorting. / The weighting matrices in the cost function are allowed to be indefinite (in particular, negative) when the diffusion term linearly depends on the control variable in the state equation. In this case, indefinite stochastic LQ control problems with jumps may still be sensible and well-posed. In an infinite time horizon, solvability of coupled generalized algebraic Riccati equations (CGAREs) is sufficient for the well-posedness of the stochastic LQ control problem with jumps. Moreover, an approach algorithm is devised to solve the CGAREs via semi-definite programming over linear matrix inequalities. On the other hand, it is shown that the well-posedness of the stochastic LQ control problem in a finite time horizon with jumps is equivalent to solvability of coupled generalized Riccati equations. / Li Xun. / "November 2000." / Advisers: Cai Xiaoqiang; Zhou Xunyu. / Source: Dissertation Abstracts International, Volume: 61-10, Section: B, page: 5541. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 115-122). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Wu Hiu-fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 86-89). / Abstracts in English and Chinese. / Abstract --- p.i / Table of Contents --- p.iv / List of Tables --- p.iii / List of Figures --- p.iv / Chapter I. --- Introduction --- p.1 / Chapter II. --- Literature Review --- p.10 / Chapter A. --- Existence and Persistence of Momentum Profitability --- p.10 / Chapter B. --- Potential Sources of Momentum Profitability and Behavioral Models Explanations --- p.11 / Chapter C. --- Evaluations of Possible Explanations --- p.14 / Chapter III. --- Data and Methodology --- p.18 / Chapter A. --- Portfolio Formation --- p.18 / Chapter B. --- Risk-Adjusted Momentum Profits --- p.20 / Chapter 1. --- Capital Asset Pricing Model (CAPM) (Sharpe (1964) and Lintner (1965)) --- p.21 / Chapter 2. --- Fama and French Three-Factor Model (Fama and French (1996)) --- p.21 / Chapter 3. --- Chordia and Shivakumar Four-Factor Model (Chordia and Shivakumar (2001)) --- p.21 / Chapter C. --- Momentum Profitability and Firm Characteristics --- p.24 / Chapter D. --- Two Alternative Momentum Strategies: Stock Specific Return Strategy and Factor Related Return Strategy --- p.28 / Chapter IV. --- Empirical Results --- p.30 / Chapter A. --- Momentum Profitability in the Hong Kong Stock Market --- p.30 / Chapter B. --- Profitability of Momentum Portfolios in Each Calendar Month --- p.33 / Chapter C. --- Properties of Momentum Portfolios --- p.34 / Chapter D. --- Risk- adjusted Return of the Zero-cost Momentum Portfolio --- p.36 / Chapter E. --- Long-run Profitability of Momentum Portfolios --- p.37 / Chapter F. --- Momentum Profitability and Firm Characteristics --- p.42 / Chapter 1. --- Momentum Profitability and Firm Size --- p.42 / Chapter 2. --- Momentum Profitability and Book-to-market Ratio --- p.44 / Chapter 3. --- Momentum Profitability and Trading Volume --- p.45 / Chapter 4. --- Momentum Profitability and Stock Price --- p.46 / Chapter 5. --- Momentum Profitability and Industry Classifications --- p.46 / Chapter 6. --- Momentum Profitability and Analyst Coverage --- p.48 / Chapter 7. --- "Momentum Profitability, Firm Size and Book-to-market Ratio" --- p.49 / Chapter 8. --- "Momentum Profitability, Firm Size and Trading Volume" --- p.50 / Chapter 9. --- "Momentum Profitability, Book-to-market Ratio and Trading Volume" --- p.51 / Chapter 10. --- "Momentum Profitability, Firm Size and Analyst Coverage" --- p.52 / Chapter 11. --- "Momentum Profitability, Book-to-market and Analyst Coverage" --- p.52 / Chapter G. --- Two Alternative Momentum Strategies: Stock Specific Return Strategy and Factor Related Return Strategy --- p.53 / Chapter V. --- Conclusion --- p.57 / Reference --- p.86