Optimal investment decisions often rely on assumptions about the models and their associated parameter values. Therefore, it is essential to assess suitability of these assumptions and to understand sensitivity of outcomes when they are altered. More importantly, appropriate approaches should be developed to achieve a robust decision. In this thesis, we carry out a sensitivity analysis on parameter values as well as model speci cation of an important problem in portfolio management, namely the optimal portfolio execution problem. We then propose more robust solution techniques and models to achieve greater reliability on the performance of an optimal execution strategy. The optimal portfolio execution problem yields an execution strategy to liquidate large blocks of assets over a given execution horizon to minimize the mean of the execution cost and risk in execution. For large-volume trades, a major component of the execution cost comes from price impact. The optimal execution strategy then depends on the market price dynamics, the execution price model, the price impact model, as well as the choice of the risk measure. In this study, rst, sensitivity of the optimal execution strategy to estimation errors in the price impact parameters is analyzed, when a deterministic strategy is sought to minimize the mean and variance of the execution cost. An upper bound on the size of change in the solution is provided, which indicates the contributing factors to sensitivity of an optimal execution strategy. Our results show that the optimal execution strategy and the e cient frontier may be quite sensitive to perturbations in the price impact parameters. Motivated by our sensitivity results, a regularized robust optimization approach is devised when the price impact parameters belong to some uncertainty set. We rst illustrate that the classical robust optimization might be unstable to variation in the uncertainty set. To achieve greater stability, the proposed approach imposes a regularization constraint on the uncertainty set before being used in the minimax optimization formulation. Improvement in the stability of the robust solution is discussed and some implications of the regularization on the robust solution are studied. Sensitivity of the optimal execution strategy to market price dynamics is then investigated. We provide arguments that jump di usion models using compound poisson processes naturally model uncertain price impact of other large trades. Using stochastic dynamic programming, we derive analytical solutions for minimizing the expected execution cost under jump di usion models and compare them with the optimal execution strategies obtained from a di usion process. A jump di usion model for the market price dynamics suggests the use of Conditional Value-at-Risk (CVaR) as the risk measure. Using Monte Carlo simulations, a smoothing technique, and a parametric representation of a stochastic strategy, we investigate an approach to minimize the mean and CVaR of the execution cost. The devised approach can further handle constraints using a smoothed exact penalty function.

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New Ventures projects emerge in response to the growing need of countries to develop and grow economically in an environment characterized by rapid changes. The importance of these projects is such that during the last decades they have played a role not only as drivers of the economy but also as sources of new jobs and innovation (Chen, 2009). Due to this importance, there have been multiple studies related to the efficient management of such projects. However, it is not sufficient for these projects to be managed properly, but the presence of limited resources makes necessary to select, prioritize and control these projects strategically within a portfolio.   This strategic management can be carried out by using the theory developed in Project Portfolio Management (PPM). The importance of PPM is the ability to integrate the world of projects with the operation of organizations, helping to minimize failures such as making unnecessary effort to undertake these projects in an appropriate manner when in fact these are not the right projects.  However, there is a lack of knowledge in the application of PPM theory for New Ventures projects, because their characteristics differ from those of any other type of projects in terms of high level of risk and, in many cases, high technical uncertainty (Mac Millan & Gunther, 2000).  This knowledge gap can be minimized using two different approaches. The first one consists in employing the theory developed by PPM in R&D projects, applying it for New Venture projects, as suggested by Mac Millan & Gunther (2000). The second approach corresponds to using the theory developed around the management of projects within Business Incubators (BIs) and Venture Capital firms (VC) in every stage of the PPM process.   This study describes how BIs and VCs in Sweden manage their New Venture projects portfolios in issues such as selection, prioritization and monitoring and control. To achieve an adequate depiction of this process, the study seeks primarily to identify the role of BIs and VCs in the PPM and the proper relationship that should exist between both organizations to ensure an ideal flow of projects at each stage of their development. In addition, it also seeks to find whether tools outlined in the literature are often used in practice.   Among the main findings of the study, the major contribution of the BIs is mainly in the feasibility analysis of projects and the support they give in their development, while VC firms are usually more focused on the selection, prioritization and monitoring and control of their portfolios. In practice there have been shortcomings in the transition of New Venture projects between BIs and VCs. These can be solved by creating a single organization that integrates the entire process of PPM between BIs and VCs, or other alternative is for VCs to start investing mainly in early stage projects.  Another important finding corresponds to the use of the expertise of BIs and VCs members as the most important tool when making strategic decisions. And although there is general satisfaction with the success of these projects in Sweden, some authors have argued that this industry is not totally mature. Therefore, this study suggests using some tools, proposed in a conceptual model, developed to achieve the maturity that New Venture projects industry requires.

“While project management and program management have traditionally focused on ‘doing work right’, portfolio management is concerned with ‘doing the right work’” (PMI,2006) Nowadays organizations are facing problems with too many projects and having limited resources to execute these projects. Therefore the role of portfolio control is gaining more importance to yield the right balance, mix and number of projects, and also to deal with the challenge of maximizing the value of the portfolio. Therefore the organizations rely on effective portfolio management and are developing new methods to deal with these challenges. Hence present study involves study of those organizations that rely on portfolio control techniques to effectively manage their portfolio of projects.   The aim of this research is to investigate the role of portfolio control techniques in achieving efficiency in project based firms, examine relationship between control techniques and the portfolio efficiency, and to find the role of contextual factors like project and governance type in impacting the portfolio efficiency. Three portfolio control factors: portfolio selection, portfolio reporting, and decision making style were identified and portfolio efficiency was explained by two measures: achievement of portfolio results and achievement of project and program level purpose.   The research was conducted at two multinational organizations, a pharmaceutical company in Europe and engineering and contracting transportation company in Asia. Case study research strategy was used, and data was collected through semi- structured interviews to investigate the impact of using these portfolio control techniques in a project based firms.   The results of the research indicate that these control techniques helps to select and analyse the portfolio from strategic, financial and risk perspective. Furthermore it helps to balance the organizational priorities by taking into consideration project type, market sector, resource constraints and product lines. The portfolio control techniques also involve portfolio reporting which is considered as formal way of communication and information sharing and is believed to be significant project-level factor contributing to portfolio efficiency. Lastly, portfolio decision making helps the organizations in making the right decision in the best interest of the organization. All these control variables were found to have a significant impact on achieving results and achieving project and programme level purpose which in our research are the dimensions of portfolio efficiency.   In our study we also found that there exists a positive relationship between the portfolio control techniques and portfolio efficiency which is affected by the contextual variables such as project type, governance type, organizational complexity, co-localization of team members, communication and clarity of goals and objectives.

This study constructs a quantitative stock selection model across multiple sectors with the application of the Bayesian method. It employees factors from the Taiwan stock market which could explain stock returns. Under this structure, each sector that has different significant factors is allowed to be imported into sub models. The factors are calculated into alpha scores and used to do stock selection. Therefore, the demonstration of both intra and inter-sector alpha scores into sector-specific integration alpha scores is an important concept in this study. Furthermore, an enhanced index fund is built based on the model and related to the benchmark to illustrate the power of this model. Once the contents of a portfolio are decided, this model could provide stock selection criterion based on the predictive power of stock return. Finally, the results demonstrate that this model is practical and flexible for local stock portfolio analysis.

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.

With the advent of globalization and knowledge economic era, organizations have to face an increasingly competitive business environment. With limited resources, it is imperative for organizations to allocate them effectively, to focus on potential projects, to choose high value-added projects, and to find out the proper project portfolio. The purpose of this research is to investigate the decision model and the operational mechanism of project portfolio selection for military investment and to analyze the characteristics of related projects. Finally, this research mainly proposes a decision model for project portfolio selection and the measurement of portfolio performance and develops a portfolio selection decision support system to provide a communicative platform and information for decision makers and project managers. This will improve portfolio performance, reduce the crowding out effect of organizational resources, and enhance the linkage of both project and organizational goals.

We analyze four alternative energy mutual funds using a multi-factor capital asset pricing model with generalized autoregressive conditionally heteroskedastic errors (CAPM-GARCH). Our findings will help portfolio managers and others who seek to predict the return on investment in alternative energy firms. We find that alternative energy firms tend to be riskier than the general US stock market, have a low, but significant and positive response to oil prices, and have a significantly high and negative response to the value of the dollar relative to other currencies. Our results also suggest that alternative energy firms should hedge against currency exchange rate fluctuation.

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<p>The financial portfolio model often referred to as the Black-Litterman model is analyzed using two approaches; a mathematical and a behavioral finance approach. After a detailed description of its framework, the Black-Litterman model is derived mathematically using a sampling theoretical approach. This approach generates a new interpretation of the model and gives an interpretable formula for the mystical parameter<b> τ</b>, the weight-on-views. Secondly, implications are drawn from research results within behavioral finance. One of the most interesting features of the Black-Litterman model is that the benchmark portfolio, against which the performance of the portfolio manager is evaluated, functions as the point of reference. According to behavioral finance, the actual utility function of the investor is reference-based and investors estimate losses and gains in relation to this benchmark. Implications drawn from research results within behavioral finance indicate and explain why the portfolio output given by the Black-Litterman model appears more intuitive to fund managers than portfolios generated by the Markowitz model. Another feature of the Black-Litterman model is that the user assigns levels of confidence to each asset view in the form of confidence intervals. Research results within behavioral finance have, however, shown that people tend to be badly calibrated when estimating their levels of confidence. Research has shown that people are overconfident in financial decision-making, particularly when stating confidence intervals. This is problematic. For a deeper understanding of the use of the Black-Litterman model it seems that we should turn to those financial fields in which social and organizational context and issues are taken into consideration, to generate better knowledge of the use of the Black-Litterman model.</p>