Essays on robust portfolio selection and pension finance

This thesis examines three different, but related problems in the broad area of portfolio management for long-term institutional investors, and focuses mainly on the case of pension funds. The first idea (Chapter 3) is the application of a novel numerical technique – robust optimization – to a real-world pension scheme (the Universities Superannuation Scheme, USS) for first time. The corresponding empirical results are supported by many robustness checks and several benchmarks such as the Bayes-Stein and Black-Litterman models that are also applied for first time in a pension ALM framework, the Sharpe and Tint model and the actual USS asset allocations. The second idea presented in Chapter 4 is the investigation of whether the selection of the portfolio construction strategy matters in the SRI industry, an issue of great importance for long term investors. This study applies a variety of optimal and naïve portfolio diversification techniques to the same SRI-screened universe, and gives some answers to the question of which portfolio strategies tend to create superior SRI portfolios. Finally, the third idea (Chapter 5) compares the performance of a real-world pension scheme (USS) before and after the recent major changes in the pension rules under different dynamic asset allocation strategies and the fixed-mix portfolio approach and quantifies the redistributive effects between various stakeholders. Although this study deals with a specific pension scheme, the methodology can be applied by other major pension schemes in countries such as the UK and USA that have changed their rules.

Innovative implementation of iLearn e-Portfolio for Personal Development Planning

iLearn is a Web 2.0 tool developed in Blackboard to help students with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the students. The e-Portfolio tool benefits students as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Students find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables students to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the assignments and how students make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, students become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for students to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the students’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their Personal Tutorial meetings and provide their Personal Tutor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their student experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares for the workplace and life beyond University including continuing professional development. Students are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for students to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

Innovative use of e-portfolio for personal development

iLearn is a quasi-Web 2.0 tool developed in Blackboard to help users with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the users, who are training to be professionals in construction management and surveying, The e-Portfolio tool benefits users as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Users find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables learners to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the tasks and how they make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, learners become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for them to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the users’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their supervision meetings and provide their supervisor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their learning experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares them for the workplace including continuing professional development. Users are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for them to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

A benchmark method for global optimization problems in structure determination from powder diffraction data

Quasi-Newton-Raphson minimization and conjugate gradient minimization have been used to solve the crystal structures of famotidine form B and capsaicin from X-ray powder diffraction data and characterize the chi(2) agreement surfaces. One million quasi-Newton-Raphson minimizations found the famotidine global minimum with a frequency of ca 1 in 5000 and the capsaicin global minimum with a frequency of ca 1 in 10 000. These results, which are corroborated by conjugate gradient minimization, demonstrate the existence of numerous pathways from some of the highest points on these chi(2) agreement surfaces to the respective global minima, which are passable using only downhill moves. This important observation has significant ramifications for the development of improved structure determination algorithms.

Optimization of protoplast production in white lupin

The influence, was investigated, of abiotic parameters on the isolation of protoplasts from in vitro seedling cotyledons of white lupin. The protoplasts were found to be competent in withstanding a wide range of osmotic potentials of the enzyme medium, however, −2.25 MPa (0.5 M mannitol), resulted in the highest yield of protoplasts. The pH of the isolation medium also had a profound effect on protoplast production. Vacuum infiltration of the enzyme solution into the cotyledon tissue resulted in a progressive drop in the yield of protoplasts. The speed and duration of orbital agitation of the cotyledon tissue played a significant role in the release of protoplasts and a two step (stationary-gyratory) regime was found to be better than the gyratory-only system.

Optimization of electroless copper plating on polyethylene films modified by surface grafting of vinyl ether of monoethanolamine

Metallized plastics have recently received significant interest for their useful applications in electronic devices such as for integrated circuits, packaging, printed circuits and sensor applications. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. There are several techniques for metal deposition on surface of polymers such as evaporation, sputtering, electroless plating and electrolysis. In this work the metallized films were developed by electroless copper plating of polyethylene films grafted with vinyl ether of monoethanoleamine. Polyethylene films were subjected to gamma-radiation induced surface graft copolymerization with vinyl ether of monoethanolamine. Electroless copper plating was carried out effectively on the modified films. The catalytic processes for the electroless copper plating in the presence and the absence of SnCl2 sensitization were studied and the optimum activation conditions that give the highest plating rate were determined. The effect of grafting degree on the plating rate is studied. Electroless plating conditions (bath additives, pH and temperature) were optimized. Plating rate was determined gravimetrically and spectrophotometrically at different grafting degrees. The results reveal that plating rate is a function of degree of grafting and increases with increasing grafted vinyl ether of monoethanolamine onto polyethylene. It was found that pH 13 of electroless bath and plating temperature 40°C are the optimal conditions for the plating process. The increasing of grafting degree results in faster plating rate at the same pH and temperature. The surface morphology of the metallized films was investigated using scanning electron microscopy (SEM). The adhesion strength between the metallized layer and grafted polymer was studied using tensile machine. SEM photos and adhesion measurements clarified that uniform and adhered deposits were obtained under optimum conditions.

Search space pruning and global optimization of multiple gravity assist trajectories with deep space manoeuvres

This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.

RBF equalizer design using directional evolutionary multi-objective optimization

Whilst radial basis function (RBF) equalizers have been employed to combat the linear and nonlinear distortions in modern communication systems, most of them do not take into account the equalizer's generalization capability. In this paper, it is firstly proposed that the. model's generalization capability can be improved by treating the modelling problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets. Then, as a modelling application, a new RBF equalizer learning scheme is introduced based on the directional evolutionary MOO (EMOO). Directional EMOO improves the computational efficiency of conventional EMOO, which has been widely applied in solving MOO problems, by explicitly making use of the directional information. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good performance not only on explaining the training samples but on predicting the unseen samples.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.