Portfolio Selection via Composite Measure Analysis: Evidence from the U.S. Stock Market

The aim of this research is to examine the pricing anomalies existing in the U.S. market during 1986 to 2011. The sample of stocks is divided into decile portfolios based on seven individual valuation ratios (E/P, B/P, S/P, EBIT/EV, EVITDA/EV, D/P, and CE/P) and price momentum to investigate the efficiency of individual valuation ratio and their combinations as portfolio formation criteria. This is the first time in financial literature when CE/P is employed as a constituent of composite value measure. The combinations are based on median scaled composite value measures and TOPSIS method. During the sample period value portfolios significantly outperform both the market portfolio and comparable glamour portfolios. The results show the highest return for the value portfolio that was based on the combination of S/P & CE/P ratios. The outcome of this research will increase the understanding on the suitability of different methodologies for portfolio selection. It will help managers to take advantage of the results of different methodologies in order to gain returns above the market.

Green supplier selection via Multiple Criteria Data Envelopment Analysis

An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Service product internationalisation : market selection for new service products in a multinational financial service firm

The significance and impact of services in the modern global economy has become greater and there has been more demand for decades in the academic community of international business for further research into better understanding internationalisation of services. Theories based on the internationalisation of manufacturing firms have been long questioned for their applicability to services. This study aims at contributing to understanding internationalisation of services by examining how market selection decisions are made for new service products within the existing markets of a multinational financial service provider. The study focused on the factors influencing market selection and the study was conducted as a case study on a multinational financial service firm and two of its new service products. Two directors responsible for the development and internationalisation of the case service products were interviewed in guided semi-structured interviews based on themes adopted from the literature review and the outcome theoretical framework. The main empirical findings of the study suggest that the most significant factors influencing the market selection for new service products within a multinational financial service firm’s existing markets are: commitment to the new service products by both the management and the rest of the product related organisation; capability and competence by the local country organisations to adopt new services; market potential which combines market size, market structure and competitive environment; product fit to the market requirements; and enabling partnerships. Based on the empirical findings, this study suggests a framework of factors influencing market selection for new service products, and proposes further research issues and methods to test and extend the findings of this research.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Review of investment strategy, portfolio management, and scorecard based portfolio selection tool for direct property investments in Finland

The lack of research of private real estate is a well-known problem. Earlier studies have mostly concentrated on the USA or the UK. Therefore, this master thesis offers more information about the performance and risk associated with private real estate investments in Nordic countries, but especially in Finland. The structure of this master thesis is divided into two independent sections based on the research questions. In first section, database analysis is performed to assess risk-return ratio of direct real estate investment for Nordic countries. Risk-return ratios are also assessed for different property sectors and economic regions. Finally, review of diversification strategies based on property sectors and economic regions is performed. However, standard deviation itself is not usually sufficient method to evaluate riskiness of private real estate. There is demand for more explicit assessment of property risk. One solution is property risk scoring. In second section risk scorecard based tool is built to make different real estate comparable in terms of risk. In order to do this, nine real estate professionals were interviewed to enhance the structure of theory-based risk scorecard and to assess weights for different risk factors.

Healthcare Information System Selection Model for Medical Clinics

The issue of selecting an appropriate healthcare information system is a very essential one. If implemented healthcare information system doesn’t fit particular healthcare institution, for example there are unnecessary functions; healthcare institution wastes its resources and its efficiency decreases. The purpose of this research is to develop a healthcare information system selection model to assist the decision-making process of choosing healthcare information system. Appropriate healthcare information system helps healthcare institutions to become more effective and efficient and keep up with the times. The research is based on comparison analysis of 50 healthcare information systems and 6 interviews with experts from St-Petersburg healthcare institutions that already have experience in healthcare information system utilization. 13 characteristics of healthcare information systems: 5 key and 7 additional features are identified and considered in the selection model development. Variables are used in the selection model in order to narrow the decision algorithm and to avoid duplication of brunches. The questions in the healthcare information systems selection model are designed to be easy-to-understand for common a decision-maker in healthcare institution without permanent establishment.

Design and Material Selection of High-Speed Rotating Electrical Machines

The increasing emphasis on energy efficiency is starting to yield results in the reduction in greenhouse gas emissions; however, the effort is still far from sufficient. Therefore, new technical solutions that will enhance the efficiency of power generation systems are required to maintain the sustainable growth rate, without spoiling the environment. A reduction in greenhouse gas emissions is only possible with new low-carbon technologies, which enable high efficiencies. The role of the rotating electrical machine development is significant in the reduction of global emissions. A high proportion of the produced and consumed electrical energy is related to electrical machines. One of the technical solutions that enables high system efficiency on both the energy production and consumption sides is high-speed electrical machines. This type of electrical machines has a high system overall efficiency, a small footprint, and a high power density compared with conventional machines. Therefore, high-speed electrical machines are favoured by the manufacturers producing, for example, microturbines, compressors, gas compression applications, and air blowers. High-speed machine technology is challenging from the design point of view, and a lot of research is in progress both in academia and industry regarding the solution development. The solid technical basis is of importance in order to make an impact in the industry considering the climate change. This work describes the multidisciplinary design principles and material development in high-speed electrical machines. First, high-speed permanent magnet synchronous machines with six slots, two poles, and tooth-coil windings are discussed in this doctoral dissertation. These machines have unique features, which help in solving rotordynamic problems and reducing the manufacturing costs. Second, the materials for the high-speed machines are discussed in this work. The materials are among the key limiting factors in electrical machines, and to overcome this limit, an in-depth analysis of the material properties and behavior is required. Moreover, high-speed machines are sometimes operating in a harsh environment because they need to be as close as possible to the rotating tool and fully exploit their advantages. This sets extra requirements for the materials applied.

Habitat selection of three rodents in a frequently burned boreal environment : productivity and habitat diversity

How does fire affect the plant and animal community of the boreal forest? This study attempted to examine the changes in plant composition and productivity, and small mammal demography brought about by fire in the northern boreal environment at Chick Lake, N.W.T. (65053fN, 128°14,W). Two 5*6 ha plots measuring 375m x 150m were selected for study during the summers of 1973 and 197^. One had been unburned for 120 years, the other was part of a fire which burned in the spring of 1969. Grids of 15m x 15m were established in each plot and meter square quadrats taken at each of the 250 grid intersections in order to determine plant composition and density. Aerial primary production was assessed by clipping and drying 80 samples of terminal new production for each species under investigation. Small mammal populations were sampled by placing a Sherman live trap at each grid intersection for ten days in every month. The two plots were similar in plant species composition which suggested that most regrowth in the burned area was from rootstocks which survived the fire. The plant data were submitted to a cluster analysis that revealed nine separate species associations, six of which occured in the burned area and eight of which occured in the control. These were subsequently treated as habitats for purposes of comparison with small mammal distributions. The burned area showed a greater productivity in flowers and fruits although total productivity in the control area was higher due to a large contribution from the non-vascular component. Maximum aerial productivity as dry wieght was measured at 157.1 g/m and 207.8 g/m for the burn and control respectively. Microtus pennsylvanicus and Clethrionomys rutilus were the two most common small mammals encountered; Microtus xanthognathus, Synaptomys borealis, and Phenacomys intermedius also occured in the area. Populations of M. pennsylvanicus and C. rutilus were high during the summer of 1973; however, M. pennsylvanicus was rare on the control but abundant on the burn, while C. rutilus was rare on the burn but abundant in the control. During the summer of 197^ populations declined, with the result that few voles of any species were caught in the burn while equal numbers of the two species were caught in the control. During the summer of 1973 M. pennsylvanicus showed a positive association to the most productive habitat type in the burn which was avoided by C. rutilus. In the control £• rutilus showed a similar positive association to the most productive habitat type which was avoided by M. pennsylvanicus. In all cases for the high population year of 1973# the two species never overlapped in habitat preference. When populations declined in 197^f "both species showed a strong association for the most productive habitat in the control. This would suggest that during a high population year, an abundant species can exclude competitors from a chosen habitat, but that this dominance decreases as population levels decrease. It is possible that M. pennsylvanicus is a more efficient competitor in a recently burned environment, while C. rutilus assumes this role once non-vascular regrowth becomes extensive.