Multilayer Aggregation with Statistical Validation: Application to Investor Networks

Multilayer networks are attracting growing attention in many fields, including finance. In this paper, we develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other improvements to their analysis: transaction bootstrapping and investor categorization. The aggregation procedure can be used to integrate security-wise and time-wise information about investor trading networks, but it is not limited to finance. In fact, it can be used for different applications, such as gene, transportation, and social networks, were they inferred or observable. Additionally, in the investor network inference, we use transaction bootstrapping for better statistical validation. Investor categorization allows for constant size networks and having more observations for each node, which is important in the inference especially for less liquid securities. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004-2009. We find that households in the capital have high centrality in investor networks, which, under the theory of information channels in investor networks suggests that they are well-informed investors

The impact of cellular characteristics on the evolution of shape homeostasis

The importance of individual cells in a developing multicellular organism is well known but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarisation makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behaviour is driven by an intra-cellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF) and cell polarisation were either present or absent. The results show that LGF has the largest positive impact on the fitness of the evolved solutions. SGF and polarisation also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behaviour is most likely to evolve when cells are endowed with long range communication, cell polarisation, and selection pressure from an unstable environment

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Dynamic Interaction Networks in modelling and predicting the behaviour of multiple interactive stock markets

The behaviour of multiple stock markets can be described within the framework of complex dynamic systems. A representative technique of the framework is the dynamic interaction network (DIN), recently developed in the bioinformatics domain. DINs are capable of modelling dynamic interactions between genes and predicting their future expressions. In this paper, we adopt a DIN approach to extract and model interactions between stock markets. The network is further able to learn online and updates incrementally with the unfolding of the stock market time-series. The approach is applied to a case study involving 10 market indexes in the Asia Pacific region. The results show that the DIN model reveals important and complex dynamic relationships between stock markets, demonstrating the ability of complex dynamic systems approaches to go beyond the scope of traditional statistical methods

Fostering innovation in a small open economy: The case of the New Zealand biotechnology sector

The New Zealand Biotechnology sector is worthy of study for several reasons. While there is a large and growing international literature on economic aspects of biotechnology innovation these studies concentrate on the United States and Europe. The New Zealand biotechnology sector may be expected to develop along a different trajectory as a consequence of a markedly different set of initial and framework conditions. Government has indicated a strong interest in fostering innovation and aims to concentrate on selected areas where New Zealand may be able to develop a new comparative advantage. One such area is biotechnology, which would build on New Zealand’s existing comparative advantage in the primary sector (dairy, forestry, meat, wool and horticulture). This paper describes the preliminary results of an ongoing study that aims to fill some of the gaps in our knowledge of innovation processes in New Zealand while using the international literature as a benchmark. The paper focuses on the drivers of innovation in the biotechnology sector; the role of networks and other linkages; the role of government and industry, the role of human and venture capital, and data from patenting

Conserving Crop Genetic Resources on Smallholder Farms in Hungary: Institutional Analysis

Hungary is home to a great diversity of plant and animal species, whose preservation is of global value. This paper focuses on the institutional aspects of the research project on on-farm conservation of crop genetic resources in three Environmentally Sensitive Areas of Hungary (Dévaványa, Orség-Vendvidék, Szatmár-Bereg). Implemented by the Institute of Environmental Management, St. István University and the Institute for Agrobotany in partnership with the International Plant Genetic Resources Institute, the project consists of an interdisciplinary institutional, economic, and scientific analysis. The main goal of the project is to develop a scientific understanding about the current and potential socio-economic role of agrobiodiversity maintained in home gardens. The first aim of the institutional analysis carried out by this paper is to identify the institutions and organisations that have significant impact on the seed choices and seed maintenance practices of farmers, and hence, on their access to genetic resources. The second aim is to identify and analyse different stakeholders’ perceptions of the issue at hand, as well as their interests and the values they ascribe to them.Crop genetic resources, Agro-biodiversity, Institutional analysis, Stakeholder analysis, New institutional economics, Qualitative research methods

Intellectual Property Rights and Biotechnology: How to Improve the Present Patent System

This paper discusses the problems related to assigning or denying intellectual property rights to biotechnological innovation, with particular reference to agro-biotechnologies and the relations between developed and developing countries. There are two types of problems to consider. First, the aim of protecting property rights on innovations is to create incentives towards research and innovation in general, which in some cases may be beneficial to society, in others not so. If the assignment of an intellectual property right does not guarantee the potential beneficial use of new knowledge, not assigning rights would not prevent its potentially dangerous utilization. Secondly, the holder of an intellectual property right has a power of exclusion which limits access by others to the newly produced knowledge. However, the production of new knowledge is very often a process which starts from a base of existing knowledge. Hence, discouraging access to existing knowledge also means discouraging the process of producing new knowledge. Paradoxically then, in protecting intellectual property we obtain the opposite result to the one expected and desired. Moreover, the holder of an intellectual property right may end up with excessive market power when commercializing the innovation. This paper will try to show that these problems cannot be solved, as sometimes is suggested, by denying protection of property rights on innovations, but by improving the procedures for awarding these rights and accompanying them with other measures such as liability rules governing potential damage and also antitrust measures.Intellectual property rights, Biotechnology, Patent system

Economic resilience : including a case study of the global transition network

This paper explores the dynamic properties of organisms and ecosystems that make them so resilient and capable of adapting to changing circumstances, allowing them to maintain an overall condition of coherence, wholeness and health while living in balance within the resources of the planet. Key principles of resilient ecological systems are explored including: self-regulation; positive and negative feedback; diversity; scale and context; cooperation; emergence and novelty; and ecological tipping points. In contrast, market based economic systems can produce unstable growth with unintended destruction of cultural and species diversity and homogenisation of global life-styles. The paper re-examines fundamental economic principles using insights from biological evolution and ecosystem dynamics to establish a foundation for more resilient economies. This involves experimenting with different models in different communities to find patterns of sustainable production and exchange appropriate to local regions. Fundamental steps in this direction include the emergence of self-organising local communities based on creative experimentation, re-localisation of core sectors of the economy (food, energy, health and education), evolution of local currencies and banking practices that support local enterprise and investment in green technologies, stimulation of decentralised renewable energy networks and economic reform aligned with ecological principles. The Transition Network provides a case study of an international community based movement that has been experimenting with putting some of these principles into practice at the local level. The aim of the Transition Network is to support community led responses to peak oil and climate change, building resilience and well-being. The concept of ecological resilience and its application to local economy is hard wired into the values and emerging structure of the network of transition communities across the globe. The movement started in the UK in 2005 and there are now over 1000 Transition initiatives spanning 34 countries across the world. Many attribute the success and phenomenal growth of the Transition Network to its emerging holographic structure that mimics cell growth within living organisms. Growing a more resilient food system in the face of the twin challenges of natural resource scarcity and climate change is central to the Transition movement. A set of principles for a post carbon resilient food economy in the UK are offered. These include an 80% cut in carbon emission in the food sector by 2050, agricultural diversification, prioritization of farming methods that establish and enhance carbon sinks, phasing out of dependence on fossil fuels in food growing, processing and distribution, promoting access to nutritious and affordable food, as well as promoting greater access to land for growing food in urban and peri-urban areas. Practical examples of Transition related projects in the food sector are presented across the following themes: access to land, low carbon production methods, food distribution systems, health and community gardens and orchards, and collaborative ownership models

Distributed Stochastic Market Clearing with High-Penetration Wind Power

Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this paper based on the DC optimal power flow (OPF). The objective is to minimize the social cost which consists of conventional generation costs, end-user disutility, as well as a risk measure of the system re-dispatching cost. Capitalizing on the conditional value-at-risk (CVaR), the novel model is able to mitigate the potentially high risk of the recourse actions to compensate wind forecast errors. The resulting convex optimization task is tackled via a distribution-free sample average based approximation to bypass the prohibitively complex high-dimensional integration. Furthermore, to cope with possibly large-scale dispatchable loads, a fast distributed solver is developed with guaranteed convergence using the alternating direction method of multipliers (ADMM). Numerical results tested on a modified benchmark system are reported to corroborate the merits of the novel framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9 figure