A CLUSTERING OF DJA STOCKS - THE APPLICATION IN FINANCE OF A METHOD FIRST USED IN GENE TRAJECTORY STUDY

Previously we employed the Gene Trajectory Clustering methodology to search for different associations of the stocks composing the DJA index, with the aim of finding different, logic clusters, supported by economic reasons, preferably different than theclustering model, data trajectory, cluster analysis

Learning in the Village Economy of Denmark. The role of Institutions and Policy in Sustaining Competitiveness.

The benefits of an international division of labour is never illustrated more clearly than in small developed nations like Denmark. Without many natural resources such countries can never be self sufficient and they need access to foreign markets in order for their firms to specialise and utilize economics of scale. The specialisation chosen is mainly in low-tech goods, where the risk of sudden domestically damaging changes in technology or demand are relatively small. Besides such general features of small developed nations, the Danish case has some special characteristics, which distinguishes it from many other nations and regions. One important feature is the century-old, deep-rooted egalitarian beliefs of the society which during the last century has intermixed with the growth of the public sector in shaping not only the welfare state, but also a strongly consensus-seeking political system - the negotiated economy - incorporating all major groups in the economy. Recently, the development towards a knowledge based world economy has increased the importance of another feature with an small egalitarian country: the kind of trust-relations, that come into existence, when everyone in an industry has known everybody else through many years. The international industrial competitiveness of the country's vast majority of small, export oriented firms are not only favoured by a reasonable adequate macro-economic policy but further enhanced by the ease in the exchange of information resulting from established trust-relations.International competitiveness, small nations, economic development, learning economy, informal institutions

Anatomy of cluster development in China: The case of health biotech clusters

Focussing on China's health biotech clusters the study explores the anatomy of interaction in as well as between various clusters. While the literature has identified the existence of a dense network of durable interactions among firms and between firms and academia at a particular location as one of the most important prerequisites for well-performing clusters, we show that network ties extending beyond regional boundaries are equally valuable for the innovative capacity of China's biotech firms. Analysing the demographic process of cluster emergence we show that there exist different types of biotech clusters in China, which are closely linked and exchange knowledge and technology amongst each other. It appears as if further analysis of these cross-cluster links may provide important insights of how learning and innovation works in China's health biotech industry. Although China's science parks and industrial bases may on an individual basis appear to be badly structured, the organization of China's health biotech industry turns out to be substantially enhanced once these external linkages are taken into consideration. --China,health biotechnology,cluster,entrepreneurship,localization

Health Biotechnology Innovation for Social Sustainability -A Perspective from China

China is not only becoming a significant player in the production of high-tech products, but also an increasingly important contributor of ideas and influence in the global knowledge economy. This paper identifies the promises and the pathologies of the biotech innovation system from the perspective of social sustainability in China, looking at the governance of the system and beyond. Based on The STEPS Centre’s ‘Innovation, Sustainability, Development: A New Manifesto’, a ‘3D’ approach has been adopted, bringing together social, technological and policy dynamics, and focusing on the directions of biotechnological innovation, the distribution of its benefits, costs and risks and the diversity of innovations evolving within it and alongside it

Networks and the Development of the Irish Biotechnology Sector

Biotechnology, an umbrella term describing combinations of engineering and scientific knowledge from an array of disciplines used to produce products and processes from living organisms, has been identified as a key sector for future economic developments among industrialised and industrialising nations as it blurs traditional boundaries between various industries. The Irish Government has introduced a series of initiatives to facilitate the development of an internationally competitive indigenous biotechnology sector since the late 1990s, yet no in-depth analysis of the sector relative to international sectoral characteristics, structures, or policy themes have informed their design or implementation. This thesis analyses the Irish sector in the context of global sectoral developments by studying the Post-Fordist organisational structure of the international sector, where biotechnology firms interact with various actors at different stages of the sectoral value chain in a variety of innovative networks determined by place specific actor and institution endowments that form local knowledge communities. Through qualitatively investigating the Irish sector's actors and collaborative network structure, the thesis analyses the implications of the nature and character of these elements for the sector's future sustainability and development, and appraises existing Government policies relating to sectoral developments. The thesis found that the on-going initiatives have facilitated significant advances, yet have not addressed the legacy of pre-initiative resource and skill capacity weaknesses, while the sectoral value chain is fragmented as actors have developed poor networking arrangements due to their conservative natures, and the relative absence of key sector actors, skills and resources. These issues demonstrate that a complex overarching policy framework is required so as to engender the long-term development of a regionally tailored, systems-based support ecosystem which addresses existing structural weaknesses, and which facilitates and drives entrepreneurial and innovative activities throughout the sector's value chain

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives

Do institutions matter for technological change in transition economies? The case of the Russia's 89 regions and republics

We explore the impact of institutions on technological change in a transition economy. We use regional panel data for Russia's 89 regions and republics during the period of recovery and growth from 1998 to 2004 to show the impact of large variation in institutional development, ranging from full enforcement of property rights in the Northwest to red belt Communist regimes in the southeast. We find an unambiguous relationship between strong and sustained institutional development and technological change. We provide one model proxying the quality of institutions by the investment risk rating compiled by the rating agency ExpertRA Regions

Financing Innovation

We review the recent literature on the financing of innovation, inclusive of large companies and new startups. This research strand has been very active over the past five years, generating important new findings, questioning some long-held beliefs, and creating its own puzzles. Our review outlines the growing body of work that documents a role for debt financing related to innovation. We highlight the new literature on learning and experimentation across multi-stage innovation projects and how this impacts optimal financing design. We further highlight the strong interaction between financing choices for innovation and changing external conditions, especially reduced experimentation costs

Identification des régimes et regroupement des séquences pour la prévision des marchés financiers

Abstract : Regime switching analysis is extensively advocated to capture complex behaviors underlying financial time series for market prediction. Two main disadvantages in current approaches of regime identification are raised in the literature: 1) the lack of a mechanism for identifying regimes dynamically, restricting them to switching among a fixed set of regimes with a static transition probability matrix; 2) failure to utilize cross-sectional regime dependencies among time series, since not all the time series are synchronized to the same regime. As the numerical time series can be symbolized into categorical sequences, a third issue raises: 3) the lack of a meaningful and effective measure of the similarity between chronological dependent categorical values, in order to identify sequence clusters that could serve as regimes for market forecasting. In this thesis, we propose a dynamic regime identification model that can identify regimes dynamically with a time-varying transition probability, to address the first issue. For the second issue, we propose a cluster-based regime identification model to account for the cross-sectional regime dependencies underlying financial time series for market forecasting. For the last issue, we develop a dynamic order Markov model, making use of information underlying frequent consecutive patterns and sparse patterns, to identify the clusters that could serve as regimes identified on categorized financial time series. Experiments on synthetic and real-world datasets show that our two regime models show good performance on both regime identification and forecasting, while our dynamic order Markov clustering model also demonstrates good performance on identifying clusters from categorical sequences.L'analyse de changement de régime est largement préconisée pour capturer les comportements complexes sous-jacents aux séries chronologiques financières pour la prédiction du marché. Deux principaux problèmes des approches actuelles d'identifica-tion de régime sont soulevés dans la littérature. Il s’agit de: 1) l'absence d'un mécanisme d'identification dynamique des régimes. Ceci limite la commutation entre un ensemble fixe de régimes avec une matrice de probabilité de transition statique; 2) l’incapacité à utiliser les dépendances transversales des régimes entre les séries chronologiques, car toutes les séries chronologiques ne sont pas synchronisées sur le même régime. Étant donné que les séries temporelles numériques peuvent être symbolisées en séquences catégorielles, un troisième problème se pose: 3) l'absence d'une mesure significative et efficace de la similarité entre les séries chronologiques dépendant des valeurs catégorielles pour identifier les clusters de séquences qui pourraient servir de régimes de prévision du marché. Dans cette thèse, nous proposons un modèle d'identification de régime dynamique qui identifie dynamiquement des régimes avec une probabilité de transition variable dans le temps afin de répondre au premier problème. Ensuite, pour adresser le deuxième problème, nous proposons un modèle d'identification de régime basé sur les clusters. Notre modèle considère les dépendances transversales des régimes sous-jacents aux séries chronologiques financières avant d’effectuer la prévision du marché. Pour terminer, nous abordons le troisième problème en développant un modèle de Markov d'ordre dynamique, en utilisant les informations sous-jacentes aux motifs consécutifs fréquents et aux motifs clairsemés, pour identifier les clusters qui peuvent servir de régimes identifiés sur des séries chronologiques financières catégorisées. Nous avons mené des expériences sur des ensembles de données synthétiques et du monde réel. Nous démontrons que nos deux modèles de régime présentent de bonnes performances à la fois en termes d'identification et de prévision de régime, et notre modèle de clustering de Markov d'ordre dynamique produit également de bonnes performances dans l'identification de clusters à partir de séquences catégorielles

Regional specialization in China's biopharmaceutical industry

Purpose: The purpose of this paper is to explore patterns as well as determinants of regional specialisation in China's biopharmaceutical industry. It identifies and characterizes different types of enterprises engaged in the biopharmaceutical sector in terms of their business organisation and regional set up. Design/methodology/approach: Based on data compilations not yet employed in academic analysis as well as personal interviews in China, structural determinants and driving forces of development are analysed against the background of the innovation systems literature. Findings: The geography of innovation in China's biopharmaceutical industry is determined by both, government policy and the strategic location decisions of entrepreneurs. While local‐government support of firm clustering has contributed to a dispersion of industrial activity throughout China, the firms networks are spanning clusters. Effectively, domestic firms are turning into multi‐regional companies locating activities such as R&D and manufacturing at different clusters. Originality/value: The paper adds to the literature in so far as it throws light on an until now under‐researched field of China's innovation system. It identifies the concept of multi‐regionalism among domestic non‐state enterprises as an important parameter for understanding success and regional distribution of the industry