BibSonomy publications for /tag/market,Thu Sep 01 13:26:03 CEST 2011Distributed Generation in Liberalised Electricity Markets2002Distributed electricity generation, market, policy Early electric power systems consisted of small generation plants located near consumers. Although most power today is produced in large central generation plants, small-scale “distributed” generation is enjoying a renaissance. Power consumers are using distributed generation technologies to ensure very high electrical reliability, to provide capacity in emergencies and, in some cases, to displace costly electricity from the grid. Network owners are using distributed generation to defer investments in network expansion. This book provides a guide to energy policy makers on this growing phenomenon. It surveys the status of distributed generation in selected OECD countries. It looks at the economics of distributed generation versus central generation. It identifies key regulatory barriers. It discusses the environmental and energy security implications of these technologies. Most of the electricity produced in the OECD is generated in large generating stations. These stations produce and transmit electricity through high-voltage transmission systems then, at reduced voltage, transmit it through local distribution systems to consumers. Some electricity is produced by distributed-generation (DG) plants. In contrast with large generating stations, they produce power on a customer’s site or at a local distribution utility, and supply power directly to the local distribution network. DG technologies include engines, small turbines, fuel cells, and photovoltaic systems. Although they represent a small share of the electricity market, distributed-generation technologies already play a key role: for applications in which reliability is crucial, as a source of emergency capacity, and as an alternative to expansion of a local network. In some markets, they are actually displacing more costly grid electricity. Worldwide, more DG capacity was ordered in 2000 than for new nuclear power. Government policies favouring combined heat and power (CHP) generation, and renewable energy and technological development should assure growth of distributed generation. This kind of generation has the potential to alter fundamentally the structure and organisation of our electric power system.Yet market conditions in some countries pose serious challenges to some generators, particularly those producing combined heat and power.Tue May 03 17:01:48 CEST 20110The American Political Tradition1948Books Market, Mass Printed Subjects, Tue May 03 17:01:48 CEST 2011First Paperback Printing0Coup d'Etat: A Practical Handbook1969Books Market, Mass Printed Subjects, Thu Jun 19 17:46:40 CEST 2008Genetic Programming Theory and Practise18291--302Enhance Emerging Market Stock Selection2003algorithms, emerging genetic market, programming, selection stock Emerging stock markets provide substantial opportunities for investors. The existing literature shows inconsistency in factor selection and model development in this area. This research exploits a cutting edge quantitative technique - genetic programming, to greatly enhance factor selection and explore nonlinear factor combination. The model developed using the genetic programming process is proven to be powerful, intuitive, robust and consistent.Thu Jun 19 17:35:00 CEST 2008Universitat Pompeu Fabra, Barcelona, SpainComputing in Economics and Finance6-8 JulyToward an integration of social learning and individual learning in agent-based computational stock markets:the approach based on population genetic programming2000Agent-Based Annealing Artificial Computation, Evolutionary Market, Modelling, Simulated Stock algorithms, genetic programming, Artificial stock market is a growing field in the past few years. The essence of this issue is the interaction between many heterogeneous agents. In order to model this complex adaptive system, the techniques of evolutionary computation have been employed. Chen and Yeh (2000) proposed a new architecture to construct the artificial stock market. This framework is composed of a single-population genetic programming (SGP) based adaptive agent with a SA (Simulated Annealing) learning process and a business school. However, one of the drawbacks of SGP-based framework is that the traders can't work out new ideas by themselves. The only way is to consult researchers in the business school. In order to make the traders more intelligent, we employ multi-population GP (MGP) based framework with the mechanism of school. This extension is not only reasonable, but also has the economic implications. How do the more intelligent agents influence the economy? Are the econometric properties of the simulation results based on MGP more like the phenomena found in the real stock market? In this paper, the comparison between SGP and MGP is studied from two sides. One is related to the micro-structure, traders? behaviour and believe. The other is macro-properties, the properties of time series. The line of research is helpful in understanding the foundation of economics and finance, and constructing more realistic economic models.Thu Jun 19 17:35:00 CEST 2008Orlando, Florida, USAGECCO-99 Student Workshop13 July374{FGP}: {A} Genetic Programming Tool for Financial Prediction1999algorithms, genetic market, prediction programming, stock Thu Jun 19 17:35:00 CEST 2008Vienna, AustriaArtificial Neural Networks - ICANN 2001 : International Conference, ProceedingsAugust 21-25759--766Lecture Notes in Computer ScienceThe Importance of Representing Cognitive Processes in Multi-agent Models21302001agent, algorithms, cognition, economics, explanation, genetic market, methodology, modelling, negotiation net, neural prediction, programming, representation, stock We distinguish between two main types of model: predictive and explanatory. It is argued (in the absence of models that predict on unseen data) that in order for a model to increase our understanding of the target system the model must credibly represent the structure of that system, including the relevant aspects of agent cognition. Merely plugging in an existing algorithm for the agent cognition will not help in such understanding. In order to demonstrate that the cognitive model matters, we compare two multi-agent stock market models that differ only in the type of algorithm used by the agents to learn. We also present a positive example where a neural net is used to model an aspect of agent behaviour in a more descriptive manner.Thu Jun 19 17:35:00 CEST 2008San Francisco, California, USAProceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)7-11 July191The Differences between Social and Individual Learning on the Time Series Properties: The Approach Based on Genetic Programming2001Agent-Based Artificial Individual Learning, Market, Modeling Poster, Social Stock algorithms, genetic programming: Thu Jun 19 17:35:00 CEST 2008Ann ArborGenetic Programming Theory and Practice {IV}1211-13 May-Genetic and Evolutionary ComputationStock Selection : An Innovative Application of Genetic Programming Methodology52006500, Arbitrage Asset Capital Information Model, Pricing Quantitative S&P Stock Technical algorithms, asset coefficient, equity genetic management market, models, programming, quantitative ratio, rules, selection selection, stock trading One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model.market,Community for tag(s) market,