Catálogo de publicaciones - libros

The paper deals with power system topology verification, being an important problem in real-time power system modeling. In the paper, the approach with use of multi-agent system is proposed. At the beginning, benefits from utilization of the agent technology are presented. Then, a theoretical background for the power system topology verification is described. Next, multi-agent system for topology verification and functions of particular agents are characterized. At the end, features of the presented approach to power system topology verification are summed up.

In this work we perform an automatic data survey to draw up an optimum portfolio, and to automate the one year forecast of a portfolio’s payoff and risk, showing the advantages of using formally grounded models in portfolio management and adopting a strategy that ensures, a high rate of return at a minimum risk. The use of neural networks provides an interesting alternative to the statistical classifier. We can take a decision on the purchase or sale of a given asset, using a neural network to classify the process into three decisions: buy, sell or do nothing.

Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of the prices curve, demonstrating that the application of these techniques is effective so to split the whole year into different groups of days, according to their prices conduct. Later, this information will be used to predict the price in the short time period. The prices exhibited a remarkable resemblance among days embedded in a same season and can be split into two major kind of clusters: working days and festivities.

In the past few years, various variants of the self-organising map (SOM) have been proposed to extend its ability for modelling time-series or temporal sequence. Most of them, however, have little connection to, or are over-simplified, autoregressive (AR) models. In this paper, a new extension termed, self-organising mixture autoregressive (SOMAR) network is proposed to topologically cluster time-series segments into underlying generating AR models. It uses autocorrelation values as the similarity measure between the model and the time-series segments. Such networks can be used for modelling nonstationary time-series. Experiments on predicting artificial time-series (Mackey-Glass) and real-world data (foreign exchange rates) are presented and results show that the proposed SOMAR network is a viable and superior to other SOM-based approaches.

Management of extreme events is required of a special consideration, as well as a sufficiently wide time horizon for solvency evaluation. Whereas their classical adjustment is usually carried out with Extreme Value Theory (EVT)-based distributions (namely, the Generalized Pareto Distribution), Evolutionary Techniques have been tried herein to fit the GPD parameters as an optimisation problem. The comparison between classical and evolutionary techniques highlights the accuracy of the evolutionary process. Data adjusted in this paper come from a Spanish motor liability insurance portfolio.

In this paper we propose to do portfolio management using reinforcement learning (RL) and independent factor model. Factors in independent factor model are mutually independent and exhibit better predictability. RL is applied to each factor to capture temporal dependence and provide investment suggestion on factor. Optimal weights on factors are found by portfolio optimization method subject to the investment suggestions and general portfolio constraints. Experimental results and analysis are given to show that the proposed method has better performance when compare to two alternative portfolio management systems.

This paper analyzes discrete time portfolio selection models with Lévy processes. We first implement portfolio models under the hypotheses the vector of log-returns follow or a multivariate Variance Gamma model or a Multivariate Normal Inverse Gaussian model or a Brownian Motion. In particular, we propose an ex-ante and an ex-post empirical comparisons by the point of view of different investors. Thus, we compare portfolio strategies considering different term structure scenarios and different distributional assumptions when unlimited short sales are allowed.

In this research, we employ Agent-Based Model to analyze how asset prices are affected by investors’ Behavior. This analysis places focus on the influence of overconfident investors on financial market. As a result of intensive analysis, we find that overconfident investors are generated in a bottom-up fashion in the market. Furthermore, it has also been found that overconfident investors have the ability to contribute to market efficiency.

The importance of modularity in product innovation is analyzed in this paper. Through simulations with an agent-based modular economic model, we examine the significance of the use of a modular structure in new product designs in terms of its impacts upon customer satisfaction and firms’ competitiveness. To achieve the above purpose, the automatically defined terminal is proposed and is used to modify the simple genetic programming.

We develop an agent-based model of the competition between payment cards by focusing on the interactions between consumers and merchants determining the subscription and usage of cards. We find that after a short period of time the market will be dominated by a small number of cards, even though there do not exist significant differences between cards and the market is fully competitive. In contrast to the existing literature we focus on the dynamics of market shares and emergence of multi-homing rather than equilibrium outcomes.