3,623 research outputs found
Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics
First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049
Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts
The usage of linguistic information involves computing with words, a methodology assuming
linguistic values as computational elements, in group decision-making environments. In recent times, a new
methodology founded on a framework of granular computing has been employed to manage linguistic
information. An advantage of this methodology is that the distribution and the semantics of the linguistic
values, in place of being initially established, are defined by the optimization of a certain criterion. In this
paper, different from the existing approaches, we present a novel approach build on the basis of a granular
computing framework that is able to cope with group decision-making problems defined in multi-criteria
contexts, that is, those in which different criteria are considered to evaluate the possible alternatives for
solving the problem. In particular, it models group decision-making problems in a more realistic way by
taking into account that each criterion has an importance weight and by considering that each decision maker
has a different importance weight for each criterion. This approach makes operational the linguistic values by
associating them with intervals via the optimization of an optimization criterion composed of two important
aspects that must be taken into account in this kind of decision problems, that is, the consensus at the level
of group of decision makers and the consistency at the level of individual decision makers.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project DPI2016-77677-P, in part by the
RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (``RobĆ³tica aplicada a la mejora de la calidad de vida de los ciudadanos.
Fase IV''; S2018/NMT-4331), funded by the ``Programas de Actividades I+D de la Comunidad de Madrid,'' and co-funded by the
Structural Funds of the EU, and in part by the research grant from the AsociaciĆ³n Universitaria Iberoamericana de Postgrado (AUIP)
and ConsejerĆa de EconomĆa y Conocimiento de la Junta de AndalucĆa
OWA-based fuzzy m-ary adjacency relations in Social Network Analysis.
In this paper we propose an approach to Social Network Analysis (SNA) based on fuzzy m-ary adjacency relations. In particular, we show that the dimension of the analysis can naturally be increased and interesting results can be derived. Therefore, fuzzy m-ary adjacency relations can be computed starting from fuzzy binary relations and introducing OWA-based aggregations. The behavioral assumptions derived from the measure and the exam of individual propensity to connect with other suggest that OWA operators can be considered particularly suitable in characterizing such relationships.reciprocal relation; fuzzy preference relation; priority vector; normalization
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