Development of dynamic cognitive networks as complex systems approximators: validation in financial time series

Dynamic cognitive networks (DCNs) define a novel approach to functionalize cognitive mapping and complex systems analysis, which were recently supported by fuzzy cognitive maps (FCMs). The modeling and inference limitations met in FCMs, especially in situations with strong nonlinearity and temporal phenomena, pushed towards DCNs; their theoretical framework is scheduled to confront the preceding weaknesses and offer wider possibilities in causal structures management. Trying to contribute to the enhancement of DCNs, at first, systemic and environmental metaphors are introduced with practical mathematical formalisms and generalized nomenclature. Nonlinear and asymmetric cause-effect relationships, decaying mechanisms, inertial forces, diminishing effects and biases formulate a powerful set of adaptive characteristics that strengthen the operational behavior of DCNs. Second, the strategic reorientation of DCNs is attempted as generalized approximation tools. This new strategic option is verified not only in classical function approximation tests, but also in the challenging area of securities markets. The platform of evaluation of DCNs involves comparisons with a linear multiple regression model, a feed-forward neural network trained with both back-propagation and evolution strategies, a radial basis function network, and an adaptive network-based fuzzy inference system (ANFIS). Through the experiments for short-term stock price predictions, multiple issues are analyzed not only about the role of diverse DCN parameters, but also about the given problem of financial markets modeling and forecasting. Simulations distinguish DCNs as a strong methodology with noticeable adaptability in complicated patterns and broad generalization capabilities while, at the same time, the all-embracing outcomes support previous findings of partially random walk phenomena in short-term stock market forecasting attempts.