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Košice, Slovakia

Tokar T.,Sors Research As | Horvath D.,Technical University of Kosice
Physica A: Statistical Mechanics and its Applications | Year: 2012

Many studies have shown that there are good reasons to claim very low predictability of currency returns; nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J. James, Simple trend-following strategies in currency trading, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the technical analysis. In this article we introduced various statistical quantities examining role of single temporal discretized trend or multitude of grouped trends corresponding to different time delays. Our specific analysis based predominantly on Euro-dollar currency pair data at the one minute frequency suggests the importance of cumulative nonrandom effect of trends on the potential forecasting performance. © 2012 Elsevier B.V. All rights reserved. Source


Zukovic M.,Sors Research As | Zukovic M.,University of P.J. Safarik
Central European Journal of Physics | Year: 2012

We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer some potential for their predictability. We employ a rolling window approach in order to capture the correlation dynamics for different window lengths and analyze the distributions of periods with statistically significant correlations. We find that for sufficiently large window lengths these distributions fit well to power-law behavior. We also measure the predictability itself by a hit rate, i. e. the rate of consistency between the signs of the actual returns and their predictions, obtained from a simple correlation-based predictor. It is found that during these relatively brief periods the returns are predictable to a certain degree and the predictability depends on the selection of the window length. © 2012 Versita Warsaw and Springer-Verlag Wien. Source


Zukovic M.,Park University | Zukovic M.,Sors Research As | Hristopulos D.T.,Technical University of Crete
Stochastic Environmental Research and Risk Assessment | Year: 2013

This paper addresses the issue of missing data reconstruction for partially sampled, two-dimensional, rectangular grid images of differentiable random fields. We introduce a stochastic gradient-curvature (GC) reconstruction method, which is based on the concept of a random field model defined by means of local interactions (constraints). The GC reconstruction method aims to match the gradient and curvature constraints for the entire grid with those of the sample using conditional Monte Carlo simulations that honor the sample values. The GC reconstruction method does not assume a parametric form for the underlying probability distribution of the data. It is also computationally efficient and requires minimal user input, properties that make it suitable for automated processing of large data sets (e. g. remotely sensed images). The GC reconstruction performance is compared with established classification and interpolation methods for both synthetic and real world data. The impact of various factors such as domain size, degree of thinning, discretization, initialization, correlation properties, and noise on GC reconstruction performance are investigated by means of simulated random field realizations. An assessment of GC reconstruction performance on real data is conducted by removing randomly selected and contiguous groups of points from satellite rainfall data and an image of the lunar surface. © 2012 Springer-Verlag. Source


Zukovic M.,Park University | Zukovic M.,Sors Research As | Hristopulos D.T.,Technical University of Crete
Atmospheric Environment | Year: 2013

We introduce the Directional Gradient-Curvature (DGC) method, a novel approach for filling gaps in gridded environmental data. DGC is based on an objective function that measures the distance between the directionally segregated normalized squared gradient and curvature energies of the sample and entire domain data. DGC employs data-conditioned simulations, which sample the local minima configuration space of the objective function instead of the full conditional probability density function. Anisotropy and non-stationarity can be captured by the local constraints and the direction-dependent global constraints. DGC is computationally efficient and requires minimal user input, making it suitable for automated processing of large (e.g., remotely sensed) spatial data sets. Various effects are investigated on synthetic data. The gap-filling performance of DGC is assessed in comparison with established classification and interpolation methods using synthetic and real satellite data, including a skewed distribution of daily column ozone values. It is shown that DGC is competitive in terms of cross validation performance. © 2013 Elsevier Ltd. Source


Tokar T.,Sors Research As | Horvath D.,Sors Research As | Hnatic M.,Pavol Jozef Safarik University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In this work the system of agents is applied to establish a model of the nonlinear distributed signal processing. The evolution of the system of the agents - by the prediction time scale diversified trend followers, has been studied for the stochastic time-varying environments represented by the real currency-exchange time series. The time varying population and its statistical characteristics have been analyzed in the non-interacting and interacting cases. The outputs of our analysis are presented in the form of the mean life-times, mean utilities and corresponding distributions. They show that populations are susceptible to the strength and form of inter-agent interaction. We believe that our results will be useful for the development of the robust adaptive prediction systems. © 2012 Springer-Verlag. Source

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