Forecasting price increments using an artificial Neural Network

Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to forecast the sign of the price increments with a success rate slightly above 50 percent can be found.Comment: 12 pages, 5 figures (to be published in Advances in Complex Systems, as the Proceeding of the WE-Heraeus Workshop on "Economic Dynamics from the Physics Point of View", Bad Honnef, Germany, March 27-30, 200

Diffusion and Aggregation in an Agent Based Model of Stock Market Fluctuations

We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting ``dominated'' by a leader's decision. The interplay of both kind of agents gives rise to complex price dynamics that is consistent with the main stylized facts of financial time series.Comment: 17 pages, 8 figures (accepted for publication in Int. J. Mod. Phys. C

On the use of the Peak Stress Method for the calculation of Residual Notch Stress Intensity Factors: a preliminary investigation

Residual stresses induced by welding processes significantly affect the engineering properties of structural components. If the toe region of a butt-welded joint is modeled as a sharp V-notch, the distribution of the residual stresses in that zone is asymptotic with a singularity degree which follows either the linear-elastic or the elastic-plastic solution, depending on aspects such as clamping conditions, welding parameters, material and dimension of plates. The intensity of the local residual stress fields is quantified by the Residual Notch Stress Intensity Factors (R-NSIFs), which can be used in principle to include the residual stress effect in the fatigue assessment of welded joints. Due to the need of extremely refined meshes and to the high computational resources required by non-linear transient analyses, the R-NSIFs have been calculated in literature only by means of 2D models. It is of interest to propose new coarse-mesh-based approaches which allow residual stresses to be calculated with less computational effort. This work is aimed to investigate the level of accuracy of the Peak Stress Method in the R-NSIFs evaluation

The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells

BACKGROUND: Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization. METHODS: We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks. RESULTS: By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations. CONCLUSIONS: The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property

Criticality of Timing for Anti-HIV Therapy Initiation

The time of initiation of antiretroviral therapy in HIV-1 infected patients has a determinant effect on the viral dynamics. The question is, how far can the therapy be delayed? Is sooner always better? We resort to clinical data and to microsimulations to forecast the dynamics of the viral load at therapy interruption after prolonged antiretroviral treatment. A computational model previously evaluated, produces results that are statistically adherent to clinical data. In addition, it allows a finer grain analysis of the impact of the therapy initiation point to the disease course. We find a swift increase of the viral density as a function of the time of initiation of the therapy measured when the therapy is stopped. In particular there is a critical time delay with respect to the infection instant beyond which the therapy does not affect the viral rebound. Initiation of the treatment is beneficial because it can down-regulate the immune activation, hence limiting viral replication and spread

Optimization of HAART with genetic algorithms and agent-based models of HIV infection

Motivation: Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI).In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection.Results: The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient.To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups.Availability: A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.htmlContact: [email protected]