Artificial Market Simulation with Embedded Complex Network Structures

Artificial neurons with arbitrarily complex internal structures

Artificial neurons with arbitrarily complex internal structure are introduced. The neurons can be described in terms of a set of internal variables, a set activation functions which describe the time evolution of these variables and a set of characteristic functions which control how the neurons interact with one another. The information capacity of attractor networks composed of these generali...

Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models

Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting finan...

Embedded Surfaces and Almost Complex Structures

In this paper, we prove necessary and sufficient conditions for a smooth surface in a smooth 4-manifold X to be pseudoholomorphic with respect to an almost complex structure on X. In particular, this provides a systematic approach to the construction of pseudoholomorphic curves that do not minimize the genus in their homology class.

Modelling complex structures by artificial neural networks

Topological Structures in the Equities Market Network

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on “Partition Decoupled Null Models,” a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application we analyze a correlation matrix der...