The mixture of two already known soft computing technics, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognice the importance...

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density. Such a density must typically be "close" to the target density to yield numerically accurate results with...

Barr and Saraceno (JEDC, forthcoming) model the firm as a type of artificial neural network (ANN) which plays a repeated Cournot game. Each period, the network/firm must estimate the relationship between environmental conditions and optimal output. Among other results, the paper develops the...

An efficient procedure is proposed to evaluate option prices using neural networks. The method considers alternatives to the procedures suggested by Hutchinson, Lo and Poggio in the Journal of Finance of 1994

One of the most critical issues when using neural networks is how to select appropriate network architectures for the problem at hand. Practitioners usually refer to information criteria which might lead to over-parameterized models with heavy consequence on overfitting and poor ex-post forecast...

Here artificial neural networks (ANNs) are employed for efficiency purposes. First, the main features of ANNs are presented. Then, common techniques of the efficiency literature are reviewed: parametric (deterministic and stochastic) and non-parametric (Data Envelopment Analysis [DEA] and Free...