Price forecasting and trading strategies modelling are examined with major international stock indexes under different time horizons. Results demonstrate that an accurate prediction is equally important as a stable saving rate for long-term survivability. The best economic performances are...

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every...

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...

This paper presents a model of adoption and diffusion of innovations that concern the technology of e-commerce. First, a model of optimal adoption of e-commerce innovation is presented. In this model web companies are assumed to behave in an imitative way: facing an innovation of uncertain...

Since their introduction Kernel Methods have proven their superior performance in many different application areas. Recently these algorithms have also been employed for different tasks in the area of finance. In this contribution we present an introduction to the methodology and give an...

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

This paper is a step towards the econometric foundation of computational intelligence in finance. Financial time series modeling and forecasting are addressed with an artificial neural network, examining issues of its topology dependency. Structural dependency of results is viewed not as a...

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...