This paper focuses on the time series’ decomposition and economic representation of its constituent parts. Wavelet transforms are used for adaptive analysis of local behaviour of heterogeneous agents. Unlike fully revealing equilibrium of homogeneous beliefs, in the environment with...

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...

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...

Artificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence...

Motivated from the charting analysis in the financial industry, Chen and He (2003) are the first to use self-organizing maps to search for and identify price patterns. Such a model is referred to as the trajectory-domain model (TDM). Chen and Tsao (2003) apply the TDMs to three American stock...

One of the key variables for a bank's management is the development of the "risk-free interest rate", which is the reference for all bond and loan rates as well as an indicator for the state of the economy and therefore the bank"s future perspectives. Turning towards long-term analysis, the...

This paper reports on the use of multi-agent games to model financial markets. Our research employs multi-agent games to address three questions which are of great practical importance in quantitative finance: how profit opportunities may be identified, large price movements predicted, and...

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

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...