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Artificial Neural Networks in Financial Modelling

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Author Info

  • Crescenzio Gallo

    ()

  • Giancarlo De Stasio

    ()

  • Cristina Di Letizia

    ()

Abstract

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological processing. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to suggest how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non-linear models to do forecasting), while in multipopulation models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent.

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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia in its series Quaderni DSEMS with number 02-2006.

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Date of creation: Jan 2006
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Handle: RePEc:ufg:qdsems:02-2006

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Keywords: artificial neural network; financial modelling; population model; market equilibrium.;

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  1. D'Ecclesia, Rita Laura & Gallo, Crescenzio, 2002. "Price-caps and Efficient Pricing for the Electricity Italian Market," MPRA Paper 10048, University Library of Munich, Germany.
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