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Artificial Neural Networks in Finance Modelling

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Author Info
Crescenzio GALLO (Università di Foggia-Dipartimento di Scienze Economiche, Matematiche e Statistiche)

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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 multi-population 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. A particular application we aim to study is the one regarding “customer profiling”, in which (based on personal and direct relationships) the “buying” behaviour of each customer can be defined, making use of behavioural inference models such as the ones offered by Artificial Neural Networks much better than traditional statistical methodologies.

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Publisher Info
Paper provided by EconWPA in its series Experimental with number 0509002.

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Length: 13 pages
Date of creation: 07 Sep 2005
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Handle: RePEc:wpa:wuwpex:0509002

Note: Type of Document - pdf; pages: 13.
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Web page: http://129.3.20.41

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Related research
Keywords: Artificial Neural Network; Financial Modelling; Customer Profiling;

Find related papers by JEL classification:
C9 - Mathematical and Quantitative Methods - - Design of Experiments

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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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. [Downloadable!]
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This page was last updated on 2009-11-25.


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