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Artificial Neural Networks in Financial Modelling

Author

Listed:
  • 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.

Suggested Citation

  • Crescenzio Gallo & Giancarlo De Stasio & Cristina Di Letizia, 2006. "Artificial Neural Networks in Financial Modelling," Quaderni DSEMS 02-2006, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
  • Handle: RePEc:ufg:qdsems:02-2006
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    File URL: http://www.economia.unifg.it/sites/sd01/files/allegatiparagrafo/29-11-2016/q022006.pdf
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    References listed on IDEAS

    as
    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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    More about this item

    Keywords

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

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models

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