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Typical activities of financial asset management, such asasset allocation, stock ranking, bond trading, stock ex-change trading and in general portfolio choices, have aconsiderable role in modern finance. Professional op-erators and especially banks are highly interested in un-derstanding and predicting financial markets' trends.

One key area of interest for banks is the asset-liability

management, that has to be optimised on the basis of thebanks expectations and predictions about financial mar-ket evolution. The banks often use simple linear modelsto evaluate the possible future scenarios and support theirstrategic decision process. My research addresses theconstruction of a neural network model of the mortgageloans' production at national level, that may be used as abasis for a more effective decision support system.

The existing econometric models of mortgage marketare traditionally based on multivariate regression. Sev-eral authors have stressed the limits of this approach forfinancial asset management, i.e. [Refenes and AzemaBarac, 1994]. After a review of these criticisms, the

reasons for using alternative models based on neural-network software technology will be taken into con-sideration.

The fundamentals of neural network technology willbe discussed [Chichocki and Unbehauen, 1994], with aspecial consideration for financial modelling relatedissues; the supposed advantages of neural networks willbe shown and documented by a comparative review ofthe existing models in the financial asset management.

The formulation of the supposed mortgage productionfunction and the neural network model identification willbe made in a methodological framework, taking intoaccount the economic theory directions and the prelimi-nary empirical results obtained in [Gardin and Virili,

1995]. In that paper a neural network model of the mort-gage market production is described, that can obtaingood out-of sample predictions in the short-term. Thesepredictions are based on the future (and lagged) values ofmortgage interest rate, inflation, disposable income,stock and price of dwellings for sale, and on the numberof dwellings in the rental sector. Unfortunately, the fu-

* e-mail: francesco.virili@acm.org

ture values of these indicators are not available at pre-diction time.

First of all, a methodological framework for the neuralmodel selection and evaluation will be adopted. Then,the preliminary model will be extended in order to in-clude the main determinants of some, if not all, the futurevalues of input variables. Each extension will be sub-

stantially equivalent to building a separate neural net-work model. At the moment I am planning for two exten-sions: one for the housing market and one for the dispos-able income.

Finally, the model will be used to come to budgetingand asset allocation decisions; as a starting approach wecan imagine that several estimations of the future valuesof mortgage interest rates and inflation will be intro-duced into the model by the user. Whatif analysis willallow the user to evaluate several scenarios of the futuremortgage production. Efforts will be devoted to show therole and relevance of the variables involved in the modeland to deal with the qualitative and judgmental factors

that can influence the future evolution of the market.

References

[Chichocki and Unbehauen, 1994] Andrzej Chichockiand Rolf Unbehauen. Neural Networks for Optimizationand Signal Processing. John Wiley & Sons, Cichester,England, 1994

[Gardin and Virili, 1995] Francesco Gardin and Fran-cesco Virili. Non-linear modelling of Dutch mortgagemarket. Economic & Financial Computing, 5(2):131145, SummerAutumn 1995

[Refenes and AzemaBarac, 1994] ApostolosPaul N.Refenes and M. AzemaBarac. Neural Network Appli-cations in Financial Asset Management. Neural Com-puting & Applications, 2(1):1339, JanuaryMarch 1994.

The use of neural network approach in financial asset management

Francesco Virili*

University of SiegenHoelderlinstrasse, 3

Siegen, Germany D-57068