A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

dc.contributor.authorSmith, M.L. (Mattie)
dc.contributor.authorBeyers, Frederik Johannes Conradie
dc.contributor.authorDe Villiers, Johan Pieter
dc.contributor.emailconrad.beyers@up.ac.zaen_ZA
dc.date.accessioned2017-05-08T08:34:52Z
dc.date.available2017-05-08T08:34:52Z
dc.date.issued2016
dc.description.abstractNo analytic procedures currently exist for determining optimal artificial neural network structures and parameters for any given application. Traditionally, when artificial neural networks have been applied to financial modelling problems, structure and parameter choices are often made a priori without sufficient consideration of the effect of such choices. A key aim of this study is to develop a general method that could be used to construct artificial neural networks by exploring the model structure and parameter space so that informed decisions could be made relating to the model design. In this study, a formal approach is followed to determine suitable structures and parameters for a Feed Forward Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with a single hidden layer. This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model. The performance of a range of more traditional time series models is compared with that of the artificial neural network models. The results suggest that, on a statistical level, artificial neural networks perform as well as time series models at forecasting the returns for financial markets. Hybrid models, combining artificial neural networks with the time series models, are constructed, trained and tested for the money market and for the rate of inflation. They appear to add value to the time series models when forecasting inflation, but not for the money market.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.departmentInsurance and Actuarial Scienceen_ZA
dc.description.librarianam2017en_ZA
dc.description.urihttp://www.actuarialsociety.org.za/Professionalresources/SAActuarialJournal.aspxen_ZA
dc.identifier.citationSmith, ML, Beyers, FJC & De Villiers, JP 2016, 'A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets', South African Actuarial Journal, vol. 16, pp. 35-67.en_ZA
dc.identifier.issn1680-2179
dc.identifier.other10.4314/saaj.v16i1.2
dc.identifier.urihttp://hdl.handle.net/2263/60256
dc.language.isoenen_ZA
dc.publisherActuarial Society of South Africaen_ZA
dc.rights© Actuarial Society of South Africa. This article is distributed under the Creative Commons Attribution 3.0 License.en_ZA
dc.subjectArtificial neural network (ANN)en_ZA
dc.subjectARIMAen_ZA
dc.subjectFinancial application of ANNsen_ZA
dc.subjectFinancial forecastingen_ZA
dc.subjectMoney marketen_ZA
dc.subjectBond marketen_ZA
dc.subjectEquity marketen_ZA
dc.subjectInflationen_ZA
dc.titleA method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial marketsen_ZA
dc.typeArticleen_ZA

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