Original scientific paper

SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS

Marijana Zekić-Sušac ; Faculty of Economics, University of J.J. Strossmayer in Osijek, Croatia
Nataša Šarlija orcid.org/0000-0003-2600-9735 ; Faculty of Economics, University of J.J. Strossmayer in Osijek, Croatia
Mirta Benšić orcid.org/0000-0001-9063-0310 ; Department of Mathematics, University of J.J. Strossmayer in Osijek, Croatia

Abstract

After production and operations, finance and investments are one of the most frequent areas of neural network applications in business. The lack of standardized paradigms that can determine the efficiency of certain NN architectures in a particular problem domain is still present. The selection of NN architecture needs to take into consideration the type of the problem, the nature of the data in the model, as well as some strategies based on result comparison. The paper describes previous research in that area and suggests a forward strategy for selecting best NN algorithm and structure. Since the strategy includes both parameter-based and variable-based testings, it can be used for selecting NN architectures as well as for extracting models. The backpropagation, radialbasis, modular, LVQ and probabilistic neural network algorithms were used on two independent sets: stock market and credit scoring data. The results show that neural networks give better accuracy comparing to multiple regression and logistic regression models. Since it is model-independant, the strategy can be used by researchers and professionals in other areas of application.

Keywords

neural networks; non-linear forward strategy; stock market prediction; credit scoring

Hrčak ID:

78281

URI

https://hrcak.srce.hr/78281

Publication date:

21.12.2005.

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