The Application of Artificial Neural Networks on the Prediction of the Future Financial Development of Transport Companies

Marek Vochozka,Penfei Sheng

doi:10.26552/com.c.2016.2.62-67

Marek Vochozka, Penfei Sheng

Open Access

https://doi.org/10.26552/com.c.2016.2.62-67

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Abstract

It is logical that in an uncertain economic environment it is necessary for companies to be able to plan better, to report more precisely and to be able to better evaluate their future financial development. For their analysis, the authors of this article focus on a particular segment of the national economy, namely transport and freight forwarding companies. The aim of this article is to utilize artificial neural networks to predict potential financial problems in transport companies in the Czech Republic. Data on all companies involved in transport and freight forwarding in the Czech Republic for the period 2003 - 2013 were used for modelling the particular neural network. The data file contained nearly 15,000 records on companies for the individual years. These records included both financial statement data and non-accounting data (e.g. data on company employees). The following networks were used for modelling the neural network: a linear network, a probabilistic neural network (PNN), a generalised regression neural network (GRNN), a radial basis function network (RBF), a three-layer perceptron network (TLP) and a four-layer perceptron network (FLP). The analysis resulted in a concrete model of an artificial neural network. The neural network is able to determine with more than ninety per cent accuracy whether a company is able to overcome potential financial problems, within how many years a company might go bankrupt, or whether a company might go bankrupt within one calendar year. The text also includes the basic statistical characteristics of the examined sample and the achieved results (sensitivity analysis, confusion matrix, etc.). The model can be utilized in practice by transport company managers, investors looking for a suitable company for capital investment, competitors, etc.

Highlights

Youssef Mohamed, Kumar and Motwani [1] evaluated selected variables of 165 production companies and transport companies in America. They analysed the ability of the companies to respond to the needs of their internal and external environments. They found out that significant differences exist between a transport company and a production company
Youssef Mohamed, Kumar and Motwani [1] claim that a transport company can be exactly identified and classified according to its response to the external environment
The weights are significant, they fluctuate just above 1. This is important with regards to the networks classification accuracy. It fluctuates at the highest level of all the models assessed so far

Summary

Introduction

Youssef Mohamed, Kumar and Motwani [1] evaluated selected variables of 165 production companies and transport companies in America They analysed the ability of the companies to respond to the needs of their internal and external environments. They found out that significant differences exist between a transport company and a production company. Companies use quality, the environment, health protection and safety management as tools with which to resolve certain problems. This applies to transport companies [2]

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