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PREDICTION OF INSTITUTIONAL SECTOR DEVELOPMENT AND ANALYSIS OF ENTERPRISES ACTIVE IN AGRICULTURE


Business Administration and Management

PREDICTION OF INSTITUTIONAL SECTOR DEVELOPMENT AND ANALYSIS OF ENTERPRISES ACTIVE IN AGRICULTURE

Name and surname of author:

Vojtěch Stehel, Jakub Horák, Marek Vochozka

Year:
2019
Volume:
22
Issue:
4
Keywords:
Agriculture enterprises, institutional sector development, prediction, artificial neural networks, value of the business
DOI (& full text):
Anotation:
The overall EU agricultural productivity growth has slowed down in recent years and has lagged behind leading global competitors, which is mainly due to decreasing number of employees in agriculture.…more
The overall EU agricultural productivity growth has slowed down in recent years and has lagged behind leading global competitors, which is mainly due to decreasing number of employees in agriculture. Technical inefficiency is then an important phenomenon of the Czech agriculture and its individual sectors. Agriculture development should be established on scientific bases. One of the basic principles of sustainable agriculture is therefore forecasting its future development. In recent years, several agricultural economists have been engaged in comparing forecasts with various other methods and their conclusions generally correspond to commonly accepted beliefs. At present, artificial intelligence can be definitely recognized as a useful tool for business analyses and forecasting. The objective of the contribution is an analysis of companies active in agriculture of the Czech Republic using Kohonen network and the subsequent prediction of their development. A data set is created, which includes complete data from financial statements of 4,201 companies active in agriculture of the Czech Republic in 2016. The set of companies is generated from the Bisnode Albertina database. The data set is subsequently subjected to cluster analysis using Kohonen network. For cluster analysis, Dell´s Statistica software, version 12 is used. The set is divided into three parts: training data set, testing data set, validation data set. Topological length and width of Kohonen network are set at 10. The number of iterations is set at 10 000. Subsequently, the individual clusters are subjected to analysis of absolute and selected indicators (or more precisely, their mean values – arithmetic average) and the results are interpreted. It can be stated that the agriculture companies show very favorable values – optimal assets level, acceptable financing structure and adequate economic result. It can be even stated that the indicators show above-average values compared to other investment options.
Section:
Business Administration and Management

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