Analysis of business companies based on artificial neural networks

Business companies have many kinds of products that they sell to other businesses, consumers, etc. They are a driving force of economies, especially in developing countries. The aim of this article is to analyse business companies in the Czech Republic using artificial neural networks and subsequently to estimate the development of this branch of the national economy. An analysis is performed to create a significant number of clusters of businesses. An analysis of the most significant clusters is also carried out. The result can be generalized and we can predict the number of companies that will be creditworthy or bankrupt in the following period. This makes it possible to estimate not only the overall growth or decline of business companies in the Czech Republic, but also to estimate the structure of the companies in terms of their size, turnover or volume of sales.

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Analysis of Transportation Companies in the Czech Republic by the Kohonen Networks – Identification of Industry Leaders

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2021.1.a32-a43 ◽

2020 ◽

Vol 23 (1) ◽

pp. A32-A43

Author(s):

Jakub Horák ◽

Petr Šuleř ◽

Jaromír Vrbka

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Czech Republic ◽

Computational Models ◽

Data File ◽

Transportation Industry ◽

The Czech Republic ◽

Kohonen Networks ◽

Artificial Neural ◽

Accuracy Of Results

Computational models of artificial neural networks are currently used in different areas. Accuracy of results exceeds the performance of traditional statistical techniques. Artificial neural networks as the Kohonen map may be used e.g. to identify industry leaders, thus replacing the traditional cluster analysis and other methods. The aim of this contribution is to analyse the transportation industry in the Czech Republic by the Kohonen networks and identify industry leaders. The data file contains results - division of companies into a total of 100 clusters. Each cluster is subjected to analysis of absolute indicators and several parameters, average, as well as absolute, are examined. In total, 88 firms may be considered as industry leaders. Consequently, a fairly small group of companies has a strong influence on development of the whole transportation industry in the Czech Republic.

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PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

Ekonomicko-manazerske spektrum ◽

10.26552/ems.2021.1.1-14 ◽

2020 ◽

Vol 15 (1) ◽

pp. 1-14

Author(s):

Zuzana Rowland ◽

Alla Kasych ◽

Petr Suler

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Czech Republic ◽

Financial Stability ◽

Grey Zone ◽

Financial Health ◽

Classification Problems ◽

Mining Companies ◽

Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

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Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: the case of the Lower Shire floodplain, Malawi

Hydrology Research ◽

10.2166/nh.2014.168 ◽

2014 ◽

Vol 45 (6) ◽

pp. 838-854 ◽

Cited By ~ 11

Author(s):

F. D. Mwale ◽

A. J. Adeloye ◽

R. Rustum

Keyword(s):

Neural Networks ◽

Developing Countries ◽

Artificial Neural Networks ◽

Water Level ◽

Lead Time ◽

Flood Forecasting ◽

Multi Layer Perceptron ◽

Raw Data ◽

Artificial Neural ◽

Forecasting And Warning

With a paradigm shift from flood protection to flood risk management that emphasises learning to live with the floods, flood forecasting and warning have received more attention in recent times. However, for developing countries, the lack of adequate and good quality data to support traditional hydrological modelling for flood forecasting and warning poses a big challenge. While there has been increasing attention worldwide towards data-driven models, their application in developing countries has been limited. A combination of self-organising maps (SOM) and multi-layer perceptron artificial neural networks (MLP-ANN) is applied to the Lower Shire floodplain of Malawi for flow and water level forecasting. The SOM was used to extract features from the raw data, which then formed the basis of infilling the gap-riddled data to provide more complete and much longer records that enhanced predictions. The MLP-ANN was used for the forecasting, using alternately the SOM features and the infilled raw data. Very satisfactory forecasts were obtained with the latter for up to 2-day lead time, with both the Nash–Sutcliffe index and coefficient of correlation being in excess of 0.9. When SOM features were used, however, the lead time for very satisfactory forecasts increased to 5 days.

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Artificial Neural Networks for Modeling of Crisis Management of National Economy

Èlektronnoe modelirovanie ◽

10.15407/emodel.42.02.109 ◽

2020 ◽

Vol 42 (2) ◽

pp. 109-120

Author(s):

N.Ya. Savka ◽

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Crisis Management ◽

National Economy ◽

Artificial Neural

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Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

SHS Web of Conferences ◽

10.1051/shsconf/20207301032 ◽

2020 ◽

Vol 73 ◽

pp. 01032

Author(s):

Marek Vochozka ◽

Zuzana Rowland

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Czech Republic ◽

Trade Balance ◽

Seasonal Fluctuation ◽

Seasonal Fluctuations ◽

Republic Of China ◽

Artificial Neural ◽

Multilayer Perceptron Networks

The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.

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