The problem of comparison of different companies is facing, when analyzing company's performance in stock exchange market. Due to many different fmancial ratios and parameters sometimes it is almost impossible to decide which company is a leader or not. One of the ways to solve this problem is the use of self-organizing (Kohonen's) neural networks. Using financial parameters as inputs, as an output we will have different groups of companies. Using the ranking, which is made before, results it is possible to determine which group consists of leading companies. By adding financial parameters of concrete company to the existing network, therefore, company will appear in one of earlier formed groups. Now it is possible to decide about mentioned...
This paper aims at using neural networks – and especially self‐organizing maps (SOMs) – to analyse a...
In this paper, a hybrid model of artificial neural networks is designed and used to evaluate the pre...
The report deals with the application of neural network modelling techniques to two categories of fi...
The article discusses the use of neural networks and attempt to reveal the peculiarities of the diff...
International audienceThe neural applications in business finance are already numerous and related f...
This paper considers the use of neural networks—namely self-organizing maps (SOMs)—to analyze and cl...
Applications of neural networks to finance and investments can be found in several books and article...
This paper sets out to determine the strategic positioning of Spanish Savings Banks, using data draw...
Neural networks are widely used for nancial time series prediction. However, the future values' pred...
The article discusses the use of neural networks and attempt to reveal the peculiarities of the diff...
This article deals with the usefulness of neuronal networks in the area of corporate finance. Firstl...
This study analyses financial data using the result characterization of a self-organized neural netw...
In this paper we propose a complete method for financial diagnosis based on Self Organizing Feature ...
Introduction We describe a neural-network-based aid to the financial analysis of companies, which i...
The content of modern management accounting is formed in conjunction with the rapid development of i...
This paper aims at using neural networks – and especially self‐organizing maps (SOMs) – to analyse a...
In this paper, a hybrid model of artificial neural networks is designed and used to evaluate the pre...
The report deals with the application of neural network modelling techniques to two categories of fi...
The article discusses the use of neural networks and attempt to reveal the peculiarities of the diff...
International audienceThe neural applications in business finance are already numerous and related f...
This paper considers the use of neural networks—namely self-organizing maps (SOMs)—to analyze and cl...
Applications of neural networks to finance and investments can be found in several books and article...
This paper sets out to determine the strategic positioning of Spanish Savings Banks, using data draw...
Neural networks are widely used for nancial time series prediction. However, the future values' pred...
The article discusses the use of neural networks and attempt to reveal the peculiarities of the diff...
This article deals with the usefulness of neuronal networks in the area of corporate finance. Firstl...
This study analyses financial data using the result characterization of a self-organized neural netw...
In this paper we propose a complete method for financial diagnosis based on Self Organizing Feature ...
Introduction We describe a neural-network-based aid to the financial analysis of companies, which i...
The content of modern management accounting is formed in conjunction with the rapid development of i...
This paper aims at using neural networks – and especially self‐organizing maps (SOMs) – to analyse a...
In this paper, a hybrid model of artificial neural networks is designed and used to evaluate the pre...
The report deals with the application of neural network modelling techniques to two categories of fi...