Scholarly record
INDICATING FINANCIAL HEALTH OF CZECH COMPANIES WITH THE SUPPORT OF MODERN METHODS OF MULTIDIMENSIONAL DATA PROCESSING
Abstract
Financial health of companies is a topic that has been historically studied under various conditions. However, the current economy is a living organism and, the conditions affecting the financial health of companies are changing dynamically. There are many ways how to evaluate the financial health of a company. In addition to the use of the ratio indicators, complex models using one numeric indicator expressing the financial health of the company can be applied. These include, for example, bankruptcy and credibility models. These models are based on the assumption that companies had been experiencing certain anomalies some years before their bankruptcy, indicating future problems in their economic activities, which are typical for units at the risk of bankruptcy. A major problem for the companys financial health research has so far been the lack of relevant electronic data over a longer timeframe. This situation is changing significantly today. One source that can be used in the Czech Republic now is the MagnusWeb database of the Bisnode company. This database provides a relatively large amount of data that can be tested and examined. With the development of computer science, applications for multi-dimensional data processing in the form of neural networks, clustering, genetic algorithms or decision trees can be used for this study. The basic element of this article is the research of financial health of companies in the Czech Republic using advanced methods of multi-dimensional data analysis. Thanks to this approach, it is possible to examine extensive data and verify the validity of existing complex models in the economic practice of the Czech companies.
Publication Impact Profile
- Captures
- Mendeley - Readers: 1
Publication details
ReferencesPending
Structured references will appear here after the reference import pass. The count is preserved now so the scholarly record is not incomplete.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
