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Exploring exit characteristics among czech female angel investors: A case study approach
(Vysoká škola Sting, 2024-06-30) Vejmělková, Lada
This article presents research findings on the identification of key exit characteristics of female business angels in the Czech Republic. The research is qualitative and based on case studies derived from semi-structured interviews with three female business angels. The primary data processing tool is content analysis of qualitative data, supported by quantification. The data included in the resulting data matrix are subjected to frequency analysis. The findings suggest that the main form of exit for female business angels is through trade sales. The share held by a female business angel is sold to a corporate investor, founders, or a venture capital fund. Furthermore, the research findings indicate that exits are the result of an opportunistic approach to planning the exit from the investee firm. Overall, it is concluded that a thorough grasp of these exit characteristics might improve the return on female business angel investments made in the Czech Republic, providing insightful information to both investors and policy makers and increased academic interest in this area.
Evaluation of the financial performance of engineering companies in the Czech Republic and Central Europe
(Entrepreneurship and Sustainability Center, 2024-12-30) Bureš, Josef; Sobotková, Nikola; Bartoš, Vojtěch
This article aims to analyse the development of the financial performance of engineering companies and evaluate the situation of the last decade in the Czech Republic and the neighbouring countries. The analysis of the development of the financial performance of engineering companies in the Czech Republic is carried out over the last 10 years and compared through the Winsorized mean with the situation in engineering in the neighbouring countries. The article is based on data collected in secondary research and relevant articles. The results of the selected financial indicators suggest that the engineering sector in the Czech Republic shows the best values only in terms of the indebtedness indicator. Other indicators show a more favourable position of engineering enterprises in Austria and Germany, demonstrating a higher degree of automation and innovation. Within the V4 countries, Hungary is the leading country according to the results of individual indicators. However, all the selected countries show problems in terms of the Liabilities Turnover Ratio, which is followed by an issue with low values in the profitability indicator. From the cited sources, previous articles, and the analysis carried out, engineering companies in the Czech Republic show average results in the selected financial indicators in the monitored period. In terms of financial performance, the engineering sector in Austria and Germany is in the leading position among the selected countries. The engineering sector in the Czech Republic should focus on increasing profitability, which would be facilitated by increasing technological progress and innovation.
BANKRUPTCY PREDICTION IN VISEGRAD GROUP COUNTRIES
(CZESTOCHOWA UNIV TECHNOLOGY, 2024-12-24) Prusak, Błażej; Karas, Michal
The novelty of the study is a comprehensive look at the problem of bankruptcy forecasting in Visegrad Group countries (V4) and making a comparison in relation to the achievements obtained in more developed western countries. The conducted research based on a systematic literature review of 151 publications indexed in Scopus and Web of Science and bibliometric analysis. The results showed that the main lines of research are from many perspectives unique compared to traditional western models, which are mainly given by different historical developments. Among the most predominant trends in the V4 countries, belongs relying on traditional classification algorithms with few exemptions of more advanced approaches, such as ensemble techniques. There are still many researchers using foreign bankruptcy forecasting models, which is an undesirable phenomenon due to their low efficiency. Although economically and culturally, these countries have many similarities, different predictors should be used in the process of developing bankruptcy prediction models. The conducted bibliometric analysis unveils the most influential papers, authors and periods of interest. Considering the number of publications and studies conducted, Hungary has a much smaller number than Slovakia, the Czech Republic and Poland. The results also showed challenges for further research, as most models rely on financial data, with limited focus on other predictors. In addition, most models are static in nature.
A LITERATURE REVIEW OF BUSINESS PERFORMANCE MEASUREMENT
(Mendel University Press, 2024-10-16) Sobotková, Nikola
The paper is aimed at a critical review of the literature dealing with the measurement of business performance. Because, nowadays the importance of implementing modern and effective management methods to maintain competitive advantage in almost all business sectors is emphasised, given the increasing competitive pressure. The measurement of business performance is also an important aspect of management and decision-making in organisations. Various indicators are currently being investigated to show the importance of modern approaches and effective measurement systems. This paper aims to identify a list of these modern methods, their bottlenecks and point out the possibility of introducing new and better indicators for performance measurement. The aim of this work is thus to create a critical review of the literature, especially about the latest findings of research articles on the selected topic. The purpose of this article is then to point out the limits of the current state of literature in the field of modern methods to measure business performance and highlight possible research gaps arising from the review in this area.
NEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIER
(Fintechalliance LLC, 2024-09-21) Lyeonov, Serhiy; Tumpach, Miloš; Loskorikh, Gabriella; Filatova, Hanna; Reshetniak, Yaroslav; Dinits, Ruslan
The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a << critical >> risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.

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