Application of Clustering in the Dimensionality Reduction Algorithms for Separation of Financial Status of Commercial Banks in Ukraine

Velykoivanenko, Halyna; Korchynskyi, Vladyslav

doi:10.13189/ujaf.2022.100116

Cited by 4 publications

(3 citation statements)

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“…Each of these methods has its advantages and areas of application and tasks, where it reveals itself in the best way. Experimental studies on comparative analysis of the effectiveness of various clustering methods are described, in particular, in scientific works Subasi, 2020;Velykoivanenko and Korchynskyi, 2022).…”

Section: Resultsmentioning

confidence: 99%

Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy

2022

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The movement of Ukrainian guest workers in the direction of Poland until 2019 was predominantly transnational in nature, as there was a constant movement of labor migrants between the national spaces of Ukraine and Poland with financial participation in the economies of the two countries at the same time. This fact is confirmed by the results of empirical sociological researches conducted by the Ukrainian Institute of the Future, the Cedos analytical agency, the Personnel Service employment agency, the Gremi Personal, and the statistical data of the International Organization for Migration, the State Migration Service of Ukraine, the State Statistics Service of Ukraine, the Ministry of Family and Social Development of Poland. However, the trends changed dramatically due to the global COVID-19 epidemic, later in 2022 with the outbreak of the war in Ukraine. The desire for temporary, pendulum labor mobility gave way to the desire to leave Ukraine forever and settle abroad with the whole family. A new migration trend may be associated with the movement to Poland of Ukrainian men who come after the end of the war to reunite with their families, who were moved there earlier since the beginning of hostilities in Ukraine.

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Section: Resultsmentioning

confidence: 99%

Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy

2022

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“…The lower efficiency of state-owned banks is evidenced by research in developing countries (Yeyati et al 2004;Zarutska 2015;Velykoivanenko and Korchynskyi 2022), in developing countries during 1995-2002(Micco et al 2007), in 16 Far East countries (Bangladesh, China, Hong Kong, India, Indonesia, Macau, Malaysia, Nepal, Pakistan, the Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Thailand, and Vietnam) during 1989-2001(Cornett et al 2010, Argentina in the 1990s (Berger et al 2005), Bangladesh in 2004-2011(Kamarudin et al 2016-2019(Ullah and Rahman 2022), China in 1994(Berger et al 2009), 1997(García-Herrero et al 2009-2011(Zhang and Wang 2014), in Egypt during 1996-1999(Omran 2007), in Ethiopia during 2011-2017(Tekatel and Nurebo 2019, in India during 2010-2012 (Aswini et al 2013), 1998-2002(Sathye 2005-2012(Sukhdev et al 2016-2014(Gupta and Sundram 2015, in Iran during 2006-2010 (Nia et al 2012), in Kenya during 2000(Barako and Tower 2007, in Malaysia during 1994-1996(Karim 2003) and 2000…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Impact of State-Owned Banks on the Sustainability of Public Finances

Davydenko,

Boiko,

Cherniavska

et al. 2023

Economies

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This paper aims to provide a retrospective assessment of Ukraine’s state policy concerning state-owned banks and evaluate their impact on the sustainability of Ukraine’s public finances. The research methodology employs an empirical study of the cash flow of public funds to state-owned banks and the reverse cash flow to determine the impact of the activity and stability of public finances. The cash flow to state-owned banks includes the expenditure of public funds for the creation of authorised capital during the establishment of state-owned banks, the acquisition of shares in operating commercial banks, additional capitalisation of state-owned banks, etc. The reverse cash flow comprises dividends paid based on the performance of state-owned banks, as well as revenue generated for public funds through the sale of shares (privatisation) of state-owned banks. This study highlights the costs associated with recapitalising state-owned banks. These costs disrupt the stability of public finances, create additional debt dependency for Ukraine, impose an additional burden on public finances, and lead to structural changes that reduce funding for social spending. As a result, Ukrainian taxpayers are financing the inefficient activities of state-owned banks while experiencing reduced investments in education, healthcare, social protection, environmental protection, and other essential areas.

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“…• Alexey Hostryk, Odessa National Economic University, Ukraine [19] • Pavlo Hryhoruk, Khmelnytskyi National University, Ukraine [20] • Muhammad Jawad, Fatima Jinnah Women University, Pakistan [21] • Nila Khrushch, Khmelnytskyi National University, Ukraine [22] • Inesa Khvostina, Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine [23] • Oksana Kovtun, University of Educational Management, Ukraine [24] • Serhii Lehenchuk, Zhytomyr Polytechnic State University, Ukraine [25] • Nataliia Maksyshko, Zaporizhzhia National University, Ukraine [26] • Abdukhakim Mamanazarov, Center of Economic Culture Development, Uzbekistan [27] • Ewa Matuska, Pomeranian University in Slupsk, Poland [28] • Inese Mavlutova, BA School of Business and Finance, Latvia [29] • Iveta Mietule, Rezekne Academy of Technologies, Latvia [30] • Dariusz Pawliszczy, Gromadka Community, Poland [31] • Oleg Pursky, Kyiv National University of Trade and Economics, Ukraine [32] • Michael Radin, Rochester Institute of Technology, United States [33] • Sultan Ramazanov, Kyiv National Economic University named after Vadym Hetman, Ukraine [34] • Kateryna Shymanska, Prague University of Economics and Business, Czechia [35] • Victoria Solovieva, State University of Economics and Technology, Ukraine [36] • Galyna Velykoivanenko, Kyiv National Economic University named after Vadym Hetman, Ukraine [37] Figure 3: Conference highlights, part 2.…”

Section: M3e2 2022 Program Committee Membersmentioning

confidence: 99%

Advances in machine learning for the innovation economy: in the shadow of war

Danylchuk,

Semerikov

2023

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.

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Application of Clustering in the Dimensionality Reduction Algorithms for Separation of Financial Status of Commercial Banks in Ukraine

Cited by 4 publications

References 37 publications

Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy

Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy

Analysis of the Impact of State-Owned Banks on the Sustainability of Public Finances

Advances in machine learning for the innovation economy: in the shadow of war

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