Prehliadanie podľa Autor "Musa, Hussam"
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Položka Bankruptcy prediction using first-order autonomous learning multi-model classifier(Český statistický úřad : Praha, 2024) Sabek, Amine; Horák, Jakub; Musa, Hussam; da Silva, Amélia FerreiraResearch background: Bankruptcy and financial distress prediction has always been an integral part of any financial management system. It gives an indication to stakeholders to take precautionary measures in order to avoid losses. The traditional approaches for prediction, including logistic regression and discriminant analysis, are constrained by their inability to deal with complex and high-dimensional data (Odom and Sharda, 1990; Min and Lee, 2005). Recent developments in the field of machine learning, and particularly autonomous learning classifiers, present a potential proposed alternative. Purpose: The purpose of this paper is to propose a first-order autonomous learning classifier (F-O ALMM0) for predicting bankruptcy of business entities and individuals. Design/methodology/approach: The data file contained a total of 352 companies obtained from the Kaggle database and incorporating 83 financial ratios. Initially, the model's performance was assessed as a preliminary step, but the results were average, followed by the application of Principal Component Analysis (PCA) to enhance the quality of the input’s variables. Afterwards, the number of independent variables was reduced to 26. Thus, the results were improved.Položka Blockchain-based financial systems: Trust, transparency, and the future of decentralized finance(Institute of Economic Research : Olsztyn, 2025) Chatterjee, Sheshadri; Musa, Hussam; Klieštik, TomášPoložka Calendar anomalies in DeFi assets and their competitive implications(Univerzita Tomáše Bati ve Zlíně, 2025) Rech, Frederik; Meng, Fanchen; Musa, Hussam; Cyrus, Isaboke; Syed Tauseef, AliThis paper investigates the presence, characteristics, and potential competitive implications of calendar anomalies within the decentralized finance (DeFi) sector, an area largely overlooked in existing financial literature. Previous research has predominantly focused on bitcoin or other cryptocurrencies selected purely based on market capitalization. In contrast, this article uniquely examines a specific sector within the cryptocurrency market, analyzing five leading DeFi assets by market capitalization, namely LINK, AAVE, MKR, SNX, and UNI, using daily data spanning November 2017 to November 2023 and estimating a GJR-GARCH model to assess day-of-the-week (DoW), month-of-the-year (MoY), and Halloween effects. The findings reveal no evidence of a consistent Halloween effect in returns or volatility. However, a strong and consistent Tuesday effect is observed in volatility, with four out of five assets exhibiting statistically significant excess volatility. A less pronounced Wednesday effect is identified in three assets, highlighting distinct volatility patterns unique to DeFi markets. The MoY analysis uncovers a pronounced January effect, with all assets except MKR exhibiting positive excess returns. This finding aligns with traditional finance yet is unprecedented within cryptocurrencies. Additionally, volatility clustering is evident, with periods of high or low volatility persisting and strongly linked to historical levels across all assets. These results enhance understanding of the competitive dynamics of DeFi markets, offering insights into how calendar anomalies influence risk, returns, and competitiveness within this rapidly evolving ecosystem.Položka Controlling tools in family and non-family businesses: a case study of woodworking and furniture industry(Institute of Economic Research : Olsztyn, 2024) Sedliačiková, Mariana; Poláková, Natália; Musa, Hussam; Schmidtová, JarmilaResearch background: Many studies point to the fact that the use of controlling in family businesses differs from that in non-family businesses and depends on factors that cannot be observed in non-family businesses. The research into the application of controlling tools in family and non-family businesses operating in the woodworking and furniture industry in Slovakia as a unique interconnection of the issues of family businesses, controlling and the Slovak woodworking and furniture industry has not been so far carried out. Purpose of the article: The aim of the paper is to identify significant differences in the application of tools of individual controlling subsystems between family and non-family businesses operating in the woodworking and furniture industry in Slovakia on the basis of a comprehensive mapping of the utilization of controlling tools in the businesses in question. Methods: The mapping of the issue was carried out by questionnaire-based method. In total, seven hypotheses were formulated. The validity of the assumed hypotheses was verified by two sample z-test. To generalize the obtained results to the entire basic set, verification of the minimum sample size was carried out. The representativeness of the sample was verified by the Pearson's Chi-square test of goodness-of-fit. Findings & value added: Based on the findings, it can be concluded that there are indeed significant differences in the use of controlling tools between family and non-family businesses operating in the industries in question. The results have showed the existence of significant differences in the use of tools of all examined controlling subsystems. It can be concluded that the application of controlling tools in the family businesses is significantly different from that in the non-family businesses. It can also be observed that family businesses of the industries in question tend to use controlling in an insufficient way and in general to a lesser extent compared to non-family businesses. The main benefit of the paper is the identification of the use of controlling tools in Slovak family businesses operating in the woodworking and furniture industry compared to non-family businesses. This knowledge can be valuable for practitioners and researchers in the field. The contribution also refers to the future direction of the development of the Slovak woodworking and furniture family businesses.Položka Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and Internet of Things financial and labor market technologies in generative artificial intelligence economics(Institute of Economic Research : Olsztyn, 2024) Lăzăroiu, George; Gedeon, Tom; Rogalska, Elżbieta; Valášková, Katarína; Nagy, Marek; Musa, Hussam; Zvaríková, Katarína; Poliak, Miloš; Horák, Jakub; Crețoiu Raluca, Ionela; Krulický, Tomáš; Ionescu, Luminița; Popa, Cătălin; Hurloiu Lăcrămioara, Rodica; Nistor, Filip; Avram, Laurenția Georgeta; Braga, VioricaResearch background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.Položka ESG performance and bank financial stability: Global evidence(Institute of Economic Research : Olsztyn, 2026) Musa, Hussam; Rech, Frederik; Grofčíková, Janka; Cúg, JurajResearch background: The link between environmental, social, and governance (ESG) performance and bank financial stability is of high academic and regulatory interest, yet global evidence is mixed. Clarifying this relation is important for resilient banking systems under rising sustainability pressures. Purpose of the article: To examine the association between ESG performance and bank financial stability, assessing both composite scores and the individual pillars, and documenting heterogeneity across bank financial stability, ESG profiles, and economic conditions. Methods: The analysis draws on a global panel of 4,466 bank-year observations from 688 banks across 84 countries over 2013–2024. ESG data come from MSCI ESG Ratings, and bank financial stability is measured using the natural logarithm of the Z-score. Baseline estimates use fixed effects with bank, country, and year effects. To account for persistence and potential endogeneity in bank financial stability, we additionally estimate dynamic panel models using the two-step Arellano–Bond GMM estimator. Findings & value added: The composite ESG score is positively associated with bank financial stability, but the effect is economically and statistically negligible in fixed effects models. Pillars diverge: governance is positive and significant, social is negative, and environmental shows no clear link. Heterogeneity is pronounced, with ESG aligning with higher bank financial stability mainly among already stable banks, while fragile banks face adverse associations. During the COVID‑19 period, the social pillar improves toward neutral or mildly beneficial, while the governance effect weakens. Dynamic GMM yields a stronger positive composite association and uniformly positive pillar effects, suggesting static models understate benefits due to endogeneity and persistence. The central contribution of this paper lies in its reconceptualization of the ESG–bank financial stability relationship as fundamentally state‑ and capacity‑contingent. By demonstrating that ESG functions not as a universal remedy but as a conditional strategic asset that benefits financially robust institutions, and by revealing how pillar‑specific effects exhibit distinct shifts during systemic crises, this paper provides a novel dynamic framework that advances both theoretical understanding and the practical design of risk management and prudential supervision in an era of escalating global uncertainty.Položka ESG výkonnosť vybraných podnikov z odvetvia automobilového priemyslu(Belianum. Vydavateľstvo Univerzity Mateja Bela v Banskej Bystrici, 2026) Kovács, Oliver; Musa, HussamThe paper deals with the analysis of ESG performance of selected companies from the automotive industry. The aim of the paper is to evaluate the ESG performance of companies from the automotive industry in the environmental, social and governance areas based on the analysis of non-financial reporting from the period 2021–2023 and subsequently propose recommendations where individual companies should improve based on a comparison of ESG performance with companies from the same industry. The subject of the paper is the ESG performance of selected companies and the object of the paper are selected companies from the automotive industry that are affected by the legal obligation to publish sustainability reports. The paper uses both qualitative and quantitative research. The literary and historical-logical methods were used in the processing of theoretical foundations. In the next part of the paper, methods such as analysis, synthesis, comparison, deduction, abstraction, generalization and graphical methods were used. The paper used secondary sources, mainly annual reports, sustainability reports, codes of ethics and remuneration reports. The paper identifies differences in the ESG performance of the companies studied, identifies their weaknesses and presents recommendations for individual companies to improve performance in individual areas - environmental, social and governance.Položka Factors affecting financial performance in the waste management industry: A comparative analysis of pre- and post- COVID-19 periods(Centre of Sociological Research : Ternopil, 2024) Grofčíková, Janka; Musa, Hussam; Lorincová, SilviaThe linear economic model is unsustainable for a long time, so the transition to a circular economy seems inevitable. By adopting a new Circular Economy Action Plan, the EU is taking concrete steps in this direction and identifying indicators to measure progress. In this context, businesses operating in the waste management industry are among the key actors helping to meet the objectives of circular economy policies. This study aims to identify and compare the determinants of the financial performance of companies in the NACE 38 industry and to quantify their impact on ROA and ROS in 2019 and 2022. Pearson's R was used to select the variables we examined using principal components analysis as one of the methods used in exploratory factor analysis. Linear regression analysis was employed to explain the influence of the extracted factors on changes in ROA and ROS. For 2019, we extracted five factors (capital structure, business policy, current assets' efficiency, operational activity, and working capital management) that explain 79.2% of the variability in profitability. For 2022, we extracted six factors (liquidity management, current assets' efficiency, asset structure, volume of available resources, capital structure and operational activity), which explain 84.17% of the variability of the variables. By comparing the findings, we concluded that while in the pre-crisis period, operating ratios appeared to be key to the financial performance of companies, in the post-crisis period, the factors of liquidity and available resources have become more critical.Položka Is it possible to build a stable ex-ante bankruptcy prediction model for Visegrad Group companies? A multi-year approach(Centre of Sociological Research : Ternopil, 2025) Musa, Hussam; Musová, Zdenka; Rech, FrederikCorporate bankruptcies pose significant challenges, impacting a wide range of stakeholders. While valuable, existing research on bankruptcy prediction primarily focuses on ex-post analysis, identifying financial indicators associated with past failures. This approach offers limited utility in proactively mitigating the negative consequences of future corporate distress. This study addresses this critical gap by developing ex-ante bankruptcy prediction models for the Visegrad Group countries. Employing Multiple Discriminant Analysis, these models are aimed at identifying companies at risk of bankruptcy up to five years before the event. A multi-model approach is utilized to construct a comprehensive V4 model that encompasses all four nations and develop individual models for each member country. Data from a sample of 25,084 companies incorporates 15 key financial ratios and 5 non-financial variables. The ratios differ significantly between bankrupt and solvent companies, and each model is calibrated with a single cut-off that caps the in-sample Type II error at 10%. Ex-ante evaluation, however, shows that this restriction does not halt the erosion of correct identification of failed firms. Rather, it drops from 90% in the test year to about one-third at a five-year horizon, even though classification of healthy firms remains above 95% and overall accuracy stays above 92%. The V4 model performed well, indicating that companies across the region share similar financial characteristics. Notably, two financial ratios, Net Income to Total Liabilities / Total Assets (X06) and Net Income / Total Assets (X04), consistently proved to be reliable indicators of financial health in all the examined models. These ratios are important because they help identify companies with strong financial stability across the V4 countries.Položka Is there a connection between ESG scores and a company’s profitability? Empirical evidence on selected Stoxx Europe 600 firms(Business Perspectives : Sumy, 2024) Musa, Hussam; Krištofík, Peter; Lysenko, Yaroslav; Medzihorský, JurajThis study scrutinizes the potential correlation between Environmental, Social, and Governance (ESG) scores and the profitability of firms listed in the selected STOXX Europe 600 index. Utilizing panel regression analysis, the study examines data from 385 non-financial companies over the period 2017 to 2021, correlating CSRHub's ESG scores and selected financial variables with corporate profitability measured by ROA. The investigation reveals that, overall, ESG scores do not have a significant impact on profitability, except for the ESG-community sub-score, which shows a slight negative influence. Thus, this paper partially supports studies that show a negative correlation between ESG and profitability, even though such results are in the minority in the literature. The overall results suggest that while ESG scores may reflect a company's ethical stance, they are not a predominant factor influencing its profitability. However, this is not the case for leverage, as the importance of capital structure for profitability is confirmed.Položka Reevaluating capital structure theories: The impact of stock yield maximization on leverage in European firms(Centre of Sociological Research : Ternopil, 2025) Musa, Hussam; Medzihorský, Juraj; Krištofík, Peter; Musová, Zdenka; Škvareninová, DagmarSeveral capital structure theories typically consider firm value maximization as the primary goal. In this paper, we propose a shift in this aim to stock yield maximization and investigate its implications. We address two specific issues: first, the redefined primary aim of stock yield maximization; second, the use of adjusted leverage measurement tools to explore the negative correlation between profitability and leverage, which similarly affects the relationship between stock yield and leverage. We verify the validity of capital structure theories under this new aim through an analysis of the relationship between stock yield and leverage, using both standard and adjusted measures. Our study focuses on European listed non-financial firms and reveals a negative correlation between stock yield (both capital and overall) and leverage with standard measures, contradicting existing theories. However, applying adjusted leverage measures confirms capital structure irrelevancy under the new aim, supporting the classical theory and the MM model. Notably, dividend yield positively correlates with leverage, aligning with investor expectations in more leveraged firms, as supported by several theories.Položka The essence and barriers to the use of controlling in the practice of manufacturing enterprises(Technická univerzita v Liberci, 2024) Potkány, Marek; Musa, Hussam; Schmidtová, Jarmila; Gejdoš, Pavol; Grofčíková, JankaThe theory, but also examples from practice confirm the fact that the use of controlling in economic practice differs significantly in the context of understanding its essence. The ambiguity of the definition of controlling, two different thought concepts, the diversity of tools and approaches, but also other barriers prevent its use to a greater extent than this managerial approach would deserve. The current research is based on the understanding of the essence of controlling in German-speaking countries and is oriented towards a coordinated predictive management approach based on precise cost reports. The research question was aimed at identifying current use and barriers of controlling in manufacturing enterprises in relation to the understanding of its essence and impact of performance through a questionnaire survey and structured interviews with managers from 2021 to 2022. A population of 2,504 enterprises was addressed by means of stratified sampling. The chi-square goodness-of-fit test was used to test how well the characteristics of the research sample fit the final population. A total of 352 manufacturing enterprises formed the resulting sample representative – enterprise size and type of industry designated according to the European standard industry classification system. Methods of contingency analysis and interval estimates of the population proportion were used to test the stated hypotheses. The testing confirmed a dependence between the practical use of a broader scale of controlling tools and the performance of enterprises measured by the return on sales (ROS) indicator, as well as the difference in the perception of barriers to the implementation of controlling depending on the size of enterprises. Controlling with the assistance of software support of the management information system, with a detailed implementation process and precisely defined competencies of employees and controllable KPI, creates the potential to increase the complexity of management and performance of enterprises as well as the elimination of potential risks.