Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.

If there is a period of unfavourable to critical economic development Business Corporation, there is no doubt about the significance of determining the actual factual moment of bankruptcy of the business corporation. In such a situation, it is crucial for further development approach of the statutory body of Business Corporation in terms of truly evaluation and timely search for a constructive solution. There are different methods of creative accounting to delaying the impending corporation bankruptcy. This paper describes the individual ways in which the creative accounting can favour the business corporation, e.g. increase the reported earnings or reduce the reported loss, manipulate with indicators used in financial analysis, conceal financial risk or strengthen the company´s access to finance. The reason for using these practices by the company management can be just a simple delay in solving the situation, trying to keep the company position as long as possible or the reasons are more complex and sophisticated. There are many model to predict the adjusting financial statements. We described and applied two of them, the CFEBT model and BENEISH M-score model.

Research background: Macroeconomic volatility leads to increased corporate indebtedness, ultimately undermining the broader economic environment and firms’ competitiveness across all industries. The COVID-19 pandemic, among all recent disruptions, was a profound systemic shock with enduring effects on the economy, offering a valuable case study for analysing its wide-ranging impact on corporate financial stability. The pandemic’s spread significantly influenced corporate debt levels, and while this was a common issue for the corporate sector, its impact on financial performance varied considerably across industries. Purpose of the article: The consequences of the pandemic are evident from various perspectives, and it is essential to monitor and evaluate the changes caused by the COVID-19 pandemic, not only for immediate understanding but also for their long-term and universal implications on economic and social systems worldwide. The main intention of this study is to describe and quantify the impact of the COVID-19 pandemic, examine changes in the growth of critical determinants and identify new trends in enterprises operating in Visegrad Group countries, providing insights that may be valuable for broader contexts and future crises. Methods: Friedman test results indicate a principal distinction between groups but do not identify which pairs of groups differ. However, as the monitored indebtedness indicators exhibit significant differences, we employ the Bonferroni correction to determine which monitored periods had the largest disparities in the individual indicators. This study employs panel data from 12816 firms from Visegrad Group countries covering 2018–2022. Findings & value added: This study provides a detailed and subtle explanation of corporate debt dynamics during the pandemic years, revealing considerable differences beyond direct effects. Based on the most recently available financial data, we present valuable long-term insights concerning enterprises’ resilience and flexibility and enrich the theoretical knowledge concerning financial stability under crisis. Moreover, the findings offer actionable advice for business leaders and policymakers seeking to enhance corporate debt management and economic competitiveness in a globalising and turbulent world. These findings are of worldwide applicability and will undoubtedly remain so for practice and scholarship in the forthcoming few years.

Financial distress prediction models have been extensively utilised to assess the financial health of companies. However, their predictive accuracy can be significantly affected by extraordinary economic disruptions, such as the COVID-19 pandemic. Traditional models, particularly those designed for stable economic conditions, necessitate evaluation and potential adaptation to maintain their effectiveness during unprecedented circumstances. This study seeks to evaluate the performance of financial distress prediction models developed by authors from the Visegrad Four (V4) when applied to Slovak automotive companies before, during, and after the COVID-19 pandemic. Initially, the best-performing models from those selected were identified in the pre-pandemic period (2017–2019). The performances of these models were subsequently analysed during the pandemic and post-pandemic periods (2020–2022). Finally, their coefficients were re-estimated to enhance accuracy while preserving the original variables, ensuring the interpretability of any changes. The objective is to identify the models with the highest performance during the pre-pandemic period, assess their reliability under crisis conditions, and suggest improvements through coefficient re-estimation. While the majority of models experienced significant declines in performance during the pandemic, some retained adequate predictive accuracy. The re-estimated coefficients improved the overall accuracy of the models and also enhanced the sensitivity of some, offering stakeholders the option to utilise either the original or adjusted models based on their specific context. To complement the analysis, we also constructed new models for the pandemic and post-pandemic periods, allowing for a more comprehensive evaluation of financial distress prediction under changing economic conditions. This study provides a framework for adapting financial prediction models to unprecedented economic conditions, contributing valuable insights for researchers and practitioners seeking to enhance predictive tools within dynamic economic environments.

Research background: Financial health is an essential factor in the success of an enterprise, its survival, competitiveness in the market and sustainable development. Therefore, predicting constraints, weak points and possible risks that could cause financial distress is crucial. Small and medium-sized enterprises (SMEs) remain a key pillar of any prosperous economy during every phase of the economic cycle, particularly in emerging countries, such as the Bucharest Nine. Purpose of this article: The objective is to specify indicators of the financial health of SMEs depending on the economic cycle through unconventional incentives under the conditions of the Bucharest Nine. It entails a longitudinal mapping of more than 30,000 enterprises during the pre-crisis, crisis and post-crisis periods, as along with data on economic growth. Methods: Financial statements from the Orbis database, covering the period 2018–2023, were used to create a robust final sample of SMEs. Logit least absolute shrinkage and selection operator with 10-fold cross-validation was employed to identify bankruptcy predictors from 75 origin predictors, including liquidity, activity, profitability, indebtedness, earnings management and business development. The resulting models for each period were validated on a test sample of prosperous and non-prosperous enterprises. Furthermore, the classification ability of all models was evaluated using the area under the receiver operating characteristic curve. Findings & value added: This research adds value by demonstrating important factors that influence the bankruptcy of SMEs and guiding financial managers to focus on these factors based on the expected economic cycle. Thus, developed prediction models are particularly beneficial for businesses themselves, enabling them to predict financial health depending on the expected state of the economy, which helps overcome the existing animosities of businesses towards predictions. The results of the present study may also prove valuable to agencies dealing with SMEs, financial database providers or auditing companies. The present study enhances the idea of financial distress prediction by including unconventional financial indicators, including earnings management and value-added variables, in traditional bankruptcy modelling frameworks. This innovative combination enhances the theoretical framework of financial economics by providing a more dynamic and context-aware method for assessing SME sustainability over the economic cycle.