Background: Artificial intelligence (AI), automation, and robotization are transforming financial business centers globally, but research on their implementation in Slovakia remains limited. Aim: This study investigates how AI, automation, and robotization are implemented in Slovak financial business centers and evaluates their impact on competitiveness. Methods: A qualitative multiple case study was conducted, including interviews with representatives from four Slovak financial business centers and detailed case analyses. Results: All centers have integrated AI, automation, and robotization into various business processes, with differing levels of maturity. These technologies enhance operational efficiency and competitive performance. Recommendations: Organizations should accelerate technology adoption, invest in employee upskilling, and strengthen collaboration with academic institutions to address implementation challenges. Further research could expand the study to additional centers in the CEE region. Practical relevance/Social implications: Findings support strategic decision-making in Slovak and Central European financial centers, promoting competitiveness, efficiency, and sustainable development. Originality/Value: This is the first in-depth study of AI, automation, and robotization implementation in Slovak financial business centers, filling a regional research gap and providing actionable guidance for managers and policymakers.

Background: The relationship between public debt and private sector profitability has long been emphasized in economic theory in the context of sectoral balances. According to Post-Keynesian economics, private debt accumulation, under certain conditions, may be a source of private sector profits. Moreover, public debt dynamics may have a strong relationship to the evolution of firms’ sector debt. Aim: This paper develops a monetary financial model of a small open economy using the stock-flow consistent and system dynamics frameworks, focusing on the interplay between the public and the private sector debts, and public debt and private sector profitability. The aim is to test – using the model – the hypothesis that public debt as an injection of net financial assets into the economy may positively influence private sector profits. Additionally, the model assesses the relationship between the public debt and the firms’ debt sector dynamics. Methodology: Stock-flow consistent approach together with nonlinear differential equations and non-equilibrium approach are used to build the model. System dynamics is used for model simulations. The model works with quarterly time periods, six sectors – central bank, government, banks, households, firms and the rest of the world, consolidated sector balance sheet items acting as stocks, and inflows and outflows changing the value of those items - as flows. Behavioral equations define the model behavior, and interest rate mechanism is used as the global feedback loop. The model tracks how monetary flows across consolidated sectors change the accumulation of stocks and a variety of real and nominal macroeconomic variables. Baseline, boom and negative shock scenarios are used to simulate the outcome of the model on simulated data. Results: According to the simulation results, public debt accumulation may contribute to private sector profitability. Public debt may also have an inverse relationship with the dynamics of firms’ sector debt. However, the introduction of export shocks can trigger a systemic decline. The model highlights a strong link between public debt and private sector debt dynamics, as well as high sensitivity of real macroeconomic variables to external flows for a small open economy. Recommendations: This paper underscores critical influence of the foreign sector, policy rule design and endogenous debt dynamics across different sectors on a small open economy and variety of its macroeconomic variables. Although it is highly recommended to apply SFC framework and system dynamics with a high level of parametrization and a variety of feedback loops – the model provides aluable insights into the discussions of public debt evolution and its implications. Relevance: This paper addresses a key topic in practical economic policy: the dynamics of public debt, its potential drivers and causes. It develops a mathematical model based on complex nonlinear relationships with a useful simulation framework. This framework might help economists and policymakers better understand the causes, implications, and intersectoral relationships associated with the public debt. Originality: This paper is original, based on the ideas of Wynn Godley, Randall Wray, Steve Keen, Marc Lavoie and Thomas Palley, providing an originally developed consolidated balance sheet of foreign sector (rest of the world), and dynamic interest rate and inflation mechanisms. Additionally, original dependencies are introduced to the model – banks’ CAR ratio, advanced interest rate feedback loop mechanism and advanced logic of sectoral flows.

Background: Between 2015 and 2023, young adults in Germany faced significant financial challenges due to escalating inflation, peaking at 10.6% between 2021 and 2023. Aim: This study aims to investigate the financial impacts of inflation on the household assets of young adults in Germany compared to other age groups. Methods: Employing a structured literature review of studies, reports, and statistical data from institutions like the European Central Bank and the Deutsche Bundesbank. Results: The findings reveal that high inflation eroded net asset returns for young households, delayed wealth accumulation, and exacerbated generational wealth disparities. Recommendations: The study recommends targeted policy actions to enhance financial resilience among young adults, such as financial education and support for wealth building initiatives. Practical Relevance/Social Implications: Addressing the unique financial vulnerabilities of young adults during inflationary periods is essential for reducing wealth inequality and promoting economic stability. Originality/Value: This research contributes originality by focusing on an underexplored demographic, shedding light on how recent inflation has specifically impacted the wealth development of young German households.

Background: Traditional econometric models like ARIMA, while foundational for time series forecasting, often rely on assumptions of linearity and stationarity. These models can fall short in capturing the complex, nonlinear dynamics frequently present in financial markets. This has led to the adoption of machine learning methods like Long Short-Term Memory (LSTM) networks, which are specifically designed to recognize long-term dependencies in sequential data, offering a potential advantage in modeling volatile financial time series. Aim: This study compares the predictive performance of a classical econometric model (ARIMA) with a deep learning approach (LSTM) in the context of stock index forecasting using the DAX 50 ESG index from 2020 to 2024. Methods: An autoregressive integrated moving average (ARIMA) model is compared against a long short-term memory (LSTM) neural network. The models are evaluated using both a static train-test split and a more rigorous expanding window forecast scheme. Predictive accuracy is measured by standard error metrics (MAE, RMSE, MAPE) and the Diebold-Mariano test. Results: The empirical results show that the LSTM model achieves lower forecast errors than the best-fitting ARIMA model in both evaluation frameworks. In the expanding window scenario (repeated retraining), the LSTM maintains a statistically significant, though modest, forecasting advantage over the ARIMA model. Originality/Value: The findings suggest that while the LSTM's ability to capture nonlinear patterns offers a forecasting edge, the improvement is incremental in a highly liquid and efficient market. This case study highlights the potential of deep learning methods in finance but also reinforces he notion that strong market efficiency can limit the forecasting benefits of such complex models.

Background: The Czech real estate market has experienced rapid growth in recent years, driven by macroeconomic trends and limited housing supply. Retail investors face increasing barriers to direct property ownership, prompting a shift toward real estate investment funds (REIFs). However, the lack of a standardized performance benchmark hinders market transparency and comparability. Objective: This study aims to design a dual-index framework to benchmark the performance of Czech real estate investment funds. It investigates how fund structure, size, and investor segmentation affect index behaviour and evaluates the implications of different methodological approaches. Methods: Two types of indices, arithmetic and NAV-weighted, were constructed separately for retail and qualified investor funds. Data were collected quarterly from 39 real estate funds, with inclusion based on data availability and reporting consistency. Indices were computed using Python-based time-series processing, with quarterly rebalancing and weight capping to reduce concentration risk. Results: Qualified investor funds achieved higher average returns and exhibited lower performance dispersion. In contrast, retail funds displayed greater heterogeneity, and the weighted index was strongly influenced by a single large, underperforming fund. The arithmetic index proved sensitive to outliers, while the weighted index highlighted capital concentration effects. Recommendation: Investors and analysts should use both index types for a comprehensive performance view. Policymakers should encourage broader data disclosure and consider the systemic impact of dominant funds on retail benchmarks. Practical relevance: The indices provide a transparent benchmarking tool for market participants, enabling better performance evaluation and investment decision-making. The framework also supports regulatory efforts to enhance market maturity. Originality/value: This study is the first to introduce a dual real estate fund index for the Czech market. It provides an analytically sound and practically applicable model for benchmarking performance across investor segments, with methodological insights relevant to other emerging real estate markets.

Background: The countries of the Visegrad Group (Czech Republic, Slovakia, Poland and Hungary) apply different personal income tax systems that reflect their different economic and social policies. Taking into account the fact that often every year changes are made in the tax legislation, there are changes in tax systems. Aim: The article is focused on measuring the progressivity of the tax on dependent activity in the Czech Republic, Slovakia, Poland and Hungary. Methods: Interval and global progression methods were used. Specifically, these are the progressivity of the average rate, the progressivity of the tax liability, the Lorenz curve, the Gini coefficient and the Musgrave and Thin index. Results: The resulting values are calculated according to model examples based on the tax laws of individual countries. Based on the results of interval progressivity, similar but also different features can be observed in individual countries. In the case of the Czech Republic, Slovakia and Poland, it can be observed that the tax on income from dependent activity is progressive. In the case of taxpayers with low incomes and in the case where a child lives with the taxpayer, the tax in some cases even has a regressive effect. On the other hand, in Hungary, a proportional tax applies throughout. Based on indicators of global progressivity, they show that the tax on income from dependent activities is the most progressive in Poland.

Background: The post-conflict reconstruction of Ukraine, especially in the housing and construction sector, has become a strategic priority for the European Union and its member states. Despite declared support, the real participation of Central European businesses, including those from the Czech Republic, remains limited due to multiple legal, security and financial barriers. Aim: The paper aims to identify the conditions, instruments and risks influencing the potential engagement of Czech enterprises in the reconstruction of Ukraine's housing and construction sector after 2022, with emphasis on investment frameworks, scenario modelling, and institutional capacities. Methods: The study combines a qualitative case study (cooperation between the University of Finance and Administration and V. N. Karazin University in Kharkiv), stakeholder analysis based on coded interviews with Czech entrepreneurs, and quantitative investment scenario modelling (2024–2033). Data triangulation was applied to ensure internal validity. Results: Findings confirm that while institutional and financial instruments (e.g., Ukraine Facility) are in place, their uptake is limited by high perceived risk and a lack of implementation facilitators. Investment scenarios range from 65 to 95 billion USD depending on security and absorption conditions. Czech SMEs face specific constraints such as insufficient legal safeguards and capacity limits yet remain strategically positioned to benefit from targeted support schemes. Recommendations: Policy actors should prioritise the development of national coordination platforms, risk insurance schemes (e.g., via EGAP), and pilot cooperation models with Ukrainian institutions. Stronger links between academia, public sector and private firms are essential to de-risk market entry and build long-term resilience. Practical relevance/social implications: The research provides applicable insights for government agencies, export organisations and business associations aiming to support Czech firms in entering high-risk post-conflict markets. Moreover, it demonstrates the role of academic institutions as platforms for international capacity building and post-war recovery. Originality/value: This is the first study focusing on the Czech context of post-war investment in Ukraine, combining scenario modelling with a grounded case study. The integration of qualitative and quantitative methods provides a comprehensive Framework for further research and policy development.

Background: In the post-2021 era, Central Europe grapples with enduringly high inflation rates, challenging the effectiveness of conventional monetary policy tools. Objective: This study aimed to shed light on these complex dynamics, offering insights into the effectiveness of fiscal strategies in an environment where conventional monetary policies appear increasingly inadequate. Methods: The Fiscal Theory of Inflation offers a straightforward model to examine the financial exchanges between governments and the public within a two-day economic setting. The framework explains the recurring patterns in government borrowing, taxation, and public expenditure, highlighting their impact on inflation and presents the focal point of this study. Results: Utilizing empirical survey data primarily from the Czech Republic and France, sourced from the Czech Statistical Office, the Czech National Bank, Banque de France, and INSEE, this study engages with the Fiscal Theory of Prices to elucidate these phenomena. Central to this analysis is the Fiscal Theory of Inflation, which argues that unanticipated inflation results in the devaluation of nominal treasury bonds, prompting a corresponding adjustment either in expected primary surpluses or the discount factor. This adjustment is crucial to ensure alignment between the total government debt's actual value and these surpluses' present value. Recommendation: The study aims to provide insights into these intricate dynamics, offering implications for the efficacy of fiscal strategies in an environment where conventional monetary policies increasingly prove inadequate. Practical relevance: This paper explores the implications of these persistent inflationary trends, focusing on the imperative of government debt recovery, as recognized by the newly elected government in the autumn of 2021. This research is helpful for any economist who opposes inflation measurement, targeting, or, most importantly, the belief that money grows on trees, allowing the government to fund public expenditures that many view as crucial. Originality/value: The paper is original, based on the ideas of J.H. Cohrane. The paper is an empirical test of the so-called fiscal theory on the example of France and the Czech Republic.

Background: The real estate market in the Czech Republic exhibits significant differences among regions, especially in terms of the influence of economic factors such as wages on property prices. Wages are one of the key determinants of house prices, but their influence may vary across regions and over time. It is important to further understand the dynamics between wages and house prices at the regional level. Objective: This study seeks to investigate whether and how the impact of wages on house prices varies among regions and how it changes over time. Methods: The fixed effects panel regression with interaction terms was used to account for regional and time effects. The model includes lagged house price values to better capture market dynamics over time. Interaction terms between wages and regions allow for the detection of region-specific effects. The Newey-West correction was used to control for heteroskedasticity and autocorrelation. Results: In some regions, such as Prague, factors other than wages (e.g. lack of supply and high demand) may play a more significant role. The analysis also confirmed that house prices exhibit time inertia, which means that past price developments have an impact on the current market. Recommendation: It is recommended to focus on promoting affordable housing in regions with high prices and on investment opportunities in emerging regions where wages and house prices are growing more steadily. Practical relevance: This study provides insights into regional differences in the Czech Republic's housing market. These insights are valuable for regional housing and economic development strategies Policymakers can use this knowledge to better respond to affordable housing challenges. Originality/value: This study provides an original analysis of the impact of wages on house prices, with an emphasis on regional specificities and time trends, allowing for a deeper understanding of regional dynamics in the housing market in the Czech Republic. This analysis provides useful insights for future research and for practical applications in real estate market and regional policy decision-making.