Background - Financial market interactions often combine rivalry with interdependence: counterparties compete for margins, order flow, and informational advantage while relying on shared infrastructure, standards, clearing, and liquidity conventions. This creates a co- opetitive setting in which cooperation expands feasible outcomes and competition or convention selects a surplus division. Aim - This paper formalizes co-opetition in financial markets as a two-layer bargaining structure in which cooperation expands the feasible bargaining set S (through infrastructure, standards, clearing, and admissible information-sharing), while competition or convention selects an agreement on the Pareto frontier P(S). Methods - Two axiomatic bargaining solutions—the Nash solution (NS) and the Kalai–Smorodinsky solution (K–S)—are used as replicable rational benchmarks. Convex bargaining environments are compared with regime-type nonconvex environments, and the behavioral layer is expressed through an implementability criterion based on proportional asymmetry. Results - In convex settings, NS and K–S provide alternative benchmark selections that differ in product efficiency and proportionality. In regime-nonconvex settings, Pareto efficiency alone does not determine the branch on which coordination should occur; the K–S ray-intersection is therefore interpreted as a publicly readable focal candidate for regime selection. Conclusions - The paper does not claim an empirical proof of market-wide behavioral stability. Instead, it formulates behavioral implementability as a testable coordination condition - under regime nonconvexity and repeated or reputational interaction, observed agreements are expected to be closer to proportional K–S-like selections than to product- efficient extremes. A minimal observational, structural, and experimental verification strategy is outlined.
Background - Central Bank Digital Currencies (CBDCs) represent a structural transformation of monetary architecture. Every major monetary innovation has reconfigured rather than eliminated systemic risk, and CBDCs follow this pattern introducing design-contingent liquidity risk, novel operational and cybersecurity vulnerabilities, and regulatory governance gaps at sovereign scale. Aim - This paper examines CBDCs through an integrated risk-based framework, analysing liquidity risk, monetary transmission effects, operational and cyber risks, and user adoption dynamics as interconnected rather than independent dimensions. Methods - A comparative analytical review synthesizes monetary economics models, macro-network simulations, institutional reports (BIS, IMF, ECB), and an Austrian user survey (n = 2,006) across three analytical expectations. Results - Liquidity and transmission effects are design-contingent; operational and cyber risks are qualitatively distinct from those of prior financial technologies due to sovereign- scale concentration; adoption dynamics interact with the risk architecture: crisis-driven conversion to CBDC poses greater systemic risk than voluntary gradual adoption. Conclusions - CBDCs do not remove systemic fragility; they redistribute it across liquidity, operational, privacy, and governance channels. The evidence suggests that policy outcomes depend primarily on design choices and rollout conditions, which makes cautious, cash-like, privacy-sensitive implementation the most prudent baseline.
Background - Assessing fiscal discipline is often based on observed outcomes such as debt and deficit levels. However, these indicators reflect policy results rather than the underlying institutional design of fiscal rules. Existing datasets primarily capture the presence of rules but fail to evaluate their functional strictness. Aim - This paper develops a multidimensional index to systematically quantify the institutional strictness of fiscal rules and applies it to a comparative analysis of Germany, the United States, and the Czech Republic over the period 2000–2024. Methods - The study introduces the Fiscal Rule Strictness Index (FRSI), which aggregates six institutional dimensions: legal bindingness, numerical tightness, coverage, enforcement, escape clauses, and monitoring. The index is constructed using qualitative legal analysis and standardized coding of de-jure fiscal frameworks based on primary legislation and international datasets. Results - The findings reveal substantial cross-country differences in institutional strictness. Germany exhibits a highly restrictive framework due to constitutional anchoring and tight numerical constraints. The Czech Republic shows a significant increase in strictness following the 2017 reform but remains vulnerable to crisis-induced relaxations. In contrast, the United States scores lowest due to the absence of binding federal fiscal rules, despite strong subnational constraints. Recommendations - Future fiscal frameworks should strengthen coverage, limit circumvention through off-budget instruments, and ensure credible return paths after the activation of escape clauses. Practical relevance/social implications - The FRSI provides policymakers with a transparent tool to assess the credibility of fiscal commitments and improve communication with financial markets, particularly in high-debt environments. Originality/value: The study contributes to the literature by introducing a theoretically grounded and empirically applicable index that moves beyond outcome-based measures and captures the multidimensional nature of institutional fiscal strictness.
Background: Digitalization has become one of the most important trends in modern tax administration and public sector management. Tax authorities increasingly rely on digital technologies to improve administrative efficiency, enhance taxpayer services, and strengthen tax compliance. Despite significant investments in digital tools, the extent to which digitalization contributes to reducing tax arrears remains an open question. Aim: The aim of the article is to analyse the factors influencing the amount of personal income tax arrears in the Czech Republic during the period 2013–2024, with particular emphasis on digitalization and selected tax administration instruments. Methods: The study is based on annual data obtained from the reports of the Czech Financial Administration. The analysis includes indicators related to the number of tax entities, tax audits, electronically filed tax returns, assessed taxes, procedures for clearing doubts, tax arrears and arrears recovered through enforcement actions. Monetary variables were adjusted for inflation using the Consumer Price Index published by the Czech Statistical Office. Pearson correlation analysis and multiple linear regression analysis were employed to examine the relationships among the selected variables. Results: The results revealed a strong positive relationship between the number of tax audits and tax arrears. The regression model explained 95.4% of the variability in tax arrears and identified tax audits as the most significant predictor. The number of tax entities exhibited a statistically significant negative effect on tax arrears, suggesting that a broader taxpayer base may contribute to lower levels of unpaid tax liabilities. Although electronically filed tax returns showed a significant negative correlation with tax arrears, their effect was not statistically significant in the multivariate regression model. This finding suggests that digitalization may influence tax arrears indirectly through broader improvements in tax administration efficiency rather than through a direct effect. Conclusions: The findings indicate that effective tax administration cannot rely solely on digitalization. While digital tools contribute to administrative modernization and may support tax compliance, the reduction of tax arrears appears to depend primarily on effective control mechanisms and the overall functioning of tax administration. The study contributes to the ongoing discussion on the role of digital transformation in tax administration and provides empirical evidence on factors associated with tax arrears in the Czech Republic.
Objective: Algorithmic and high-frequency trading have become a permanent fixture of modern financial markets over the past two decades — and regulators on both sides of the Atlantic have been trying to keep up, with mixed results. This paper asks how well current regulatory frameworks, specifically the EU's MiFID II and the SEC's supervisory approach, hold up against the challenges that autonomous trading algorithms create. These are not purely technical issues — market manipulation, flash crashes, and the technological gap between different market participants all have real consequences for how the financial system functions. The paper works through three questions: what empirical research tells us about HFT's effects on liquidity, price formation, and market stability; how MiFID II and the SEC each try to get these risks under control; and where exactly current regulation breaks down — whether in the rules themselves or in how they're enforced. Methodology: The paper draws on a structured literature review. This covered peer reviewed empirical studies in market microstructure, reports from regulatory and international bodies (ESMA, SEC, BIS, IOSCO, FSB, FCA), and legal-policy analyses of the EU AI Act. Sources were pulled from five academic databases — IEEE Xplore, SpringerLink, ScienceDirect, SSRN, and Google Scholar — and each was assessed against four criteria: breadth of regulatory coverage, real-world enforceability, how well regulation adapts to technological change, and documented effects on market stability and integrity. Findings: The central finding is that regulatory effectiveness has less to do with whether formal rules exist and more to do with whether those rules can actually be applied in real time. The EU's preventive ex-ante model and the U.S. ex-post traceability approach both have blind spots — and technologically sophisticated trading firms know how to use them. The paper also argues that the ethical problems associated with algorithmic trading — manipulation, the information gap between institutional and retail participants, opaque AI models — are not really independent moral concerns. They are more like the visible surface of a deeper regulatory and technological failure. Part of the analysis maps how the EU AI Act interacts with MiFID II, tracing how requirements around high-risk system classification, transparency, human oversight, and post-deployment monitoring connect with existing RTS 6 obligations. From this, a four-pillar evaluation framework is proposed — fairness, accountability, transparency, and robustness — as a working tool for assessing algorithmic trading systems on ethical grounds. Practical relevance: The paper's conclusions have concrete implications for regulators, compliance teams, and trading firms alike. Supervisory authorities need to invest in real time monitoring infrastructure, push for greater cross-border enforcement consistency, and bring AI Act conformity assessment into MiFID II compliance processes. Trading firms, meanwhile, should stop treating algorithmic impact assessments, independent model validation, and explainable AI as optional extras — these should be standard practice. The proposed framework is intended as a starting point for ethics audits in automated trading, and to help narrow the gap between what regulation promises on paper and what actually happens in the market.
