We propose a responsibility-anchored employment theory framework grounded in endogenous responsibility costs. When AI systems replace human labor, runaway risks (e.g., explosions in chemical plants, misdiagnoses by medical robots) create irreversible responsibility gaps; individual corporate owners, neither cognitively nor financially equipped to bear such liabilities, face catastrophic societal damage (environmental destruction, casualties, etc.). By anchoring the balance between automation levels and responsibility-bearing capacity, the model identifies a critical threshold: surpassing the AI-to-labor ratio threshold triggers an responsibility vacuum. A dual institutional mechanism is proposed to resolve this vacuum: governments impose an automation responsibility tax on threshold-breaking firms while redistributing tax revenues as social insurance subsidies to sectors retaining human oversight. Numerical simulations demonstrate that this policy pre-emptively internalizes societal risks, allowing economies to harness AI-driven productivity gains while cushioning the shocks of rapid labor displacement. Specifically, the introduction of the Automation Responsibility Tax would alleviate 30% of unemployment without compromising productivity in the short term following AI-driven replacement. In the long term after AI impacts, under mechanisms addressing responsibility vacuums and social security deficits, it would enhance overall social welfare by 20% at the cost of a 6% reduction in total production. Furthermore, comparative analysis with alternative policies—such as direct taxation on automation volume and government-funded labor training programs—demonstrates the unique effectiveness of the Automation Responsibility Tax framework. This study proves that institutionalized responsibility redistribution is not only an essential pillar of societal stability but also a potential foundational framework for sustainable human–AI coexistence.

Purpose: The world economy is undergoing digital innovation in the field of finance and thereby creating a centralized currency. This study is based on expert interviews that explores the formation of attitudes towards Central Bank Digital Currencies (CBDCs) in India, addressing factors contributing to payment system transition and potential financial inclusion enhancement. This study adapts Extended Attitude Formation theory as its underpinning. It aims to understand the equivalence between existing payment systems and CBDCs by comparing the benefits, concerns, and limitations of both.

Methodology: This study employed a constructivist grounded theory approach and conducted 13 semi-structured interviews with experts from the banking sector, academia, and financial technology sectors. This study conducted a Reflexive Thematic Analysis, integrating both deductive and inductive coding techniques to develop a comprehensive analytical framework.

Findings: The analysis revealed four major themes: Technology Infrastructure and Security, User Experience and Adoption, Financial Inclusion & Accessibility, and Implementation and Integration. Key theoretical constructs emerged, including Trust Evolution Theory, Cultural Transformation Theory, and Implementation Strategy Theory.

Originality: This study provides a comprehensive qualitative exploration of CBDC attitude formation in the Indian context, offering a unique perspective on how existing digital payment experiences, cultural factors, and institutional trust intersect to shape users' perceptions of digital currencies.

This research investigates the application of multiple solver engines to enhance the optimization of nonlinear programming models, focusing on Evolutionary Strategies. These strategies mitigate the limitations of other optimization techniques, offering a robust approach to complex problems. Quadratic programming is especially beneficial as it allows for more flexible modeling compared to linear programming, accounting for correlations between asset pairs. This paper explores various algorithms for solving quadratic optimization issues, particularly in the financial sector using the mean-variance Markowitz model. The quadratic framework integrates risk factors through a quadratic term in the objective function.

Nonlinear optimization poses significant challenges, with the absence of universal solutions underscoring the importance of understanding the problem at hand to select appropriate methods and parameters. A key difficulty is the tendency of many algorithms to get trapped in local optima, leading to suboptimal results. Evolutionary Strategies, employing iterative trial and error and randomization, help address this by improving the likelihood of finding global optima, although they typically work slower than deterministic solvers. This research highlights Particle Swarm Optimization as an effective solver.

Data from 15 major international stock market indices, covering Europe, Asia, and North America from January 2020 to December 2024, are used to construct a buy-and-hold portfolio over a five-year horizon. Portfolio performance is evaluated using metrics, such as Portfolio Return (mean), Risk (standard deviation), Sharpe Ratio, Treynor Ratio, Sortino Ratio, Omega Ratio, Systematic Risk (Beta, Jensen's Alpha), Portfolio Drawdown (Calmar Ratio), Value at Risk (VaR), Expected Shortfall (ES), and distribution characteristics like Skewness and Kurtosis, to provide a comprehensive analysis of performance.

This study investigates the linkage between policy events and abrupt stock market fluctuations in China during 2024, analyzing how regulatory agencies—including the People’s Bank of China (PBOC), China Securities Regulatory Commission (CSRC), and China Banking Regulatory Commission (CBRC)—formulate pre-emptive policies to mitigate sudden market volatility, prevent asset bubbles, and curb systemic financial risks. Integrating behavioral finance theory, monetary economics, emergency event theory, and monetary policy frameworks, we redefine "emergency events" within China’s institutional context and conduct a micro-level analysis using event study methodology supplemented by a Principal Component BP Neural Network (PC-BPNN) algorithm. Focusing on the Shanghai Composite Index (SCI) as a market proxy, we address three core questions: (1) whether China’s monetary policy exerts macro-level intervention effects on stock markets; (2) whether PC-BPNN outperforms existing models in predicting stock prices and deriving normal returns; and (3) whether monetary policy retained significant influence amid frequent 2024 market emergencies.

Methodological innovations include redefining stock market emergencies, applying PC-BPNN for price prediction, and evaluating policy efficacy through event studies. Our key findings reveal the following:

（1）Monetary Policy Effectiveness: China’s monetary tools demonstrate measurable macro-level market intervention capabilities, validating their role in market regulation.

（2）PC-BPNN Superiority: The PC-BPNN model achieves higher accuracy in price forecasting compared to traditional methods, establishing its utility for subsequent research.

（3）Policy Attenuation Mechanism: Frequent abnormal market declines in 2015 nullified monetary policy’s significance (p > 0.05), exposing a self-reinforcing vicious cycle: investor pessimism and distrust reduced policy responsiveness, exacerbating sell-offs and liquidity drain. Concurrently, emergency events amplified negative sentiment, weakening policy transmission and undermining regulatory control, a dynamic that intensified bubble risks while rendering stabilization measures ineffective.

The delisting of Binance USD (BUSD) marks a critical moment in stablecoin regulation, raising concerns about market concentration and systemic risk in the cryptocurrency ecosystem. While the existing literature examines stablecoins as safe havens and the impact of banking crises on cryptocurrency markets, the implications of regulatory actions on stablecoin market structure remain underexplored. This paper employs local projections, including nonlinear specifications, to investigate the effects of the BUSD delisting on the broader cryptocurrency ecosystem. Our findings reveal that the liquidity displaced from BUSD primarily concentrates in USDT and USDC, reinforcing their dominance and exacerbating systemic risk due to increased market concentration. Furthermore, our analysis uncovers asymmetric effects on traditional cryptocurrencies, with Bitcoin and Ethereum experiencing short-term liquidity contractions, while smaller cryptocurrencies fail to emerge as viable alternatives. Notably, algorithmic and decentralized stablecoins like DAI and FRAX exhibit negligible absorption capacity, reflecting market skepticism toward their stabilization mechanisms during crises. These results suggest a potential unintended consequence of regulatory scrutiny, where increased regulation of major players leads to greater market concentration, potentially creating new systemic vulnerabilities. This highlights the importance of a nuanced approach to stablecoin regulation that considers its impact on market structure and systemic risk within the cryptocurrency ecosystem. Our study contributes to the growing body of literature on cryptocurrency market dynamics and provides valuable insights for policymakers, regulators, and market participants. We call for a balanced regulatory framework that promotes both stability and innovation in the rapidly evolving digital asset landscape.

Abstract

Traditional credit assessment methods rely on individuals' credit history and their interactions with financial institutions. However, these methods are insufficient for individuals, leading to limitations in financial inclusion. The integration of alternative financial data sources enables more comprehensive and accurate credit risk predictions. This study will examine the role of mobile payment history, bill payments, and e-commerce behavior in credit risk assessment. Open data sources will be utilized to enhance financial inclusion and improve credit evaluation processes.

In this study, the World Bank Global Financial Inclusion Database, the Brazil Open Data Portal, and the UK Open Banking API will be used as data sources. The World Bank database will be employed to analyze financial accessibility. The Brazil Open Data Portal will provide comprehensive insights into e-commerce behavior, while the UK Open Banking API will supply extensive data on digital banking transactions.

To process these data sources, Logistic Regression, Decision Tree, and XGBoost algorithms will be used. Logistic Regression will be applied to provide interpretable results for binary classification tasks. Decision Tree will be used to better understand dataset structures and efficiently process information from alternative data sources. XGBoost will be used to achieve high accuracy in large-scale datasets. As a result of this study, we aimed to analyze alternative credit score evaluation methods with Machine Learning.

Introduction
Asset prices should be relevant to the rate of depletion of a basket of essential resources, rather than the money market numeraire, whose price is almost a deterministic function of time. Basket weights can then be calibrated to the stability of asset prices. Then in a complete market, we can argue that there is a risk-neutral measure under which the sensitively discounted asset prices are stable.
Methods
Our key considerations for developing the resource-linked numeraire will include:
• Choice of resource base: The numeraire could be tied to a basket of essential resources (e.g., energy, water, soil fertility, rare minerals). It could be weighted based on global availability, depletion rates, or ecological impact. Finding the optimal weight would require a number of methods to be applied, including, but not limited to, Machine Learning, Dimensionality Reduction, and Mathematical Optimization.
• Scarcity-driven valuation: As resources become scarcer, their contribution to the numeraire increases, making depletion more costly. This could be modeled similarly to commodity-backed currencies but dynamically adjusting for sustainability.
Results
The paper would result in a number of potential models such as, but not limited to,
• Energy-backed currency: Pegging the numeraire to available units of sustainable energy (e.g., solar, wind) instead of fossil fuel reserves.
• Eco-certificate scheme: A parallel financial system where economic activity is balanced against resource consumption quotas.
• Resource depletion index: An index tracking key essential resources, which could be used as a dynamic numeraire.
Conclusions
Developing a financial numeraire aligned with the depletion of essential resources is possible and could provide a more sustainable economic framework. The idea would be to create a unit of account that reflects the scarcity and consumption of critical natural resources, such as water, fossil fuels, arable land, or biodiversity.

This study examines how Artificial Intelligence (AI) verifies financial statements by using machine learning algorithms together with natural language processing and predictive analytics to spot errors and potential fraudulent activities. AI-based audit systems evaluate financial data accuracy by matching it against historical patterns along with industry standards and regulatory requirements. AI boosts auditing efficiency by using real-time monitoring while reducing human biases in the audit process.
AI-powered financial audits enable organizations to predict and identify financial risks before they escalate by facilitating predictive forecasting beyond basic error detection. AI systems allow for the rapid processing of extensive data sets that traditional techniques cannot match while simultaneously improving transparency and reducing undetected fraud risks. The integration of AI into financial auditing necessitates robust data governance strategies combined with sophisticated cybersecurity protocols and thorough regulatory compliance to address potential algorithmic bias issues and prevent data misinterpretation risks. AI keeps advancing financial auditing even with existing difficulties since it creates a demand for combining machine intelligence with human judgment to achieve precise and trustworthy financial reports. A hybrid mode, combining AI with traditional audit, will be the trend of the future. AI will carry out risk assessments and identify unusual trends in Financial Statements, and auditors will talk with their clients to confirm those high-risk areas and make further investigations.

This study examines the relationship between inflation and the performance of the Nigerian stock market from 1990 to 2022, using market capitalization and consumer price indices (CPIs) as key variables. Employing an ex post facto research design, this study utilizes secondary data from the Central Bank of Nigeria (CBN) Statistical Bulletin (2022) and applies multiple regression analysis using the Ordinary Least Squares (OLS) method. The findings reveal a negative correlation between inflation and market capitalization, with the results indicating that the headline CPI (HCPI), core CPI (CCPI), and food CPI (FCPI) significantly impact stock market performance. Additionally, this study identifies structural constraints, including high tax rates, insider trading concerns, and limited market depth due to the "buy and hold" strategy, which hinders stock market growth. The research underscores the necessity for policy reforms aimed at enhancing market efficiency, improving investor confidence, and ensuring macroeconomic stability. These findings contribute to the ongoing discourse on inflation’s impact on Nigeria’s financial markets and provide valuable insights for policymakers and investors in mitigating inflationary risks while fostering economic growth. Based on these findings, it is recommended that the Central Bank of Nigeria (CBN) implements policy tools to control inflation and prevent it from eroding stock gains. Additionally, the government should focus on reducing inflation and poverty through improved living standards and infrastructure development to support economic stability and market performance.

Abstract

The rapid growth of financial technologies and the emergence of digital assets have transformed traditional economic systems, raising serious issues of money laundering, illicit transactions, and financial security. This paper addresses the role of Central Bank Digital Currencies (CBDCs) in fighting financial crimes in the cashless economy where digital bartering has achieved broad popularity. Analysing the transformation from old bank money to digital assets, this paper addresses the implications of anonymity, privacy, and trust in the process of financial transactions.

This study evaluates the opportunities and risks of embracing CBDCs and their potential to lower the use of cryptos in illicit commerce, promote financial inclusion, and maintain monetary stability. However, this study also poses questions regarding the disintermediation of banks and the challenge of achieving effective regulatory oversight of decentralised finance systems. This paper also discusses the growing application of digital assets in dark web trade, comparing the utility and worth of Bitcoin and other digital tools to traditional fiat currencies and physical high-value commodities such as gold and luxury goods.

Based on historical monetary policies and recent trends in finance, this research emphasises the increasing complexity of digital transactions and the urgent need for effective anti-money laundering (AML) mechanisms. The report contends that while CBDCs will offer aregulated and open alternative to existing digital currencies, their usefulness in the prevention of illicit finance is predicated on trust, technological security, and regulatory flexibility. This research concludes by advancing a balanced approach to the use of CBDCs, emphasising financial literacy, regulatory innovation, and more advanced forensic tools to combat financial offenses in the digital economy.