Abstract: This paper presents a structured synthesis of the statistical arbitrage literature, tracing the evolution of the field from classical mean-reversion and cointegration frameworks to contemporary machine learning and reinforcement learning architectures. Through a comparative analysis of prior empirical studies across equity, ETF, and cryptocurrency markets, the paper finds that although rising market efficiency and structural complexity have weakened the persistence of traditional signals, statistical arbitrage retains meaningful potential when supported by adaptive modeling and robust risk management. To address challenges related to tail-risk exposure, model fragility, and declining interpretability, this study proposes a human–AI collaborative execution framework. In this system, machine intelligence focuses on high-dimensional signal extraction and pattern recognition, while human oversight enforces risk constraints, contextual judgment, and interpretability requirements. This complementary structure is operationalized through CVaR-based pre-execution screening, multi-horizon drawdown controls, and explanability-guided intervention, forming a resilient and practically deployable architecture for risk-aware statistical arbitrage.

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This article examines how large-scale fiscal–financial crime schemes in Brazil exploit legal and regulatory fragmentation, non-bank intermediation channels and institutional blind spots to generate systemic fiscal risk. Drawing on recent national operations in the fuel, logistics, beverage, retail and e-commerce sectors, it analyses how fintechs, payment institutions, investment funds, holding companies and shell entities have been used as parallel financial systems to sustain chronic tax evasion (devedores contumazes), money laundering and competitive distortions. Methodologically, the study adopts a qualitative, document-based approach, relying on official investigations, judicial records, government reports and regulatory documents. It integrates insights from financial regulation, public financial management, macro-supervision and organized crime to construct an analytical framework for understanding how fiscal–financial crime operates within the legal architecture of emerging markets. The findings show that fragmented supervisory mandates, gaps in the regulatory perimeter and limited data-sharing across tax, financial and sectoral authorities enabled criminal groups to operate at scale for long periods. These structures weakened state capacity, eroded public revenue and embedded illicit flows in key markets, thereby amplifying systemic vulnerabilities. The article contributes to the legal and regulatory literature by consolidating lessons from Brazil’s recent large-scale operations—such as Carbono Oculto, Poço de Lobato and Tank—into an integrated model of chronic tax evasion as a source of systemic fiscal risk. It concludes with a set of regulatory and public financial management recommendations that are relevant for both emerging markets and advanced jurisdictions facing similar legal and supervisory challenges.

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This study empirically investigates the impact of both the level and risk of cash dividend distributions on the stock value of companies listed on the Saudi Stock Exchange (Tadawul). Utilizing a proportional stratified random sample of 120 companies across 21 sectors over the period 2020-2024, the research employs third-degree polynomial regression models to analyze complex, non-linear relationships. The findings reveal a significant cubic relationship, identifying an optimal dividend per share of 5.91 SAR that maximizes stock price. Furthermore, dividend volatility (risk) exhibits an inverted S-shaped relationship with price, with an optimal standard deviation of 5.04 SAR, indicating that the market rewards a dynamically stable payout policy. The study also uncovers strong sectoral effects, with Telecommunication, Health Care, and Energy sectors commanding significant valuation premiums, while Real Estate and Financial Services trade at discounts. The results robustly confirm that both dividend level and stability are critical, sector-dependent determinants of firm value in the Saudi market. These insights provide valuable guidance for corporate dividend strategy, investment decision-making, and policy formulation within the context of Saudi Vision 2030.

Abstract: Financial resilience has increasingly captured the attention of scholars, as it directly impacts general well-being, inclusive education, and sustainable economic development. This study provides evidence on how university students view financial resilience, based on their perceptions, experiences, and actions taken to address difficult financial situations in recent times. Thus, it contributes to SDG 3 by promoting economic and psychological well-being; to SDG 4 by strengthening financial education as an essential element of quality education; and to SDG 8 by facilitating development. The scale is based on financial health indicators aligned with the Center for Financial Services Innovation. Data were analyzed using SEM methodology. Findings reveal a three-factor model explaining perceptions of financial health indicators, including lived experiences and resilient actions taken in the face of adverse financial situations. The three-dimensional model is not fully supported, as indicators that do not favor the final model due to low factorial loads are excluded.

Abstract: This study employed machine learning methods to predict household credit access concerns using comprehensive financial and demographic data from the 2022 Survey of Consumer Finances (SCF), analyzing 4,595 households to examine which characteristics predict whether families have been turned down for credit or feared credit denial in the past five years, a critical measure of financial vulnerability affecting approximately 84% of surveyed households. Two classification models were developed and compared: an XGBoost gradient boosting model and a logistic regression model, both using 263 principal components derived from the original feature space. The XGBoost model (Model A) achieved exceptional predictive performance (AUC = 0.9885, accuracy = 96.65%, precision = 97.52%, recall = 82.01%), substantially outperforming the logistic regression model (Model B: AUC = 0.7955, accuracy = 80.34%, precision = 44.93%, recall = 78.37%), demonstrating that credit access concerns follow highly systematic patterns. Feature importance analysis revealed that asset-based financial indicators dominated predictions, with Equity to Income, Homeownership, Credit application history, Emergency Savings, and Leverage Ratios emerging as the top five predictors, while behavioral and historical factors particularly payment delinquencies and prior credit experiences exhibited substantial importance, supporting path-dependent theories of financial exclusion. Race ranked 14th among predictors, suggesting that observed disparities operate substantially through differential economic circumstances, though structural barriers persist. The findings have immediate implications for financial institutions, which can deploy similar predictive models to identify at-risk customers for targeted financial counseling, develop alternative credit scoring approaches accounting for asset ownership and emergency savings, and design early warning systems flagging households with declining liquid assets or increasing leverage ratios for preventive assistance. Financial advisors can use these insights to prioritize asset-building strategies over simple income increases, emphasize emergency savings establishment as critical for credit access maintenance, counsel clients on credit history management following past delinquencies, and recognize that payment delinquencies create enduring barriers requiring proactive rehabilitation.

Abstract: This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel of 62 companies observed from 2006 to 2024, we employ a three-stage empirical strategy that integrates a Probit model to estimate the likelihood of impairment, a Tobit model to assess the magnitude of the loss, and a Heckman two-step procedure to correct for potential self-selection. The results show that goodwill impairment reflects key economic and financial fundamentals, including revenue growth, book-to-market ratios, and operating performance. However, both real and accrual-based earnings management significantly influence the probability and intensity of impairment, particularly through abnormal cash flows and income-smoothing behavior. Discretionary accruals become significant only after correcting for selection bias, indicating that they do not drive the recognition decision but contribute to determining the size of the impairment once it has been recorded. The findings are robust across multiple specifications and contribute to the broader literature on financial reporting quality under IAS/IFRS, while enriching empirical evidence on managerial discretion and earnings management in emerging-market environments.

Abstract: We investigate the impact of government economic policy uncertainty (GEPU) on bank risk, distinguishing short- and long-term effects. We argue that heightened GEPU increases bank risk in the short run by raising borrowers’ default probabilities under adverse economic conditions, while reducing risk in the long run by discouraging banks from extending risky loans due to the higher option value of waiting under uncertainty. Using bank-level data from 22 countries over 1998–2017, we find that elevated GEPU raises bank risk contemporaneously but lowers it with a lag of two to four years. These results are robust to endogeneity concerns, alternative measures of bank risk and GEPU, variations in sample composition, and different estimation techniques. Our findings highlight the dual role of policy uncertainty in shaping bank risk-taking behavior and have implications for regulatory design and macroprudential policy.

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This research conducts a systematic review of Tunisian stakeholders' perceptions of green finance, microfinance, and gender through the lens of the Business Model Canvas (BMC). Within this framework, a systematic search was conducted until October 2024 in electronic databases and grey literature. The findings indicate a dual perception of women as both vulnerable victims and active agents in the ecological transition. The BMC analysis reveals major weaknesses in the value proposition, distribution channels, and cost structures of gendered green microfinance offerings. The study highlights the crucial role of the regulatory and institutional context in these perceptions. It proposes an updated conceptual framework for thinking about more inclusive and sustainable green microfinance models.

Abstract: The Role of Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain in Augmented Finance Augmented finance is emerging as an important approach to tackle the complex financial challenges that accompany climate change. This systematic review aims to provide existing research, identifying how these technologies may help in sustainable finance. Thus, following the PRISMA guidelines, we reviewed and analysed 42 peer-reviewed studies released between 2018 and 2025. Our results are applicable in three general areas: (1) increased MRV of the environmental impacts by employing IoT and blockchain, to ensure transparency and traceability, (2) better physical and transition risk control using predictive AI modelling and (3) better ESG analysis and the detection of greenwashing and risk reduction via alternative data. We highlight the power of these technologies to address stubborn problems such as information asymmetry and transparency holes in impact chains. However, significant challenges persist, such as algorithmic bias, difficult data governance, and regulatory lag. The present study contributes to this landscape by offering a scientific framework of augmented finance in a climate context. It also suggests a proposed future research agenda with emphasis on impact assessment, algorithmic transparency, and impact on financial stability.

Abstract: This article examines how fintechs and payment institutions in emerging markets have been used as parallel banking infrastructures that facilitate tax evasion, large-scale money laundering and regulatory arbitrage. Focusing on Brazil, it analyzes recent high-profile investigations—such as the Carbono Oculto, Tank and Quasar operations—to show how criminal organizations exploited payment institutions, digital accounts and card schemes to move value outside the visibility of traditional banks and supervisory authorities. The study employs a qualitative, document-based approach, drawing on official reports, judicial decisions, supervisory guidance and financial intelligence materials to map the architecture of fintech-enabled illicit finance. The findings show that fintechs and payment institutions provided anonymity, opacity and transactional mobility through fragmented regulatory perimeters, lighter reporting requirements and gaps between financial, tax and AML/CFT oversight. These blind spots allowed parallel banking structures to operate at national scale while remaining formally within the legal financial system. The article contributes by proposing an integrated conceptual framework for understanding fintech-enabled parallel banking in emerging markets and by outlining policy recommendations to close supervisory gaps, align reporting obligations across institutions and strengthen cooperation between financial regulators, tax authorities and law-enforcement agencies.

Abstract: Losses resulting from the opportunistic use of insider information are detrimental, and therefore it is essential to mitigate this behavior through robust corporate governance practices. This research analyzes the determinants of corporate governance that reduce the signs of opportunistic insider trading, considering the assumptions of information asymmetry and opportunistic behavior. The hypotheses formulated consider that compa-nies listed on the Novo Mercado or Level 2 of Corporate Governance, with independent Boards of Directors and greater female representation, active Fiscal Councils, consolidated ESG practices, non-family firms, robust Audit Committees, and audits not linked to the Big Four, tend to show a lower propensity for opportunistic conduct. 237 firms were analyzed, representing 51% of the firms listed on the [B]3 between 2010 and 2021, with 2,175 obser-vations. The panel data analysis confirmed the proposed hypotheses. Therefore, the theo-retical conclusion is that the greater the level of corporate governance practices, the lower the incidence of opportunistic insider trading in the Brazilian capital market. This work contributes to the literature by highlighting the unique characteristics of the largest stock market in Latin America and emphasizing the importance of transparency, formal moni-toring, and informal mechanisms, such as social and reputational pressure on insiders, in-fluencing ethical behavior, and restricting the misuse of privileged information.

Abstract: Hedge funds and other non-bank financial intermediaries have become central players in global capital markets, yet their leverage and liquidity risks remain imperfectly captured by macroprudential frameworks that were designed primarily for banks. Episodes such as the March 2020 turmoil in US Treasury markets, the collapse of Archegos Capital and recent stress in gilt markets have highlighted how leveraged non-bank positions can generate systemic shocks that propagate across borders, including to emerging markets. This article develops a conceptual analysis of the channels through which hedge fund and non-bank leverage interacts with macroprudential policy, focusing on lessons from the United States, the European Union and the United Kingdom and their relevance for emerging market economies. Drawing on official reports and the academic literature, it examines how macroprudential tightening and regulatory fragmentation in advanced economies can encourage risk migration to non-banks and spillovers to emerging market asset prices, funding conditions and exchange rates. The article then discusses policy options for emerging markets, including the design of macroprudential toolkits, the monitoring of cross-border non-bank exposures and the coordination with advanced-economy regulators and central banks. It concludes by outlining an agenda for integrating non-bank and cross-border dimensions more systematically into macroprudential policy in emerging markets.

Abstract: Real option instruments provide a solid model to portray a trustworthy valuation pa-rameter for the attractiveness of private equity, given its natural variability. By applying the Cox-Ross-Rubinstein (CRR) binomial lattice (Cox et al., 1979) for private equity valuation, we provide a more suitable framework which reflects early exercise flexibility, staged investment and sensitivity to interest rate dynamics. The analysis shows how real option valuation is driven by American puts, providing stable protection against losses in various interest rates scenarios, while calls are more limited and rate-sensitive, showing interest rates mainly affect upside potential rather than downside risk.

Abstract: Asia presently houses some of the top and dynamic economies in the world. These nations have also experienced a high rate of fintech adoption within their banking sectors. This paper examines the impact of fintech adoption and integration on the efficiency and stability of banks in 9 Asian countries, using panel data from 85 banks spanning 11 years from 2014 to 2024. The paper first analyzes the impact of fintech on banks across all selected countries and then, on a stratified basis, divides them into three categories: developed economies, large economies, and emerging countries. We use NPL and PLL as proxies for risk, efficiency ratios, and the cost-to-income ratio as efficiency measures, and the stability ratio and Z-score as indicators of stability. Ordinary least squares and fixed-effect techniques have been applied to estimate the results. Results show that fintech adoption reduces bank risk, presents mixed effects on efficiency, and strongly supports bank stability. Moreover, total assets and ROA consistently demonstrate lower risk, higher efficiency, and greater stability. Based on the findings, this research suggests that policymakers must adopt strategies to maximize the benefits.

Abstract: Promoting the synergistic development of green finance (GF) and green technology innovation (GTI) is crucial for achieving sustainable economic development. Based on the sample data of 30 provinces in China from 2010 to 2023, this study first sorts out the theoretical mechanism of interactive coupling, and then employs methods including Dagum Gini coefficient, spatial kernel density estimation, spatial correlation analysis, and GTWR model to explore the spatiotemporal pattern, evolution trend, and driving factors of the coupling coordination between GF and GTI. The findings are as follows: (1) The CCD is about to transition from the moderate imbalance stage to the near imbalance stage, presenting a distinct spatial pattern of "higher levels and faster development in the east, while lower levels and slower development in the west". (2) Gini coefficient of the CCD shows an upward trend, with the degree of imbalance increasing year by year; the main sources of the overall differences follow the order: Gw > Gb > Gt. (3) The CCD between GF and GTI exhibits a positive spatial correlation, and the agglomeration degree is constantly strengthening; the High-High Cluster areas are mainly concentrated in northern China. (4) Economic development level, financial development level, population scale, and urbanization level drive the coupling coordination between GF and GTI. This study provides new theoretical and empirical evidence for the complex coupling relationship and driving factors of GF and GTI, and offers a key scientific basis for the Chinese government to formulate differentiated regional policies, thereby promoting the effective implementation of the green and low-carbon development strategy.

Abstract: Dynamic adjustment of capital structure is crucial for corporate financial stability and long-term resource allocation, and it is also an important foundation for sustainable economic development. In the context of global climate change, carbon finance is playing a key role in incentivizing emissions reductions and promoting the transition to a low-carbon economy. While the existing literature has explored the macroeconomic effects of carbon finance, its impact at the firm or micro—level, particularly on capital structure decisions, remains largely unexamined. This study aims to investigate the effect of carbon finance on the speed of dynamic capital structure adjustment and the degree of deviation of enterprises. Data were collected from China's A-share-listed companies between 2014 and 2024 to construct the provincial carbon finance development index, and a partial adjustment model was applied to measure the speed of capital structure adjustment. The findings indicate that carbon finance has a significantly positive effect on the speed of adjusting the capital structure and that carbon finance is consistent in keeping firms in line with their target leverage ratio, according to a number of robustness tests. Notably, cross-sectional analyses show that this effect is more pronounced among firms with higher green innovation outputs and greater indebtedness. Further research indicates that the underlying mechanism driving this relationship lies in alleviating financing constraints and reducing financing costs. By bridging the gap between market-oriented environmental regulations and corporate financial policies, our study provides policymakers with evidence to improve carbon finance mechanisms and gives managers a basis for using green finance tools to create sustainable value.

Abstract: This study examines the impact of corporate governance mechanisms on bank risk, focusing on the moderating role of CEO extreme overconfidence. Using a sample of all commercial banks in Tunisia from 2000 to 2021, the study explores how ownership structure and board characteristics influence three key types of risk: insolvency risk, operational risk, and equity risk. The findings show that managerial ownership and larger boards generally reduce insolvency and operational risk, while ownership concentration and excessive board independence may increase equity volatility. When CEO overconfidence is considered, it weakens the risk-reducing effect of governance on insolvency risk but strengthens its impact on operational risk. Equity risk, however, remains largely unaffected. A governance quality score is used to confirm these results, highlighting the limitations of structural governance when leadership traits are not considered. The study underscores the need to integrate behavioural assessments into governance practices to better manage bank risk.

Abstract: This study examines the dynamic interplay between digitalization, deposit insurance systems, bank resolution mechanisms, and policy guarantee frameworks in fostering financial stability, with a particular focus on Indonesia's Deposit Insurance Corporation (LPS). A quantitative analysis was employed, and data were collected from 265 senior financial experts, including banking executives, regulatory officials, and policymakers. SmartPLS was used to explore the relationships among digitalization, deposit guarantee resilience, resolution mechanisms, governance quality, and financial stability. Our findings indicate that digitalization significantly enhances financial stability by streamlining processes, enabling real-time monitoring, and facilitating proactive risk management. Furthermore, robust deposit insurance systems and effective resolution strategies mitigate systemic disruptions and minimize contagion risks. Governance quality has emerged as a critical moderating factor that aligns regulatory objectives with sustaining market confidence, for Indonesia and similar institutions in emerging economies. Our study provides policy insights by integrating advanced digital platforms into deposit insurance operations, refining resolution frameworks through the timely utilization of data, and strengthening governance mechanisms to ensure policy responsiveness. By linking digital innovation with institutional resilience mechanisms, our study provides both theoretical and empirical contributions to the financial stability literature.

Abstract: This study will focus on the connection between financial overconfidence and financial vulnerability, using data from the 2024 FINRA National Financial Capability Study (NFCS). The main question was whether people who think they know more about money than they actually do would end up taking more financial risks. After applying Exploratory Factor Analysis, the study identified three key areas that include financial confidence, financial knowledge, and financial vulnerability. These were combined to create an overconfidence score that was then used in several prediction models. Five machine learning models were tested on ten random sample seeds. Among these tested models, XGBoost performed the best to have explained about 60% of the variation in financial vulnerability. To better understand the results, SHAP analysis was then applied to see which factors mattered most. Confidence turned out to be the strongest influence, followed by overconfidence and knowledge. Income and education had smaller effects. Surprisingly, the results did not show that overconfidence increases vulnerability. Instead, a moderate level of confidence actually tends to be financially protective, especially for people with lower knowledge levels. Overall, it was figured that confidence may act more as a form of psychological support than as a risk factor. This finding suggests that financial education should help people build both knowledge and self-belief.

Abstract: This article critically examines the role of financial education as a catalyst for financial freedom, understood not only as economic autonomy, but also as the real capacity for agency and deliberation about material life. Based on a qualitative, descriptive, and documentary study, a thematic content analysis was applied to academic literature, institutional reports, and classic and contemporary works on personal finance.The results show that financial literacy, although necessary, is insufficient if it is not articulated with psychological, socio-emotional, and cultural dimensions, such as financial mindset, discipline, emotions, and beliefs about money. From a transdisciplinary approach, the interrelationships between financial education, self-education, the psychology of money, and decision-making are explored, proposing a comprehensive model that links knowledge, action, and being.It is argued that financial education must transcend the mere transmission of technical content to become a process of ethical and political empowerment, oriented toward the development of sustainable habits, critical thinking, and socio-emotional skills that allow for conscious control over the economy and thoughtful participation in the structures that shape collective life.It concludes that, in order to be transformative, financial education must be multidimensional, situated, and adaptive, capable of responding to the challenges of a global environment characterized by inequality and technological acceleration. It proposes moving toward evidence-based programs that strengthen civic agency and deliberative participation as constitutive dimensions of contemporary political subjectivity.