This paper documents an increase in co-arranged loans in private loan markets, where multiple lead arrangers jointly participate in a loan contract. We show that lenders co-arrange loans for high-quality borrowers (large, highly profitable, and with strong credit ratings), indicating a strategic response to increased credit market competition. Using the Gramm–Leach–Bliley Act as a natural experiment, we find that the regulatory change, which encouraged lending competition by allowing greater involvement from nonbank institutions, significantly increases the likelihood of co-arrange loan issuance. We also find that co-arranged loans carry higher interest rates. Overall, our findings suggest that credit market competition shapes debt ownership structures and raises financing costs for borrowers.
As carbon transition and regulatory risks escalate, understanding the impact of local carbon emissions on corporate financial strategies is essential, particularly in shaping cash holding policies to manage environmental liabilities. This study investigates the impact of neighborhood carbon emissions on corporate cash holdings, introducing a novel exogenous metric that quantifies carbon emission levels within a specific radius around each firm. Our baseline regression results indicate a positive correlation between higher neighborhood carbon emissions and increased corporate cash holdings, and a series of robustness checks confirm the stability of these results across different model specifications. The mechanism analysis suggests that higher neighborhood carbon emissions lead to an increase in cash holdings by reducing corporate ESG performance, increasing financing constraints, and strengthening precautionary expenditures. Moreover, heterogeneity tests reveal that the impact of neighborhood carbon emissions on cash holdings is significant in nonstate-owned enterprises and nonenergy industries, particularly in regions characterized by stringent environmental regulations, higher climate policy uncertainty, and located in the eastern provinces of China. These results underscore the importance of understanding localized environmental risks and their implications for corporate financial strategies.
Abstract In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States based on monthly metrics of oil price uncertainty (OPU) and the broader energy uncertainty index (EUI). This approach addresses the previous literature’s limitations of narrowly focusing on crude oil prices and restricted geographic coverage by offering a more comprehensive analysis of energy uncertainty’s predictability for stock market volatility across all 50 U.S. states. We find that over the daily period of (February) 1994 to (September) 2022 and various forecast horizons, in 37 out of the 50 states, the GARCH-MIDAS model with EUI outperforms the benchmark, i.e., the GARCH-MIDAS-realized volatility (RV), which, in turn, holds for at most 18 cases under OPU. This evidence is further strengthened with the detection of higher utility gains delivered for 42 states by the GARCH-MIDAS-EUI in comparison to the GARCH-MIDAS-RV. Policymakers can utilize EUI-driven high-frequency forecasts to predict state-level economic activity, enabling timely interventions to mitigate regional recessions. For investors, incorporating broader energy market uncertainty into strategies would improve risk management, portfolio allocation, and hedging decisions, potentially enhancing risk-adjusted returns.
This study aims to identify key themes and research trends in auditing through a comprehensive analysis of 5961 Scopus-indexed articles published from 1988 to 2024. Latent Dirichlet allocation (LDA) was employed to uncover dominant themes and associated conceptual groupings. Additionally, regression and correlation analyses were conducted to reveal temporal trends in topic probabilities and to uncover relationships among topics, citations, and research entities. The results highlight the transformative role of advanced technologies in auditing—particularly artificial intelligence, blockchain, and big data analytics. These tools play a critical role in enhancing data security, fraud detection, and risk management. However, there is a noticeable gap concerning the ethical, regulatory, and governance-related impacts of these technologies. Journals such as Auditing and Managerial Auditing Journal significantly influence theoretical and practical advancements in the field. Future projections indicate increased emphasis on themes such as “Auditing and Client Relationships,” “Accountability and Audit Approaches,” and “Earnings Management,” whereas interest in “Auditing Profession” and “Management and Performance” is expected to diminish. Geographic differences were observed, with Denmark and Italy focusing on strategic dimensions, while the UK and Australia prioritize accountability-oriented audit approaches, and Germany alongside Australia emphasizes sustainability. This comprehensive thematic analysis provides a data-driven foundation for future auditing research and offers strategic guidance for both scholars and practitioners.
Abstract Capital market liberalization provides foreign investors with greater access to direct investment in mainland China’s stock market. This study uses the expansion of the Shanghai–Hong Kong Stock Connect as a quasi-natural experiment. The sample consists of A-share listed companies on the Shanghai Stock Exchange from the first quarter of 2021 to the first quarter of 2024. Employing a difference-in-difference (DID) model, we examine the impact of the expansion on stock price crash risk among small- and medium-cap stocks in the A-share market. Empirical results show that the Shanghai–Hong Kong Stock Connect expansion significantly increases the stock price crash risk of these stocks. Mechanism analysis reveals that the policy elevates crash risk by aggravating managerial short-termism, reducing information transparency, and weakening corporate governance. Furthermore, we find that this effect is more pronounced in non-state-owned enterprises, firms facing high financing constraints, companies with low free-float market capitalization, and high-tech enterprises. This study offers a new perspective on the relationship between stock connect expansions and stock price crash risk and provides policy insights for improving the Shanghai–Hong Kong Stock Connect and similar market access mechanisms in the future.
Abstract This study examines systemic risks in decentralized lending protocols stemming from “mega-whales”—large users with transaction volumes exceeding $100 million—who dominate liquidity dynamics. Using high-frequency transaction-level data from major protocols ( Aave v2/v3 , Compound v2/v3 , Morpho Blue , Flux , and SparkLend ) spanning August 2024 to August 2025, we identify 550 mega-whales and analyze their impacts across market size, structural risks, and user activity. Key findings reveal the following: (1) The net growth in both mega-whale deposits and borrowing is negatively correlated with the market-size dynamics of lending protocols, while arbitrage-driven behaviors (such as token diversification and platform switching) further fragment the growth of Total Value Locked (TVL); (2) Although whale borrowing serves to temporarily stabilize leverage ratios, deposit activities may adversely impact systemic stability; however, an increase in mega-whale deposits is found to positively stimulate the growth and participation of unique depositors; (3) Utilizing event-window controls around DeFi shocks, we confirm that these effects are strongly associated with mega-whale activities rather than market-wide events. These results point to participation patterns that may be inconsistent with DeFi’s egalitarian ethos, suggesting that economic concentration could influence market dynamics.
This study examines market adaptability in the Moroccan stock market from 2013 to 2025 within the adaptive market hypothesis (AMH) framework using long short-term memory (LSTM) networks. Daily Moroccan All Shares Index (MASI) returns are modeled jointly with macrofinancial variables, and changes in market conditions are identified through the time variation of out-of-sample forecasting errors as indicators of regime dynamics. The LSTM outperforms a historical-mean benchmark during relatively stable periods RMSE (0.0064 vs. 0.0085) and MAE (0.0044 vs. 0.0055), consistent with its ability to learn gradual shifts in the return–environment relationship. However, predictive performance deteriorates during abrupt shocks (e.g., COVID-19 and the Russia–Ukraine war), highlighting limits to real-time learning under extreme uncertainty. An adaptive retraining mechanism partially reduces postshock error volatility, although its effectiveness is context dependent. Complementary nonparametric tests provide additional evidence of time-varying regimes. Methodologically, the paper proposes a data-driven, nonparametric framework to detect regime dynamics without imposing ex ante break assumptions. Overall, the findings provide evidence consistent with the AMH and offer implications for investors and policymakers in volatile emerging markets.
This study proposes a novel data fusion-based feature selection and reconstruction (DFFSR) method for assessing debt maturity risk based on multi-dimensional data from publicly traded Chinese companies between 2000 and 2023. The DFFSR approach maps the fused data into a lower-dimensional embedding space using a stacked autoencoder (SAE), thereby enabling feature reconstruction while preserving data heterogeneity. It employs a CancelOut layer to identify a salient subset of features and reduce indicator redundancy. The DFFSR model also enhances managers’ understanding of the decision-making process related to debt maturity. The comprehensive ranking of the relative importance of risk factors enables enterprises to manage risk more effectively by focusing on key indicators, optimizing their liability structure, and improving overall performance.
Blockchain-enabled instruments have gradually filtered into financial intermediation, disrupting traditional institutions. This paper discusses the benefits of blockchain focusing on financial intermediation services (FIS). We draw upon a collection of theories on technology innovation, barriers, and diffusion. Our main aim is to highlight points of incompatibility with current institutional frameworks and regulations and develop a conceptual model of the main, interconnected, barriers to its widespread diffusion (i.e., regulatory, technological, and environmental). We argue that lowering these barriers would displace traditional institutions with blockchain technology and could raise welfare. While we highlight the few steps that have already been made towards the removal of these obstacles, both at a regulatory and technical level, we outline the main areas that still need addressing if the full benefits of a blockchain-enabled FIS are to be reaped.
A fundamental question for stakeholders in the active management industry is what level of active management fee could be considered fair when factoring in the active risk deployed to generate the gross active return. To explore this topic, we introduce a new, simple and effective metric, the information ratio threshold (IRT), which reframes the fee signature of an active manager in terms of its cost intensity: the ratio between a fund’s fee and its tracking error volatility (TEV). Intuitively, the higher the cost intensity, the higher the bar for the investors to obtain a net (i.e., after fee) outperformance against the relevant benchmark, all else equal. We test the IRT on a sample of 3937 share classes belonging to 1230 active equity and bond funds in 14 main asset allocation categories. Our data confirm the intuition that the greater the IRT, the lower the net information ratio (IR) delivered to investors. We believe that our findings could support funds’ due diligence, product governance, and assessment-of-value considerations.
This study investigates the distributional determinants and time-varying dynamics of U.S. retail electricity sales using a monthly dataset spanning from January 2008 to December 2023. To move beyond traditional linear modeling, we conceptualize a Fundamental–Systemic Nexus that interacts in two stages. First, we examine the roles of market fundamentals, fossil fuel receipts (RFFEP), average generation costs (ACFFEG), retail prices (ARPE), and fuel quality (QFFEG) using multivariate quantile-on-quantile regression. Second, we employ quintuple wavelet coherence (QWC) to link unexplained demand anomalies to four external systemic uncertainty factors: oil price uncertainty, energy-specific uncertainty (ERU), ESG-related uncertainty (ESGU), and geopolitical risk. Our findings reveal pronounced nonlinearities where fossil fuel receipts (RFFEP) show a strong positive association with demand, with coefficients reaching as high as 0.92 during moderate demand conditions. Conversely, average retail prices (ARPE) are negatively associated with demand, with elasticities ranging from −0.12 to −0.56, significantly intensifying under peak demand. The average generation costs (ACFFEG) show the strongest negative association at the tails, with coefficients reaching −0.30 during high-demand regimes. To assess unexplained variation, QWC was applied to residuals from the multivariate quantile regression model. The results indicate that negative demand residuals exhibit strong, persistent long-term coherence (exceeding 0.8) with ESG-related (ESGU) and energy-specific (ERU) uncertainty at 32–64 month horizons, particularly during major regulatory shifts. In contrast, positive residuals display only weak and episodic coherence. These findings underscore the importance of integrating distribution-sensitive and scale-aware methodologies to guide risk-informed energy governance and demand resilience amid decarbonization.
Corporate financialization diverts resources from productive investment and undermines long-term competitiveness, yet the role of technological infrastructure in shaping this trend remains underexplored. This paper investigates whether and how computing power infrastructure affects corporate financialization. Using Chinese A-share listed companies from 2012 to 2023, we exploit the staggered establishment of National Supercomputing Centers (NSCs) as quasi-natural experiments and employ a staggered difference-in-differences model for causal identification. Baseline results show that computing power deployment significantly reduces corporate financialization levels by approximately 1.1 percentage points. Mechanism analysis reveals two transmission channels: enhanced data factor capitalization capability and improved intelligent decision-making efficiency. Both channels strengthen core business returns and weaken the motivation to allocate funds to financial assets. Heterogeneity analysis shows that the inhibitory effect is more pronounced in computing-intensive industries and firms with low analyst coverage and is concentrated in speculative rather than precautionary financial assets. Extended analysis confirms that computing power deployment promotes real investment reallocation, with significant increases in fixed assets, R D, and capital expenditure intensity. Spatial Durbin model estimation further reveals that the inhibitory effect extends beyond host city boundaries to surrounding cities. These findings highlight the potential of digital infrastructure investment as a policy instrument for correcting excessive financialization and guiding corporate resources back to the real economy.
This study investigates the nonlinear, asymmetric, and regime-contingent determinants of fuel oil prices across three major European hubs: Antwerp, Barcelona/Valencia, and Rotterdam. It contributes to the literature by being the first to jointly model fuel oil price determinants across these key ports using a multivariate quantile-on-quantile framework that captures tail-specific and regime-dependent spillovers absent in existing models. Employing a dual-quantile Granger causality approach, the research examines the transmission of macrofinancial indicators, clean-energy sentiment, and policy uncertainty across the fuel–oil return distribution. Within this framework, causality is interpreted in the Granger sense, reflecting predictive patterns rather than structural identification. The findings reveal significant cross-country heterogeneity, confirming that fuel oil pricing is profoundly shaped by state-dependent effects. Specifically, the study documents incomplete and regime-specific pass-through from crude oil and exchange rates. In extreme market regimes, such as the upper tail of the price distribution in the Netherlands, the magnitude of the crude oil impact is up to three times larger than at the median, indicating pronounced tail amplification. These insights provide a foundation for developing state-contingent risk management tools, such as environmentally linked derivatives, to navigate fuel price formation under global decarbonization pressures.
This study examines the relationship between digital remittance receipt and access to mobile-money-enabled alternative financial services among unbanked individuals in South Asia using the 2021 World Bank Global Findex dataset. The analysis focuses on unbanked domestic remittance recipients and compares individuals who received remittances through mobile money account with those who received remittances through non-digital channels. We use propensity score matching as the main empirical strategy and complement the analysis with Lewbel’s heteroskedasticity-based IV estimation as a robustness check. The outcomes include utility bill payments, mobile money savings, government payment receipts, payments made, and wage receipts. The results show a positive and significant association between digital remittance receipt and access to mobile-money-enabled alternative financial services, except for mobile money savings. Heterogeneity analysis further shows that the associations are larger among women, rural households, individuals with secondary education, and middle- and higher-income groups. These findings suggest that digital remittances may serve as an entry point into broader mobile-money-enabled financial service use among underserved populations in South Asia.
Abstract The increasing integration of green cryptocurrencies into financial markets raises critical questions about their effectiveness as diversification and hedging instruments. This study examines their role relative to traditional green assets, including the S&P Green Bond Index, S&P Global Clean Energy Index, and S&P ESG Leaders Index, via quantile vector autoregression (QVAR) over the period November 2017–July 2024. The results reveal a U-shaped connectedness pattern, where spillovers between green assets intensify under extreme market conditions, diminishing their diversification benefits. Green cryptocurrencies, particularly Cardano (ADA) and Stellar (XLM), function as primary transmitters of volatility, especially during extreme market conditions. Conversely, green assets, traditionally perceived as low risk, act as net receivers of volatility, failing to provide consistent downside protection and challenging their reliability in risk mitigation. Hedging analysis demonstrates limited risk mitigation from traditional green assets, with certain cryptocurrencies, such as NANO, providing superior hedging potential. These findings have important implications for investors and policymakers. Investors should reassess their reliance on traditional green assets for risk management and consider adaptive hedging strategies incorporating green cryptocurrencies. Regulators must address systemic risks associated with the growing influence of clean cryptocurrencies by implementing volatility thresholds and transparency measures. Future research should examine the regulatory impact and the evolving role of green financial instruments in sustainable portfolio management.
This paper introduces Recurrence Quantification Analysis (RQA) as a robust, interpretable framework to evaluate the dynamic behavior of cryptocurrency markets. Using optimized recurrence plot thresholds, we assess the long-term market behavior of 100 cryptocurrencies. The results show that RQA can effectively distinguish periods of stability and instability, identify structural differences between assets, and capture hidden nonlinearities that may affect market efficiency. We validate the generalizability of our method by applying it to FinTech-related indices, suggesting that RQA has potential applications beyond cryptocurrencies. These insights are relevant to investors, who require tools for navigating volatility, and regulators seeking to monitor systemic risk.
This study investigates the effects of economic policy uncertainty, oil volatility, oil prices, and gold prices on pessimistic and optimistic sentiment toward Bitcoin. The research employs the Quantile Autoregressive Distributed Lag-Error Correction Model (QARDL-ECM) to analyze the relationships among economic policy uncertainty, oil volatility, the gold price, and Bitcoin sentiment from January 2014 to December 2020. The econometric analysis reveals that both the short-term and long-term behaviors of Bitcoin sentiment remain consistent in both the pessimistic and optimistic scenarios. The findings of this study have significant implications for legislators, stockholders, and investors, as they provide valuable insights for making informed decisions regarding investment allocation in Bitcoin sentiments. Moreover, the study offers a unique framework that can be utilized by portfolio managers and speculative investors in these markets.
Abstract Artificial intelligence (AI) in finance is commonly reviewed by method, data type, or application domain. These perspectives are essential, but they understate a deeper shift: AI is moving from a predictive tool to a component of human–AI hybrid financial decision systems. This integrative and conceptual review synthesizes literature across finance, management, human–computer interaction (HCI), and AI to examine how humans and AI jointly participate in information acquisition, prediction, recommendation, approval, execution, monitoring, and learning. We argue that the central question is moving from model performance to decision architecture: how authority, oversight, and accountability should be allocated across financial workflows. We show that human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance. We also argue that AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely. The review integrates evidence on methods, data, scenarios, explainability, trust, governance, financial large language models (FinLLMs), and agentic finance, and organizes the field around an integrated decision-system framework consisting of five connected constructs—delegation frontier, reliance wedge, decision-useful explainable artificial intelligence (XAI), meaningful oversight, and reflexive AI loop—to support cumulative research on investment, trading, credit, asset management, risk, compliance, and financial regulation.
This paper focuses on the significant differences in the impact of green finance and digital finance on energy transition. Using a panel data from Chinese cities, we found that, overall, both green finance and digital finance play a positive role in promoting energy transition. Employing the Panel Smooth Transition Regression (PSTR) model, this paper reveals key findings: robust development of green finance is necessary, while strategic allocation of digital finance can maximize the effect of energy transition. The heterogeneity of cities requires policymakers to avoid blindly following any single financial instrument. This study not only emphasizes the important role of emerging financial instruments in energy transition and dual carbon goals but also provides practical guidance for the expansion direction of urban financial sectors.
