Search Results (96)

From the perspective of interactions between energy firm behavior and government intervention strategies, this study develops a contagion control model for energy firm debt default risk utilizing cellular automata and complex network theory. This research investigates the spatio-temporal evolution of risk transmission and evaluates the efficacy of various mitigation protocols through computational simulation. The research results indicate that: (1) An escalation in both the transmission likelihood and the rate of immunity decay significantly amplifies the propagation strength of debt default risks. Conversely, the stability of the energy firm network is bolstered as the probabilities of immunity and recovery increase. (2) The contagion intensity for debt default risk is positively correlated with market noise, the risk appetite of energy firms, and their corporate influence. It is negatively correlated with risk awareness, creditworthiness, regulatory intensity, and policy subsidies. Furthermore, it exhibits an inverted U-shaped relationship with investor sentiment. (3) Within the interconnected network of energy firms, risk contagion can be effectively mitigated not only by enhancing risk perception and credit standing but also by guiding risk preference and managing firm influence. Furthermore, the integration and adjustment of government intervention strategies, such as regulatory intensity and policy subsidies, can more efficiently accelerate the eradication of debt default risk among energy firms. Full article

Prolonged downturns in the global economy have simultaneously increased banks’ credit risk exposures and intensified the need for effective liquidity management. This study develops a dynamic agent-based financial network comprising banks, depositors, firms, and the central bank to examine trade-offs in bank resolution under coupled liquidity and credit risks from the perspective of systemic stability. The simulation results show that, for liquidity risk management, when banks adopt the asset-sale strategy, both default probability and expected returns in the banking system exhibit a nonlinear pattern: they first decline and then rise as the asset depreciation ratio increases. Furthermore, at moderate levels of asset depreciation, the asset-sale strategy helps preserve heterogeneity within the banking system, thereby preventing excessive risk concentration, and performs better than the liability-expansion strategy. Regarding credit risk resolution, the debt-relief strategy significantly improves systemic stability, whereas the effectiveness of the debt-extension strategy depends critically on liquidity management conditions. Under liability-expansion scenarios, default risk initially declines but later rises as debt maturity is extended, whereas expected returns move in the opposite direction. Under asset-sale conditions, the debt-extension strategy enhances systemic stability only when the allowable number of debt extensions is sufficiently high. The analysis of strategic trade-offs indicates that combining the debt-relief strategy with the asset-sale strategy generates a positive synergistic effect and strengthens systemic resilience, whereas the interaction between the debt-extension and asset-sale strategies produces offsetting effects. These findings offer useful implications for banks and regulators in designing coordinated and adaptive frameworks for risk resolution and systemic stability. Full article

The objective of this paper is to offer a mathematical formulation of economic value added (EVA) that incorporates distance-to-default (DD) and thus a default-free capital structure. The latter is extended via the weighted average cost of capital (WACC) to introduce a default-free EVA. The data include the nonfinancial firms listed in the DJIA30 and NASDAQ100 covering the period 1992Q2–2023Q3. The results of standard specification tests and the GMM estimator show that (a) DD causes an increase in WACC and thus, EVA decreases; (b) the interest coverage ratio can be used effectively to compensate for default risk, thus adjusting the default-free EVA positively; (c) both EVA and default-free EVA can effectively be managed via common determinants, namely, net working capital ratio, total liabilities to EBITDA, sales growth rate, debt–equity ratio, and earnings per share; (d) the positive impact of the inflation rate on both EVA and default-free EVA justifies the use of default-free EVA as a metric for equity risk premium; and (e) the robustness of the results via stochastic geometric Brownian motion shows that the determinants of default-free EVA are stable. This paper contributes to related studies by incorporating credit risk via the DD into default-free EVA. Full article

Does the digital transformation of major customers foster collaborative financing for upstream suppliers, or does it amplify their bargaining power for rent extraction? This study investigates these competing hypotheses by examining the digital spillovers from major customers to supplier trade credit. Using a unique hand-collected dataset linking Chinese listed suppliers with their top five customers by accounts receivable from 2010 to 2021, we document a “dual enhancement effect”: major customer digitalization significantly increases trade credit scale and improves trade credit quality, effectively rejecting the rent extraction hypothesis. Specifically, trade credit quality is reflected in lower bad debt provision ratios, shorter receivable aging, and lower material default risk. Mechanism tests suggest that improved information transparency and stronger customer market competitiveness are important channels through which digitalization affects supplier trade credit. Cross-sectional analyses show that these effects are more pronounced for non-state-owned or low-asset-specificity suppliers, and for customers with higher asset specificity or lower importance. After ruling out alternative explanations, we further find that this digital spillover strengthens supply chain resilience. Overall, the evidence is more consistent with the collaborative financing view than with the rent extraction view, suggesting that major customer digitalization may help foster more sustainable and cooperative financing relationships within supply chains. Full article

Economic shocks in international systems propagate not only through financial channels but also through real-sector interactions, creating feedback effects that can amplify systemic risk across countries. However, country-level systemic risk assessments often rely on single-layer analyses, potentially overlooking such cross-channel dynamics. To investigate how country-level systemic risk interpretations differ across propagation layers, we constructed a multilayer network that integrates cross-border financial exposures and real-sector trade linkages. Using BIS Locational Banking Statistics and UN Comtrade data for 20 countries from 2000 to 2023, we developed a multilayer contagion framework that combines continuous within-layer propagation based on DebtRank with a threshold-based mechanism that activates cross-layer contagion when critical loss levels are exceeded. Initial shocks were calibrated using sovereign credit default swap (CDS), which implies default probabilities, to reflect market-based credit risk conditions. The results show that countries’ systemic roles and risk transmission patterns vary across layers and over time, and that incorporating cross-layer amplification reveals vulnerabilities not captured by single-layer models. Full article

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks’ loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9) as the financial reporting standard. Unlike its predecessor (i.e., International Accounting Standard 39, IAS 39), IFRS 9 accelerates the recognition of losses by requiring provisions to cover both already-incurred losses and some losses expected in the future by calculating the expected credit loss (ECL). To evaluate if the obligor’s credit quality has deteriorated, the IFRS 9 standard requires banks to compare the obligor’s probability of default (PD) at the inception phase of the loan and at the reporting date. Thus, three methodologies are explored in this study (i.e., Cox proportional hazard (PH), Extended Cox PH, and Random Boosting Forest (RBF)) for computation of the PD term structures using Kaplan–Meier as the benchmark model under IFRS 9. The purpose of this research is to illustrate the application of three methodologies on the publicly available mortgage loan portfolio from Freddie Mac using different measures of goodness-of-fit and the predictive accuracy measure, i.e., the Concordance index (C-index). The comparison analysis reveals that the extended Cox PH and RBF models provide better predictive accuracy (higher C-index) but at the cost of increased complexity and potential overfitting (higher information criteria). However, Cox PH has shown the most efficient fit, and offers a stable and understandable hazard trajectory. Finally, for reproducibility, the SAS and R codes are included to illustrate how each of the results (in form of a table or figure) were obtained. Full article

This paper proposes a continuous-time dynamic clearing model on a multilayer financial network to study systemic risk propagation and optimal intervention. The model incorporates interbank credit, equity crossholdings, and overlapping portfolios, and models bankruptcy as a jump event triggered by insolvency or illiquidity. Based on the system’s dynamic structure, we develop a model predictive control (MPC) framework that enables forward-looking and flexible allocation of limited bailout resources between debt relief and capital injection. Numerical results show that the proposed MPC strategy substantially outperforms both no-intervention and rule-based policies in terms of financial stability and resource efficiency. Compared with no intervention, the MPC strategy reduces the number of defaulting banks by approximately 56%. In contrast, the simple rule-based intervention achieves a reduction of about 48.83%, while improving rescue efficiency by approximately 28.57%. Overall, the framework provides a unified and effective approach to systemic risk control in financial networks. Full article

Internal accounting control personnel constitute the operational foundation through which firms ensure the accuracy and reliability of financial reporting, yet their relevance to capital market outcomes remains insufficiently documented. This study evaluates whether investment in internal accounting control personnel is incorporated into corporate bond pricing by considering both the quantitative dimension of staffing levels and the qualitative dimension of personnel expertise. Corporate bond issuance data are merged with mandatory disclosures on internal accounting control personnel for manufacturing firms listed on the Korea Exchange between 2011 and 2021. The analysis shows a significantly negative association between internal accounting control personnel and corporate bond yield spreads, with personnel expertise further reinforcing this relationship. These patterns are consistent with the view that enhanced monitoring capacity and stronger reporting credibility reduce information asymmetry and perceived default risk among bond investors. The evidence positions internal accounting control personnel as an operational and signaling indicator of internal control effectiveness reflected in debt market pricing and suggests that investment in internal control staff extends beyond compliance to produce measurable financial benefits through lower borrowing costs. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

This study investigates the valuation of defaultable corporate bonds using a two-factor model of Markov-modulated stochastic volatility with double exponential jumps (2FMMSVDEJ). This model captures long- and short-term SV and asymmetrical jumps in the underlying asset value. Concurrently, the firm’s debt dynamics are governed by a Markov-modulated GBM (MMGBM) model to reflect state transitions. A dynamic measure change technique is employed to determine the pricing kernel, and the resulting credit spreads and default probabilities are analyzed. Full article

This study examines the role of algorithmic fairness in alleviating digital financial stress among consumers across OECD countries, utilizing panel data spanning from 2010 to 2023. By introducing a digital financial stress index—constructed from indicators such as household credit dependence, digital debt penetration, digital default rates, and financial complaint frequencies—the research quantitatively captures consumer financial anxieties within AI-driven e-commerce platforms. Employing two-way fixed-effects regression and system-GMM methods to address endogeneity and dynamic panel biases, findings robustly indicate that increased algorithmic fairness significantly reduces digital financial stress. Furthermore, the moderating analysis highlights digital literacy as a critical factor amplifying fairness effectiveness, revealing that digitally proficient societies derive greater psychological and economic benefits from equitable algorithmic practices. These results contribute to existing scholarship by extending discussions of algorithmic ethics from individual-level analyses to a macroeconomic perspective. Ultimately, this research underscores algorithmic fairness as a crucial policy lever for promoting consumer welfare, calling for integrated national strategies encompassing ethical algorithm governance alongside enhanced digital education initiatives within OECD contexts. Full article

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., $70.77\%$ on Lending Club, $93.25\%$ on Australia, $77.85\%$ on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search’s accuracy while reducing runtime by up to $75.7$-fold (e.g., $3.19$ vs. $241.47$ min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below $0.4\%$ under $\pm 10\%$ perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation $> 0.95, p<0.01$) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions. Full article

Many of the existing theoretical and empirical studies ignore the two-way relationship between a sovereign’s credit risk and economy. To address this gap, we develop a theoretical model that incorporates the feedback effects of sovereign-debt credit risk on a country’s economy and then provide empirical implications. The model links the risks of sovereign debt and economic fundamentals through a two-way transmission mechanism. In doing so, it demonstrates how economic-fundamentals-driven sovereign-debt credit risk can have a significant impact on economic fundamentals through a feedback effect that has the potential to significantly raise the sensitivity of a country’s economic performance to shocks from both the credit risk associated with sovereign debt and economic fundamentals. The outcomes of the theoretical model are then verified by empirically testing the feedback effects using a structural equation model (SEM) framework on data covering sovereign debt defaults worldwide. We demonstrate how disregarding feedback effects may result in information that is insufficient and less helpful to public-debt-management policymakers. Full article

Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates the contagion path from individual liquidity crisis to systemic repayment crisis. In order to test whether bank financial technology (hereinafter referred to as “BankFintech”) can mitigate SDLU and reduce the possibility of financial risks, this study matched the loan data of China’s A-share listed companies with the patent data of bank-invented Fintech from 2013 to 2022 to construct the BankFintech Development Index for empirical analysis. The empirical results show that the development of BankFintech can significantly inhibit SDLU. The mechanism test reveals that BankFintech reduces bank credit risk and liquidity risk by lowering firms’ risk-weighted assets, improving capital adequacy and liquidity ratios, tilts banks’ lending preferences toward duration-matched long-term financing, and “forces” enterprises to take the initiative to improve their financial health and information transparency, enhance their ability to obtain long-term loans, and realize the active management of mismatch risk. Heterogeneity analysis finds that the effect is more significant in non-state-owned enterprises and technology-intensive industries. Further analysis shows that the level of enterprise digitization, the intensity of financial regulation, and related financial policies significantly moderate the marginal effect between the two. This study verified the “Porter’s Risk Mitigation Hypothesis” of Fintech, providing empirical evidence for effectively cracking the financial vulnerability caused by debt maturity mismatch and deepening financial supply-side reform. Full article