Paulo Alcarva

Drawing on a panel of 1,250 Portuguese SMEs between 2015 and 2023, this study evaluates how different liquidity positions (cash holdings, current ratios, working capital efficiency) are associated with firm survival, profitability, and debt servicing capacity during and after economic shocks. Using dynamic panel regression models (System-GMM), we isolate the effect of liquidity buffers on firm outcomes, controlling for firm size, sector, leverage, and governance indicators. Our findings suggest that higher pre-crisis liquidity levels significantly increased the probability of operational continuity during COVID-19 and fostered faster revenue recovery in subsequent years. The study contributes to the growing literature on financial resilience in the European periphery and offers practical insights for SME managers and policymakers on the strategic importance of liquidity reserves, not only as a buffer but as a forward-looking instrument of competitiveness. The results carry implications for regulatory bodies and development banks in shaping liquidity support mechanisms and credit access frameworks for SMEs in Portugal.

Green bonds and green bank loans coexist as instruments for financing the low-carbon transition, yet the strategic mechanism through which issuers select between them remains weakly formalized. This paper models green debt instrument choice as a three-stage extensive-form game with perfect information in which a firm first selects a financing route, a bank then chooses credit enhancement in the bond branch or loan terms in the loan branch, and investors decide whether to subscribe. Because the bank’s credit enhancement enters the investors’ participation condition, the three players are genuinely strategically interdependent, and because the bond branch carries only a small standby cost rather than the full loan cost, the regulatory regime affects the choice of instrument and not merely the level of financing cost. The subgame-perfect equilibrium is characterized analytically by backward induction and is then implemented numerically over the eight admissible states of demand, issuer credibility and regulatory regime through a Monte Carlo experiment of 200,000 parameter draws. The share of simulated parameter configurations yielding a green bond equilibrium falls from 93.4% under high demand and strong credibility to 14.2% when both deteriorate; the regulatory regime flips the equilibrium instrument in 19.4% of draws, against 0% in the degenerate special case without the enhancement channel. A variance-based global sensitivity analysis (Sobol and Morris) and an incomplete-information extension with a noisy credibility signal complete the analysis. All reported quantities are numerical solutions of the model under an illustrative calibration, not empirical estimates.

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), we estimate three model families—a regularized logistic regression, a gradient-boosting machine, and a multi-layer perceptron—under a fully crossed design in which each family is evaluated without mitigation and under pre-processing (reweighing), in-processing (an exponentiated-gradient reduction, applicable to any base learner, together with adversarial debiasing where gradient-based training permits it), and post-processing (reject-option) interventions, so that the mitigation effect is no longer confounded with the choice of estimator. No sensitive-group field enters any estimated specification; group membership is used exclusively for auditing. Predictive performance (AUC-ROC, Brier score and Brier skill score relative to the base-rate forecast, F1 on the default class, Gini, and the Kolmogorov–Smirnov statistic) is reported jointly with group fairness (demographic-parity and equal-opportunity differences, disparate-impact ratio, Theil index) and with group-conditional calibration, at an explicitly stated and economically justified decision threshold. Every fairness quantity is accompanied by stratified-bootstrap confidence intervals and, for stochastic learners, by seed-level dispersion. The interpretable benchmark attains an AUC of 0.780 and a Brier score of 0.104 against 0.129 for the constant base-rate forecast, and the high-capacity models improve on it by under one AUC point. Disparity is present but is located geographically rather than in the composite group label: the disparate-impact ratio is 0.724 [0.693, 0.754] for the lowest socio-economic neighborhood cluster, excluding the four-fifths screening value, against 0.809 [0.776, 0.840] for the ethno-socioeconomic proxy, whose interval contains it, and no measurable gender disparity. Group membership is recoverable from the neutral feature set at an AUC of 0.654, and 42% of the group gap in predicted risk travels through the bureau credit score alone, so feature deletion cannot close the channel. Feature attributions and an auxiliary group-recoverability test locate the proxy pathways through which disparity arises, and a misclassification-sensitivity analysis bounds the effect of error in the group proxy, which attenuates measured disparity toward parity. We map the results onto Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744, the GDPR as interpreted in SCHUFA Holding, Directive (EU) 2023/2225, EBA loan-origination guidance, and Solvency II, EIOPA, and IAIS expectations, and propose fairness-risk controls organized around impact assessment, independent validation, and three lines of defense governance. Because the evidence comes from credit origination at a single lender, the insurance argument is developed at the level of regulatory and governance architecture rather than as an empirical transfer of estimates.

Green bonds and green bank loans coexist as instruments for financing the low-carbon transition, yet the strategic mechanism through which issuers select between them remains weakly formalized. This paper models green debt instrument choice as a three-stage extensive-form game with perfect information in which a firm first selects a financing route, investors then decide whether to subscribe to a green bond issuance, and a bank sets lending conditions under regulatory incentives. The subgame-perfect equilibrium is characterized analytically by backward induction and is then implemented numerically: the equilibrium correspondence is mapped over the parameter space and evaluated through a Monte Carlo experiment of 200,000 parameter drawing across the eight admissible states of demand, issuer credibility and regulatory regime. Equilibrium green bond issuance requires two conditions to hold jointly, namely investor participation and a demand-driven advantage exceeding the fixed issuance and certification cost. The simulation shows that green bond adoption falls from 79.2% under high demand and strong credibility to 1.5% under moderate demand and weak credibility, with a sharp discontinuity at the participation threshold, and that bank concessionally shifts financing levels without altering the bond-versus-loan margin. The framework provides a tractable basis for assessing sustainability-related financing risk and for the empirical separation of feasibility, optimality and institutional transmission channels.

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), we compare a regularized logistic regression with gradient-boosting and neural-network classifiers, estimated under baseline and fairness-aware configurations (reweighing, adversarial debiasing, and reject-option adjustment), evaluating performance (AUC-ROC, Brier score, F1) jointly with group fairness (demographic-parity and equal-opportunity differences, disparate-impact ratio, Theil index). The interpretable benchmark attains an AUC of 0.734 and a Brier score of 0.162 yet already disadvantages the marginalized ethno-socioeconomic subgroup (disparate-impact ratio 0.67; equal-opportunity difference −0.165), despite excluding sensitive attributes. Higher-capacity models raise accuracy while widening disparities: the neural network reaches AUC 0.792 but a ratio of only 0.58. Mitigation reverses this cheaply, lifting the ratio to 0.85 for 1.1 AUC points under reweighing and to 0.84 while retaining an AUC of 0.765 under adversarial debiasing, so the presumed fairness–accuracy trade-off is weaker than assumed. We map these results onto the EU AI Act, GDPR, EBA, Solvency II, EIOPA, and IAIS frameworks and propose fairness-risk controls based on impact assessment, validation, and three-lines-of-defense governance.

The main purpose of this research is to examine the significance of green bonds and green banking loans as financing tools for ecologically sustainable projects in the face of increasing worldwide environmental issues. This research seeks to uncover the determinants of both instruments’ issuance and the obstacles to their acceptance. A thorough systematic literature review will be conducted to assess the efficacy of these tools in improving company financial performance and cost of debt, advancing environmental sustainability, and influencing investor behavior. This methodology guarantees a comprehensive and impartial examination of peer-reviewed publications from reputable sources such as Web of Science and Scopus. Although issues such as greenwashing, market liquidity, and regulatory discrepancies still exist, both tools are growing steadily in the sustainable financing spectrum. The results also suggest that both instruments are influenced by several factors, often overlapping due to their common focus on financing sustainable projects. The credit rating, financial health, and overall environmental performance of the issuing entity significantly influence the attractiveness and pricing of green bonds, as do the market conditions, regulatory frameworks, and certification. The environmental profile and creditworthiness of the borrower are key determinants for green banking loans. The review enhances the current body of knowledge by presenting a theoretical structure for comprehending the dynamics of green debt markets and proposing practical recommendations for policymakers and financial institutions. Furthermore, it emphasizes the deficiencies in existing research, including the need for further longitudinal investigations into green bank loans and a more thorough examination of the notion of ‘greenium’. We searched Web of Science and Scopus up to 26 April 2024. Eligibility criteria included peer-reviewed English-language studies on green bonds or green banking loans. After screening, 128 studies were found to have met the inclusion criteria.

Family firms play a key role in the world economy, yet their success is often plagued by family-related issues and conflict. Succession is one of the most critical hurdles they face. The purpose of this paper is to illustrate how specific governance instruments, such as the family protocol, can aid mitigate the conflicts by establishing a set of guidelines and procedures to aid govern the operation, management and continuity of family firms. To study the underlying strategic decision making process of the multiple stakeholders involved, this paper resorts to game theory. The results will provide valuable determinants of the family protocol which may aid smooth managerial transfer in the family firm and uphold harmony in the family setting.