Search Results (414)

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

Customer churn refers to the termination of a customer’s business relationship with a bank, representing a direct loss of future revenue. Product downsell manifests as a reduction in the number of financial products held or a downgrade in service tier, often signaling early customer disengagement. Accurately identifying customers at risk of these two behaviors has become a cornerstone of profitable growth in the competitive retail banking industry as downsell frequently serves as a precursor to total churn. However, the existing research typically treats these highly correlated behaviors as independent prediction tasks, overlooking their intrinsic link and failing to address the critical challenges of class imbalance and regulatory demands for model interpretability. To tackle these problems, we propose an enhanced multi-task learning network (EMTL-Net), a deep learning framework specifically designed to capture the nuanced interplay between churn and downsell behaviors. EMTL-Net introduces an explicit feature interaction module to enhance the modeling of high-order feature relationships and utilizes a shared representation layer to extract universal customer risk patterns, enabling the joint prediction of churn and downsell. Furthermore, we employ Focal Loss as the training objective to dynamically adjust sample weights, effectively mitigating the class imbalance problem. Critically, to meet financial compliance requirements, we implement a SHAP-based interpretation mechanism that is compatible with multi-task outputs, providing preliminary insights into feature importance. Formal validation of interpretability claims remains an important direction for future research. The experimental results on a publicly available pedagogical bank customer benchmark dataset demonstrate that EMTL-Net achieves excellent performance on both tasks. For churn prediction, the model achieves an AUC of 0.8259, an accuracy of 0.8361, and an F1-score of 0.6235, significantly outperforming the existing baseline models. For downsell prediction (noting that the downsell label is rule-derived from the number of products held), the model achieves an AUC of 0.8932, an accuracy of 0.8571, and an F1-score of 0.7504. Ablation studies confirm the critical contributions of the explicit feature interaction module, Focal Loss, and the residual structure to model performance. Crucially, the interpretability analysis corroborates business intuition by identifying customer age, account balance, and product holdings as dominant churn drivers—a consistency that reinforces the model’s credibility and practical utility in high-stakes financial environments. Full article

In an increasingly volatile global economy, countries are scaling up financial investment in innovation to sustain their competitive advantages. Financial institutions, specifically banks, are facing the challenge of aligning more radical financial policies for innovation with their own business sustainability, and innovation has thus become a key issue confronting banking. Existing studies mostly explain financial innovation from an external incentive perspective, leaving theoretical divergences and empirical paradoxes regarding core innovation dynamics unclear. Adopting a novel perspective of constraint-induced innovation theory, which challenges the dominant external pull view in the existing literature, this paper takes China’s tech–finance integration pilots as a quasi-natural experiment and uses a double/debiased machine learning model to identify causal effects by using panel data from 142 Chinese regional banks from 2008 to 2024. We find that tech–finance integration policies induce regional bank financial innovation through the dual channels of profitability and safety constraints. Banks must innovate to cope with the pressure exerted by new policies, and constraint-induced innovation is the main dynamic mechanism. In addition, financial innovation amplifies the policy’s impact on banks’ business sustainability. This study bridges the gap between existing theory and empirical evidence, extending the boundaries of financial innovation dynamic research. Full article

This study examines the accounting determinants of the capital structure of Portuguese firms in the textile, clothing, and leather sectors, based on a sample of 6469 firms over the period 2010–2022, using panel data models. The relevance of this study lies in its focus on specific industrial sectors characterized by a high predominance of small and medium-sized enterprises (SMEs) and a strong dependence on bank financing. In addition to the traditional analysis of leverage determinants, this study introduces a conditional approach to accounting variables based on firms’ structural characteristics, namely size and age. Robustness checks and data treatment procedures were conducted to mitigate the potential impact of outliers in the financial variables. The results show that profitability, liquidity, and risk negatively affect indebtedness, whereas asset structure and growth exert positive effects. The effective tax rate has a negative impact on debt. Firm size and age significantly condition the relationship between variables. SMEs’ financing decisions exhibit differentiated patterns depending on firm size and age. The findings support the predictions of the Pecking Order Theory and, to a lesser extent, the Trade-Off Theory. The study highlights the importance of considering firm heterogeneity when designing financing policies and strategies for Portuguese SMEs. Full article

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders. Full article

This paper examines the effect of trade openness on corporate tax revenue in the Middle East and North Africa (MENA) region, where increased economic integration might incite more business activity and expand taxable corporate income but also intensify losses due to practices such as profit shifting. The study follows a quantitative empirical approach and applies a panel ARDL model to secondary data collected from international databases (World Bank and IMF), such as GDP, trade openness (exports and imports as % of GDP), inflation, corporate tax revenues, foreign direct investment inflows and tax evasion via informal economies, for a sample of ten developing countries from the MENA region, including Morocco, Tunisia, Egypt, Jordan, Lebanon, Algeria, Saudi Arabia, Oman, the United Arab Emirates, and Bahrain, over the period 2010–2023. We employ a PMG ARDL model to study our panel data, allowing the analysis of both short-run and long-run effects to investigate the relationship between trade openness and tax revenues. Our results show that in the long run, export-driven economies generate higher corporate tax revenues by expanding profitability and the tax base, and imports also positively affect revenues, indicating that trade openness stimulates economic activity. Conversely, FDI inflows reduce corporate tax revenues, consistent with profit shifting and tax incentives in developing countries. GDP growth does not necessarily increase tax receipts, likely due to tax elasticity effects and growth-oriented tax structures. Also, tax evasion appears to decline, likely reflecting improved compliance, and no significant short-run effects are observed. The results contribute to the literature on tax compliance and economic integration in the case of open economies in developing countries. From a practical perspective, our findings have implications for policymakers and tax regulators in the MENA region, as they highlight the dual nature of globalization for developing countries and their tax systems and underscore the need for effective compliance measures in trade and investment policies. Full article

This study examines the impact of Corporate Social Responsibility (CSR) and green banking practices on the financial and risk sustainability of commercial banks in Bangladesh. Using panel data of 300 observations over the period 2013 to 2024, this study employed random effects regression models to assess how CSR and green banking influence profitability, financial stability, and risk exposure. A System GMM model is additionally used to address endogeneity concerns and validate the robustness of the results. The empirical findings indicate that CSR engagement by banks contributes positively to financial performance with superior shareholder returns. This positive relationship remains robust under the GMM framework. CSR also shows a significant negative association with non-performing loans, which becomes stronger after controlling for endogeneity. Green banking practices also exhibit a positive and statistically significant relationship with ROE under the baseline model. However, this relationship becomes statistically insignificant in the GMM estimation, indicating that the effects of green banking are sensitive to endogeneity. Full article

This study examines the impact of mobile wallet adoption on the profitability of a banking institution in Peru during the period 2021–2025, in a context of rapid digital transformation in financial services. The research adopted a quantitative, non-experimental, longitudinal, and explanatory design based on a single-bank case study. Mobile wallet adoption was measured through a synthetic index (IAD) constructed from five indicators using principal component analysis, while profitability was assessed through return on assets (ROA), return on equity (ROE), and aggregate monetary profitability. The effect of the IAD on profitability was estimated using generalized estimation equations with HAC-type robust standard errors. The results show that mobile wallet adoption exerts a positive and statistically significant effect on all three profitability indicators, with the strongest effect on aggregate monetary profitability, followed by ROE and ROA. These findings contribute to the literature by providing longitudinal evidence from an underexplored emerging economy and by showing that the financial effects of digital adoption differ according to the profitability measure considered. Overall, the study highlights the relevance of mobile wallet adoption as a strategic digital factor in banking performance within emerging financial contexts. Full article

Capital structure decisions are among the most critical financial choices for firms, as they directly influence them. There is an ongoing debate among researchers regarding the optimal capital structure, motivating this study to examine the impact of various factors on firms’ capital structure, while also considering the COVID-19 pandemic as an influential external factor. This study investigates the financial behavior of 147 non-financial firms listed on the Egyptian Stock Exchange over the period of 2011–2022, using firm year observations. Firms were classified into 14 sectors, excluding banking and non-banking financial services, due to their unique regulatory environments. Data were collected from multiple reliable sources, including financial statements, corporate reports, and EGX databases. Advanced econometric techniques, including the Panel Generalized Method of Moments (GMM), the Arellano–Bond test, and Johansen cointegration analysis, were employed to address endogeneity and explore long-run relationships. The results show that leverage is persistent over time, is positively associated with firm size, tangible assets, and growth opportunities, and is negatively related to profitability, cash flow, and liquidity. The COVID-19 pandemic had a small but significant positive effect, and sectoral differences were also observed. The findings provide robust insights into corporate financing behavior in emerging markets, highlighting the interplay between firm characteristics, external shocks, and financing decisions. Full article

This paper examines the relationship between artificial intelligence (AI) innovation and bank performance, the organizational channels through which these relationships operate, and the role of firm-wide adoption in shaping outcomes. Using patent-based measures of AI innovation for 31 large U.S. commercial banks from 2015 to 2024 based on the Federal Reserve’s Large Bank classification and employing panel regressions with bank and year fixed effects, we find that AI innovation is associated with improved asset quality but higher operating costs and lower profitability in the short run. Our two-step mediation analysis implies that AI innovation induces organizational changes through diminishing employee scale and branch networks, which mitigates management efficiency and profitability. Importantly, firm-wide AI adoption mitigates the adverse association between AI innovation and both management and profitability prior to adoption, suggesting that the realization of AI’s benefits requires organizational adaptation and coordinated deployment. Dynamic tests further support the productivity “J-curve” of AI innovation. Our findings suggest that bank managers should align AI investment with organizational restructuring and coordinated deployment, while regulators should account for short-term adjustment costs when evaluating the performance implications of AI adoption. Full article

Multi-unit common value auctions in which bidders submit demand functions are used for a variety of purposes, including selling government debt (Treasury auctions) and allocating liquidity (repo auctions). Typically, either a discriminatory or a uniform-price format is used. In this paper, we consider the incentive for participation by relatively uninformed bidders in the presence of more informed bidders under these formats. We characterize the equilibrium under a discriminatory auction and show that discriminatory pricing inhibits uninformed participation. In contrast, the equilibria we construct under a uniform pricing rule show that profitable uninformed participation can occur. The usefulness of widening participation in Treasury auctions makes the latter format a natural choice in these auctions, providing an explanation for the switch to the uniform-price format in US Treasury auctions. We also apply our results to repo auctions and show that a uniform-price format can reduce the ability of a central bank to steer interest rates. This sheds light on the reason for the switch away from the uniform-price format by several central banks in conducting repo auctions. We also consider the question of information aggregation and show that uniform-price auctions might fail to do so. The results also offer an explanation for the fact that the ECB, as well as several other central banks, prefer to allocate liquidity through a fixed-rate tender rather than either uniform-price or discriminatory auctions. Full article

This study examines the influence of diversification on commercial bank performance, specifically considering the role of monetary policy and shifting macroeconomic conditions. PRISMA 2020-based systematic literature review was conducted using Scopus data from 2006–2025. Bibliometric analysis using VOSviewer 1.6.20 and RStudio 4.4.3 was employed to identify key themes and citation patterns. Findings are heterogeneous and context-dependent: diversification can improve profitability and valuations in some settings, yet may increase earnings volatility and reduce risk-adjusted performance in others. In particular, the impact of monetary policy is identified as significant yet mediated by broader macroeconomic factors. These insights assist bank managers in aligning strategies with economic landscapes and aid policymakers in designing regulations adaptive to macroeconomic fluctuations. This study contributes a comprehensive synthesis of diversification strategies, emphasizing the often-overlooked interplay with monetary policy and providing a future research agenda. Full article

Unlike purely domestic firms, multinational companies have distinctive opportunities to engage in sophisticated international tax planning strategies. This study investigates whether banks perceive potential earnings repatriation taxes as a significant source of risk when designing loan agreements for these firms. Our findings reveal that U.S. multinationals facing higher potential repatriation tax burdens are subject to wider loan spreads, indicating increased risk premiums. Moreover, this effect is especially pronounced among firms with low profitability or limited financial flexibility, highlighting the risk-sensitive nature of these loans. We also observe that lenders are more likely to demand collateral and impose stricter financial covenants in loans to firms with substantial repatriation tax exposure, further underscoring that banks regard these taxes as a firm-specific risk factor. By exploring the intersection of international tax considerations, potential earnings repatriation taxes here, and debt contracting, our research makes a valuable contribution to the literature, shedding light on how global tax issues influence credit markets and lending behavior. Full article

This study examines the determinants of profitability for 21 Bulgarian commercial banks over the period from the first quarter of 2007 to the first quarter of 2025, using financial statement data. Bank profitability is measured by return on assets (ROA) and return on equity (ROE) and modeled within a panel autoregressive distributed lag (PMG-ARDL) framework. The empirical specification combines bank-specific and macroeconomic variables, allowing for the identification of both long-run equilibrium relationships and short-run bank-level dynamics. The long-term results indicate that the net interest margin (NIM), net fee and commission margin (NFM), government bond yields, the growth of the gross domestic product (GDP), and the loan-to-deposit ratio (LDR) positively affect profitability. On the other hand, higher unemployment, rising housing prices, increased loan loss impairments, and the ratio of cash holdings to total assets reduce profitability. The findings provide policy-relevant insights for bank management, regulators, and macroprudential authorities regarding efficiency, income diversification, and credit risk management. The findings facilitate a more comprehensive assessment of banking sector resilience and provide a foundation for the development and refinement of macroprudential and supervisory policy measures. Full article

Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector. Full article