Purpose This study aims to examine the role of productive competitiveness in capital market development, emphasizing its importance in countries with weaker institutional environments. Productive competitiveness is defined as a country's capability to produce a diverse range of complex products, measured through the economic fitness index (EFI).Design/methodology/approach Using panel data for 98 countries during 1997-2022, the analysis applies fixed-effect instrumental variable regressions to assess the effect of EFI on capital market development and tests the interaction between EFI and institutional quality. The economic complexity index is used as a robustness check.Findings Results show that productive competitiveness is a key driver of financial deepening. The interaction term indicates that the effect of the EFI becomes more important in countries with weaker institutional quality, suggesting that productive sophistication plays a comparatively stronger role in explaining capital market development when institutional conditions are less favorable.Practical implications EFI can guide firm-level diversification strategies and serve as an information-signaling metric for investors by revealing the sophistication and scalability of firms' productive capabilities. For policymakers, EFI enables the prioritization of high-complexity sectors and helps attract long-term capital, broadening market participation and strengthening financial system resilience. These strategies should be coordinated with institutional improvements, given the interaction between EFI and institutional quality.Originality/value This study suggests that productive competitiveness, measured through the EFI, is a significant driver of capital market development and becomes more important where institutional quality is weak. These results are grounded in the evolutionary and sequential view of financial development, in which productive sophistication precedes financial sophistication.
Purpose - This study examines the direct linkages between relational bonds (RB), customer engagement (CE), and customer psychological ownership (CPO), and explores the mediating role of CPO in the relationship between RB and CE in the hospitality industry. Design/methodology/approach - A total of 629 valid responses were collected from three-star and above hotels in Jammu and Kashmir and Ladakh, Union Territories of India (UTs), using purposive sampling during the second half of 2024. Data were analysed using partial least squares structural equation modelling. Findings - The findings revealed that RB have a significant positive impact on CE and CPO. CPO also has a significant effect on CE. Moreover, CPO partially mediates the relationship between RB and CE. Research limitations/implications - This research is limited to selected areas of the UTs in India (Ladakh and Jammu and Kashmir), and further research is needed to determine the relative impact of RB on CE and CPO. Practical implications - The present study highlights the importance of RB and CPO in keeping customers engaged and reaping the benefits thereof. In addition, decision-makers will be able to devise comprehensive strategies to instil CPO and remain ahead of the competition. Originality/value - This study includes the "customisation bond" in the existing framework to offer a more comprehensive understanding of the impact of RB on CE and CPO. Furthermore, it adds a novel contribution by exploring the mediating role of CPO in the relationship between RB and CE.
Purpose-Board diversity has become a central issue in corporate governance due to its potential role in fostering innovation and long-term strategic thinking. This paper aims to evaluate the influence of board composition-specifically ownership structure, board structure, and demographic diversity-on innovation outcomes in Latin America's metal mining sector. This study introduces a fuzzy inference system (FIS) to assess the marginal impact of these governance variables on innovation capacity. Design/methodology/approach-This research applies a Mamdani-type fuzzy logic model to a sample of 12 listed metal mining firms in Latin America. The study draws on corporate governance theory and upper echelons theory to structure three blocks of input variables: ownership (institutional and promoter participation), board structure (size and independence) and demographic diversity (gender and age). Each block is modeled using fuzzy rules and linguistic variables to capture the complexity and interdependence of governance attributes under uncertainty. Findings-The findings are exploratory and should be interpreted as patterns observed within this specific sample. The results suggest that promoter ownership contributes more significantly to innovation than institutional investors. Smaller boards are associated with higher innovation scores than larger or highly independent boards. Gender diversity demonstrates a meaningful correlation with innovation, while age diversity yields inconclusive results. The fuzzy model enables the ranking of firms based on innovation scores, reflecting governance maturity levels and their alignment with innovation strategies. Originality/value-This study offers an original contribution by applying fuzzy logic to board-level governance variables, a method not commonly used in the governance-innovation literature. It addresses the analytical gap in evaluating ambiguous and non-linear relationships between governance composition and innovation, particularly in high-capital, low-frequency sectors such as mining. This approach may support firms and policymakers in designing governance frameworks that enhance innovation under conditions of limited data and contextual variability.
Purpose This study aims to assess the effects of macro-level factors on forecasts of price volatility for crude oil, natural gas, coal, and uranium, and to identify the key factors influencing predictive models in order to enhance the accuracy and reliability of energy price forecasts. Design/methodology/approach The study uses the Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model, combining factor selection techniques within a single modelling framework. This captures complex interdependencies among variables and improves analytical precision. In addition, the study incorporates the log likelihood function with an adaptive Least Absolute Shrinkage and Selection Operator (LASSO) penalty (ALASSO), providing robust estimation of volatility dynamics and causal relationships. Findings The model identified four key determinants that significantly influence West Texas Intermediate (WTI) crude oil price volatility - default-yield spread, financial market uncertainty, geopolitical risk uncertainty, and macroeconomic uncertainty. The three most informative determinants of natural gas price volatility are default-yield spread, financial market uncertainty, and macroeconomic uncertainty. Coal price volatility is primarily shaped by two determinants: demand and supply. Finally, uranium price volatility is largely determined by the demand factor and geopolitical risk uncertainty. The out-of-sample analysis further indicates that incorporating these variables significantly improves the prediction accuracy of all models compared with that of traditional baseline models. Originality/value The present analysis departs from previous studies, which typically exclude variables such as the industrial production index, crude oil demand and supply, and natural gas demand and supply, thereby suggesting that the previously reported influence of these factors may have been overstated. Uncertainty indices have proven to be robust and comprehensive predictors of market analysts and investors when assessing natural gas and crude oil price fluctuations and volatility. Accordingly, energy market participants are advised to focus on default yield spreads, financial market uncertainty, geopolitical risk uncertainty, and macroeconomic uncertainty to improve the overall efficiency of their risk management strategies. This focused approach is instrumental in refining decision-making processes during volatile market conditions.
PurposeThis paper develops and empirically tests a novel big data analytic model to measure financial well-being (FWB) throughout the customer life cycle. The study uses a comprehensive, proprietary dataset that encompasses demographic and transactional data for nearly 430,000 clients across multiple financial products, approximating an "open finance" setting.Design/methodology/approachThe model combines objective indicators of financial behavior with subjective measures of perceived well-being into a composite, customer-level index. To ensure robustness and practical relevance, the model was refined using feedback from nearly one hundred industry experts and validated through extensive sensitivity analyses. Furthermore, the composite index is closely aligned with independent and nationally representative studies conducted by the Development Bank of Latin America and the Caribbean (CAF), which highlights its external validity.FindingsThe study shows that the big data analytics model can capture the main features of the proposed conceptual model for measuring FWB at different stages of life. The model produces data-driven analytical weight loads that reflect most of the implications of the lifecycle consumption theory. The results validate the analytical model's suitability to be scaled and implemented in an open finance setting.Originality/valueBy providing a scalable, actionable, and empirically validated tool, this research contributes to the literature on FWB measurement and offers financial institutions and consumers the ability to continuously monitor their financial health, while accommodating AI-driven recommendations tailored to improve well-being.
PurposeThis study examines how equitable taxation can be designed under conditions of severe informational scarcity, where standard assumptions regarding income observability and the reliable estimation of behavioral elasticities do not hold. It proposes a structure-based approach to redistribution, in which observable economic networks provide indirect but policy-relevant information for fiscal design. Design/methodology/approachThis paper integrates graph neural networks with distributionally robust optimization to infer income-relevant structural embeddings from observable non-monetary characteristics such as education, occupation, region and formality status. Individuals are represented as nodes within an economic network, where links capture economic similarity and opportunity structure. Tax rules are optimized over Wasserstein ambiguity sets to explicitly account for income uncertainty and limited observability. The framework is evaluated through simulations of two stylized economies - a cohesive network and a fragmented network - using empirically calibrated synthetic data grounded in household surveys and administrative aggregates. FindingsTax rules based on GNN-derived structural embeddings consistently outperform benchmark scoring rules that rely solely on observable proxies. The proposed approach achieves larger reductions in post-tax inequality and lower regressivity while exhibiting greater robustness to income ambiguity and network perturbations. Redistributive performance improves with higher structural connectivity, underscoring the role of informational topology as a determinant of fiscal capacity. Research limitations/implicationsThe analysis abstracts from strategic tax evasion, labor supply responses and general equilibrium effects. Instead, it focuses on the informational foundations of redistributive capacity under partial observability. Practical implicationsThe framework provides tax administrations with a method to transform existing non-monetary registries and fragmented administrative data into actionable fiscal information without relying on intrusive income monitoring. Social implicationsBy enhancing structural observability, the proposed approach has the potential to strengthen fairness, transparency and trust in fiscal governance, particularly in economies characterized by high informality and limited administrative capacity. Originality/valueThis study contributes to the literature on optimal taxation by demonstrating that progressive redistribution remains feasible under severe informational constraints when the economic structure serves as a substitute for direct income observability. By combining network-based representation learning with distributionally robust fiscal optimization, it introduces a transparent and policy-relevant framework for tax design in data-poor environments.
PurposeThis study aims to investigate how social media recommendations influence investor attention and retail investor activity in stock investing.Design/methodology/approachUsing data from the Organisation for Economic Co-operation and Development (OECD)'s 2023 Adult Financial Literacy Survey across a sample of countries, the analysis employs a logistic regression model. To address potential endogeneity, both instrumental variable and propensity score matching techniques are applied.FindingsInvestor attention to social media influencers shows a positive and significant effect on stock investment. After adjusting for endogeneity, the main results remain robust. However, the moderating role of digital financial literacy is not consistent across all countries in the sample.Research limitations/implicationsThe influencer recommendation question was newly introduced in this survey, limiting longitudinal comparisons. Additionally, the results are based on self-reported data from four countries.Originality/valueThis research contributes to the emerging field of digital behavioral finance and offers insights for the design of financial inclusion and education policies in increasingly digital environments.
Purpose This study provides a comprehensive scientometric analysis of publication trends and thematic evolution in behavioral finance, spanning four decades (1984-2024). It aims to map the intellectual structure, identify key thematic shifts and analyze the impact of 7,053 Scopus-indexed journal articles. Design/methodology/approach A comprehensive bibliometric analysis was conducted using RStudio (bibliometrix) and VOSviewer. The methodology analyzed publication volume, citation impact, co-citation networks and co-word mapping to objectively visualize the field's performance, influential entities and conceptual clusters. Findings The analysis reveals an exponential growth in the number of publications and a corresponding increase in citation impact over the period. A significant finding is an interdisciplinary shift, evidenced by the high volume of research published in sustainability-oriented outlets such as the Journal of Cleaner Production, as well as by the emergence of financial literacy as a central theme. Traditional core areas, including decision-making, risk assessment and overconfidence, remain foundational. Research limitations/implications The study is limited to Scopus-indexed journal articles. It provides a foundation for future qualitative studies on influential themes and proposes new quantitative research avenues in algorithmic behavioral finance and cross-cultural biases.Practical implicationsThe findings guide policymakers and practitioners in designing behaviorally informed interventions, such as using nudge theory to enhance investor protection and developing tailored financial education programs. Social implications Potential outcomes include improved financial decision-making, greater financial literacy and the promotion of responsible investment practices. Originality/value This study offers a data-driven, comprehensive roadmap of the field by defining its intellectual core and providing a unique analytical explanation for the rise of sustainable behavioral finance in the academic literature, thereby challenging conventional assumptions about research outlets.
PurposeThis paper aims to examine the relationship between the implementation of the sustainable development goals (SDGs) and governments' financial sustainability. It analyzes whether progress toward the 2030 Agenda is associated with increasing public debt, thereby highlighting the potential fiscal implications of sustainability-oriented policies.Design/methodology/approachThe study uses a balanced panel of 145 countries covering the period 2016-2023. A dynamic model is estimated using the two-step system generalized method of moments. The dependent variable is general government gross debt (% of gross domestic product (GDP)), while the key independent variable is the SDG Index. Additional control variables include GDP per capita, unemployment, population metrics, revenue, political ideology, gender representation and voter turnout. Several robustness checks have also been conducted, including alternative estimation methods, variable substitutions and subsample analyses.FindingsThe results show a significant and positive relationship between SDG performance and public debt levels. Countries with higher SDG scores tend to exhibit greater indebtedness, suggesting that SDG progress is frequently financed through borrowing.Originality/valueThis is among the first empirical studies to examine how SDG implementation affects national debt. It offers new insights into the financial trade-offs of sustainable development, highlighting the importance of aligning sustainability strategies with sound fiscal planning. The findings contribute to debates on financing the 2030 Agenda and inform public finance and policy decisions.
PurposeThis study analyzes inter-industry reversal, or whether loser or underperforming industries yield higher returns than winner or outperforming industries in Latin America. The phenomenon is likewise examined in market segments that are more prone to inefficiencies and short-selling barriers. It also investigates intra-industry reversal by assessing whether loser stocks outperform winner stocks within the same industry. The analysis is then extended to market segments defined by stock characteristics.Design/methodology/approachLong-term reversal for industry portfolios is evaluated following the portfolio simulation approach proposed by Jegadeesh and Titman (1993). When testing multiple hypotheses simultaneously, the probability of reporting false positives increases substantially. To account for multiple hypothesis testing, p-values are adjusted using several well-established approaches.FindingsNo evidence of inter-industry reversal for the whole market or for specific market segments was found. Moreover, in both the entire market and certain segments, a contrarian intra-industry reversal strategy does not yield profits. Overall, investors in Latin American industries would have been unable to profit from exploiting return reversion across and within industries in the region.Research limitations/implicationsThe study focuses on formation periods of up to five years and holding periods of up to a year, as constrained by data availability. This limitation restricts the range of reversal strategies that can be analyzed (e.g. formation periods of a decade are not considered). As additional data become available, this limitation will be less severe.Practical implicationsThis paper builds on our previous paper that explored industry return continuation or momentum. Overall, neither momentum nor reversal at the industry level appears to be significant in Latin America's most important equity markets. Violations of weak-form market efficiency at the industry level in Latin America are not supported by our findings.Originality/valueThis paper contributes by providing new evidence on both inter- and within-industry reversal in a region that is frequently overlooked in international studies. It also adds to the literature by analyzing reversal in market segments related to industry and stock characteristics such as size or market cap. In addition, the study addresses the issue of multiple hypotheses testing, which is often neglected in existing literature.
PurposeThis study analyzes the effects of country-level shareholder protection and agency costs on the cash-holding policies of family firms.Design/methodology/approachData were collected for 2,159 European firms for the period 2010-2019. The authors estimate the model using the generalized method of moments.FindingsAgency costs are found to have a stronger effect than low country-level shareholder protection on decision-making related to cash-holding policies. In line with previous literature, the results show that the absence of agency costs between ownership and control results in holding more cash for the firms. In addition, family firms with a dominant shareholder and young firms hold more cash than family firms without a dominant shareholder and old firms, respectively. The study also finds that firms in countries with a low level of shareholder protection hold more cash than firms in countries with a high level of shareholder protection, in turn. However, the effect of agency costs outweighs the effects of low country-level shareholder protection.Originality/valueThis study advances the literature on family firms by examining the interplay between ownership, governance and agency costs in shaping cash-holding decisions, particularly in the context of European firms with varying levels of shareholder protection. Additionally, it provides a valuable perspective by analyzing how different types of agency costs influence cash holdings in family firms, demonstrating that these costs have a stronger impact than country-level shareholder protection in determining corporate liquidity policies.
PurposeThis study explores the mediating role of innovation output in the relationship between green management and the performance of small and medium-sized enterprises (SMEs).Design/methodology/approachThis research is based on a dataset collected by the Ibero-American SME Observatory, comprising 3,966 manufacturing SMEs in Latin America. Data were gathered between February and May 2022 and analyzed using partial least squares structural equation modeling.FindingsInnovation output, as a strategic resource or capability, mediates the relationship between green management and SME performance, which implies that the impact of green management on SME performance is bolstered by innovation output.Originality/valueGrounded in the resource-based view, this study contributes to the field of innovation by identifying innovation output as a mechanism that strengthens the relationship between green management and SME performance. The findings are therefore relevant for manufacturing SMEs seeking to address environmental challenges and ensure market survival through innovation, as well as for policymakers.
PurposeThis study develops a comprehensive discrete numerical model for option valuation that explicitly incorporates risk preferences, which may deviate from risk neutrality. Unlike the traditional binomial tree models - strictly under the risk-neutral paradigm - our framework embeds a constant relative risk aversion (CRRA) utility specification, capturing heterogeneous attitudes toward risk while preserving the arbitrage-free pricing rule.Design/methodology/approachThe model extends the multiplicative binomial recombination tree (MBRT) by adjusting key parameters - transition probabilities, growth factors, discount rates and drift/diffusion terms - to reflect the investor's degree of risk aversion. The classical Cox-Ross-Rubinstein binomial tree (CRR) emerges as a special case when risk aversion is set to zero. The methodology remains consistent with geometric Brownian motion (GBM) dynamics and is benchmarked against a modified Monte Carlo simulation to ensure robustness.FindingsResults show that option values can be consistently derived under both traditional risk-neutral settings and preference-driven settings. Sensitivity analysis highlights the impact of time to maturity, volatility, strike price and the risk-free rate under varying levels of risk aversion.Research limitations/implicationsWhile this research offers significant theoretical and practical contributions, certain limitations warrant further study. Computational complexity: the CRRA-based valuation method introduces additional numerical challenges, requiring precise calibration and advanced optimization techniques. Dependence on risk aversion estimates: the model assumes that investor risk preferences can be accurately measured and remain stable, which may not always reflect dynamic market conditions. Absence of a closed-form solution: our proposed approach lacks an analytical closed-form solution. Therefore, it is crucial to dedicate efforts to its development.Practical implicationsThe integration of CRRA utility functions into derivative valuation represents a key innovation, as it explicitly accounts for investor risk preferences beyond the traditional risk-neutral paradigm. This framework advances the literature on utility-based and nonlinear risk-adjusted pricing by demonstrating how variations in the relative risk aversion (RRA) coefficient shape option values. From a practical perspective, the model offers a flexible tool for portfolio managers, traders and policymakers by aligning valuations with observed market behavior while preserving consistency with classical models under specific conditions. Accurate calibration of risk preferences thus becomes essential for reliable pricing and policy design.Originality/valueThe novelty of this research lies in bridging utility-based preferences with recombining lattice valuation: while prior studies focused exclusively on risk-neutral or arbitrage-based approaches, our model incorporates explicit risk aversion into the numerical structure. By deriving general algebraic expressions and validating the framework through numerical experiments, this study offers a tractable and versatile tool for analyzing option prices under heterogeneous risk attitudes, without losing the analytical clarity of traditional methods.
PurposeThis study aims to examine the effects of currency depreciation and volatility on services trade balance and trade partner concentration and dynamics in Japan at a disaggregated level over the 2006M01 to 2023M09 period.Design/methodology/approachIt applies linear and non-linear autoregressive distributed lag models and correlation analysis (including cross- and rank correlations) to identify strength, asymmetry and lags in the effects.FindingsExcept for a few cases, the evidence of both J- and S-curves was found weak and generally not asymmetric, as was the effect of currency volatility on trade balance. Trade partner concentration remained stable, and the dynamics of trade partner ranks were slow. The results are explained in terms of the specific response of services (as opposed to goods) trade to depreciation, the nature of competitive processes and corporate reorganisations in Japan and the macroeconomic policy of the recent 2 decades.Originality/valueThe study is one of a kind that examines J- and S-curve effects in services, incorporates volatility and considers trade partner composition effects.
PurposeThis study examines the impact of green finance (GFIN) and green innovation (GTI) on environmental sustainability in seven South American countries from 2000 to 2020.Design/methodology/approachThe study employs panel data econometric techniques using the Method of Moments Quantile Regression approach to explore the relationships between carbon dioxide (CO2) emissions, GFIN, GTI, economic growth (GDP), renewable energy (REN) and non-renewable energy (NRE) globalization (GLO) and population (POP). The robustness of the results is confirmed through additional analyses using bootstrap quantile regression, feasible generalized least squares and panel corrected standard errors.FindingsThe findings reveal that GFIN significantly reduces CO2 emissions across all quantiles, with stronger effects at higher quantiles. However, GTI shows a positive association with emissions in higher quantiles, suggesting rebound effects. Renewable energy decreases emissions, while NRE, GLO, population and GDP growth contribute to environmental degradation, indicating no evidence of the environmental Kuznets curve hypothesis. Additionally, the Dumitrescu-Hurlin causality test reveals bidirectional causality between carbon dioxide (CO2), GDP, NRE and POP, and unidirectional causality from CO2 to GFIN, GTI and REN, highlighting dynamic interactions.Practical implicationsThe results suggest that policymakers should promote accessible GFIN, enhance the efficiency of green innovation and invest in REN sources to support environmental sustainability.Originality/valueThis study offers novel insights by applying a quantile-specific approach to examine the impacts of GFIN and innovation on environmental sustainability in South America, addressing a significant gap in the literature where such distributional effects in emerging economies have been largely overlooked.
PurposeThis paper examines the non-financial determinants influencing Sustainable Development Goals (SDGs) disclosure among Latin American banks.Design/methodology/approachThe study employs an explanatory methodological approach characterized by quantitative analysis and a longitudinal perspective. It applies a multiple linear regression model to examine the non-financial determinants influencing SDG compliance among banks listed on the national stock exchanges of the six Latin American countries with the highest nominal GDP in USD (World Bank, 2022). This group includes the four members of the Pacific Alliance (Chile, Colombia, Mexico, and Peru), along with Brazil-the only Latin American member of the BRICS-and Argentina, recognized for its significance in South America and its membership in the Group of Twenty (G-20).FindingsThe findings reveal a direct and significant relationship between three variables of interest and financial institutions' disclosure of priority SDGs: board member independence, adherence to International Integrated Reporting Council guidelines in reporting, and the audit of sustainability reports by one of the Big Four firms. The variables of board size and the proportion of female employees exhibited a notable inverse relationship.Originality/valueThis study provides empirical evidence from the Latin American context, advancing research on non-financial determinants in the sustainability reporting of financial services, and underscoring banks' commitment to sustainable development.
PurposeThe purpose of this paper is to analyze the speed at which credit unions adjust their capital ratios.Design/methodology/approachThe systemic Generalized Method of Moments (GMM-Sys) was applied to 704 Brazilian individual credit unions from 2014 to 2022, with a total of 5,864 observations.FindingsThe results indicate that the median Basel Ratio was higher than the Leverage Ratio, without breaching the regulatory minimum. Credit unions show differences in the speed of adjustment, with faster adjustment for the Basel Ratio compared to the Leverage Ratio. Size influences the speed of adjustment, with larger credit unions being more flexible in adjusting their Leverage Ratio more quickly, while smaller credit unions adjust more slowly to the Basel Ratio. During economic crises, the speed of adjustment of the Basel Ratio was higher, probably due to more thorough analysis by supervisors and stakeholder expectations.Originality/valueThe originality of this study lies in the analysis of the speed of adjustment of capital ratios in credit unions, focusing on the differences between the Basel and Leverage Ratios. This study fills a significant gap in the literature, offering insights into how these institutions adjust their capital ratios in the context of Basel III. The research also explores the impact of credit union size and economic crises on these adjustments, contributing to a deeper understanding of the financial behavior of these institutions and its implications for regulators and supervisors.
