Search Results (119)

This paper develops a theoretical framework for corporate financial resilience under incomplete-market conditions, in which firm-specific equity derivatives are structurally unavailable or only weakly developed. Using the Romanian capital market and the Bucharest Stock Exchange (BSE) as a focal context rather than as the paper’s sole relevance, the study links Tobin’s q, liquidity policy, capital structure, ESG governance, and the domestic quasi-risk-free benchmark ($R_f^{ROM}$) to explain how firms may partly support financial flexibility when direct hedging instruments are missing. This is a conceptual framework paper: it does not provide empirical tests or validated firm-level results but instead formulates empirically testable propositions (P1–P4) and a future empirical research agenda. Building on selective hedging theory, Tobin’s q investment theory ESG finance and organisational resilience research, the framework identifies six assumptions of the classical model that are violated and four limitations affecting q measurement on the BSE. Within thin and illiquid markets, Tobin’s q is treated as a noisy, imperfect valuation signal rather than as a precise decision threshold. The paper contributes by delimiting the scope conditions under which classical q-based and selective-hedging assumptions weaken in derivative-constrained markets by reframing financial flexibility as a conditional resilience mechanism rather than a hedge substitute and by specifying falsifiable propositions for future empirical testing in the Romanian capital-market context. Full article

This paper examines the strategic incentives to adopt sustainability measures within the positioning perspective on competitive advantage. While much of the existing literature emphasizes “win–win” opportunities, suggesting that a commitment to sustainability can simultaneously enhance economic, social and environmental performance, this study adopts a more critical stance. Drawing on the logic of trade-offs inherent in competitive strategy, it argues that the internalization of environmental and social externalities often entails costs that must be justified through price premiums. Integrating insights from strategic management and consumer research, the paper analyzes how demand-side conditions shape the viability of sustainability as a basis for differentiation, with particular attention to consumer involvement and information transparency across niche and mass markets. To capture these dynamics, the paper develops a conceptual 2 × 2 framework identifying how varying levels of involvement and transparency shape firms’ incentives for sustainability differentiation and greenwashing. The analysis suggests that sustainability is most viable as a differentiation strategy in niche markets characterized by high involvement and transparency, whereas its prospects in mass markets remain limited due to price sensitivity, low engagement, and imperfect information. These findings challenge optimistic assumptions about the scalability of sustainability through competitive market mechanisms and highlight the structural constraints that favor cost-based competition and greenwashing. Full article

This study investigates the determinants of capital structure in Moroccan agricultural SMEs, with particular emphasis on the distinction between interest-bearing debt and non-interest-bearing liabilities in a context characterized by persistent credit constraints. While traditional capital structure theories typically treat debt as a homogeneous aggregate, such an approach may obscure important financing dynamics in financially constrained environments. Using a panel dataset of 52 agricultural SMEs observed over the period 2017–2022, the analysis employs a correlated random effects model to control for unobserved heterogeneity. The results indicate a negative relationship between profitability and both total and short-term debt, consistent with the predictions of the Pecking Order Theory. Liquidity, asset tangibility, and firm size are negatively associated with non-interest-bearing current liabilities, suggesting that trade-based financing may serve as an adjustment mechanism when access to formal credit is limited. In contrast, long-term debt is only weakly explained by firm-level characteristics, pointing to potential supply-side constraints in agricultural credit markets. Overall, the findings suggest that financing patterns in agricultural SMEs appear to be more closely associated with credit market imperfections than with optimal trade-off considerations. By distinguishing between different debt components, this study contributes to the literature by highlighting the importance of debt composition when analyzing capital structure in emerging and financially constrained economies. Full article

Hydrogen energy storage serves as a pivotal technology for integrating high proportions of renewable energy, yet its development faces constraints due to substantial investment requirements and imperfect market mechanisms. Green Asset-Backed Notes (ABNs) offer potential to alleviate financing constraints; however, their synergistic effects with hydrogen storage market strategies remain unexplored. This paper constructs a two-layer Stackelberg game model integrating ABN financing with day-ahead trading. Multi-scenario analysis reveals that ABN financing costs significantly influence the operational economics of energy storage: low-cost financing enhances hydrogen storage’s price responsiveness and arbitrage capabilities, whereas high costs suppress its market participation. The research provides quantitative evidence for leveraging financial instruments to enhance hydrogen storage competitiveness. Full article

This paper examines the role of agricultural credit in shaping farm performance and technology-related outcomes in Peru, using nationally representative microdata from the Encuesta Nacional Agropecuaria (ENA). In a context characterized by credit rationing and institutional constraints, access to finance may influence agricultural income, productivity, and the adoption of improved practices through multiple direct and indirect channels. To address the non-random allocation of credit, the analysis employs a quasi-experimental framework that combines propensity score trimming, block-based common support restrictions, entropy balancing, and doubly robust treatment-effect estimators (IPWRA and AIPW). Descriptive evidence documents substantial heterogeneity in credit sources, loan uses, and rejection reasons, highlighting structural barriers related to collateral, land tenure, and risk. Regression results on the balanced sample indicate positive and statistically significant associations between credit access and both real agricultural income and land productivity. However, estimated treatment effects are sensitive to the estimation strategy: while IPWRA estimates suggest economically meaningful gains among credit recipients, AIPW estimates are smaller and not always statistically distinguishable from zero. Exploratory results further suggest that credit access is positively associated with technology adoption and managerial capacity, consistent with, but not identifying, a potential association between credit approval and technological practices. Overall, the findings are consistent with a growing body of evidence showing that the impacts of agricultural credit are modest, heterogeneous, and context dependent. From a sustainability perspective, the results underscore the importance of complementary interventions—such as land tenure security, risk management instruments, and tailored financial services—in enhancing the effectiveness of rural credit programs in agricultural systems characterized by imperfect markets and high production risk. Full article

This study advances a hierarchical bilevel optimization paradigm to rigorously characterize the intertwined processes of strategic bidding and regulatory market participation in electricity systems increasingly dominated by renewable resources. At the upper tier, a central regulatory authority orchestrates participation rules, renewable integration mandates, and incentive mechanisms with the overarching aim of maximizing system-wide social welfare while driving decarbonization and reliability objectives. At the subordinate level, profit-maximizing generation firms—each managing heterogeneous renewable portfolios—pursue strategic bidding under deep uncertainty, conceptualized as a multi-agent game governed by imperfect and asymmetric information. The interaction between these tiers is formalized as a bilevel Stackelberg game that encapsulates price-responsive demand, intertemporal reserve adequacy, and policy-driven incentive structures. To ensure both computational tractability and robustness against strategic indeterminacy, the lower-level equilibrium is reformulated into a mathematical program with equilibrium constraints (MPEC), enabling a hybrid solution procedure that combines penalty-based regularization with exact decomposition algorithms. The framework’s efficacy is validated through a stylized multi-zone case study featuring diverse renewable assets and strategic participants, revealing how policy signals, capacity ceilings, and market power asymmetries reshape efficiency frontiers and bidding equilibria. A set of high-resolution post-processing visualizations is further employed to illustrate the dynamic evolution of marginal prices, equilibrium trajectories, and regulatory impacts under uncertainty. Full article

This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the COVID-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the COVID-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets. Full article

Defect inspection of Printed Circuit Board (PCB) is essential for maintaining the safety and reliability of electronic products. With the continuous trend toward smaller components and higher integration levels, identifying tiny imperfections on densely packed PCB structures has become increasingly difficult and remains a major challenge for current inspection systems. To tackle this problem, this study proposes the Multi-scale Edge-Aware Enhanced Detection Transformer (MEE-DETR), a deep learning-based object detection method. Building upon the RT-DETR framework, which is grounded in Transformer-based machine learning, the proposed approach systematically introduces enhancements at three levels: backbone feature extraction, feature interaction, and multi-scale feature fusion. First, the proposed Edge-Strengthened Backbone Network (ESBN) constructs multi-scale edge extraction and semantic fusion pathways, effectively strengthening the structural representation of shallow defect edges. Second, the Entanglement Transformer Block (ETB), synergistically integrates frequency self-attention, spatial self-attention, and a frequency–spatial entangled feed-forward network, enabling deep cross-domain information interaction and consistent feature representation. Finally, the proposed Adaptive Enhancement Feature Pyramid Network (AEFPN), incorporating the Adaptive Cross-scale Fusion Module (ACFM) for cross-scale adaptive weighting and the Enhanced Feature Extraction C3 Module (EFEC3) for local nonlinear enhancement, substantially improves detail preservation and semantic balance during feature fusion. Experiments conducted on the PKU-Market-PCB dataset reveal that MEE-DETR delivers notable performance gains. Specifically, Precision, Recall, and mAP_50–95 improve by 2.5%, 9.4%, and 4.2%, respectively. In addition, the model’s parameter size is reduced by 40.7%. These results collectively indicate that MEE-DETR achieves excellent detection performance with a lightweight network architecture. Full article

Modular construction is generally defined as a typical offsite construction approach that can improve environmental sustainability throughout the building project lifecycle. Based on this situation, identifying the strengths, weaknesses, opportunities, and threats (SWOT) while promoting this sustainable construction method effectively during the urbanisation process is essential. Generally, modular construction is a sustainable building approach that can improve project sustainability, considering the environmental, social, economic, and technological aspects. A comprehensive understanding of the basic situation of prefabricated construction is worthwhile to ensure the widespread adoption of this offsite building method. By employing the SWOT analytical framework, this study adopts a literature review approach to conduct the investigation. In terms of the project results, the core strengths of using modular construction include improving environmental sustainability, enhancing management effectiveness, and improving construction safety and quality. The major weaknesses, on the other hand, are a lack of expertise and research, excessively high initial costs, and difficulties in stakeholder coordination. On the other hand, the major opportunities include promoting the SDGs and other policies, the Industrial Revolution 4.0, and urbanisation and building demands. The main threats, however, include substitute construction technologies, imperfect building codes and standards, and a lack of social and market acceptance. Further research can increase the sample size and collect more accurate firsthand data to validate the results of the current investigation, which can increase the effectiveness of promoting modular construction in the targeted regions. Full article

Obtaining accurate estimates of the true probabilities of sporting events remains a long-standing problem in sports analytics. In this paper we propose a new domain-driven approach that infers true probabilities from betting odds. This task is not trivial, as betting odds are noisy because of bookmaker margins (vig), insider bets, and model imperfections. In this study, we present a novel approach that integrates estimates across multiple groups of betting markets to obtain more robust estimates of true probability. Our method takes market structure into account and constructs a constrained optimisation problem that is solved using the Dixon–Coles model of a football match. We compare our approach with a wide range of existing methods, using a large dataset of 359035 matches from more than 6000 leagues. The proposed method achieves the lowest log-loss and the best probability calibration among all tested approaches. It also performs the best in terms of expected profit convergence in Monte Carlo simulations, outperforming its competitors in terms of MSE and bias. This study contributes both to a new margin-removal (devig) method and provides a comprehensive comparative analysis of other known methods. Beyond football, this approach has potential applications in other sports with discrete scoring systems and potentially in other areas involving stochastic processes and market inference, such as prediction markets, finance, reliability engineering, and social prediction systems. Full article

This study develops an integrated quality inspection and production optimization framework for an imperfect production system, where system deterioration follows a zero-inflated non-homogeneous Poisson process (ZI-NHPP) characterized by a power-law intensity function. Parameters are estimated from historical data using the Expectation-Maximization (EM) algorithm, with a zero-inflation parameter π modeling scenario where the system remains defect-free. Operating in either an in-control or out-of-control state, the system produces products with Weibull hazard rates, exhibiting higher failure rates in the out-of-control state. The proposed model integrates system status, defect rates, employee efficiency, and market demand to jointly optimize the number of conforming items inspected and the production run length, thereby minimizing total costs—including production, inspection, correction, inventory, and warranty expenses. Numerical analyses, supported by sensitivity studies, validate the effectiveness of this integrated approach in achieving cost-efficient quality control. This framework enhances quality assurance and production management, offering practical insights for manufacturing across diverse industries. Full article

The market imperfection hypothesis posits that market frictions undermine economic efficiency and amplify economic fluctuations. As an emerging corporate evaluation framework and behavioral norm, ESG (environmental, social, and governance) performance helps mitigate such market imperfections. This study empirically examines the impact of corporate ESG performance on investment volatility and its underlying mechanisms. Using panel data from Chinese listed companies, we find that higher ESG ratings significantly reduce corporate investment volatility. Mechanism tests reveal that ESG practices curb investment fluctuations through two key channels: alleviating information asymmetry and reducing agency costs, thereby addressing fundamental market frictions. Moderating effect tests indicate that patient capital suppresses the smoothing effect of ESG on corporate investment volatility. Heterogeneity analysis further demonstrates that this stabilizing effect is more pronounced in non-state-owned enterprises, larger firms, and financially constrained firms. These findings highlight the economic value of ESG practices in promoting corporate investment stability and provide relevant insights for policy design and market participants. Full article

The forest food industry, as a typical low-carbon green ecological industry, holds strategic significance in addressing global food security challenges. This review takes forest protein resources as an example to analyze the current development status, opportunities, and challenges from a global industrial perspective. Research indicates that forests, as a vital food treasure for humanity, can provide diverse protein sources such as insects, plants, microorganisms, and bio-manufactured proteins. Currently, numerous technological innovations and market practices have emerged in fields such as insect protein (e.g., there are over 3000 edible insect species globally, with a market size of approximately USD 3.2 billion in 2023, projected to reach USD 7.6 billion by 2028), plant-based alternative protein (e.g., plant-based chicken nuggets by Impossible Foods in the United States), microbial fermentation protein (e.g., the production capacity of Solar Foods’ production base in Finland is 160 tons per year), and cell-cultured meat (e.g., cell-cultured chicken is sold in Singapore), demonstrating significant potential in alleviating food supply pressures and reducing environmental burdens. However, industrial development still faces practical challenges including insufficient resource exploration, incomplete nutritional and safety evaluation systems, low consumer acceptance, high costs of core technologies (e.g., the first cell-cultured meat burger in 2013 cost over 1 million USD/lb, and current costs need to be reduced to 17–65 USD/kg to achieve market competitiveness), and imperfect regulatory mechanisms (e.g., varying national standards lead to high compliance costs for enterprises). In the future, it is necessary to achieve efficient development and sustainable utilization of forest protein resources by strengthening resource exploration, clarifying the basis of nutrients, promoting multi-technology integration and innovation, and establishing a sound market access system, thereby providing solutions for global food security and high-quality development of the food industry. Full article

The transition to a circular economy depends on the widespread adoption of sustainable products by consumers. However, the point-of-sale purchase decision is a complex process, influenced not only by ethical arguments but also by sensory cues. This study investigates how the aesthetics (visual design) and materiality (tactile sensation) of green products shape value perception and purchase intention. Using a qualitative methodology based on a focus group, the research directly compares consumer reactions to green products (e.g., a bamboo toothbrush) versus their conventional alternatives (e.g., plastic). Thematic analysis of the data reveals a fundamental dichotomy among consumers: while one segment associates high-tech aesthetics and perfect finishes with quality and hygiene, another segment values natural materials and their “imperfections” as signs of authenticity and responsibility. The results demonstrate that there is no single, universally accepted “sustainable aesthetic” and highlight the need for designers and marketers to align the visual and tactile language of products with the value system of the target consumer segment. The study provides a framework for understanding how design can act as either a barrier to or a catalyst for the adoption of sustainable products. Full article

Emotion detection in Brazilian Portuguese is less studied than in English. We benchmarked a large language model (Mistral 24B), a language-specific transformer model (BERTimbau), and the lexicon-based EmoAtlas for classifying emotions in Brazilian Portuguese text, with a focus on eight emotions derived from Plutchik’s model. Evaluation covered four corpora: 4000 stock-market tweets, 1000 news headlines, 5000 GoEmotions Reddit comments translated by LLMs, and 2000 DeepSeek-generated headlines. While BERTimbau achieved the highest average scores (accuracy 0.876, precision 0.529, and recall 0.423), an overlap with Mistral (accuracy 0.831, precision 0.522, and recall 0.539) and notable performance variability suggest there is no single top performer; however, both transformer-based models outperformed the lexicon-based EmoAtlas (accuracy 0.797) but required up to 40 times more computational resources. We also introduce a novel “emotional fingerprinting” methodology using a synthetically generated dataset to probe emotional alignment, which revealed an imperfect overlap in the emotional representations of the models. While LLMs deliver higher overall scores, EmoAtlas offers superior interpretability and efficiency, making it a cost-effective alternative. This work delivers the first quantitative benchmark for interpretable emotion detection in Brazilian Portuguese, with open datasets and code to foster research in multilingual natural language processing. Full article