We examine how academic journal reviewers' experience with the peer-review process influences their propensity to recommend manuscript acceptance or rejection. We use data on the total recommended rejections and acceptances for all referees who reviewed at least one paper for the Journal of Financial Economics (JFE) between 1994 and 2020. We show that reviewers who write more reports are more likely to recommend the acceptance of manuscripts. We also find that older reviewers, those who graduated from or are affiliated with prestigious universities, and those with more and highly cited publications are more likely to recommend acceptance. There is also some evidence that reviewers with doctoral training in economics, mathematics, physics, and engineering are more likely to recommend acceptance than those with a PhD in finance. We find no consistent evidence of significant differences between genders or among reviewer demographic characteristics. We also document that reviewers who themselves publish more successfully in JFE and publish highly cited articles are, ceteris paribus, more likely to recommend rejection of reviewed manuscripts. Our study utilizes a unique research setting to gain new insights into the determinants of the peer-review process in scientific journals.

Disentangling deprivation: Differential associations of the area deprivation index- 3 factors with clinical, cognitive, and mood outcomes in epilepsy.

ABSTRACT Chinese economists produce large and increasingly high-quality research output, yet they have not been considered for the Nobel Prize in Economics. Prevailing explanations, cultural bias or insufficient theoretical depth, rely on general assumptions. We integrate Bourdieu’s field theory and Merton’s normative structure of science to operationalize three Nobel-alignment dimensions: Nobel Query (NQ), Nobel Context (NC), and Nobel Time (NT). Using textual and metadata evidence from 2,604 articles in the Economic Research Journal (2000–2019), top in China and representative of the trend, we analyze the question types, levels of analysis, and timeline. Results show that applied “how” questions and domestically bounded contexts (firm/industry/national) dominate in those empirical studies. Despite strong empirical sophistication, Nobel-style inquiries are concentrated in a small set of subfields—especially econometrics, institutional economics, and financial economics. The timeline suggests a gradual convergence towards the Nobel Prize criteria since 2013. These patterns suggest that under-recognition reflects not only external gatekeeping but also internal research orientations that shape symbolic visibility. The NQ–NC–NT framework clarifies where convergence is occurring and identifies levers for translating policy relevance into generalizable contributions and for building international scholarly capital, with implications for journal strategy, collaboration networks, and training that privileges abstraction.

This study examines herding behaviour in stock markets from a financial economics perspective by integrating informational, psychological, and social determinants within a unified empirical framework. Using primary data collected from 249 individual investors in Ahmedabad, India, the study investigates the impact of information asymmetry, investor sentiment, risk perception, social influence, and reputational concerns on herding behaviour, and further analyzes the effect of herding behaviour on market volatility and market efficiency. Multiple regression analysis was employed to test the proposed relationships. The results indicate that information asymmetry, risk perception, social influence, and reputational concerns have significant positive effects on herding behaviour, while investor sentiment does not exhibit a statistically significant independent influence. Among the predictors, risk perception and reputational concerns emerge as the strongest determinants of herding behaviour. Further analysis reveals that herding behaviour significantly increases market volatility and also exerts a strong influence on market efficiency. These findings highlight the central role of behavioural and informational factors in shaping collective investment decisions and market outcomes. The study contributes to the behavioural finance and financial economics literature by offering an integrated individual-level framework to explain herding behaviour and its consequences in an emerging market context. The results provide important implications for investors, policymakers, and regulators in designing strategies to mitigate excessive herding and promote stable and efficient financial markets.

Abstract This paper investigates the impact of volatility on expected corporate bond returns in China by using transactional data from 2010 to 2022. Portfolio analysis and Fama‐MacBeth regression show that volatility has a negative impact on expected corporate bond returns. After controlling for credit rating, maturity, liquidity, stock volatility and risk exposures, the volatility effect remains significant. By incorporating a new volatility factor and the bond market factor into the term‐default two‐factor bond pricing model of Fama and French ( Journal of Financial Economics , 1993, 33, 3), we construct a new four‐factor pricing model of corporate bonds. The proposed model captures the premium of volatility risk well and makes a significant marginal contribution to explaining the excess returns of corporate bonds. In addition, we find that volatility has a predictive effect on the default of corporate bonds.

All information related to regional financial performance can be accessed by the public through officially published local government financial reports. This study aims to examine the effect of regional financial performance ratios on economic growth across regencies and cities in Aceh Province. Economic growth is measured by the growth rate of Gross Regional Domestic Product (GRDP), while regional financial performance is represented by the growth ratio of fiscal independence, the growth ratio of local revenue (PAD) effectiveness, the fiscal growth ratio, and the regional expenditure growth ratio. The population of this study includes all regencies and cities in Aceh Province, with a total sample of 23 local governments selected using a saturated sampling method, and an observation period of five years (2020–2024). Hypothesis testing is conducted using panel data regression analysis with a significance level of 0.05. The results show that the growth ratio of fiscal independence and the growth ratio of Region original income effectiveness have positive but insignificant effects on economic growth, while the fiscal growth ratio has a negative and significant effect on economic growth. In contrast, the regional expenditure growth ratio has a positive and significant effect on economic growth. These findings indicate that regional expenditure growth plays a more dominant role in driving economic growth than revenue-based fiscal indicators.

Financial Technology Innovation, Data Transparency, and the Quality of Corporate Financing: An Empirical Inquiry Based on Financial Economic Data

Behavioral finance has evolved as a transformative field that bridges cognitive psychology and financial economics, challenging traditional assumptions of rational decision-making in business environments. Modern organizations increasingly recognize that managerial decisions are influenced by heuristics, biases, emotional responses, and social dynamics, all of which significantly affect strategic outcomes. This research paper investigates how behavioral finance principles shape managerial decision-making and how psychological insights can be integrated into business strategy to enhance organizational performance. By reviewing theoretical constructs such as heuristics, prospect theory, overconfidence bias, anchoring, loss aversion, and herd behavior, the study explains why managers deviate from rational models predicted by classical finance. The paper further discusses how behavioral interventions, such as debiasing techniques, nudge strategies, and structured decision frameworks, can strengthen managerial judgment. Case studies from industries including investment management, technology, and banking illustrate practical applications. Ultimately, this paper argues that incorporating behavioral finance into strategic management not only improves forecasting accuracy and risk assessment but also cultivates adaptive leadership capable of navigating uncertainty in dynamic markets.

Purpose: The study puts forth the comparison and effects of numerous variables and indicators of economic growth, financial inclusion on healthcare access using panel and time series data across 7 developing economies in South Asia. Design/Methodology/Approach: The study was correlational and empirical quantitative approach was used, we tested the numerical data as empirical panel, the other methods and tests used were correlation, unit root, regression, fixed and random effects, model fit, Hausman test and correlation matrix and for comparative analysis with time series data, unit root and OLS regression were used. Findings: The findings that are also aligned with previous research demonstrates financial inclusion heavily impacts the healthcare access and mortality in specifics; GDP has a weak and inconsistent effect on healthcare access which suggests that enhance financial inclusion and focus on microlevel financial betterment helps the cause for better healthcare. Implications/Originality/Value: The outcome of this study yields noticing implications for policymakers and developmental organizations that they can use to expand the healthcare network and access using improved financial inclusion policies merged with healthcare infrastructure for greater results in national healthcare across the developing South Asian countries.

The aim of this study was to provide a comprehensive analysis of the theoretical and methodological foundations for assessing the investment attractiveness and identifying the risks associated with the implementation of innovative circular economy business models. The article examined the essence of circular business models, their key characteristics, operational mechanisms, and their potential to enhance enterprise competitiveness within the context of global economic, environmental, and technological transformations, including the energy transition and the implementation of the European Green Deal initiatives.A systematic analysis of the main barriers and threats limiting investments in the implementation of circular business models was conducted, including technological, financial, regulatory, and market risks. The results of an analytical comparison were synthesized to identify key directions for evaluating investment attractiveness and potential obstacles to the adoption of innovative circular business models. A conceptual multi-level approach to assessing investment attractiveness was proposed, integrating financial, environmental, and innovation criteria. The application of this approach allowed a comprehensive evaluation of potential benefits and the identification of strategic priorities for investors and enterprises in implementing circular business models. To reveal structural imbalances in circular economy financing and identify barriers to innovative business models, a comprehensive study of the risk sector was carried out.The results indicated that systematic risk management and the removal of key barriers facilitated the acceleration of circular model implementation, enhanced enterprise competitiveness and resilience, and supported the achievement of sustainable development goals. The integration of financial, environmental, and innovation criteria increased the efficiency of investments in circular business models and contributed to their successful practical implementation.It was determined that innovative circular business models possessed significant investment potential but required well-grounded strategic management, careful risk assessment, strengthened regulatory support, and the development of technological infrastructure. Only through a balanced approach could their effective implementation be achieved, transforming them into a stable mechanism for sustainable economic growth.The study also developed directions for adapting circular business models to the specifics of the Ukrainian market, taking into account financial, technological, and institutional factors, and provided recommendations on digitalization, partnerships, and incentives to accelerate the circular transformation of Ukrainian enterprises.

General Background: Stock price prediction remains a central concern in financial economics due to its role in guiding investment decisions and managing market risk. Specific Background: Conventional forecasting techniques often inadequately address uncertainty, adaptability to new information, and the integration of prior knowledge, whereas Bayesian analysis offers a probabilistic framework that updates beliefs using historical and incoming data. Knowledge Gap: Despite growing interest in Bayesian methods within finance, empirical evidence demonstrating their practical efficiency in closely matching predicted and actual stock prices remains limited. Aims: This study aims to examine the efficiency of Bayesian analysis as a technical tool for predicting stock prices and supporting investor decision-making. Results: The findings indicate that Bayesian analysis generates predictions with minimal deviation from actual stock prices, confirming the robustness and reliability of the model. Novelty: The study reinforces the applied value of Bayesian analysis by empirically demonstrating its predictive efficiency within a real stock market context. Implications: These results suggest that Bayesian-based forecasting can enhance investors’ analytical independence, reduce reliance on costly external financial analysts, and promote more informed, data-driven investment strategies in dynamic financial markets.Keywords : Bayesian Analysis, Stock Price Prediction, Bayesian Forecasting Models, Financial Time Series, Investment Decision MakingHighlight : Forecast outputs closely matched observed market values, indicating minimal deviation across tested periods. Continuous updating with incoming information improved adaptability under changing market conditions. Empirical application demonstrated practical usefulness for investment decision-making without reliance on external advisors.

Subject. This article discusses the issue of relationship between the financialization of economy and accounting. Objectives. The article aims to describe the role of accounting in designing the profitability of the financial economy, in the interests of which the current accounting standards regimes (US GAAP and IFRS) operate. Methods. For the study, I used the dialectical method of scientific cognition, the method of collecting theoretical and statistical information, and analysis and synthesis, observation, and comparison. Results. The article finds that in 2021–2022, the operational profitability of the financial economy (financial and insurance activities) of the European Union was more than twice as high as the profitability of the real sector (industry, construction and market services). Similarly, in the USA, the operational profitability of the financial economy was 1.2–21.3 times higher than the operational profitability of non-financial industries (chemical, aluminum, construction, metallurgical and agricultural industries) throughout 2024 and Q1 2025. The article puts forward a hypothesis that modern accounting standards regimes (US GAAP and IFRS) contribute to the outflow of capital from the manufacturing (real) sector to the financial economy. Conclusions. In the scientific literature, many issues related to the role of accounting in economy are not thoroughly considered. The article assumes that accounting is a neutral technology that contributes to the efficient allocation of capital, however, this mechanism has not been adequately studied. The article identifies promising areas for the development of the research topic.

This article investigates the fiscal underpinnings of multilevel climate governance (MLCG) by examining how competitive project grants shape local decarbonisation efforts in the United Kingdom. While MLCG literature has focused on institutional structures and regulatory authority, we argue that intergovernmental fiscal relations constitute a critical but underexplored dimension of climate governance. In particular, the literature has neglected that much of local climate action in Western countries gets financed through conditional, short-term, and competitive project-based grants. Drawing on a mixed-methods study, including 45 interviews and a survey of local authority officers, we show how the UK’s centralized and fragmented funding regime imposes ‘rules of the game’ that prioritize upward accountability, short-term deliverables, and visibility over locally grounded, long-term strategies. These dynamics fragment local bureaucracies, constrain strategic planning, and exacerbate regional inequalities in climate governance capacity. Our findings challenge assumptions about local autonomy and leadership in climate action, revealing how fiscal instruments function as tools of centralised control. We call for greater attention to the political economy of public finance in multilevel climate governance.

The prediction of stock market dynamics remains a central challenge in financial economics due to the complexity and volatility of financial time series. Traditional econometric approaches, while useful, struggle to capture nonlinear patterns and long-term dependencies inherent in stock market behavior. Recent advances in artificial intelligence (AI), particularly in deep learning and ensemble learning, offer promising alternatives for improving predictive accuracy and robustness. This study revisits the Moroccan stock market, building upon prior research that tested neural network architectures such as MLP, RNN, CNN, and LSTM. Using daily data from the MASI index and seven sectoral indices from 2017 to 2024, we propose a hybrid methodology combining the Temporal Fusion Transformer (TFT) with gradient boosting models (XGBoost and LightGBM) in a stacking ensemble. The results demonstrate that hybrid models outperform standalone deep learning architectures, offering more reliable forecasts and improved economic backtesting performance. Our findings highlight the potential of probabilistic AI models to enhance financial decision-making and risk management in emerging markets.

Faced with limitations to transnational transactions, Chinese students in Russia engage in informal currency exchanges. Through fieldwork in a Siberian city, we discovered how, against the backdrop of sanctions, fluctuating official exchange rates, and cumbersome bank procedures, Chinese students build informal networks on WeChat, a Chinese multifunctional mobile application that combines messaging, social media, and payments. Such a group-based money exchange reflects students’ economic activities in a foreign country and their reliance on informal financial channels. In addition, this type of exchange highlights the contradictions between the financial system of the sanctions-hit host country and the needs of internationally mobile subjects—that is, students. In this essay, we elucidate new understandings of social and financial strategies from grassroots-level operations that function like a black market exchange system. The outcome provides a fresh ethnographic case for rethinking the informal financial economy in a localised and transnational context.

In recent years, within the framework of sustainable development, the issues of improving green financing technologies, as well as the directions for their modernization, have been examined in the scholarly works of contemporary economists. In developed economies, projects aimed at developing modern investment ecosystems that promote the growth of environmentally friendly (green) bonds are currently being successfully implemented. The purpose of this work is to identify the main directions for developing green finance instruments in the Republic of Kazakhstan based on global trends. The article analyzes international practices in achieving sustainable development goals, emphasizing the role of the green bond market as a driving force behind green, social, and sustainable bonds. The analysis shows that many European countries demonstrate positive results in sustainable development, while international organizations act as key players in issuing and promoting green finance instruments. Based on the conducted practical analysis, the article provides an assessment of the current state of the green financing market in the Republic of Kazakhstan and identifies contemporary trends in the development of green and sustainable bonds, in particular, the measures being taken to integrate ESG criteria (Environment, Social, Governance) into the strategies and business models of financial organizations. The results of the study allowed the authors to determine the directions of state policy in Kazakhstan for promoting the development of green financing through the implementation of sustainable development instruments by harmonizing regulatory documents and government programs in the field of green economy financing. To achieve the research objective, the authors employed methods of generalization and systematization of information, which made it possible to identify current and future trends, challenges, and opportunities for green financing amid changes in the business economy in the Republic of Kazakhstan. A comparative analysis of the green instruments market was conducted based on parameters from the Climate Bonds Initiative database.

Problem. The full-scale military aggression against Ukraine has fundamentally altered the architecture of logistics systems, rendering traditional «Just-in-Time» supply chain models ineffective. The blockade of Black Sea ports and the constant threat of shelling have forced a strategic shift of cargo flows to western land borders. This environment is characterized not by risk (stochastic uncertainty with known probabilities), but by epistemic (deep) uncertainty, where historical data becomes irrelevant. Consequently, classic deterministic and stochastic models, which focus on minimizing financial costs, are inadequate for humanitarian logistics, where the priority lies in minimizing social consequences and ensuring the physical survival of the population. Goal. The primary objective of this study is to enhance the efficiency and reliability of transport-technological schemes (TTS) for international cargo delivery in conditions of military conflict. This is achieved by developing a robust optimization methodology that, unlike classical approaches, minimizes total social costs and effectively accounts for the epistemic uncertainty of logistics network parameters. Methodology. The research methodology is based on robust optimization (RO) principles, which utilize «uncertainty sets» rather than probability distributions. The study develops a mathematical model centered on a composite objective function that integrates three key components: direct logistics costs, the cost of risk (threats to cargo and personnel), and the cost of deprivation (a non-linear economic valuation of the humanitarian impact caused by delivery delays). To handle time uncertainty, the authors formulate robust constraints using the concept of an «uncertainty budget» and propose a mechanism for dynamic parameter adaptation using Digital Twin technology. Results. A new mathematical model for selecting rational TTS has been developed. Unlike commercial models, the proposed function prioritizes social needs over financial economy in critical situations. The study successfully formulated a system of robust constraints that guarantees the adherence to delivery deadlines (Hard Time Windows) even under the realization of worst-case delay scenarios on a specified number of route segments. Scenario analysis of four strategies («Peace», «Humanitarian Crisis», «High Threat», «Maximum Uncertainty») demonstrated that adjusting the model's weight coefficients allows for the adaptation of the logistics system to current priorities–ranging from cost minimization to the maximization of safety or speed, regardless of economic factors. Originality. The scientific novelty lies in the integration of the deprivation cost function into a robust routing model specifically designed for conditions of epistemic uncertainty characteristic of wartime. The authors substantiate the shift from the paradigm of minimizing expected costs (stochastic approach) to minimizing costs in the worst-case scenario (robust approach) for critical humanitarian cargo. Practical value. The practical significance of the work involves the proposed concept of dynamic TTS management through the integration of the robust model with Digital Twin technology. This architecture enables operational route re-calculation in real-time based on heterogeneous data regarding traffic, infrastructure destruction, or military threats. This transforms static plans into flexible response mechanisms, ensuring the reliability of humanitarian corridors and the security of supply chains in highly volatile environments.

The signaling theory suggests that dividends signal future prospects of a firm. However, recent empirical evidence from the US and the Uk does not offer a conclusive evidence on this issue. There are conflicting policy implications among financial economists so much that there is no practical dividend policy guidance to management, existing and potential investors in shareholding. Since corporate investment, financing and distribution decisions are a continuous function of management, the dividend decisions seem to rely on intuitive evaluation.

The signaling theory suggests that dividends signal future prospects of a firm. However, recent empirical evidence from the US and the Uk does not offer a conclusive evidence on this issue. There are conflicting policy implications among financial economists so much that there is no practical dividend policy guidance to management, existing and potential investors in shareholding. Since corporate investment, financing and distribution decisions are a continuous function of management, the dividend decisions seem to rely on intuitive evaluation.

The signaling theory suggests that dividends signal future prospects of a firm. However, recent empirical evidence from the US and the Uk does not offer a conclusive evidence on this issue. There are conflicting policy implications among financial economists so much that there is no practical dividend policy guidance to management, existing and potential investors in shareholding. Since corporate investment, financing and distribution decisions are a continuous function of management, the dividend decisions seem to rely on intuitive evaluation.