Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
A study of Implicit disagreement in MPC meetings for selected Eastern European Economies using the Taylor Rule
This paper in an empirical attempt to study implicit disagreement among MPC members of central banks contained in the minutes of the MPC meetings for three Eastern European economies, viz., Poland, Czech Republic and Hungary. NLP sentiment analysis techniques, specifically a BERT model trained and fine-tuned on the corpus of the text of the minutes of the MPC meetings of the three countries, are applied to extract sentence level sentiment scores in terms of hawkish, dovish or neutral sentiment which are then used to calculate meeting level Disagreement Index(DI) variable by taking the Standard Deviation of the sentence level sentiment scores. We augment the standard Taylor rule formula for setting monetary policy rates with this DI variable and see that it impacts the interest rate negatively, suggesting that higher implicit disagreement might restrain the MPC members from being too hawkish which might be influenced by higher uncertainty about economic conditions, especially fears of a looming recession. So, we find that disagreement has a significant impact, even implicit disagreement, on the policy rate decisions of the MPC members. This offers insights into why central banks might hesitate to raise rates immediately in face of higher inflation or output gap pressures.
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A Deterministic Trajectory-Level Evaluation Framework for Learning-Based Agentic Systems
In the recent past, learning-based agentic systems are being increasingly used to tackle complex decision environments where reliability, transparency, and compliance with governance are key. However, the dominant evaluation strategies are mostly outcome-centric, with a focus on aggregate performance measures like accuracy, rewards, or task completion rates. These evaluation strategies provide little information on the internal decision-making processes that lead to the observable outcomes, especially for reasoning-based agentic systems with multiple steps. This paper proposes a Deterministic Trajectory Level Evaluation Framework (DTLEF) for learning-based agentic systems. The proposed framework transforms the evaluation paradigm from traditional outcome-oriented metrics to an evaluation process that focuses on the trajectories of states and actions within controlled execution conditions. The DTLEF integrates standardized states, comprehensive action trace logging, deterministic replay validation, and behavior verification against governance constraints. The evaluation process, in which the agentic system runs in controlled inference mode and the trajectory traces are compared, enables the identification of policy instabilities, reasoning drift, and non-deterministic behavior. Moreover, the evaluation process also ensures that the trajectories are consistent with the predefined constraints. Unlike traditional performance metrics, the proposed framework does not depend upon empirical data or domain-specific metrics. Instead, it provides a methodology for evaluating agentic systems, including autonomous decision pipelines, tool-enhanced language agents, and cyber-physical control systems, at an architecture level. The proposed framework increases transparency, reproducibility, and compliance without modifying training procedures. This research provides a scalable and domain-independent evaluation methodology for validating learning-based autonomous agents in environments where behavioral reliability is as important as functional performance by formally defining trajectory-level determinism as a primary evaluation criterion.
Agentic Systems, Trajectory-Level Evaluation, Deterministic Replay, Learning-Based Autonomous Agents, AI Governance and Reliability
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Impact of ERP-Enabled Digital Transformation on Cost Accounting Practices in Saudi Firms
Enterprise Resource Planning (ERP) systems are major instruments of organizational digital transformation as they integrate operational and financial processes and uplift the overall performance. This research focuses on the effect of ERP implementation on cost accounting activities in Saudi firms, the mediating roles of process integra-tion and automation being the primary emphasis. Information was obtained from 60 companies with a total of 290 employees from the manufacturing, construction, retail, energy, and service sectors. Reliability and validity were established through Cronbachs alpha, composite reliability, and average variance extracted. For data analysis, multiple regression, independent t, tests, and Partial Least Squares Structural Equation Modeling were utilized. Findings suggest that the adoption of ERP leads to a significant improve-ment in cost accuracy, cost control effectiveness, variance analysis quality, and managerial decision, making. In addition, process integration and automation partially mediate these relationships, thus advocating the strategic ERP adoption aligned with Saudi Vision 2030 goals.
Cost Accounting, ERP, Digital Transformation, Integration, cost accuracy, cost control effectiveness, variance analysis quality, managerial decision making
Machine Learning Models for Detecting Hidden Collusion Networks in U.S. Corporate Finance
Hidden collusion networks in U.S. corporate finance present a significant challenge to market integrity and regulatory oversight. Such networks are difficult to detect due to the indirect and distributed nature of collusive behavior, which is often embedded within legitimate financial transactions and governance relationships. This study aims to develop and evaluate machine learning models capable of detecting latent collusion structures by combining firm-level financial indicators with relational and governance-based network information. The goal is to uncover clusters of firms that exhibit coordinated behavior while providing interpretable insights for regulatory decision-making. A multi-stage detection framework was implemented, incorporating classical machine learning classifiers, tree-based ensembles, graph neural networks (GNNs), and hybrid models combining feature-driven and structure-driven learning. Corporate entities were represented as nodes within financial and governance networks, with edges encoding ownership ties, shared executives, transactional dependencies, and temporal co-movements. Models were evaluated using precision, recall, F1-score, AUC-ROC, AUC-PR, and network consistency metrics, while robustness analyses examined performance under class imbalance and sparse labeling conditions. Graph-based models outperformed traditional baselines, achieving F1-scores up to 0.91 and AUC-ROC values up to 0.94. Hybrid ensembles that combined tree-based and graph-based predictions achieved the highest overall performance (F1-score = 0.93, AUC-ROC = 0.95, AUC-PR = 0.91). The models successfully identified densely connected collusive clusters and key hub firms, highlighting the importance of relational and temporal features over isolated firm-level indicators. Ablation studies confirmed that financial metrics alone were insufficient to detect coordinated behavior without network structure. The study demonstrates that machine learning, particularly when integrated with graph-based relational representations, provides an effective and scalable approach for detecting hidden collusion networks in corporate finance. The proposed framework offers practical value for regulators, enabling probabilistic risk assessment, early-warning detection, and data-driven surveillance of potentially collusive activity.
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Real-Time Payments and Real-Time Fraud: A U.S. FinTech Risk Framework for RTP Rails and Consumer Protection
Real-time payment (RTP) rails convert retail payments from a batch, revocable process into an always-on, irrevocable, data-rich transfer of value. In the United States, this transition accelerated during 2012–2023 as mobile wallets, account-to-account (A2A) transfers, and instant credit transfers expanded, culminating in nationwide rail competition between private-sector networks and the Federal Reserve’s FedNow Service. The same design features that make RTP economically attractive—immediacy, finality, and API-based integration—also magnify fraud externalities and operational-resilience demands. Authorized push payment (APP) scams exploit consumer initiation and social engineering, while mule accounts, synthetic identities, and rapid cash-out compress detection windows from days to seconds. Regulatory liability regimes (e.g., U.S. Regulation E) were built around unauthorized transfers and do not fully internalize APP harms, creating incentives that may underinvest in preventive controls. This paper develops a pragmatic–institutional risk framework for U.S. RTP ecosystems that integrates (i) fraud typologies and externalities, (ii) operational-resilience requirements for 24/7 settlement, and (iii) consumer-protection and governance mechanisms spanning federal and state authorities. Methodologically, we specify a replicable measurement architecture using a simulated institution–region–year panel (2012–2023) that combines RTP intensity, fraud loss proxies, incident-response performance, and governance strength indicators. We outline identification strategies suitable for U.S. data constraints—fixed-effects panels, staggered adoption difference-in-differences, and instrumented uptake using broadband penetration and legacy payment frictions. Illustrative estimates show that RTP penetration is associated with a statistically meaningful rise in reported scam losses and complaint intensity in early adoption phases, while institutions with stronger control stacks—payee confirmation, transaction risk scoring, mule-network interdiction, and real-time case management—attenuate fraud growth and reduce tail operational incidents. We translate these findings into a U.S.-specific governance blueprint that aligns rail operators, depository institutions, nonbank PSPs, and regulators around a shared ‘prevent–detect–respond’ control taxonomy, standardized reporting metrics, and consumer redress principles. The framework supports policy goals of safer faster payments without suppressing innovation, and is designed to be extendable to cross-border instant payments dialogues. Research highlights. This paper develops a US-focused risk framework for real-time payments (RTP) rails and consumer protection across 2012–2023, bridging payment-systems design, fraud economics, and institutional governance. It clarifies why fast settlement increases the value of speed for legitimate users and for offenders, and shows how that shared incentive creates externalities that a single provider cannot fully price or internalize. Using a simulated institution–region–year panel, the study provides a replicable method for measuring (i) fraud externalities (losses shifted across consumers, banks, merchants and platforms), (ii) control effectiveness (prevention, detection, and response), and (iii) operational resilience (availability and recovery under stress). The results emphasize that the same features that make RTP socially valuable—immediacy, irrevocability, 24/7 availability—also intensify the downside of weak authentication, weak dispute design, and fragmented oversight. Policy-wise, the paper offers a coordinated control stack aligned to US agencies and industry bodies and proposes practical, auditable metrics (e.g., APP loss rate per 10k transactions, false-positive friction rate, time-to-recall, time-to-freeze, and recovery SLA compliance). The governance contribution is a ‘rails-and-responsibilities’ map that connects economic function to supervisory responsibility, supporting activity-based regulation and reducing arbitrage across state and federal regimes.
Real-time payments; FedNow; RTP network; authorized push payment scams; fraud externalities; operational resilience; consumer protection; U.S. financial regulation; payment governance
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Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability
Financial technology (FinTech) has become a core component of the United States financial system, reshaping payment infrastructure, consumer credit markets, and data-driven financial intermediation. While FinTech innovation has improved efficiency and expanded financial access, its rapid scaling has also introduced new sources of systemic risk, regulatory fragmentation, and consumer vulnerability—particularly as artificial intelligence increasingly governs financial decision-making. This study examines FinTech innovation through an integrated governance lens, focusing on how AI-enabled intermediation interacts with financial stability and public-interest outcomes in the United States. Using a multi-source, institutionally grounded panel dataset spanning 2012–2022, the analysis conceptualizes FinTech as part of national financial infrastructure rather than as a peripheral technological disruption. The empirical strategy combines a composite measure of FinTech intensity with indicators of explainable AI adoption and institutional governance strength to assess their joint effects on financial stability risk and public-interest performance. Fixed-effects panel models, interaction specifications, and robustness checks are employed to isolate institutional and temporal dynamics. The results reveal three central findings. First, greater FinTech intensity enhances intermediation efficiency and financial inclusion but is associated with elevated short-term financial stability risks when deployed without adequate oversight. Second, the adoption of explainable AI significantly moderates these risks by reducing algorithmic opacity and improving auditability and supervisory visibility. Third, governance capacity—reflected in regulatory coordination, disclosure mandates, and supervisory engagement—emerges as a decisive factor in aligning technological innovation with systemic resilience and consumer trust. These findings suggest that the principal challenge facing the U.S. financial system is not technological innovation itself, but the institutional lag between innovation and governance. By demonstrating that explainable AI functions as a stabilizing mechanism rather than merely a compliance tool, the study advances both financial stability theory and the literature on AI governance in finance. The paper contributes a conceptual–methodological framework that integrates innovation, governance, and public interest, offering actionable policy insights for regulators, financial institutions, and FinTech firms. More broadly, the analysis underscores the importance of governing AI-enabled finance in a manner that supports long-term financial stability, equity, and national economic resilience.
FinTech, artificial intelligence, financial stability, governance, algorithmic accountability, consumer protection, United States
Trade Realignments, Exchange Rate Volatility, and Financial Stability in Sub-Saharan Africa
This study examines the impact of trade realignment, exchange rate volatility, and financial depth on financial stability in Sub-Saharan Africa. Recognizing the critical role of financial stability in promoting economic growth and resilience, the study aims to analyze both the long-run and short-run dynamics of macro-financial interactions and provide evidence-based policy insights. The scope of the study covers a panel of 40 Sub-Saharan African countries over the period 2000 to 2024. A Panel Vector Error Correction Model (VECM) is employed to capture the cointegration relationships and short-run adjustments among financial stability, trade realignment, exchange rate volatility, GDP growth, inflation, and financial depth. The findings reveal a significant long-term relationship between financial stability and the explanatory variables, with deviations from equilibrium gradually corrected over time. In the short run, financial stability responds to lagged effects of exchange rate volatility, trade realignment, financial depth, GDP growth, and inflation, highlighting the delayed transmission of shocks. The joint significance of these variables confirms that short-run fluctuations in financial stability are influenced by their combined effects rather than isolated changes. The study recommends enhancing financial sector depth, carefully managing trade realignments, mitigating exchange rate volatility, controlling inflation, and coordinating macroeconomic and financial policies to strengthen resilience. Policy implications include integrating trade and financial sector policies, implementing forward looking risk management frameworks, and promoting regional coordination to reduce cross-border vulnerabilities. Future research should explore the role of digital finance, country-specific heterogeneity, external shocks, and institutional quality in shaping financial stability in Sub-Saharan Africa.
Financial Stability, Trade Realignment, Exchange Rate Volatility, Financial Depth, GDP Growth, Inflation
Mega-Sporting Events and Soft Power: Perceived Changes in Qatar’s Nation Brand Following the 2022 FIFA World Cup
Countries increasingly use mega-sporting events as instruments of soft power and nation branding, yet empirical evidence of their impact on international perceptions remains limited, particularly in non-Western contexts. This study examines changes in Qatar’s nation brand following the 2022 FIFA World Cup by assessing how international audiences evaluated key nation-brand dimensions before and after the event. Drawing on Anholt’s Nation Brand Index framework, a quantitative, perception-based design was employed using a cross-sectional survey of an international sample. Respondents retrospectively assessed Qatar across six dimensions: governance, culture, people, tourism, exports, and immigration and investment. The findings indicate a statistically significant but modest improvement in Qatar’s overall international image. Perceptual gains were most evident in the dimensions of people, culture, and governance, while economically oriented dimensions showed limited or no change. These results suggest that the World Cup functioned as a reputational enhancer rather than a transformative nation-branding intervention, generating selective and uneven effects. The study contributes to nation-branding and sports-diplomacy research by providing perception-based evidence from a non-Western context and highlighting the importance of aligning mega-events with long-term strategic initiatives to achieve sustainable soft-power outcomes.
Nation Branding, Soft Power, Nation Brand Index, Mega Sporting Events, Qatar
The Impact of Shanghai Cooperation Organization Membership on Pakistan’s Manufacturing Exports: A Difference-in-Differences Gravity Analysis
This study examines the impact of Pakistan’s membership in the Shanghai Cooperation Organization (SCO) on its manufacturing export performance, employing a Difference-in-Differences (DID) framework within a gravity-type trade model. Using panel data covering pre- and post-integration periods, the analysis compares Pakistan’s manufacturing exports to SCO member countries with those to non-SCO trading partners to identify the causal effect of SCO accession in 2017. Descriptive evidence indicates a gradual but more stable growth in manufacturing exports following SCO membership, with notable sectoral variation across industries. Empirical results reveal a positive and statistically significant effect of SCO membership on Pakistan’s manufacturing exports, confirming that regional integration has enhanced trade performance beyond pre-existing trends. The validity of the DID approach is supported by pre-treatment parallel trend tests, while robustness checks including placebo tests and Poisson Pseudo-Maximum Likelihood (PPML) estimation confirm the consistency of the findings. Heterogeneity Sectoral analysis shows stronger export growth in healthcare-related products, whereas traditional sectors such as textiles, minerals, and rubber exhibit relatively modest gains, reflecting ongoing structural constraints. Overall, the findings suggest that SCO integration has contributed positively to Pakistan’s manufacturing export expansion, though the benefits remain uneven across sectors. The study underscores the importance of complementary domestic policies, industrial upgrading, and trade facilitation measures to fully realize the export potential offered by regional economic integration.
SCO; Manufacturing Exports; Difference-in-Differences; Regional Integration; Gravity Model; Pakistan
Bridging Prediction and Profit: Deep Learning models with Trading Evaluation for FTSE 100
This study examines stock price forecasting for FTSE 100 companies using deep learning and XAIl. The research addresses the disconnect between predictive accuracy and interpret ability in financial models by integrating data-driven forecasting with transparent feature attribution. Four neural architectures: LSTM2, Bi-LSTM3, GRU4 and CNN5 are compared to classical benchmarks: SMA6 and EMA7. Models are trained on OHLCV8 data augmented with technical indicators. Evaluation uses a threshold-based trading strategy. The findings indicate that a lower prediction error does not necessarily result in higher profitability. Although LSTM achieved the lowest prediction error, GRU and Bi-LSTM produced more stable cumulative returns (16%), compared to the EMA benchmark (2%). SHAP9 analysis demonstrates that recent price movements and momentum indicators, particularly SMA, drive model decisions.
FTSE 100, LSTM, GRU, Explainable AI, SHAP, Trading strategy, Stock market prediction, BILSTM, financial data, machine learning
Trends and Gaps in the Literature about Green Washing in Asia
This bibliometric study explores the trends and gaps in the literature on greenwashing in Asia, analyzing key research patterns, thematic clusters, and geographical contributions. The results highlight an increasing scholarly focus on greenwashing, particularly from 2021 onwards, reflecting growing awareness of environmental issues and deceptive corporate sustainability practices. China and India emerge as leading contributors, with institutions like Shandong University and Massey University playing prominent roles in advancing the research. Thematic clusters emphasize topics such as corporate social responsibility, green marketing, sustainable development, and circular economy, with "greenwashing" serving as a central concept linking these themes. Despite the rising volume of research, gaps persist in understanding the effectiveness of regulatory frameworks, consumer perceptions, and cross-sectoral impacts of greenwashing. This study underscores the need for interdisciplinary approaches and actionable strategies to address greenwashing, supporting authentic sustainability practices and policies.
Reconstruction as a Lever for Sustainable Development in Gaza: Between Reality and Prospects for Recovery After the 7 October 2023 War
The 2023 conflict in the Gaza Strip resulted in unparalleled human, material, and structural damage, valued at approximately USD 18.5 billion, equivalent to almost a full year of Palestinian GDP. Beyond the extent of physical devastation, the hostilities revealed the profound vulnerability of Gaza’s economy, public systems, and communal structure, and severely aggravated hardship, joblessness, and involuntary movement. This analysis explores how post-2023 rebuilding can surpass a brief humanitarian reaction to become a strategic instrument for enduring progress in Gaza. Employing a descriptive–analytical and specific-example method, the research utilizes recent evaluations by the World Bank, the UN, and ESCWA (2024–2025) to chart the extent and sectoral nature of the harm, pinpoint major organizational, fiscal, and political barriers, and dissect the connections between rebuilding and the economic, societal, and ecological foundations of permanence. The paper suggests a conceptual model that regards reconstruction as an evolving procedure rooted in “build back superior” tenets, strong local administration, and alignment with the 2030 Agenda. It then details a policy plan for 2025–2035 connecting accommodation, essential structures, earning potential, welfare provisions, rubble handling, and clean power into a unified national blueprint. The paper finishes by asserting that progress in Gaza will only serve as a true impetus for lasting development if it revitalizes populations, the marketplace, and organizations concurrently, under a domestically controlled structure that harmonizes emergency assistance with protracted structural changes.
Reconstruction; Gaza Strip; 2023 conflict; Enduring progress; Post-conflict recuperation; Relief effort; Durability; Leadership; Rubble handling; Clean economy; Societal fairness.
How Smart City Initiatives Economically Affect Small and Medium-Sized Enterprises (SMEs): Innovation Capacity and Competitiveness – A Comparative Study of Japan and the UK
This paper will look at the impact of smart city projects on the competitiveness and economic performance of small to medium enterprise (SMEs) in the United Kingdom and Japan. The selection of a qualitative comparative research design to be used was based on the systematic analysis of the academic literature, national policy papers and institutional reports. The results indicate that the Japanese long-term, highly centralized type of governance helps in facilitating systemic coordination, structural integration, whereas the UK model that is decentralized, market-driven encourages flexibility, fast experimentation, and data-driven innovation. Nevertheless, having differences, both models unveil that smart city programs can only be translated into the competitiveness of SMEs in case of coherent policies, digital literacy, and collaborative ecosystems. The research has implications on policymakers who want to reconcile technology change with an inclusive economic development in the urban settings.
Smart cities, Small and medium-sized enterprises (SMEs), Innovation and competitiveness, Society 5.0 (Japan), Digital transformation, Open data and innovative governance, Comparative economic analysis (Japan–UK)
Tourism as a Catalyst for Economic Diversification in Saudi Arabia: Vision 2030 and Beyond
Saudi Arabia’s Vision 2030 reform program positions tourism as a cornerstone for economic diversification, aiming to reduce reliance on oil revenues and foster a knowledge-based economy. This paper critically examines the evolution of Saudi Arabia’s tourism sector under Vision 2030, analyzing its economic, social, and cultural impacts while identifying key challenges and opportunities. Drawing on secondary data from the Saudi Ministry of Tourism, OECD, UNWTO, and World Bank, the study reviews flagship projects such as NEOM, the Red Sea Project, and Diriyah Gate. It also explores the role of technology, sustainability, and global partnerships in shaping the sector’s future. The paper concludes with a comprehensive policy framework and actionable recommendations to ensure sustainable, inclusive, and competitive tourism growth by 2030.
Tourism, Economic Diversification, Vision 2030, Saudi Arabia, NEOM, Red Sea Project, Sustainability, Cultural Heritage, Employment, Smart Tourism
Does Corporate Governance Improve Bank Performance? A study of Rural Banks in West Java, Indonesia
The objective of this research is to examine the correlation between Sound Corporate Governance and Financial Performance within Rural Banksing Institutions. The design/methodology uses balanced panel data multiple regression analysis on EViews 13 software. The study sample comprises Rural Banks located in West Java Province. The study's findings demonstrate that the inclusion of female directors significantly enhances ROA. The directors adversely impact both ROA and ROE. Furthermore, the board of commissioners and independent commissioners have no impact on ROA but negatively impact ROE. This study is unique in that it focuses on Rural Banks, which have received less attention in earlier research.
Keywords: Good Corporate Governance, Financial Performance, Rural Banks.
Identifying Hidden Fraud Indicators through Longitudinal Analysis of U.S. Financial Misconduct
Systemic risk of fraud in U.S. finance and health care has continued to have an adverse impact on the economy, stretch government programs and reduce public trust in institutions. This study provides a 10 year cross-sectoral (finance and health care) study (years 2010-2020) utilizing databases from the Consumer Financial Protection Bureau (CFPB), Securities Exchange Commission (SEC), Department of Justice (DOJ), Center for Medicare Services (CMS) and Health and Human Services Office of Inspector General (HHS-OIG) to identify potential early warning signs which may occur prior to the detection of fraud by regulatory bodies. Using Longitudinal Trend Analysis, Correlation Mapping, K-means Clustering, and Regression Modeling; this study demonstrates a sustained increase in Fraud Occurrence Rates (FOR) for each sector, and a high positive correlation between the two sectors (Sectoral Correlation Index SCI= .72). The three early warning signs identified within this study: Complaint Escalation Rate (CER), Detection Lag (DL), and Fraud Method Diversity (FMD); were found to possess statistically significant predictive capability for future confirmed fraud events. The trends also demonstrated that spikes in public complaints and healthcare recoveries could potentially serve as an actionable signal to regulators of the need for increased oversight or investigation. Based upon these findings, the study proposes an Early Warning Fraud Detection Framework (EW-FDF) and advocates for the development of a Unified Fraud Intelligence Network (UFIN) to enable predictive analytic capabilities across agency boundaries.
Hidden Fraud Indicators, Fraud Analysis Longitudinal, Financial Misconduct, Predictive Fraud Detection and U.S. Regulatory Oversight
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ESG and Commercial Credit Financing: Based on Government Subsidies and Polluted Areas
This paper investigates whether and how firms’ Environmental, Social and Governance (ESG) performance affects commercial credit financing (SCF) in the context of Chinese listed companies. Using panel data on A-share firms and ESG ratings, this study constructs a comprehensive measure of SCF based on supplier-provided trade credit and estimate fixed-effects models. The results show that ESG performance is positively and significantly associated with SCF. Further analysis reveals pronounced heterogeneity: ESG enhances SCF only in non-heavily polluting industries and among firms receiving government subsidies, while the effect is insignificant for heavily polluting and non-subsidised firms. These findings suggest that ESG operates as a credible financing signal when it reflects voluntary engagement. The results imply that firms can treat ESG as part of their financing strategy and use it to improve access to supplier credit. Suppliers can also incorporate ESG indicators into credit-risk assessment. Policymakers may strengthen ESG disclosure rules and subsidy design to support more efficient, sustainability-oriented supply-chain finance.
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Accountability of Mosque Financial Management: A Case Study of Pangeran Diponegoro Mosque, Yogyakarta, Indonesia
This study aims to describe how the Pangeran Diponegoro Mosque instituted open and accountable financial management related to the mosque’s activities. Specifically, this study aims to: (1) describe the forms of activity and financial reporting practiced at the mosque; and (2) analyze the extent to which these practises reflect principles of accountability and transparency for non-profit entities; and (3) identify supporting and constraining factors that shape accountability in reporting within a mosque funded by its community. The research employed a qualitative approach with a single-case study design, utilizing in-depth interviews with mosque administrators (takmir) and congregants/donors, in addition to an analysis of financial documents. Data analysis was conducted by adapting accountability and transparency indicators from previous studies. The results indicate that financial reporting is conducted regularly through simple cash reports, which include receipts, expenditures, and balances, along with occasional announcements to the congregation. This practice incorporates elements of accountability and transparency, as evidenced by periodic reports, deliberations, and informal disclosures. However, it lacks support from specific internal regulations, has never undergone third-party auditing, and does not leverage digital media platforms, such as Instagram, for disseminating financial information. Key supporting factors include the management's religious commitment, social closeness with the congregation, and the convenience of cashless donations via transfer/QR. Conversely, inhibiting factors involve the limited accounting skills of mosque committees and the absence of up-to-date operational guidelines from the Ministry of Religious Affairs regarding a practical reporting format for mosque financial management. This research provides input to the Ministry of Religious Affairs regarding the need to develop national guidelines for mosque financial reporting that are simpler, more uniform, and in accordance with Sharia principles. It also highlights the importance of improving basic financial reporting capacity at the mosque committee level to strengthen accountability and transparency across mosques in Indonesia.
Current Snapshot of Business Climate and Sustainable Economy. Does AI Play a Crucial Role?
This study aims to provide an analysis of the business climate on a sustainable economy by considering the effects of AI adoption in the era of global uncertainty (2024). The study obtained data from 50 countries, which were selected based on the specified criteria. The main analysis of this study applied moderation regression analysis, while the additional analysis applied multiple regression analysis. This research uses the EViews 13 application to analyze the data. The main analysis indicates that the business climate has a positive effect on the sustainable economy, and AI does not moderate the influence of the business climate on the sustainable economy. Additional analysis shows consistency that all indicators in the business climate have a positive effect on the sustainable economy. The findings of this study are contributing to the motivation and evaluation of each country to enhance a sustainable economy through the implementation of a stable and supportive business climate, as well as the promotion of the adoption of AI as a catalyst for sustainability in various sectors, specifically business and the economy.
Sustainable Economy, Business Climate, Artificial Intelligence
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The Economics of Fast Food: Impact on Society, Employment, and Consumption Patterns: A Comprehensive Study Integrating Global and Indian Perspectives
This research article examines the economics of the fast-food industry, focusing on its societal impact, employment dynamics, and evolving consumption patterns. Integrating global and Indian perspectives, the study traces the historical development of fast food, analyses employment trends including youth and gig economy participation, and investigates consumer behavior and pricing strategies. It further scrutinizes public health and environmental challenges, evaluates policy responses, and considers the industry's future in light of technological and regulatory trends. The study employs a data-driven approach, incorporating infographics, and tables to illustrate key trends. The findings offer valuable insights for students, researchers, and policymakers into the complexities and implications of the fast-food sector worldwide and in India.
Fast food industry, economics, consumption patterns, public health, consumer behavior, pricing strategies, gig economy, environmental challenges, sustainability initiatives, market adaptation.
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Internal, External, and Forensic Audits as Determinants of Fraud Prevention in Ghana’s Metropolitan, Municipal, and District Assemblies
Fraud and corruption are major global challenges that have a profound effect on individuals, countries, and economies. They incur substantial costs and hinder societal advancement. In Ghana, fraud and corruption present serious obstacles for the Metropolitan, Municipal, and District Assemblies (MMDAs), compromising governance and development efforts. This research assesses how well internal, external, and forensic audits function in curbing fraudulent financial activities within Ghana's MMDAs. A quantitative cross-sectional survey design was utilised to gather data from 343 finance directors, accountants, and internal auditors across 261 MMDAs. Descriptive statistics and multiple regression analysis were used to evaluate the data. The results indicate that internal audits are viewed as effective for identifying early indicators of financial mismanagement (mean = 4.87) and for preventing fraud (mean = 4.60), although issues like lack of independence (mean = 4.23) and insufficient resources (mean = 4.23) exist. External audits are conducted regularly (mean = 5.44) but face challenges with the weak enforcement of their recommendations (mean = 2.56). Forensic audits, despite their limited usage (mean = 1.68 regarding the hiring of forensic auditors), show the most substantial association with fraud reduction (β = 0.228, p < 0.001). Together, audit activities account for 12.2% (R² = 0.122) of the variance in fraudulent practices, revealing their significant yet constrained effectiveness. The study concludes that although audits are vital for preventing fraud, their success is undermined by systemic shortcomings. It suggests enhancing forensic audit capabilities, ensuring the enforcement of external audit recommendations, and preserving the independence of internal audits to improve fraud prevention in Ghana's local governments. These findings add value to both academic discussions and practical policy reforms in the management of public sector finances.
Predictive Modeling of U.S. Stock Market Trends Using Hybrid Deep Learning and Economic Indicators to Strengthen National Financial Resilience
The stock markets are too complex to predict, despite their non-linearity, volatility, and multifactorial nature. Current econometric and deep learning models often do not take into account the macroeconomic context, which restricts predictive accuracy at times of financial volatility. This paper attempts to fill this gap by proposing a Hybrid Deep Learning Model (HDLM), a machine that combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and an attention mechanism that can jointly include both the dynamic behavior of the market and the economic links in the trends of the U.S. market. The analysis is based on a strictly tested data set of 20102023, which was retrieved from the Federal Reserve Economic Data (FRED), Yahoo Finance, and the Bureau of Economic Analysis. These include all significant equity indexes such as the S&P 500, NASDAQ Composite, and Dow Jones Industrial Average, prime macroeconomic indicators such as the GDP growth, inflation rates, interest rates, and unemployment levels. Correlation analysis, multiple regression, Granger causality tests, Johansen cointegration procedures, and volatility modeling using ARCH GARCH models are the statistical methods used. The findings show that GDP growth has a statistically significant positive impact on market returns ( = 0.58, p = 0.001), though inflation ( = -0.21, p = 0.021), interest rates ( = -0.34, p = 0.002), and unemployment ( = -0.42, p = 0.001) have significant negative predictive power (R 2 = 0.68). The HDLM achieves a better predictive performance with RMSE = 1.89, MAPE = 4.9% and DA = 83.6% > 23% better than the baseline LSTM and CNN- LSTM settings. The model minimizes prediction error by 36.1% in cases of strong market shocks, which occur under simulated stress conditions, confirming an increased financial strength. Taken together, the results of the studies support the idea that the incorporation of macroeconomic intelligence into hybrid neural systems significantly enhances forecast reliability and stability of the system. The current research, therefore, adds a strong, interpretive, and policy-related framework to the research fields of predictive finance and resilience engineering systems to predict market upheavals.
Deep learning, financial resilience, macroeconomic indicators, stock market forecasting, U.S. economy
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
The Internal Audit Roles and Financial Discipline of the Technical Universities in Ghana: The Moderating Role of Effective Leadership
This study examined the relationship between internal audit roles and financial discipline, with effective leadership as a moderating factor, in Ghana’s technical universities. Using an explanatory research design, data were collected from 127 key officeholders across the Technical Universities in Ghana. The findings show that internal auditors play a pivotal role in promoting financial discipline through strategic advisory services, operational efficiency, risk management, reporting, and compliance enforcement. More importantly, the results demonstrate that effective leadership practices centred on integrity, transparency, accountability, strategic vision, and sound financial decisions significantly strengthen the positive effects of internal auditing on financial discipline. The financial discipline remains a significant challenge, as evidenced by ongoing issues such as fund misallocation, procurement manipulation, weak budgeting practices, ineffective debt management, and the neglect of internal audit recommendations. The study recommends that technical universities complement national policies and legal frameworks with internally developed financial discipline policies tailored to their specific contexts. Central to this should be the establishment of a comprehensive Code of Ethics for Financial Discipline, with explicit provisions requiring strict adherence by leaders and senior officials and enforcing stringent penalties for breaches. Such context-specific policies, aligned with national directives, will reinforce ethical conduct, strengthen financial integrity, and promote sustainable accountability in technical universities.
Financial Discipline, Strategic Advisory Services, Risk Management, Policies, Financial Mismanagement, Sound Financial Decisions, budgeting
Corporate Investment in Indonesia: Literature Review
This research aims to conduct in-depth research on corporate investment decisions in Indonesia. The company's investment decision is an important factor in a business that will determine the company's condition in the future. There are cases of failure due to investment decisions made by companies, which is not in line with several studies that show the importance of corporate investment decisions. The investment decisions made by a company are different from those made by individual investors, this study will only analyze the investment decisions made by a company. Analysis is needed to find out the factors that affect a company's investment decisions or are influenced by investment decisions. The method used in this study is systematic literature review analysis. Using a database from a journal indexed by Sinta with the addition of the Scopus base with cases in Indonesia. Data collection uses systematic stages, which are then reviewed manually by the document. A company's investment decision is influenced by several variables and divided into financial and non-financial categories. This research has implications for investment decisions made by a company and is an additional literature for researchers in the future.
Investment Decisions, Corporate Investment Decisions, Literature Review, Corporate Investment
Cryptocurrency Accounting Frameworks and Tax Compliance Challenges: Toward a Unified Digital Financial Reporting Model
The swift growth of cryptocurrencies has highlighted several issues with current accounting and tax compliance regimes given that there is little to no accounting guidance in the current standards like IFRS or US GAAP regarding the measurement, recognition and disclosure of crypto-assets . (Rajaram & Aboluwodi, 2024; Mpanza, ). This paper analyzes the existing literature along with case practices to present the main concerns behind unreliable financial reporting and inefficient tax enforcement as being those of volatility, lack of clarity in legal categorization, problems of tax jurisdiction in cross- border transactions, and anonymity. Drawing on and comparing existing structures found in policy documents, financial statements, and guidelines, it makes the case for a theory of a singular digital financial reporting method utilizing the blockchain as a financial reporting and auditing platform through standardized measures and advanced disclosures of tax information. The suggested model attempts to harmonize accounting measurement with sound tax compliance as well as safeguard the users of financial statements from artificial liquidity, profitability and asset measurement or valuation. The results apply particularly to standard setters, regulators, and crypto-assets firms to move towards coherence in regulation and risk reduction in the digital financial ecosystem.
field of accounting for cryptocurrencies. Financial reporting of digital assets. Blockchain accounting standards. Crypto taxation issues. Consolidated model of reporting. IFRS and cryptocurrency value
Cash Flow Volatility in Ghanaian Banks (2004– 2024): A CV, Moving Average, and GARCH-Based Analysis
In this paper, the researcher will examine the volatility of cash flow among commercial banks in Ghana in the years 2004-2024 using simulated monthly data. It represents both cyclical behaviour and systemic shocks by using both descriptive metrics (coefficient of variation and moving averages) and sophisticated econometric modelling (GARCH (1,1) including exogenous covariates). Findings indicate that net cash flows vary considerably, especially in the years 2008 global financial crisis, the 2014 macroeconomic turmoil, the 2017-2019 banking sector clean-up, and the COVID 19 pandemic. GARCH (1,1) findings are consistent with volatility clustering and regime changes, structural-break-dummy variables in the years 2008, 2015, 2017-2018 and 2020 exhibit a significant positive influence on the conditional variance equation, revealing that the risk is higher in those years. The results imply that banks of Ghana have increased the level of liquidity risks and that more advanced forecasting instruments, contingency liquidity planning, and proactive, based on volatility, macroprudential supervision are needed.
Cash Flow Volatility; Ghanaian Banks, A CV, Moving Average, GARCH-Based Analysis
Economic Viability of Organic Farming Practices in Developing Countries
Organic farming has become a better option compared to conventional farming methods, offering sustainability, health benefits, and improved rural livelihoods. Organic farming practices face unestablished economic feasibility challenges in developing countries because these areas often deal with resource limitations along with market system faults. This paper analyzes available studies to understand how costs and benefits interact with market structures and policy structures to determine organic agricultural sustainability in these regions. Various studies show organic farming provides lasting economic advantages, together with ecological advantages, though transition difficulties and short-term disadvantages prevent many farms from adopting this system. Organic systems gain viability when policies support them and when knowledge and market integration develop effectively. Future empirical work should focus on developing solutions to barriers that limit organic agriculture expansion in developing economies, according to the recommendations for policymakers and development practitioners.
Economic Viability; Organic Farming Practices; Developing Countries
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Structural Changes in Indian Economy since the 1950s: A Markov Chain Analysis
Understanding the evolution of the production structure of an economy has been one of the core macroeconomic issues for academicians, professional economists, policymakers and governments for decades. Within this context, this study investigated the structural changes in the Indian economy since the 1950s using a time-varying Markov chain (TVMC) model—a data-oriented non-parametric methodology—within a panel data framework. It aimed to understand whether India’s production structure followed the Classical–Chenery path of transformation or deviated towards a service-led trajectory. The production sector of the Indian economy was classified into three sectors–agriculture, industry and service. The data on the sectoral shares in the real gross value added (GVA) in the economy was taken from the National Statistical Accounts and deflated using the Wholesale Price Index (2011–12 as base year). The period under investigation was divided into three distinct phases: the dirigiste pre-liberalisation period (1951–1991), the neoliberal post-liberalisation period (1991–2011), and the digital integration period associated with the digital era (2011–2021). The findings showed that the Indian economy took a different growth path, experiencing direct service-led structural transformation of its production system. Its growth path showed (i) persistent decline in the GVA share of the agricultural sector throughout the study period, which was consistent with the agricultural transformation observed in developed economies; (ii) a stagnant GVA share of the manufacturing sector, indicating limited industrial deepening; and (iii) a sharp, sustained and premature expansion in the GVA share of the service sector. While projections about the production structure suggest that the service sector will sustain and improve its GVA share in the long-run, it would pave the path of jobless growth in the Indian economy. The novelty of this study lies in integrating historical data with a probabilistic model to analyse sectoral transformation, providing insights for future industrial and employment policies. However, its contribution to the literature is methodological, offering insight on how long-run historical data can enhance our understanding of structural change in an economy, which remains crucial for long-run policy framing.
Economic development, Indian economy, Projection, Markov chain, Structural change
Determinants of small farmers’ integration and agricultural yield in inclusive business models: Insights from the cocoa sector in Côte d'Ivoire
This paper examines the factors influencing the integration of smallholder farmers and their agricultural yields within inclusive business models (IBs). The Heckman two-stage selection method was used for the analysis. The first-stage results showed that the farmer's age, household size, possession of a Tax Declaration of Existence, and association membership were determinants of smallholders’ probability of being integrated into IBs. Concerning the second stage, the number of working days, gender (male), and the farmer's experience had a positive and significant influence on agricultural yield.
Inclusive business models, smallholder farmers, agricultural performance, cocoa sector
Deep Learning Architectures for High-Frequency Stock Price Prediction: An Empirical Evaluation
High-frequency trading (HFT) generates vast amounts of financial data at millisecond intervals, presenting both opportunities and challenges for accurate stock price prediction. Traditional econometric and statistical models, while useful for low-frequency data, often fail to capture the nonlinear dependencies, temporal correlations, and microstructural patterns inherent in high-frequency financial markets. In this study, we present an empirical evaluation of multiple deep learning architectures including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and Transformer-based models for high-frequency stock price forecasting. Using a dataset of tick-level order book information and intraday price movements from major U.S. exchanges, we assess each model’s predictive power, robustness, and computational efficiency. Evaluation metrics include root mean squared error (RMSE), mean absolute percentage error (MAPE), and directional accuracy, alongside financial performance measures such as cumulative returns and Sharpe ratio in a simulated trading environment. Results reveal that attention-based architectures, particularly Transformers, consistently outperform recurrent and convolutional counterparts in capturing complex temporal dependencies, while TCNs demonstrate superior efficiency in low-latency scenarios. The findings highlight the trade-offs between accuracy, interpretability, and latency, providing actionable insights for practitioners in algorithmic trading, risk management, and market microstructure research.
High-frequency trading; Stock price prediction; Deep learning architectures; LSTM; GRU; Temporal Convolutional Networks (TCN); Transformers; Financial time series forecasting; Market microstructure; Algorithmic trading