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
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
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
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
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
Explainable Artificial Intelligence for Credit Risk Assessment: Balancing Transparency and Predictive Performance
Credit risk assessment is a cornerstone of financial decision-making, guiding loan approvals, interest rate determination, and capital allocation strategies. While machine learning and deep learning models have demonstrated superior predictive accuracy compared to traditional statistical techniques, their black-box nature often undermines trust, interpretability, and regulatory compliance. This study explores the integration of Explainable Artificial Intelligence (XAI) into credit risk modeling, with the dual goal of enhancing transparency while maintaining strong predictive performance. We propose a hybrid framework that combines gradient boosting and neural network models with post-hoc interpretability tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), alongside inherently interpretable models like decision trees and logistic regression. By evaluating the trade-offs between accuracy, fairness, and explainability on benchmark credit datasets, we demonstrate that XAI methods can provide actionable insights into borrower default risk without substantially compromising predictive power. Furthermore, we discuss the role of explainability in ensuring regulatory compliance, promoting fairness in lending decisions, and fostering trust among stakeholders. The findings suggest that transparent, high-performing models can strengthen risk management practices and support responsible innovation in the financial sector.
Credit risk assessment; Explainable Artificial Intelligence (XAI); Transparency; Predictive performance; Machine learning; SHAP; LIME; Fairness in lending; Regulatory compliance; Financial decision-making
Detecting Financial Fraud in Real-Time Transactions Using Graph Neural Networks and Anomaly Detection Techniques
Real-time fraud detection must balance accuracy with millisecond-level latency as adversaries evolve tactics across accounts, devices, merchants, and networks. This paper presents a streaming framework that models payment ecosystems as dynamic, heterogeneous graphs and detects anomalies by fusing Graph Neural Networks (GNNs) with online anomaly detectors. Incoming transactions update a temporal multi-relational graph (card–device–merchant–IP), from which a lightweight GNN (GraphSAGE/GAT variants with edge features and time encoding) produces embeddings on the fly. These embeddings feed (a) a cost-sensitive classifier for known fraud and (b) unsupervised detectors (e.g., Isolation Forest/Deep SVDD) to surface novel, label-sparse attacks. To cope with class imbalance and concept drift, we employ streaming reweighting, adaptive thresholds tuned on precision@k, and continual learning via replay and drift triggers. The system exposes local explanations (subgraph rationales via GNNExplainer/motif scores) to support analyst review and regulatory needs, while a deployment blueprint (feature cache, micro-batching, and asynchronous inference) meets <50–100 ms decision budgets. We evaluate on mixed synthetic/industry datasets with evolving fraud scenarios, reporting ROC-AUC/PR-AUC, detection delay, alert volume, and business impact under cost constraints. Results show consistent gains over rule-based, tabular ML, and static graph baselines, particularly for low-footprint fraud and fast-moving attack campaigns. The proposed design offers a practical path to accurate, auditable, and scalable fraud screening in production payment streams.
Financial fraud detection; Graph neural networks; Dynamic/temporal graphs; Real-time streaming analytics; Anomaly detection; Concept drift; Class imbalance; Explainable AI; Cost-sensitive learning; FinTech deployment architecture
AI-Powered Financial Analytics and Visualization Tools: The Evolving Landscape of Strategic Finance
The incorporation of artificial intelligence (AI) in financial analytics and visualization has become a strategic necessity of contemporary finance in the age of inspired technology change. The topic covered in this research paper is the changing landscape of the use of AI to power financial applications and the nature and benefits of such applications in transforming the traditional financial management systems into systems of data-driven decision making. Through the utilization of machine learning, natural language processing, and predictive analytics ARTS insight and forecasting are no longer just the preserve of data scientists and machine learning specialists, rather with the new tools companies can now provide real-time insights and add to their forecasting models with a much higher degree of accuracy and capital allocation strategies can be optimized. The research study uses a large data set which contains a wide range of the indicators of global economies such as GDP, inflation, interest rates, and the gross national income which will be analyzed using AI enabled visualization tools like Tableau and Python. The discussed analysis shows that AI models are capable of unearthing hidden trends, sources of anomalies, and modeling hypothetical circumstances that enhance financial robustness. Some of the most interesting findings are of how AI is being deployed in terms of strategic finance teams, which focus on reducing the amount of financial data that is being input into a highly visual relative form of data to improve budgeting and planning decisions involving investment and risk assessment. The suggested use of AI algorithms can help companies to be proactive in case of macroeconomic changes and political instabilities by creating automated insights in time. The paper has come to the conclusion that the analytical technology AI is not only redefining the support to frontline finance functions, but is also redefining strategic planning at the organizational level. As they keep developing, these technologies have the potential to transform the finance role into an agile, intelligent, predictive and adaptive system. The paper adds to the body of knowledge about financial digitalization because it highlights the role of AI in enhancing efficiency, transparency, and long-term competitiveness in long-term competitiveness in strategic finance.
Artificial Intelligence, Financial Analytics, Data Visualization, Strategic Finance, Predictive Modeling and Economic Forecasting
Predicting and Preventing Drug Shortages: A Big-Data Digital-Twin Framework for Pharmaceutical Supply-Chain Optimization
Drug shortages put patients at risk and make the health system less efficient. This research suggests a comprehensive big-data simulation framework to forecast and preempt shortages within multi-echelon pharmaceutical supply chains, encompassing APIs, manufacturers, wholesalers, and both hospital and community pharmacies.We consolidate demand signals (claims, EHR orders, syndromic trends), production and quality data (recalls, batch yields, inspections), logistics (lead times, port congestion), and market structure (single-source risk, inventory) within a data lake. A hybrid pipeline merges gradient-boosted risk scoring, graph/network analytics, and a discrete-event/agent-based digital twin fine-tuned over five years of operations. Scenario experiments test policies for reducing risk, such as dynamic allocation, therapeutic substitution, prioritised rationing, selective dual sourcing, and targeted safety stocks. The framework cuts expected shortage days by 30–44% and backorders by 32–51% compared to current policies, while keeping logistics and holding costs within a 5–9% range. The early-warning risk scores (AUC≈0.85) give you 2–6 weeks to start taking steps to reduce the risk. A Pareto frontier appears: small, well-timed reallocations and selective dual sourcing provide the most benefits; blanket inventory inflation is the main issue. Health systems, wholesalers, manufacturers, and regulators can prioritise scarce vials, negotiate contingency capacity, and test emergency-use or import flexibilities in silico. Explainable models show which upstream nodes (API sites, fill-finish lines) cause risk, which helps make decisions that can be checked. We combine policy search with predictive learning in a digital twin that is aware of networks for supply chains that are important for humanitarian purposes.Originality/value: This playbook takes data from the beginning to the end and turns early warning signs of risk into cost awareness. These grounded actions stop drug shortages while keeping service levels high at scale.
Copyright (c) 2024 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Artificial Intelligence in Corporate Financial Strategy: Transforming Long-Term Investment and Capital Budgeting Decisions
Artificial Intelligence (AI) is increasingly transforming the landscape of corporate financial strategy, particularly in the domain of long-term investment and capital budgeting. Traditional evaluation methods—such as Net Present Value (NPV), Internal Rate of Return (IRR), scenario analysis, and real options valuation—are essential tools for strategic decision-making but are constrained by rigid assumptions, historical data limitations, and the inability to adapt quickly to uncertainty. AI-powered models, by contrast, offer dynamic, data-driven approaches that enhance forecasting accuracy, risk assessment, and strategic flexibility. Through machine learning, natural language processing, and predictive analytics, corporations can analyze vast amounts of financial and non-financial data, identify hidden patterns, and generate real-time insights to guide investment decisions. This paper examines how AI augments the capital budgeting process by refining cash flow estimation, enabling advanced scenario simulations, and integrating external factors such as macroeconomic volatility, market sentiment, and ESG considerations into corporate financial planning. The study further investigates the role of AI in real options valuation, where adaptive models provide managers with enhanced flexibility in responding to evolving market conditions, technological disruptions, and competitive pressures. In addition, ethical and governance implications are discussed, particularly regarding the transparency and interpretability of AI-driven financial decisions. By bridging traditional corporate finance theories with advanced AI applications, this research highlights the potential for firms to optimize capital allocation, reduce uncertainty in investment planning, and maximize long-term shareholder value. Ultimately, the study argues that AI is not merely a supportive tool but a transformative force capable of redefining corporate financial strategy and positioning organizations for sustainable growth in increasingly complex global markets.
Corporate Finance, Capital Budgeting, Net Present Value (NPV), Internal Rate of Return (IRR), Scenario Analysis, Predictive Analytics, Investment Decision-Making, Risk Management, Financial Forecasting, ESG Integration
Managing Purchasing Power in Today’s Global Economy: Challenges and Strategic Approaches
This paper explores the evolving nature of purchasing power in the context of current global economic dynamics. Drawing insights from the book 'Purchasing Power Management,' the study examines the underlying causes of declining real purchasing capacity among middle and lower-income populations, especially in post-colonial and developing economies. The paper emphasizes the role of inflation, unstable income distribution, import dependency, and monetary policy in shaping modern consumer power. It proposes a conceptual model integrating real income, price levels, and employment factors to better understand this phenomenon. Through a review of global policy practices and theoretical insights, the study provides strategic recommendations for governments to stabilize and strengthen household purchasing power. This includes approaches such as wage indexation, targeted subsidies, price regulation, and local production support. The study aims to provide a structured academic framework to inform policymaking in emerging and vulnerable economies.
Purchasing Power, Inflation, Income Inequality, Consumer Economics, Post-Colonial Development, Economic Policy, Global Markets
The Frozen Frontier: United States Arctic Strategy in a Shifting Economic Landscape
The Arctic region has rapidly evolved into a focal point of 21st-century geopolitical competition, emerging as a critical arena for economic opportunity and global power projection due to its vast resource reserves, emerging trade routes, and increasing strategic relevance. This paper examines the dynamics of modern Arctic geopolitics, analyzing the growing involvement of major powers—namely, the United States, Russia, and China—with a particular focus on the United States' policy and expansion in the region. In contrast to Russia's militarized approach and China's economic engagement, the United States is shown to possess relatively limited capabilities in the region, hindered by an underdeveloped icebreaker fleet, infrastructure gaps, and diplomatic challenges arising from its increasingly isolationist posture. This analysis calls for a measured but assertive approach to Arctic expansion, grounded in enhanced polar capabilities, infrastructure investment, and multilateral cooperation to balance geopolitical risks. The paper concludes that such expansion is not only necessary to safeguard American strategic interests but is also key to counterbalancing adversarial advances and ensuring long-term energy security and access to trade routes, driving both economic growth and regional stability. The paper contends that America's long-term economic and geopolitical success in the Arctic rests upon decisive action, resilient infrastructure development, and the careful management of alliance dynamics and collective defense in the region.
Copyright (c) 2025 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Forecasting Currency Volatility in Global Markets Using Transformer Models: Implications for U.S. Trade and Investment Strategies
Currency volatility has become one of the most significant challenges for global financial stability and U.S. trade competitiveness. Traditional econometric approaches, such as GARCH and VAR models, often fail to capture the nonlinear dependencies and structural breaks inherent in currency markets. This paper proposes the use of transformer-based deep learning models for forecasting short- and medium-term exchange rate volatility across major and emerging market currencies. By leveraging self-attention mechanisms, transformers can model long-range dependencies in high-frequency financial data, capturing hidden structures often overlooked by conventional models. Empirical analysis demonstrates that transformer models outperform GARCH, LSTM, and GRU baselines in predictive accuracy and volatility clustering detection. Furthermore, the study evaluates the strategic implications of currency volatility forecasts for U.S. trade policy, hedging strategies, and foreign investment decisions. Results highlight the potential for AI-driven forecasting systems to provide U.S. firms, investors, and policymakers with actionable insights for risk management, portfolio allocation, and international trade strategy.
Currency volatility; exchange rate forecasting; transformer models; deep learning; financial risk management; trade strategy; U.S. foreign investment; macro-financial stability; attention mechanism; AI in finance
AI-Powered Early Warning Systems for Emerging Market Crises: Enhancing U.S. Foreign Investment Risk Strategy
Emerging markets (EMs) exhibit nonlinear dynamics and contagion pathways that can rapidly amplify local shocks into systemic crises, exposing U.S. investors to outsized downside risk. This paper proposes an AI-powered early warning system (EWS) that forecasts the probability and timing of EM crisis events currency crashes, sovereign distress, and capital-flow sudden stops over 3/6/12-month horizons. The framework integrates multi-modal data: macro–financial indicators (FX, rates, CDS, reserves, external balances), market microstructure signals (order-flow imbalance, jump intensity), and alternative data (news and social sentiment, trade/shipping activity, satellite night-lights). Methodologically, we combine temporal transformers for regime-aware sequence modeling with graph neural networks over exposure networks (trade, banking, portfolio flows) to capture spillovers, and gradient-boosting models for calibrated probabilities. A composite Crisis Risk Index (CRI) is produced via isotonic calibration, with uncertainty bands from conformal prediction. Model transparency is ensured through SHAP-based global and local explanations, counterfactual analysis for policy levers (e.g., reserve adequacy, rates), and stability checks against data revisions. Backtests benchmark against canonical EWS rules and logistic/KLR-style baselines, evaluating AUC, Brier score, precision, false-alarm costs, and average lead-time. We illustrate decision utility for U.S. foreign investment strategy through three use cases: (i) dynamic country allocation and hedging, (ii) pre-trade risk budgeting with crisis-conditioned scenarios from a generative stress engine, and (iii) portfolio-level loss mitigation under liquidity and currency constraints. The system operationalizes an end-to-end pipeline ingestion, nowcasting, horizon forecasting, and alert governance suitable for investment committees and risk offices. Results indicate materially improved early warning lead-time and fewer false positives versus traditional indicators, enabling earlier de-risking and more resilient U.S. exposure to EM cycles.
Early warning systems; emerging market crises; sovereign risk; currency crash; sudden stop; machine learning; temporal transformers; graph neural networks; explainable AI (SHAP); alternative data & news sentiment; contagion and spillovers; U.S. foreign investment risk management
The Future of Enterprise Finance: Lessons from ECC-to-S/4HANA Migrations Across Industries
Enterprise Resource Planning (ERP) systems are the foundation of modern financial and operational management. For decades, SAP ECC (ERP Central Component) has provided the backbone for industries ranging from manufacturing and healthcare to retail and the public sector. However, with SAP announcing the end of mainstream maintenance for ECC by 2027 (and only offering costly extended support until 2030), organizations face a critical inflection point. This paper examines the drivers compelling migration, the challenges of transformation, and the lessons learned from cross-industry programs moving from ECC to SAP S/4HANA, SAP’s next-generation digital core. Unlike ECC, S/4HANA leverages an in-memory database to deliver real-time analytics, cloud-native scalability, embedded compliance, and integration with emerging technologies such as AI and machine learning. Drawing on global benchmarks, industry case studies, and policy frameworks, the paper positions migration not as a technical IT upgrade but as a strategic re-architecture of enterprise finance. It further highlights the executive implications for CFOs and CIOs, the policy perspective of finance as critical infrastructure, and future research directions in AI-enabled and quantum-resilient finance systems. The findings make clear that delaying migration magnifies risk—technical debt, regulatory exposure, and obsolescence—while early adopters are seizing competitive advantage. The imperative is clear: SAP S/4HANA is the future of enterprise finance.
SAP ECC, SAP S/4HANA, ERP migration, enterprise finance, digital transformation, compliance, cloud ERP, AI in finance
Social Return on Investment of Establishing a Cancer Drug Fund in Thailand
Cancer remains the leading cause of death in Thailand, with 336 new cases and 221 deaths daily, yet access to high-cost cancer therapies under the universal health coverage system remains limited. This disparity creates significant treatment inequities and highlights the need for innovative financing mechanisms to expand access to life-saving drugs. Drawing lessons from the United Kingdom’s Cancer Drug Fund, this study evaluates the potential social return of establishing a similar fund in Thailand. A two-pronged framework was adopted to quantify social benefits: (1) improvements in quality of life, measured by changes in Disability-Adjusted Life Years using an Interrupted Time Series with Control Group model comparing the United Kingdom (treatment group) and Germany (control group), and (2) reductions in inequality of health access, assessed using the Transferability of Economic Evaluation framework by applying per-patient cost data from the UK CDF to the Thai context. Monetary values were derived using Thailand’s GDP per capita as a financial proxy. The ITSCG analysis indicated a reduction of 16.95 DALYs per 100,000 population associated with CDF implementation, equivalent to THB 3,381.76 million in social value. In addition, under a simulated annual budget of THB 15,000 million, expanding access to high-cost cancer drugs was estimated to benefit 8,367 patients, generating THB 14,808.68 million in social value. Combining these outcomes, the total social benefit was estimated at THB 18,190.45 million, yielding a Social Return on Investment ratio of 1.21. These findings suggest that establishing a CDF in Thailand could deliver a substantial positive social return by improving health outcomes, reducing access inequalities, and alleviating the long-term burden of cancer. Policy adoption of such a fund could enhance equity, ensure financial sustainability, and strengthen Thailand’s public health system
Cancer Drug Fund, Social Return on Investment, DALYs, Quality of Life, Health Access, Interrupted Time Series, Thailand
Decentralizing Real Estate Markets: Evaluating REITs and Blockchain Tokenization Approaches
Blockchain-based tokenization is transforming the real estate sector, presenting a compelling alternative to the traditional model of Real Estate Investment Trusts (REITs). As the industry shifts from financialization to decentralization, driven by technological advancements, these two models offer different approaches to democratizing real estate investment.REITs have been a foundational aspect of real estate financialization, enabling individual investors to participate in large-scale real estate ventures through fractional ownership of diversified property portfolios. This has broadened the investor base and improved market liquidity. However, the emergence of blockchain technology and decentralized finance (DeFi) introduces a new paradigm: real estate ownership can now be fractionalized into digital tokens. This enhances liquidity, transparency, and accessibility through global 24/7 trading platforms. While REITs have made significant strides in expanding access to real estate investment, blockchain-based tokenization can further enhance these achievements by lowering entry barriers, reducing transaction costs, and decentralizing market operations. Nevertheless, the adoption of blockchain technology in real estate also comes with challenges, including regulatory uncertainties, technological risks, and the need for robust governance frameworks. As the lines between finance and technology continue to blur, it is essential to adapt regulatory frameworks and investment strategies to navigate this evolving landscape. The critical review highlights the future implications of these trends, emphasizing the importance of continued research and regulatory innovation to fully realize the potential of decentralized real estate markets. This is particularly relevant in addressing issues of housing inequality and affordability, as housing serves not only as an investment vehicle but also as a fundamental shelter for people.
Evaluating Determinants of Public’s Financial Readiness to Pay for Enhanced Bus Public Transport Options in Dar Es Salaam, Tanzania.
Globally, most developing countries faces a continuing challenges on how to smoothern public transport operation. Users disatification on bus public transport service has forced customers struggling to shift from public transport to private transport. Private transports has faced them with the increased cost. This study evaluated the determinants of the public’s financial readness to pay for the enhanced public transport options in Dar es salaam, Tanzania. Traveler distance identification, on road time waste, fare pricing and readness to pay for improved public transport was all evaluated. A primary survey data with a total of 296 respondents was analysed. The research results revealed that the mean readnesness to pay for major road routes was under Tanzanian shillings 750/=. Additionally, transport users was ready to pay the average amount of Tanzanian shillings 1100/= per trip within Dar es salaam city. Morever, all major routes operating under Tanzanian shillings 500 , transport users were ready to pay average amount of Tanzanian shillings 880 per trip, routes operating under Tanzanian shillings 450, transport users were ready to pay the average amount of Tanzanian shillings.780 per trip, and the routes operating under Tanzanian shillings 400, transport users were ready to pay the average amount of Tanzanian shillings 750 per trip. The results also revealed that travel distance and travel time have a significant effect on the readness to pay for improved public transport. Bases on this research findings, researcher recommend public transport government polic review from time to time to enhance the continuation of time to time improvement of the public bus service quality.
Return Anomalies Under Constraint: Evidence from an Emerging Market
This study examines the presence of the MAX effect in the Tehran Stock Exchange (TSE) in Iran, an emerging market with unique regulations. The MAX effect, typically observed in developed markets, describes the inverse relationship between a stock's maximum daily return in a month and its subsequent performance. Using univariate, bivariate, time-series, and panel regression models, this research finds no significant evidence of the MAX effect in the TSE, even when controlling for market size, book-to-market ratio, momentum, liquidity, and market risk. The absence of the MAX effect is attributed to the TSE's regulatory environment, including price limits and restrictions on short-selling, which hinder arbitrage and reduce mispricing opportunities. These findings highlight the challenges of applying asset pricing models from developed markets to emerging ones and suggest further research into the role of regulatory frameworks, market efficiency, and investor behavior in shaping asset pricing anomalies in emerging markets.
Return Anomalies, Emerging Markets, MAX Effect, Cross-sectional Stock Returns, Lottery-like Payoffs
Study of the Impact of Government Expenditures on the Trade Balance of Afghanistan from 2005 to 2020
The effects of government expenditures, which are the results of the implementation of government financial policies, on the current account balance, especially on the trade balance of Afghanistan, during the period from 2005 to 2020 have been investigated. The purpose of this research is to study and investigate the effects of government spending on Afghanistan's trade balance during specific periods of time. The information used in this research is from the official reports of the Ministry of Finance, Ministry of Economy, Ministry of Trade and Industry, National Administration of Statistics and Information, World Bank, magazines and books. The method used in this study is a simple regression model, which is taken to estimate the parameters of the model, the Autoregressive Distributed Lag. Also, for the use of information, the generalized Dickey-Fuller method of validity and invalidity of information was obtained, and invalid information was removed by differentiating their unit root and used later in the table. The results of this research show that there is no significant relationship between government expenditure and Afghanistan's trade balance. The lack of correlation between the variables of government expenditure and trade balance may be due to the higher volume of imports and lower volume of exports, the weak economic structure and the limited amount of Afghanistan's budget during the considered years.
government expenditure, government budget deficit, trade balance, current account and Afghanistan.
Awareness of Employees in Financial Departments of Governance Principles in Saudi Government Institutions
This study aimed to assess the level of awareness among financial department employees in Saudi government institutions regarding the principles of governance, and to examine how such awareness—alongside departmental interest in implementation—impacts institutional financial performance. Employing a descriptive-analytical methodology, the researchers distributed an electronic questionnaire to 67 employees and conducted statistical analysis using T-tests and linear regression via SPSS. Findings revealed varying levels of governance awareness, coupled with clear institutional interest in applying governance, particularly in internal control and transparency practices, which contributed to enhanced financial performance. Regression results showed that 77.8% of the variance in governance implementation could be attributed to employee awareness and departmental commitment, with the latter having a stronger effect. The study recommended fostering a unified understanding of governance principles, organizing specialized training programs, reinforcing internal control systematically, and conducting organizational restructuring to reduce task overlap. Limitations included the study’s confinement to government institutions in 2024 and reliance on electronic data collection. Future research was encouraged to explore additional variables such as organizational culture and leadership style, and to expand sample size for broader generalizability.
Financial Departments; Governance Principles; Saudi Government Institutions
The Relationship between Financial Literacy and Financial Behavior: A Systematic Literature Review
Financial literacy (FL) has become an essential determinant of financial behavior, influencing how individuals manage money, make investments, and prepare for long-term financial security. As financial systems grow increasingly complex, the need for a comprehensive understanding of FL's impact on personal and small business financial behavior has become more pressing. This systematic review seeks to consolidate and synthesize the findings from 45 empirical studies published between 2014 and 2024, examining the relationship between financial literacy and financial behavior across various demographic groups and contexts. The review focuses on how financial literacy affects key financial behaviors, such as saving, investing, debt management, and budgeting, and explores the significant demographic factors such as age, gender, income, and education that influence these relationships. The review highlights disparities in financial literacy across populations, with particular emphasis on gender, socio-economic status, and geographical location. It also addresses the role of financial education programs in improving financial literacy and their subsequent impact on behavior. The findings underscore the moderating role of behavioral traits like financial self-efficacy and risk tolerance, which influence the strength of the relationship between literacy and behavior. This review identifies several research gaps.
Financial Literacy, Financial behavior, financial decisions, Financial self-efficacy, financial education
Poverty Accounting and Measurement: A Systematic Literature Review
The paper is intended to be a systematic literature review (SLR) of the developing area of poverty accounting and measurement. It finds out how the old and the new methods think about and measure poverty, determines where ethical, philosophical, and accounting concepts fit in, and charts what all this means in terms of policy, institutional responsibility, and further inquiry. The paper is a synthesis of 118 scholarly and organizational publications released in 2020-2025. It recognizes the replacement of unidimensional income-dependent poverty measures with multidimensional concepts that take account of health, education, well-being and social inclusion. The results indicate an increased focus on moral reasoning, participatory paradigms, data justice, and the obligation of accountability in measures of poverty. It also reveals theoretical shortcomings in how reflexivity, normative ethics and institutional reporting can be incorporated into measurement frameworks. Thematic synthesis grouped results in twelve main areas, such as multidimensional poverty indices (MPIs), data shortcomings, governance systems, and useful implementations in open and corporate reporting.The review offers substantive recommendations to policymakers, development practitioners, educators, and institutions through proposing the hybrid ethically based metrics that would combine the objectivity with contextual validity and participatory validity. It places the arrival of poverty accounting in national budgets, ESG reporting and social impact assessment. In addition, the review facilitates moral and ethical literacy in the education programs to shape the future practitioners. The paper fits in the literature because it provides the interdisciplinary framework able to integrate accounting principles and ethical and philosophical aspects of measuring poverty. It is among the few SLRs that have included various SLRs- development economics; moral education; institutional reporting; and artificial intelligence in conceptualizing the operational and ethical aspects of poverty.
Poverty measurement, Poverty accounting, Multidimensional poverty, Accountability, Institutional reporting, Systematic Literature Review
A Review of Human-Centric AI in Industry 5.0: Integrating Data Science with Mechanical Automation
The shift towards Industry 5.0 from Industry 4.0 represents the paradigm shift in industry, not only highlighting automation and efficiency but also human-centered innovation, resilience, and sustainability. Central to this transformation is the synergy between Artificial Intelligence (AI) and Data Science with mechanical automation to produce intelligent, adaptive, and collaborative industrial environments. This review identifies the new frontier of human-centered AI in Industry 5.0 as the intersection of data-driven intelligence, mechanical engineering, and human-robot collaboration (HRC). It methodically examines how models of AI/Machine Learning (ML), such as explainable AI (XAI), prediction analytics, and systems of human-in-the-loop are redefining mechanical automation into cognitive, user-oriented settings. A systematic methodology based on major scientific databases was employed in order to choose more than 150 high-impact articles published between the years 2015 and 2025. Fundamental enabling technologies like collaborative robots (cobots), digital twins, cyber-physical systems, and edge AI are discussed in detail, with special emphasis on how they facilitate ergonomic, transparent, and secure interaction between humans and machines. In addition, the review discusses how data science frameworks are implemented to maximize the performance, trust, and well-being of humans in automated machinery systems. The paper also identifies some key missing gaps, such as the absence of scalable explainability of industrial AI, poor integration of ergonomic models with robotics, and difficulty in implementing real-time feedback systems from humans. In overcoming these challenges, this review provides a research and development pathway towards ethically oriented, resilient, and inclusive production. The research is expected to be used as a basis of reference by academics, engineers, and policy-makers who are leading the humanity-oriented shift of smart production systems.
Human-Centric AI; Industry 5.0; Data Science; Mechanical Automation
Enhancing Supply Chain Resilience through SAP APO and S/4 HANA Integrated Planning Frameworks
In times of endemic supply chain disruption—ranging from global pandemics and international tensions through to raw material shortages—resilience has become the imperative organizational need to ensure operational continuity and competitiveness. Enterprise Resource Planning (ERP) and Advanced Planning Systems (APS), while performing well in isolated disciplines, fail to deliver the integrated, real-time reactivity necessary to confront volatility and complexity in rapidly changing conditions. The paper presents a digitally integrated planning framework, one drawing on the complementary capabilities of SAP Advanced Planning and Optimization (APO) and SAP S/4 HANA, to amplify the resilience of the supply chain. By integrating advanced demand planning, supply network planning, and execution levels within real-time, in-memory architecture, the framework provides predictive analytics, agile scenario modeling, and coordinated decision-making across functional silos. Methodologically, the research takes a conceptual modeling stance, underpinned by illustrative simulations of typical supply chain disruption, the paper critiques the proposed framework against the resilience drivers of responsiveness, adaptability, visibility, and risk-absorbing capacity, and demonstrates significant benefits over legacy siloed systems. This research contributes to the literature by bridging the capability disconnect between legacy planning tools and next-generation digital ERP systems, providing both strategic and operational recommendations to supply chain managers operating under uncertainty. As future avenues, empirical validation within industry contexts as well as extension toward AI-infused autonomous planning systems are anticipated to be of interest.
Supply Chain, Digital transformation, SAP, APO, ERP Systems
Detecting Financial Fraud in Real-Time Transactions Using Graph Neural Networks and Anomaly Detection
The exponential growth of digital financial services has amplified the risk and complexity of fraud in real-time transactional systems. Traditional rule-based or statistical approaches are often inadequate for detecting evolving and covert fraudulent behaviors embedded within large-scale financial networks. This paper proposes a novel, data-driven framework that leverages Graph Neural Networks (GNNs) combined with unsupervised anomaly detection to identify fraudulent activity in real-time transaction streams. By modeling financial transactions as a dynamic graph, where nodes represent users/accounts and edges represent transactions, the system captures the intricate relational patterns and dependencies among entities. A GNN is then trained to learn latent representations of nodes and edges, which are subsequently analyzed using density-based anomaly scoring techniques such as Isolation Forest and Local Outlier Factor (LOF). Our experimental results, conducted on publicly available and simulated financial datasets, demonstrate that the proposed hybrid model significantly outperforms baseline methods in terms of detection accuracy, precision, and false positive rates. Furthermore, the system offers real-time inference capabilities, making it highly applicable for deployment in fraud monitoring engines of banks, fintech platforms, and payment gateways. This study establishes the effectiveness of graph-based deep learning and unsupervised anomaly detection as a unified solution for modern financial fraud prevention.
Graph Neural Networks (GNN), Financial Fraud Detection, Real-Time Transactions, Anomaly Detection, Dynamic Graphs, Isolation Forest, Local Outlier Factor, Deep Learning, Transactional Networks, Cybersecurity in Finance
Cryptocurrency Volatility Forecasting Using Transformer-Based Deep Learning Models and On-Chain Metrics
Cryptocurrencies have emerged as highly dynamic digital assets, characterized by extreme price volatility and driven by both speculative behavior and network-level activities. Traditional volatility forecasting methods, including GARCH and LSTM-based models, often fall short in capturing the complex, nonlinear, and temporal dependencies inherent in crypto markets. This paper proposes a novel deep learning framework that leverages Transformer-based architectures originally designed for natural language processing to forecast short-term cryptocurrency volatility with enhanced accuracy and temporal sensitivity. By incorporating a comprehensive set of on-chain metrics (such as transaction volume, wallet activity, miner behavior, and token circulation), our model captures both market sentiment and blockchain-level dynamics. The proposed Transformer model is benchmarked against LSTM and GRU networks using Bitcoin and Ethereum datasets spanning multiple market cycles. Experimental results show that the Transformer-based model outperforms recurrent architectures in predicting both realized and implied volatility, particularly during high-turbulence periods. These findings suggest that attention mechanisms, combined with on-chain data, provide a powerful tool for managing risk and making informed decisions in the rapidly evolving digital asset ecosystem.
Cryptocurrency; Volatility Forecasting; Transformer; Deep Learning; Bitcoin; Ethereum; On-Chain Metrics; Attention Mechanism; Financial Time Series; Blockchain Analytics