Search Results (52)

This study investigates the relationship between stock returns and income inequality in South Africa, a country marked by persistently high levels of income disparities and a sophisticated and structurally unique financial market. Despite the Johannesburg Stock Exchange (JSE) being one of the most developed and liquid markets in Africa, stock ownership remains limited to a small segment of the population, often reinforcing pre-existing income inequalities. This study determines the relationship between stock returns and income distribution using the ARDL bound test methodology. Using time series data from 1975 to 2024, the study examines the extent to which stock market returns influence income distribution. The findings of the study suggest a positive relationship between stock returns and income distribution. This relationship suggests that higher stock market development disproportionately benefits capital holders. The long-term relationship seems to have limited feedback from inequality to stock returns. The findings aim to inform policies on inclusive financial participation and broad-based wealth generation to address South Africa’s structural inequalities. Full article

The rapid digitalization of financial services in the European Union (EU) has not only enhanced convenience and inclusion but also increased exposure to sophisticated online financial fraud. Digital financial literacy (DFL) is widely promoted as a key tool for empowering consumers and reducing fraud victimization. However, the empirical and conceptual landscape linking DFL to fraud reduction within the specific sociolegal context of the EU remains fragmented. This study uses bibliometric analysis to map the research area, define major themes within the field, and determine the role of DFL in reducing online financial fraud in the EU. Peer-reviewed journal articles were targeted to ensure academic rigor, with a publication window of 2010–2025 reflecting key fintech and regulatory developments. After adhering to PRISMA principles, 87 peer-reviewed publications were chosen out of a total of 568 records identified through OpenAlex and Web of Science, coauthorship, keyword co-occurrence, citation, temporal, and density representations were analyzed using VOSviewer. Findings indicate an increasingly diffuse research field with new clusters concentrating on macroeconomic policy, business technology, social psychology, and interdisciplinary foundations. Results demonstrate that successful implementation of DFL interventions combines behavioral insights, technological protection, and non-discriminatory policy considerations. The study concludes by identifying major gaps in research and providing a path forward for future evidence-based policy efforts toward enhancing consumer protection in the EU digital financial market. Full article

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANN–LSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions. Full article

The deep integration of digital technologies has significantly improved the operational efficiency of electricity markets, but it has also introduced increasingly severe and sophisticated cybersecurity challenges. As a highly coupled cyber–physical system (CPS), the electricity market is increasingly vulnerable to attacks that exploit weaknesses in both market mechanisms and information infrastructure. Unlike existing reviews, this study makes three key contributions: First, it provides a hierarchical analysis of cyberattacks targeting electricity market operations, detailing how such attacks manipulate outcomes for profit or disruption. Second, it proposes a novel full-lifecycle dynamic defense framework tailored to the cyber–physical–market nature of the electricity market, coordinating defenses across the entire attack lifecycle to ensure market stability and financial integrity. Third, it analyzes key enabling technologies for attack–defense games and identifies fundamental challenges to market resilience. Looking ahead, the manuscript outlines a strategic research agenda, emphasizing breakthroughs in intelligent and collaborative technologies. These advancements are expected to drive the evolution of the electricity market’s defense from a passive–reactive model to a state of active immunity, which can anticipate, withstand, and autonomously recover from complex cyber threats. Full article

This paper investigates the effect of Environmental, Social and Governance (ESG) scores and Credit Default Swap (CDS) spreads on the profitability of Halkbank, one of the biggest state-owned banks in Türkiye, an emerging economy. To this end, we employ Non-linear Autoregressive Distributed Lag (NARDL) and Markov Switching Regression (MSR) methods, taking into account non-linear market risks, using Halkbank’s quarterly data consisting of 63 observations for the period 2009Q1–2024Q3. Moreover, to prevent multicollinearity, we aggregate banking-specific and macroeconomic indicators into a single composite index using Principal Component Analysis (PCA). Our MSR findings suggest that ESG scores and CDS spreads negatively affect bank profitability and that these effects are particularly pronounced during periods of high market volatility. Similarly, NARDL findings suggest that ESG scores have asymmetric effects on bank performance, with both positive and negative changes in ESG performance having a negative impact on profitability, and moreover, negative changes have a more negative impact on profitability. This means that the bank’s sustainability initiatives may be costly and negatively affect profitability in the short run, but these effects will be more negative if initiatives deteriorate. Our findings emphasize the need for banks to adopt a gradual ESG approach that enables them to increase their capacity without compromising financial stability and for regulatory structures to have a flexible and sophisticated risk management framework capable of rapidly adapting to different market conditions. Therefore, our study provides valuable insights to sector managers and policymakers regarding the financial implications of sustainability approaches. Full article

Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution. Full article

This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques. Full article

This research examines the time–frequency co-movement patterns among the Johannesburg Stock Exchange (JSE) size-based indices, utilizing daily data covering the period from November 2016 to December 2024. To conduct the analysis, three sophisticated wavelet techniques are applied: the Maximal Overlap Discrete Wavelet Transform (MODWT), the Continuous Wavelet Transform (WTC), and the Wavelet Phase Angle (WPA) model. Subsequently, the Multivariate Generalized Autoregressive Conditional Heteroscedasticity–Asymmetric Dynamic Conditional Correlation (MGARCH-DCC) model is employed to evaluate the robustness of the findings. The results reveal that the co-movement among the JSE size-based indices is influenced by investment holding periods and prevailing market conditions. Notably, a lead–lag relationship is identified, indicating that a single size-based index often drives the co-movement of the others. These findings carry important implications for investors, policymakers, and portfolio managers. Investors should account for optimal holding periods to avoid increased correlation and reduced diversification benefits. Policymakers are advised to mitigate financial market uncertainty, particularly during bearish phases, to manage excessive index co-movement. Portfolio managers must integrate both holding periods and market conditions into their investment strategies. This research offers a novel contribution to the South African investment landscape by providing practical and risk-mitigating insights into the role of JSE size-based indices within diversified portfolios—a topic that has received limited attention despite its growing relevance. Full article

The growing complexity of the contemporary financial systems requires the emergence of sophisticated computational and statistical methods that are capable of managing uncertainty, lack of normality and structural variability of multivariate data. The TS charts defined by Hotelling are widely applicable but have been observed to be susceptible to asymmetrical distributions and outliers and are therefore inapplicable in a dynamic real-world example, such as cryptocurrency markets. We present a computationally efficient ambiguity-aware framework in this work, which generalizes the robust covariance estimation methods, which are MVE and MCD, into a neutrosophic logic-based framework. This adaptation also allows the proposed charts to model and react to the intrinsic data ambiguity and indeterminacy with improved robustness and additional multivariate process monitoring. The methodology is validated by a combination of simulation experiments and empirical research on high-frequency financial data of the Binance Exchange, with the focus on the BTCUSDT and ETHUSDT trading pairs. The evaluation of the performance is performed based on total and generalized variance measures that give a holistic picture of the sensitivity and adaptability of the method to noise in data and complexities arising in the presence of noise and complexity of data. The results demonstrate that the proposed approach is considerably superior to conventional TS charts and their robust variants, particularly in terms of detecting a small shift and trends of multivariate financial procedures. Thus, it is a contribution to the growing body of knowledge about applying computational statistics and data science to a scalable, uncertainty-sensitive system of high-dimensional process monitoring in volatile financial settings. Full article

This study investigates the relationship between green trade, economic complexity, and green indicators in Asian countries using a Poisson Pseudo Maximum Likelihood (PPML) fixed effects model. This study uses panel data from 33 countries in the Asia region, focusing on the national level of each country from 2010 to 2023. The analysis explores how economic sophistication and environmental indicators influence the capacity of economies to engage in sustainable trade. The findings reveal that economic complexity significantly enhances green trade, underscoring the role of knowledge-intensive production structures in fostering environmentally friendly export performance. Among the green indicators, green economic opportunities demonstrate a positive and significant effect on green trade, which indicates that economies allocating greater financial resources to renewable energy and sustainable infrastructure are better positioned to expand their participation in eco-friendly markets. This signals strong trade readiness and market-driven incentives. Conversely, green innovation shows a negative and significant effect, indicating that innovation is not yet translating into export competitiveness, is still costly, and is in an early phase. Moreover, economic complexity and renewable energy show positive and significant effects, reflecting that higher complexity enables the adoption of green technologies, the embedding of sustainability in value chains, and the export of high-value green products. These results suggest that green economic opportunities and regional dynamics play a complementary role in shaping outcomes, with proximity to innovation hubs amplifying the capacity for sustainable trade. The study contributes to the literature by linking economic complexity with green trade in the Asian context, offering evidence-based recommendations to enhance sustainability-driven growth. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

Reliable financial reporting is critical for maintaining market confidence and guiding stakeholders’ decision-making, yet traditional audit methods often fail to detect sophisticated fraud schemes that are hidden within large volumes of transactional data. This systematic literature review synthesizes 43 empirical and theoretical studies published between 2010 and 2024 that utilize data analytics techniques for the prevention and detection of fraud in financial statements. Following the PRISMA guidelines, we conducted a four-phase review—identification, screening, eligibility assessment, and inclusion—to ensure transparency and reproducibility. Our analysis categorizes techniques into supervised machine learning classifiers (e.g., decision trees and neural networks), statistical anomaly detection methods, network-based analyses, and real-time monitoring frameworks. We evaluate each approach’s comparative effectiveness, highlight persistent challenges such as data imbalance, model interpretability, and governance constraints, and also trace evolving methodological trends over time. The review reveals that integrating predictive analytics and continuous monitoring into accounting information systems can transform audits from reactive investigations into proactive fraud prevention mechanisms. We conclude by proposing a future research agenda focusing on developing explainable AI models for audit applications, establishing robust data governance frameworks to support automated monitoring, and conducting longitudinal field studies to assess the real-world impact of analytics-driven controls. Full article

The burgeoning economic relationship between Türkiye and Poland, marked by a targeted $10 billion trade volume, has catalyzed significant Turkish engagement in the Polish construction sector. Ranked second globally in international contracting, Turkish firms are increasingly undertaking complex infrastructure projects in Poland, making it a critical European market to analyze. This study develops a comprehensive framework to identify and evaluate the sources of sustainable competitive advantage for Turkish contractors operating in this dynamic environment. The research adopts a qualitative, single-case study methodology, centered on the extensive project portfolio of a leading Turkish firm in Poland. The analytical approach is twofold. First, it employs Porter’s Diamond Framework to deconstruct the existing competitive advantages, revealing a shift from traditional low-cost models to a sophisticated synergy of superior labor management capabilities, strategic local partnerships, and expertise in complex project delivery. These strengths are shown to align directly with Poland’s critical needs, particularly its skilled labor shortage and ambitious infrastructure agenda. Second, a Foresight Analysis is conducted to map plausible future scenarios through 2035, addressing key uncertainties such as geopolitical shifts and the pace of technological adoption. The findings demonstrate that the sustained success of Turkish contractors hinges on their ability to deliver targeted value. The study concludes by proposing a set of “no-regrets” strategies—including accelerated ESG and digital up-skilling, forging deep local partnerships, and developing financial engineering capabilities—designed to secure and enhance their competitive positioning. The results provide an actionable roadmap for industry practitioners and valuable insights for policymakers fostering bilateral economic collaboration. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

This paper presents a comprehensive deep reinforcement learning (DRL) framework for developing strategic trading models tailored to the Indian NIFTY 50 index, leveraging the temporal and nonlinear nature of financial markets. Three advanced DRL architectures deep Q-network (DQN), double deep Q-network (DDQN), and dueling double deep Q-network (Dueling DDQN) were implemented and empirically evaluated. Using a decade-long dataset of 15-min interval OHLC data enriched with technical indicators such as the exponential moving average (EMA), pivot points, and multiple supertrend configurations, the models were trained using prioritized experience replay, epsilon-greedy exploration strategies, and softmax sampling mechanisms. A test set comprising one year of unseen data (May 2024–April 2025) was used to assess generalization performance across key financial metrics, including Sharpe ratio, profit factor, win rate, and trade frequency. Each architecture was analyzed in three progressively sophisticated variants incorporating enhancements in reward shaping, exploration–exploitation balancing, and penalty-based trade constraints. DDQN V3 achieved a Sharpe ratio of 0.7394, a 73.33% win rate, and a 16.58 profit factor across 15 trades, indicating strong volatility-adjusted suitability for real-world deployment. In contrast, the Dueling DDQN V3 achieved a high Sharpe ratio of 1.2278 and a 100% win rate but with only three trades, indicating an excessive conservatism. The DQN V1 model served as a strong baseline, outperforming passive strategies but exhibiting limitations due to Q-value overestimation. The novelty of this work lies in its systematic exploration of DRL variants integrated with enhanced exploration mechanisms and reward–penalty structures, rigorously applied to high-frequency trading on the NIFTY 50 index within an emerging market context. Our findings underscore the critical importance of architectural refinements, dynamic exploration strategies, and trade regularization in stabilizing learning and enhancing profitability in DRL-based intelligent trading systems. Full article