Search Results (180)

This study investigates the transformation of energy security in distributed energy systems under growing uncertainty, geopolitical shocks, and fuel market volatility. A risk-based multi-objective optimization framework is proposed that integrates economic performance (LCOE, CAPEX, and OPEX), system reliability, and systemic risk measured by Conditional Value-at-Risk (CVaR). The empirical analysis is based on European electricity market data for 2010–2025 and combines historical analysis with stochastic scenario generation and Monte Carlo simulation to evaluate the impacts of exogenous shocks. The results reveal a structural shift in the European electricity market after 2021, characterized by increased sensitivity to fuel price fluctuations and a transition to a more volatile operating regime. Although a higher share of renewable energy improves economic and environmental performance, it does not ensure system resilience without complementary flexibility measures, including energy storage and demand-side management. The proposed framework demonstrates that integrating these measures substantially reduces systemic risk under crisis conditions. The analysis also identifies a persistent post-crisis risk pattern, reflected in elevated CVaR values after market stabilization, which is consistent with the hypothesis of risk hysteresis. Rather than proving hysteresis, the results indicate sustained risk persistence following major external shocks. The proposed framework extends existing approaches to energy security assessment by integrating economic efficiency, reliability, and risk within a unified optimization model. Its modular structure enables adaptation to different electricity markets through recalibration of local parameters, providing a practical decision-support tool for strategic planning under uncertainty. Full article

This study investigates the relationship between economic growth, trade openness, energy intensity, and carbon dioxide (CO₂) emissions in South Africa within the Environmental Kuznets Curve (EKC) framework over the period 1970–2022. Using quarterly time series data and the Autoregressive Distributed Lag (ARDL) modelling approach, the study examines both the short-term and long-term dynamics between economic activity and environmental degradation. Descriptive statistics, correlation analysis, unit root tests, ARDL bounds testing, error-correction modelling, Granger causality analysis, and diagnostic tests were employed to ensure robust empirical results. The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests indicate that all variables are integrated of order one, I(1), thereby satisfying the conditions for ARDL estimation. The ARDL bounds test confirms the existence of a long-term cointegrating relationship among carbon emissions, economic growth, trade openness, and energy intensity. The long-term results reveal a statistically significant negative coefficient for economic growth and a positive coefficient for the squared income term, indicating a U-shaped relationship between income and carbon emissions. Consequently, the conventional Environmental Kuznets Curve hypothesis is not supported for South Africa. The findings suggest that economic growth initially reduces environmental degradation; however, beyond a certain income threshold, further economic expansion increases carbon emissions. Trade openness and energy intensity exert positive and statistically significant effects on carbon emissions in the long run, implying that increased integration into global markets and continued dependence on energy-intensive production contribute to environmental degradation. The Error-Correction Model (ECM) reveals a negative and highly significant adjustment coefficient (−0.928), indicating that approximately 92.8% of short-term disequilibrium is corrected within one period. Granger causality results further show a unidirectional causal relationship running from trade openness to carbon emissions, while no significant causal relationship is found between economic growth and carbon emissions. The study concludes that economic growth alone is insufficient to achieve environmental sustainability in South Africa. Policy efforts should therefore focus on promoting renewable energy adoption, improving energy efficiency, strengthening environmental regulations, encouraging cleaner production technologies, and integrating environmental considerations into trade and industrial policies. These measures are essential for achieving sustainable economic development while meeting national climate-change-mitigation objectives. Full article

This study provides an exploratory predictive analysis to examine how different dimensions of digital infrastructure—capital market development, digital payment adoption, e-commerce penetration, and market volatility—predict the financial performance metrics of fintech ventures in Egypt. Using panel data from ten fintech ventures listed on the Egyptian Stock Exchange over the period 2017–2023, the research employs Random Forest machine learning algorithms alongside Logistic Regression as a baseline comparator. Feature importance analysis identifies the most significant predictors of profitability across four performance metrics: gross revenue, sales growth, gross margin, and net profit margin. This study employs Random Forest with five-fold cross-validation. Hyperparameters were optimized via grid search, and feature importance scores are reported with cross-validation standard deviations. To address panel structure concerns, we additionally employ leave-one-firm-out cross-validation. All findings reflect predictive associations only; no causal claims are made due to potential reverse causality. Findings show that capital market development emerges as the most important predictor across all profitability metrics, accounting for 45% of feature importance for net profit margin and 42% for gross revenue (mean importance across five folds; SD = 0.07–0.08). Digital payment adoption exhibits a paradoxical dual association—positively associated with revenue and margins through operational efficiency (38% importance for gross margin; SD = 0.08) while negatively associated with sales growth (22% importance; SD = 0.10). Gross online sales show limited predictive efficacy, affecting only gross margin. Market volatility correlates solely with sales growth. Random Forest consistently outperforms Logistic Regression across all models, with accuracy rates ranging from 68% to 76% (compared to a chance level of 50% and a majority-class baseline of 52–58%). Due to the limited sample of 70 firm-year observations, these findings must be interpreted as strictly exploratory and hypothesis-generating; they apply uniquely to publicly listed fintech firms on the Egyptian Stock Exchange and cannot be generalized to private, early-stage, or unlisted fintech startups without further empirical validation. Full article

The paper examines the multifractal and asymmetric behavior of oil, freight and exchange rate markets in the global trade system with the help of the Asymmetric Multifractal Detrended Fluctuation Analysis (AMF-DFA) technique. Based on daily data of West Texas Intermediate (WTI) crude oil, the Baltic Dry Index (BDI), and the exchange rate between the RMB/USD over the period of post-COVID-19 (2021–2024), the analysis focuses on whether efficiency in the markets varies across time scales and directional regimes. The findings show that there is strong evidence of multifractality in all markets, which implies that the scaling behavior is heterogeneous, and that it is long-range-dependent. Notable directional persistence is found between up and down movements with oil and exchange rate markets showing stronger directional persistence, especially at longer horizons with the freight markets displaying relatively weaker directional persistence. Additional results imply that temporal dependence and nonlinearity are the main drivers of multifractality in oil and exchange rate markets, and short-term fluctuations are prevalent factors in the dynamics of the freight market. These findings not only refute the classical Efficient Market Hypothesis but also provide empirical evidence in support of the Adaptive Market Hypothesis, and how efficiency is dynamic, dependent on scale, and directionally asymmetric. The study contributes by examining asymmetric multifractal efficiency across three trading markets during the post-COVID period, while recognizing that formal cross-market spillover analysis remains a direction for future research. Full article

This study investigates Bitcoin price forecasting using integrated sentiment analysis and gradient boosting within digital financial ecosystems. Two XGBoost models were developed using sentiment scores derived from Bitcoin news (2021–2024) and technical indicators, including GARCH-estimated volatility, Bollinger Bands, MACD, and RSI. The analysis uses 1042 daily Bitcoin observations and 10,025 sentiment records. Two model configurations were evaluated: one using only technical indicators and another incorporating daily aggregated sentiment scores. Model performance was assessed using Diebold–Mariano tests with Newey–West HAC variance estimation and walk-forward validation across 40 rolling windows. Contrary to expectations, sentiment features provided no statistically significant improvement over the technical-only model (p = 0.4888). Both models achieved identical test performance (R² = −0.16%). Walk-forward validation revealed substantial temporal instability (Mean R² = −126.30%, Std = 233.05%), highlighting the challenges of forecasting daily Bitcoin returns. Nevertheless, both XGBoost models significantly outperformed the random walk benchmark (DM statistic = −8.58, p < 0.0001), indicating that technical indicators capture exploitable market structure despite limited predictive accuracy for practical trading. These findings support the efficient market hypothesis and have implications for digital financial ecosystems integrating multimodal information. Full article

This study examines the relationship between valuation multiples and investment performance in the U.S. stock market. Specifically, it tests whether portfolios constructed with high-multiple stocks consistently outperform portfolios with low-multiple stocks. The analysis spans the Technology, Consumer Staples, and Healthcare sectors from 2018 to 2022. A sector-based portfolio construction framework was employed using quarterly portfolio-return data. Quantitative financial modelling, including regression analysis and descriptive statistics, was applied to assess the correlation between portfolio returns and valuation multiples (P/E and EV/EBITDA), while interpreting results within the broader context of market volatility and the COVID-19 period. The results show no statistically significant relationship between valuation multiples and portfolio performance. Low-multiple portfolios demonstrated marginally higher average returns over the period, offering weak support for value-based investment strategies. Results further suggest limited standalone predictive power in high-multiple valuations. Drawing on the Efficient Market Hypothesis, Value Investing, Growth Investing, and the Fama-French Three-Factor Model, this paper empirically tests the impact of valuation multiples within a sector-based portfolio framework. Accordingly, the study adds to the asset pricing literature by offering a structured null-result framework, demonstrating that valuation multiples, when applied in isolation, may not provide sufficiently reliable standalone signals for portfolio performance. The COVID-19 period is interpreted as an economically meaningful contextual regime characterized by elevated volatility, liquidity intervention, and sectoral divergence, rather than as a formally estimated event-study framework. Full article

The efficient market hypothesis, which holds that prices completely reflect available information, is commonly used in financial market analysis. However, emerging empirical evidence shows that market efficiency develops with time, as posited by the adaptive market hypothesis (AMH), with predictability varying across shifting economic and behavioural regimes. Despite the increasing use of deep learning in financial forecasting, there has been little systematic investigation into whether neural network topologies can successfully identify time-varying efficiency trends across diverse markets. Furthermore, the relevance of transfer learning in studying adaptive behaviour between foreign exchange markets and extremely volatile cryptocurrency markets has received little attention. Using these data, we investigate the AMH by comparing the forecasting performance of various deep learning architectures and determining whether knowledge transfer from a relatively stable fiat currency market, Botswana Pula/US Dollar (BWP/USD), improves the predictive accuracy in a highly volatile cryptocurrency market, Bitcoin/US Dollar (BTC/USD). We use daily data from 1 January 2015 to 11 January 2026 to develop deep neural networks (DNNs) and alpha-recurrent neural networks, and, for generalisation, we benchmark using a recurrent temporal neural network (RTNN), a domain-adversarial neural network (DANN), and KLIEP-based importance-weighted regression. A transfer learning technique is used, in which models are initially trained on BWP/USD and then re-estimated on BTC/USD without freezing any network layers, ensuring complete flexibility and enabling parameters to respond to changing market dynamics. Out-of-sample accuracy measures and rolling long-memory diagnostics are used to evaluate forecast performance in terms of time-varying efficiency. The findings reveal that the RTNN regularly outperforms other forecasting models across marketplaces. Predictive accuracy fluctuates with time, and rolling long-memory measurements show persistent departures from random walk behaviour, which supports the AMH. Transfer learning improves predictive stability in the cryptocurrency market by identifying the existence of transferable informational structures between fiat and digital asset markets. Overall, our results support the idea that market efficiency is dynamic rather than static, and they show that adaptive deep learning systems are an excellent way to test the AMH. The paper suggests that cross-market transfer mechanisms and adaptive modelling methodologies be investigated further in growing foreign exchange and cryptocurrency markets. Full article

This paper proposes a new model with the goal of improving several aspects of the modern portfolio theory: (i) investor behavior, (ii) depiction of behavior in the fractal stochastic differential equations about price efficiency in chaotic dynamics (Tsallis statistics) and the fractal market hypothesis, (iii) the introduction of the novel Evolving Returns Integrated System (ERIS) in portfolio selection in the fractal behavioral convolution, and (iv) the selection of an accurate classifier (ERIS) among three neuro-genetic hybrids of 66 models: 22 modular, 22 Jordan–Elman and 22 generalized feedforward networks. Our model demonstrates superior classification performance across the Greek (1996–1998) and NYSE (2008–2010) equity market datasets examined in this study. Full article

Background: Lyapunov exponent has been used in many science and engineering problems to quantify chaos in systems and understand their nonlinear dynamics. In financial engineering and forecasting, evaluation of chaos in financial data helps determine whether the data are predictable and if profits can be generated. The purpose of this study is to examine presence of chaos in cryptocurrency markets. Methods: To examine chaos, Lyapunov exponent is computed from a set of 50 cryptocurrencies and statistical one-sided and two-sided Student-t tests are performed to check if on average the computed Lyapunov exponents are equal, less, or larger than zero. Results: The statistical results reveal strong evidence that prices, returns, and trading volume changes are all chaotic; hence, they show nonlinear and deterministic characteristics. Conclusions: Prices, returns, and trading volume changes in cryptocurrencies could be predicted in the short run; for instance, on a daily basis. In this regard, active traders and investors may implement predictive systems to generate daily profits. Full article

This study investigates time-varying market efficiency and cross-market correlations in cryptocurrency markets across South Korea, the United States, and Japan. Using rolling-window multifractal detrended fluctuation analysis (MF-DFA) and dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC-GARCH), we analyze 11 cryptocurrency–fiat pairs—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Bitcoin Cash (BCH) denominated in Korean Won (KRW), US Dollar (USD), and Japanese Yen (JPY)—from January 2018 to September 2025. MF-DFA results confirm persistent multifractality and significant time-variation in market efficiency across all markets, consistent with the Adaptive Market Hypothesis (AMH). DCC-GARCH estimates reveal a structural divergence between return integration and efficiency correlations: return-based correlations for same-asset cross-fiat pairs are exceptionally high (mean dynamic conditional correlation of approximately 0.96–0.98), whereas efficiency-based correlations are far more heterogeneous, with cross-asset pairs approaching near-zero synchronization. We interpret the Kimchi Premium as a product of institutional frictions that impede price-level arbitrage while leaving volatility transmission largely unaffected. These findings suggest that cryptocurrency market integration is multidimensional—globally synchronized in risk dynamics, yet locally segmented in the structural quality of information processing. Full article

Assessing electricity market efficiency is important for power market reform and the development of sustainable power systems. Efficient prices can improve resource allocation and provide better signals for system operation, system flexibility and low-carbon transition. Against this background, this study examines the efficiency of three representative provincial electricity spot markets in China, Shandong, Shanxi and Guangdong, using day-ahead and real-time price data from January 2022 to August 2024. A multi-method framework including unit root tests, price convergence tests, detrended fluctuation analysis and sample entropy is employed to evaluate market efficiency and compare differences across provinces. The results show that none of the three markets satisfies the weak-form Efficient Market Hypothesis. The fractal analysis and entropy results further suggest that market efficiency remains limited. Cross-provincial differences are nevertheless observed, which may be partly related to intraday load patterns, generation mix, market structure, and market design. This study provides useful evidence for deepening electricity market reform, as well as promoting the efficient and sustainable development of power systems. Full article

This paper studies the effects of leasing on credit risk and access to credit. The repossession of a leased asset is generally easier than the enforcement of collateral associated with securing a standard loan agreement. We argue that this greater efficiency in enforcement mitigates, ceteris paribus, the counterparty’s moral hazard. To test this hypothesis, we developed a credit rationing model in which income is privately observed and non-verifiable, and financial intermediaries share credit risk information about borrowers. Financial contracts that are more rapidly enforced, such as in leasing, enable the screening of relatively safer projects or credit rationing reduction. We provide empirical evidence consistent with this prediction for the Italian credit market and considerations for the effects of monetary policy variables on the model’s equilibrium. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article

This study offers a comprehensive assessment of financial market modeling through a PRISMA-based systematic review, bibliometric analysis, and content synthesis. We examined 67 review articles (1990–2024) from Web of Science to build a conceptual framework, and 4982 articles (1990–2024) were analyzed with Biblioshiny. Five main clusters emerge: AI and deep learning for prediction; hybrid models that combine traditional and computational approaches; theoretical foundations, including the Efficient Market Hypothesis and critiques; high-frequency prediction and volatility analysis; and modeling of cryptocurrencies and digital assets. Temporal patterns show a shift from traditional econometrics to hybrid and deep learning methods, heightened attention to uncertainty and volatility during crises, rapid growth in crypto-focused modeling, and increased use of sentiment/news data after 2017. The content analysis highlights key gaps and future directions: standardized open benchmarks and reproducible frameworks; regime-sensitive validation; interpretable hybrid models that merge econometric structure with machine-learning flexibility; and wider applicability across assets, markets, and data types. The study provides a structured guide to intellectual and applied modeling, supporting future advances in forecasting, risk management, and policy design. Full article

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study ($N=25$, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is $\beta =0.0036$ ($p=0.495$) and the Alphabet Rank coefficient is $\beta =-0.0027$ ($p=0.642$), with the results robust to all tested parameterizations ($\lambda \in [0.05,0.20]$; $p>0.50$ throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. Full article