Search Results (1,649)

This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article

Accurate mortality prediction is essential to actuarial practice as it is directly linked to insurance pricing, reserving, and the management of longevity risk. This study proposes a deep neural network (DNN) model for the mortality rates of multiple populations; it is composed of long short-term memory (LSTM) and convolutional neural network (CNN) components. As mortality trends evolve over long time horizons, and as capturing the complex dependencies among mortality rates across countries or regions with a linear model is challenging, the LSTM and CNN were applied to mortality modeling. The former can automatically learn long-term dependencies of sequential data, whereas the latter can extract local features from grid or sequential data. Formulated as a nonlinear generalization of the Lee–Carter decomposition, the model maps the log-mortality matrix $logM$ to future $logm(x,t)$ end-to-end and generates multi-step forecasts through dynamic recursive prediction. Then, the DNN and baseline models were used to fit mortality data of 21 countries from the Human Mortality Database (HMD), which were divided into training and test sets with the year 2000 as the split point. Extensive numerical experiments from the perspectives of accuracy, stability, and reliability of long-term forecasting revealed that DNN models yield better predictive performance, particularly the LSTM-CNN model. It combines the LSTM, CNN, and fully connected network (FCN) layers and thus exploits each deep neural network to fit nonlinear age, period, and cohort effects as well as their interactive terms to achieve better predictive performance. However, the CNN still outperformed other models for certain groups. In addition, the conclusions hold for remaining life expectancy. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

Educational equality is essential for achieving social justice and sustainable development. Accurately predicting the trend of educational inequality is important for improving education systems and ensuring equitable resource allocation. In this paper, the Educational Gini (E-Gini) index is calculated based on the population aged 6 and above in China from 2002 to 2024, quantifying educational inequality. To forecast the future trend in the E-Gini index, a hybrid prediction framework based on the grey prediction model (GM(1,1)) and Cuckoo search-support vector regression (CS-SVR) model is proposed. This framework incorporates three influencing factors, including government budget spending on education, per capita consumption expenditure on education, and the Consumer Price Index (CPI) for education. The results show that the E-Gini of China generally declines from 2002 to 2024 with fluctuations. The proposed approach predicts the E-Gini value of 2024 as 0.220130, while the actual value is 0.2206, corresponding to an absolute error of 0.000470 and a relative error of 0.213%. In the benchmark comparison, the proposed model outperforms the linear trend model, the univariate GM(1,1), the naive persistence model, ARIMA, and the standard SVR model. The comparative analysis demonstrates that the proposed framework effectively captures the inherent patterns of educational inequality and reveals its trends. The proposed framework serves as a valuable tool for forecasting trends in educational inequality and informing policy decisions. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

With the implementation of market-oriented electricity trading in an increasing number of countries, accurate electricity price forecasting can not only help participants in the electricity market to make more reasonable decisions but also enable regulators to have a more reliable regulatory basis. Therefore, it is necessary to propose an appropriate electricity price forecasting method. In view of the insufficiency of the traditional models in dealing with nonlinear and non-stationary data, to improve the detection ability of the model for hidden information in data and considering the high randomness of electricity price data, this paper proposes an electricity price forecasting method based on singular spectrum analysis (SSA) to decompose the original sequence and combines it with an extreme learning machine (ELM) optimized by the grey wolf optimizer (GWO). First, SSA is used to decompose the original sequence, and then the ELM is used to predict each subsequence and add them, in which the number of neurons in the hidden layer of each ELM is jointly optimized by the GWO. To verify the effectiveness of the SSA–GWO–ELM model, a total of 2106 days of electricity price data in Victoria, Australia, were selected for modeling. The results show that the prediction accuracy of the model proposed in this paper is significantly higher than that of the other comparison models, and the R² score is as high as 0.989, which is 0.017 higher than that of the suboptimal SSA–ELM. It can also maintain strong robustness and high prediction accuracy for heterogeneous data on power demand. SSA has the potential for real-time prediction, which can provide reliable data support for electricity market participants and supervisors. Full article

This study examines the dynamic interlinkages among energy, food, and metal commodity markets under geopolitical tensions using daily data from January 2022 to July 2025. The empirical framework integrates correlation analysis, Granger causality tests, and a Vector Error Correction Model (VECM) to capture both short- and long-run transmission mechanisms, with robustness assessed through impulse response functions, forecast error variance decomposition, and a Diebold–Yilmaz connectedness analysis across three structurally distinct geopolitical event windows. The results reveal asymmetric and sector-specific transmission patterns in which geopolitical risk significantly influences key commodity prices—particularly WTI crude oil, wheat, copper, and aluminium—confirming its role as a primary external shock driver. WTI emerges as the dominant transmitter of shocks, while industrial metals exhibit strong internal connectedness. Critically, gold’s role proves to be conditional and context-dependent: within an integrated energy–food–metal network under geopolitical stress, it functions primarily as a net receiver and passive absorber of macroeconomic uncertainty rather than as a systemic transmitter, a finding that complements, rather than contradicts, its established safe-haven role in financial asset pricing frameworks. These findings are subject to limitations, including reliance on futures price data and a linear VECM framework that may not fully capture nonlinear or regime-dependent dynamics. Full article

Using daily data from Investing.com South Africa, this study investigates the forecasting performance of four Rand currency rate markets (EUR/ZAR, CHF/ZAR, BRL/ZAR, and CNY/ZAR) from 13 February 2018 until 24 February 2025. The predictive fitness of three competing models, Support Vector Regression (SVR), Principal Component Regression (PCR), and eXtreme Gradient Boosting (XGBoost), is explored between 80%/20% and 95%/5% training-testing splits. Forecasting accuracy is evaluated based on evaluation errors, i.e., Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The Diebold–Mariano test is employed to check for statistical significance. Empirical results show that the linear SVR model outperforms PCR across all markets, while XGBoost achieves competitive predictive accuracy on average; the trade-offs between SVR and XGBoost are often very small. The data indicate that linear kernel methods provide a robust prediction pipeline, especially when macroeconomic factors (gold, oil, platinum prices, and the USD/ZAR exchange rate) and calendar-based factors are taken into account, and offer a strong framework for predicting daily exchange rate fluctuations. The results of this research provide practitioners (traders, risk managers, and policymakers) with insights into the relative efficiency of the kernel vs. ensemble learning approaches for forecasting the value of emerging-market currencies in the presence of structural volatility. Full article

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative H-day log-returns from OHLCV-derived information and converting them to implied price forecasts. All model families share a homologated design: causal feature construction, a strictly chronological split with an explicit purging rule to prevent label-window overlap for multi-day targets, training-only robustification (winsorization and adaptive clipping), and a unified metric suite computed consistently in return and price spaces. The framework benchmarks transparent baselines (zero- and mean-return), gradient-boosted trees (XGBoost), and deep temporal models (LSTM and CNN/TCN). Lookback length $L\in \{60,180,500\}$ is selected via an internal walk-forward procedure on the pre-evaluation block, and final performance is reported on an external hold-out segment (last 15% of instances). Experiments on daily data for MT, DELL, and the S&P 500 index (through 3 February 2026) show that all families achieve similarly strong price-level fit at $H=1$, largely driven by persistence in the price process, while separation across families becomes more visible at $H=5$. However, predictive performance in return space remains weak, with $R^2$ close to zero or negative, and Diebold–Mariano tests do not provide consistent evidence of statistical superiority over naive benchmarks. Under an operational rule that minimizes hold-out RMSE on the price scale, selected models are asset- and horizon-dependent, supporting horizon-wise selection rather than a single global architecture. Overall, the primary contribution lies in the proposed leakage-controlled evaluation and benchmarking framework rather than in demonstrating consistent predictive gains in financial time series forecasting. Full article

The transition from non-renewable to renewable energy sources has emerged as a pressing global issue, driven by concerns over climate change, resource depletion, and the need for sustainable development. This study compares Canada, an energy-rich nation, and Bangladesh, an energy-scarce country, to understand the structural, institutional, and market factors driving their respective renewable energy transitions. Using univariate time-series models (ARIMA, ETS, and Prophet) for energy demand forecasting and extensive literature-based policy evaluation, the paper examines trends in energy production, consumption, and trade from 1990 to 2024. Our analysis indicates that Canada’s vast reserves of both renewable and non-renewable energy sources, its diversified energy portfolio, and carbon-pricing framework support a stable decarbonization pathway, with renewables projected to account for more than 20% of total supply by 2030. However, regional disparities and political resistance from the established energy sector continue to delay transition outcomes. On the other hand, Bangladesh has limited renewable and non-renewable energy sources, with its primary energy resource being natural gas reserves. Consequently, its heavy reliance on imports (over 75% of primary energy) and institutional bottlenecks expose its energy system to commodity-price volatility, undermining energy security and slowing renewable investment. Despite these challenges, targeted solar programs and concessional financing have modestly increased the penetration of renewable energy. The analysis highlights that commodity market fluctuations, technological innovations (such as smart grids and energy storage), and market-based policy instruments critically shape each country’s transition trajectory. A coordinated policy linking market stabilization, innovation investment, and social inclusion is essential for achieving a just and secure low-carbon transition in both countries. Full article

This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the ${\mathfrak{K}}_1<1$ condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications. Full article

Macroeconomic conditions are widely believed to influence the direction of equity markets, yet most forecasting models either ignore macroeconomic information or incorporate it through a small set of ad hoc predictors. We propose XAttnFusion, a macro–market fusion architecture that jointly learns from high-frequency market data and lower-frequency macroeconomic time series for equity return prediction. The model comprises three branches: a 1D convolutional network that encodes 40-day market windows (price, volume, and technical indicators), a temporal convolutional network that encodes 24-month macro sequences, and a feedforward branch for volume-at-price structure features. These representations are integrated through multi-head cross-attention, in which the current market state queries the macro sequence to produce a fused representation for directional forecasting. We evaluate XAttnFusion on daily SPY returns from 2012 to 2024 using purged cross-validation with a 5-day embargo to prevent information leakage. To address potential look-ahead bias from macroeconomic publication lags, all macro inputs are lagged by two months. The model achieves a mean out-of-sample AUROC of $0.63\pm 0.05$, representing a 27% improvement over random and an 8.1% improvement over the best concatenation baseline. In a fair comparison where each model is independently hyperparameter-tuned, cross-attention fusion improves AUROC by 0.047 over concatenation ($p=0.031$, Wilcoxon signed-rank test). The model also generalizes to QQQ and IWM, where cross-attention consistently outperforms concatenation fusion. Crucially, the model’s discriminative ability is state-dependent, indicating that the value of macro–market fusion is itself conditioned on market structure. Permutation-based feature importance shows that macro and market branches contribute on a comparable scale (approximately 48% and 36%, respectively), so the gains come from jointly fusing two comparably weighted sources rather than from a single dominant input. Our results show that explicitly modeling macro–market interactions with interpretable attention improves predictive accuracy over naive fusion strategies and provides insight into the time-varying relevance of macroeconomic information in financial forecasting and equity market prediction. Full article

This study aimed to compare RNN algorithms and select the best-performing one between the GRU and LSTM for forecasting South African adjusted closing gold prices. The study used weekly secondary data sourced from Yahoo Finance and partitioned into three regimes, pre-COVID-19, COVID-19, and post-COVID-19, as well as the overall sample. The results indicated that the GRU algorithm consistently outperformed the LSTM algorithm across all evaluation periods based on the selected metrics, except during the COVID-19 period, where LSTM exhibited slightly better performance. Consequently, the GRU algorithm was identified as the best-performing algorithm for the South African adjusted closing gold price series. The relative effectiveness of GRU and LSTM algorithms in financial time series forecasting was clarified by the results. By integrating GRU-based forecasts into development finance frameworks, stakeholders can strengthen resilience against global shocks, improve financial planning, and foster more stable pathways for economic development. The authors recommended that future studies explore the performance of the GRU and LSTM with other advanced algorithms like Transformer architectures, hybrid algorithms, or traditional statistical methods to further enhance the forecasting robustness. Full article

This study presents a structured methodology for mid-term electricity demand forecasting in the Greek interconnected power system, incorporating climate-sensitive and socio-economic variables. A set of linear regression models was developed to produce forecasts at both monthly and daily resolutions, aiming to balance accuracy with transparency and computational efficiency. Monthly demand was modeled using macro-trend variables such as GDP, population, and energy prices, while daily demand was approached through a disaggregated modeling structure, assigning a distinct regression model to each day of the week. Temperature effects were introduced at both levels using cooling and heating degree days, estimated based on seasonal weather forecasts provided by 51 meteorological models. The modeling approach developed shows a high predictive value. The monthly electricity demand forecast over a six-month horizon exhibits a mean absolute percentage error and a maximum error of approximately 1.4% and 3.9%, respectively, when actual meteorological data are employed, and 3.7% and 8.5%, respectively, when seasonal meteorological forecasts are used for the entire year 2022, in which it has been tested. Adjusting the model for projecting, the monthly peak load in the same time horizon, presents less accurate yet satisfactory results, with a mean and maximum error of 2.9% and 9.6%, respectively, when actual meteorological data are used, and 5.3% and 12.9%, respectively, when seasonal meteorological forecasts are employed. Full article

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article