Search Results (130)

Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. This study examined volatility transmission from global commodity shocks to Kenya’s USD/KES exchange rate and the NSE 20 Share Index using daily data from November 1997 to December 2024. While GARCH specifications capture clustering, they are sensitive to structural breaks and regime changes, which distort persistence and weaken risk measures. Machine learning approaches provide alternatives capable of capturing nonlinear dependencies, abrupt volatility bursts, and regime-independent dynamics. Empirical evidence demonstrates that the 2008 Global Financial Crisis and COVID-19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES and the NSE 20 Share Index. Structural break-detection was integrated through the Iterative Cumulative Sum of Squares algorithm, alongside APARCH, FIGARCH models and ML architectures (XGBoost, LSTM). In Kenya volatility is characterized by strong persistence and long-memory dynamics, with limited evidence of leverage effects. Break-adjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. Volatility transmission is nonlinear, break-sensitive, and state-dependent; hybrid ML–econometric methods enhance crisis forecasting and regime-sensitive financial stability analysis. 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

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

Heat recovery from greywater is one solution for improving the energy efficiency of buildings and reducing greenhouse gas emissions. Particular attention is paid to systems utilizing heat from shower water, which, due to its high temperature and regularity, represents a promising energy source. However, the interplay of parameters determining the financial and environmental effectiveness of such a solution has not yet been fully explored. Therefore, the aim of this paper was to identify key variables influencing the feasibility of using a shower heat exchanger operating under unbalanced flow conditions and to assess the consistency between financial and environmental effects. The analyzed net present values ranged from −€1381 to €52,168. Greenhouse gas emission reduction values ranged between 61 kgCO₂e and 37,207 kgCO₂e. The analysis was conducted using predictive modeling and the SHAP (SHapley Additive exPlanations) method, which allows for the interpretation of the impact of individual variables on the forecasted net present value and potential greenhouse gas emission reduction. A global analysis was carried out to determine the relative importance of variables, as well as a local analysis for selected cases. The results showed that operational variables related to shower use, particularly shower length and mixed water flow rate, significantly influenced the prediction results of both models. In the case of emission reduction, greenhouse gas emission intensity and its change over time also had a significant impact, whilst the financial effects were determined by the energy price from the perspective of the subsequent years of the system’s operation. Full article

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–2018 training data + 2019–2023 validation data + 2024 testing data) to train and evaluate the model provides realistic forecasting and prevents temporal leakage. The global panel dataset for more than 250 and 60+ year countries and regions demonstrate that all of the proposed models are better than all classical machine learning models, stand-alone deep learning models, and naive persistence models. The hybrid model shows the most significant prediction error reduction with R² as 0.98, proving long-horizon currency forecasting is extremely robust. Full article

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis. Full article

This paper offers a hybrid forecasting approach that merges a local-level state space Kalman filter with a Long-Short-Term Memory (LSTM) neural network to assess the downside risk of the Botswana Pula versus the US Dollar (BWP/USD). Inspired by the inability of conventional econometric models to capture complex latent structural shifts and nonlinear patterns, our architecure uses a bottom-up hierarchical methodology in which the smoothed level component of the exchange rate is isolated by the Kalman filter and subsequently fed into the LSTM architecture. Three key indicators for assessing downside risk—Maximum Drawdown (MDD), Conditional Drawdown-at-Risk (CDaR), and Downside Deviation—are used to assess model performance across various time-frames (7, 30, 90, 180, and 240 days). As confirmed by Kupiec and Christoffersen’s backtesting processes, the findings show a high degree of alignment between projected and actual values, with negligible downside deviation bias and robust calibration. Moreover, global economic and geopolitical shocks, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the 2015–2016 Shanghai Stock Exchange crash, are important factors that influence exchange rate volatility, according to explainable artificial intelligence techniques, particularly SHAP (SHapley Additive exPlanations) analysis. Downside risk is also greatly increased by regional currency links, especially the impact of the ZAR/BWP exchange rate. On the other hand, domestic temporal variables, such as week, quarter, and month, have very little impact. These results emphasise how Botswana’s currency rate is structurally vulnerable to external shocks and how crucial it is to include both global and regional considerations in risk analysis. The research concludes that the accuracy and transparency of projections for exchange rate risk significantly improve when practical filtering is combined with deep learning and explainable AI. To improve macroeconomic resilience and guide successful financial risk management plans in emerging market environments, policymakers are advised to employ AI-driven forecasting techniques, enhance regional monetary coordination, and set up real-set learning systems. Full article

Exchange rate forecasting is an important issue in financial market analysis. Currency rates form a dynamic network of connections that can be efficiently modeled using graph neural networks (GNNs). The key mechanism of GNNs is the message passing between nodes, allowing for better modeling of currency interactions. Each node updates its representation by aggregating features from its neighbors and combining them with its own. In convolutional graph neural networks (GCNs), all neighboring nodes are treated equally, but in reality, some may have a greater influence than others. To account for this changing importance of neighbors, graph attention networks (GAT) have been introduced. The aim of the study was to evaluate the effectiveness of GAT in forecasting exchange rates. The analysis covered time series of major world currencies from 2020 to 2024. The forecasting results obtained using GAT were compared with those obtained from benchmark models such as ARIMA, GARCH, MLP, GCN, and LSTM-GCN. The study showed that GAT networks outperform numerous methods. The results may have practical applications, supporting investors and analysts in decision-making. Full article

This paper uncovers a paradoxical disconnect between two widely used metrics for forecast evaluation: Mean Directional Accuracy (MDA) and the correlation between the forecast and the target variable. We show that a forecast that is more strongly correlated with the target may deliver poorer sign predictions than a less correlated alternative. Within a Gaussian framework, we derive analytical expressions showing that directional accuracy depends not only on correlation but also on the standardized means of both the forecast and the target variable. As a consequence, higher correlation does not guarantee superior sign predictability. We illustrate this paradox through analytical examples and derive formal conditions under which it cannot arise. Interestingly, we show that when forecasts are efficient, the MDA Paradox is impossible. Finally, we present an empirical application from the exchange rate literature that demonstrates the practical relevance of our results. Full article

Rapid urbanization and infrastructure expansion in Ghana have intensified demand for concrete, yet reliable, context-specific forecasting tools to support long-term infrastructure planning and resource management remain limited. Existing demand models are largely developed for advanced economies or focused on cement production rather than final concrete consumption, limiting the applicability to rapid urbanizing developing countries. This study addresses this gap by developing an integrated forecasting framework to quantify and project concrete demand in Ghana. Using time-series data spanning 2000–2025, the study employs a modelling approach that combines the Autoregressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to examine both short- and long-run relationships between concrete consumption and key macroeconomic indicators, including GDP, population, GDP growth, concrete prices, housing loan interest rates, lending rates, and exchange rates. Forecast results for 2025–2030 indicated a sustained upward trend in concrete consumption, increasing from 39,278.52 m³ in 2026 to 99,430.53 m³ in 2030, with an average annual growth rate of 26.3% and a mean projected demand of 67,730.83 m³. Model evaluation metrics demonstrated high predictive accuracy, confirming the robustness of the proposed framework. The study contributes to the literature on construction demand forecasting by providing a context-specific, empirically validated model of concrete consumption in a developing economy. The findings offer actionable insights for policymakers, urban planners, and construction managers, underscoring the need to proactively scale local production capacity, strengthen supply-chain logistics, and promote sustainable material sourcing to support infrastructure development. Full article

The Meese–Rogoff puzzle suggests that exchange rate models rarely outperform a random walk in out-of-sample forecasting. This paper re-examines that puzzle in the context of the German hyperinflation, an environment in which monetary forces dominate economic behavior. Using simple bivariate specifications derived from the quantity theory of money, purchasing power parity, and the monetary model of exchange rates, the paper evaluates forecasting performance against a random walk benchmark. The results show that during the most intense phase of hyperinflation, these fundamentals-based models can outperform the random walk in terms of root mean square error. This finding indicates that exchange rate predictability is regime-dependent and that, under extreme monetary instability, basic theoretical relationships can regain forecasting power. Full article

Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the network structure of the Korean won exchange rate against 36 major trading-partner currencies. The model combines the generalised Yule–Walker equations with structured penalisation to jointly estimate instantaneous and lagged interactions in a data-driven manner. This approach allows for the recovery of economically meaningful spillover networks while maintaining tractability in high dimensions. Using daily data from 2019 to 2023, the results reveal pronounced contemporaneous spillovers among currencies closely tied to Korea’s trade and financial networks, notably the U.S. dollar, Chinese yuan, Japanese yen, and key ASEAN currencies. Monte Carlo simulations confirm the estimator’s consistency and convergence properties, while empirical forecasting exercises demonstrate systematic improvements in both mean-squared and robust error metrics relative to benchmark VAR and spatial autoregressive models. The evidence highlights that modelling sparse, high-dimensional time series structures enhances predictive accuracy and interpretability, particularly under nonstationary and heterogeneous conditions. The proposed framework provides a flexible tool for exploring interconnected time series in economics and finance, offering new insights into exchange-rate linkages and risk transmission in globally integrated markets. Full article

To address the nonlinear nature of exchange rates where drivers vary by time horizon, this paper proposes a CEEMDAN-PE-CatBoost-SHAP framework. Analyzing USD/CNY data (2012–2024), we decomposed rates into high, medium, and low frequencies to bridge machine learning with economic interpretability. Empirical results revealed distinct frequency-dependent drivers: high-frequency fluctuations depend on market sentiment; medium-frequency variations follow Fed policies; and low-frequency trends reflect fundamentals like gold prices. SHAP analysis provides transparent attribution of these factors. This multi-scale approach isolates heterogeneous drivers, offering policymakers and investors a nuanced paradigm for managing currency risks. The study significantly clarifies how different economic factors shape exchange rate dynamics across varying time scales. Full article

Youth unemployment remains one of the most persistent and structurally sensitive challenges in emerging economies, particularly in environments characterized by macroeconomic volatility and frequent shocks. This study investigates the dynamics and forecasting performance of youth unemployment in Turkey by adopting a symmetry-based multivariate framework that explicitly contrasts equilibrium-oriented and asymmetric temporal behaviors. Using monthly data covering the period 2009–2024, youth unemployment is modeled jointly with key macroeconomic indicators, including economic growth, inflation, overall unemployment, labor force participation, migration, exchange rates, and consumer confidence. The empirical strategy integrates traditional econometric models and modern machine learning approaches under a unified and leakage-free evaluation protocol. Stationarity and long-run properties of the series are examined using unit root tests and the Bayer–Hanck cointegration approach, followed by long-run coefficient estimation via FMOLS and DOLS. Forecasting performance is then compared across VARIMA, Prophet, and deep learning models (RNN, LSTM, and GRU), including both vanilla and hyperparameter-tuned specifications. The results reveal a clear performance hierarchy. VARIMA models, particularly the VARIMA (p = 2, q = 0) specification, consistently outperform all alternatives by a wide margin, achieving exceptionally low forecast errors. This finding indicates that youth unemployment in Türkiye is predominantly governed by symmetric co-movements and long-run equilibrium relationships among macroeconomic variables. Prophet and GRU models capture short-term and regime-sensitive fluctuations more flexibly, reflecting asymmetric temporal responses, but at the cost of higher forecast dispersion. In contrast, RNN and LSTM models exhibit limited generalization capability and are prone to overfitting in the small-sample macroeconomic context. As a result, this study positions the estimation of youth unemployment as both an econometric challenge and a symmetry-based analytical problem, offering new methodological and conceptual insights consistent with a fresh perspective. Full article

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but its sustainability benefits depend on reliable, constraint-aware nutrient dosing under delayed inflow effects. Aquaponics involves coupling hydroponics with aquaculture but is difficult to control because the greenhouse/crop state at the current time step ($t$) must anticipate water-quality changes that arrive at the next time step ($t+1$), under hard EC–pH and dose constraints. We propose the Analysis System for Nutrient Requirements in Hydroponics (ASNRH), a two-module, constraint-aware framework that directly regresses next-step elemental supplementation (N, P, K; mg·L⁻¹). First, the Fish-farm By-product Prediction Module (FBPM) uses a lightweight GRU forecaster to predict inflow chemistry at $t+1$ (e.g., NH₄⁺/NO₂⁻/NO₃⁻, alkalinity) from standard aquaculture sensors. Second, the Nutrient Requirement Prediction Module (NRPM) encodes the current hydroponic and crop state at t in parallel with the FBPM inflow at $t+1$ via a dual-branch architecture and fuses both representations to produce non-negative dose recommendations while penalizing forecasted EC/pH violations and excessive actuation volatility. The data pipeline assumes low-cost greenhouse and aquaculture sensors with chronological, leakage-free splits. A protocol-first simulation evaluates ASNRH against time-series and rule-based baselines using accuracy metrics (MAE/RMSE/R²), EC/pH violation rates, and robustness under missingness/noise; ablations isolate the contributions of the inflow branch, constraint-aware losses, and lightweight physics priors. The framework targets deployability in decoupled or coupled aquaponics by structurally resolving $t$ vs. $t+1$ asynchrony and internalizing domain constraints during learning; procedures are specified to support reproducibility and subsequent field trials. By operationalizing anticipatory dosing from reused aquaculture byproducts under EC/pH feasibility constraints, ASNRH is designed to support sustainability goals such as reduced nutrient wastage and fewer corrective water exchanges in coupled or decoupled aquaponics. Full article