arXiv subjects

Lutfu Sua

Publications and source records attributed to Lutfu Sua.

6 recordsLinked to original sources

arXiv2026

Energy Market and Carbon Emission Spillovers in Critical Minerals Investment: A Dynamic Connectedness Approach

Design/methodology/approach A time-varying parameter vector autoregression (TVP-VAR) model is employed to quantify dynamic connectedness and directional volatility spillovers using daily data from May 1, 2013, to May 2, 2023. The study isolates the impact of extreme events by splitting the data into pre- and post-COVID-19 samples based on the February 2020 stock market crash. Purpose This paper examines the daily financial risk spillovers associated with investing in critical minerals. It examines the dynamic interconnectedness between seven critical mineral Exchange-Traded Fund (ETF) portfolios and key economic-wide variables, including the energy market, carbon emissions, market sentiment, and global infrastructure. Findings Portfolios with high Environmental, Social, and Governance (ESG) scores significantly contribute to shock spillovers. Net directional connectedness analysis reveals that West Texas Intermediate (WTI) crude oil and carbon emission futures consistently act as "net receivers," absorbing volatility from the system. Conversely, Cobalt and Aluminum ETFs primarily act as "net givers," transmitting volatility. The pandemic caused significant structural shifts in these transmission roles. Practical implications The identification of specific net givers and receivers provides actionable insights for investors, facilitating better hedging strategies against time-varying structural breaks and broader economic shocks. Originality This study uniquely utilizes financial ETF data rather than physical mineral prices to capture accessible investment risks. It is among the first to link ESG scores to the directional role (giver vs. receiver) of critical mineral assets within a broader macro-financial network.

econ.EM↗

arXiv2025

The Impact of Trade and Financial Openness on Operational Efficiency and Growth: Evidence from Turkish Banks

This paper examines the relationship between trade and financial openness, as well as the operational efficiency and growth of Turkish banks, from 2010 to 2023. Utilizing CAMELG-DEA and dynamic panel data analysis, the study finds that increased trade openness significantly enhances banking efficiency, primarily due to heightened demand for banking services related to international trade. Financial openness further boosts growth by facilitating capital flows, expanding banks' credit portfolios, and increasing fee income from cross-border transactions. However, poverty levels have a negative impact on bank performance, reducing financial intermediation and innovation opportunities. The results underscore the crucial role of trade and financial openness in fostering banking sector growth in developing economies.

econ.GN↗

arXiv2025

Exploring Trade Openness and Logistics Efficiency in the G20 Economies: A Bootstrap ARDL Analysis of Growth Dynamics

This study examines the relationship between trade openness, logistics performance, and economic growth within G20 economies. Using a Bootstrap Autoregressive Distributed Lag (ARDL) model augmented by a dynamic error correction mechanism (ECM), the analysis quantifies both short run and long run effects of trade facilitation and logistics infrastructure, measured via the World Bank's Logistics Performance Index (LPI) from 2007 to 2023, on economic growth. The G20, as a consortium of the world's leading economies, exhibits significant variation in logistics efficiency and degrees of trade openness, providing a robust context for comparative analysis. The ARDL-ECM approach, reinforced by bootstrap resampling, delivers reliable estimates even in the presence of small samples and complex variable linkages. Findings are intended to inform policymakers seeking to enhance trade competitiveness and economic development through targeted investment in infrastructure and regulatory reforms supporting trade facilitation. The results underscore the critical role of efficient logistics specifically customs administration, physical infrastructure, and shipment reliability in driving international trade and fostering sustained economic growth. Improvements in these areas can substantially increase a country's trade capacity and overall economic performance.

econ.GN↗

arXiv2025

Deep Learning in Renewable Energy Forecasting: A Cross-Dataset Evaluation of Temporal and Spatial Models

Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy domain. Given the nonlinear relationships among variables in renewable energy datasets, DL models are preferred over traditional machine learning (ML) models because they can effectively capture and model complex interactions between variables. This research aims to identify the factors responsible for the accuracy of DL techniques, such as sampling, stationarity, linearity, and hyperparameter optimization for different algorithms. The proposed DL framework compares various methods and alternative training/test ratios. Seven ML methods, such as Long-Short Term Memory (LSTM), Stacked LSTM, Convolutional Neural Network (CNN), CNN-LSTM, Deep Neural Network (DNN), Multilayer Perceptron (MLP), and Encoder-Decoder (ED), were evaluated on two different datasets. The first dataset contains the weather and power generation data. It encompasses two distinct datasets, hourly energy demand data and hourly weather data in Spain, while the second dataset includes power output generated by the photovoltaic panels at 12 locations. This study deploys regularization approaches, including early stopping, neuron dropping, and L2 regularization, to reduce the overfitting problem associated with DL models. The LSTM and MLP models show superior performance. Their validation data exhibit exceptionally low root mean square error values.

cs.LG↗

arXiv2025

Renewable Energy Prediction: A Comparative Study of Deep Learning Models for Complex Dataset Analysis

The increasing focus on predicting renewable energy production aligns with advancements in deep learning (DL). The inherent variability of renewable sources and the complexity of prediction methods require robust approaches, such as DL models, in the renewable energy sector. DL models are preferred over traditional machine learning (ML) because they capture complex, nonlinear relationships in renewable energy datasets. This study examines key factors influencing DL technique accuracy, including sampling and hyperparameter optimization, by comparing various methods and training and test ratios within a DL framework. Seven machine learning methods, LSTM, Stacked LSTM, CNN, CNN-LSTM, DNN, Time-Distributed MLP (TD-MLP), and Autoencoder (AE), are evaluated using a dataset combining weather and photovoltaic power output data from 12 locations. Regularization techniques such as early stopping, neuron dropout, L1 and L2 regularization are applied to address overfitting. The results demonstrate that the combination of early stopping, dropout, and L1 regularization provides the best performance to reduce overfitting in the CNN and TD-MLP models with larger training set, while the combination of early stopping, dropout, and L2 regularization is the most effective to reduce the overfitting in CNN-LSTM and AE models with smaller training set.

cs.LG↗

arXiv2024

Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models

Numerous established machine learning models and various neural network architectures can be restructured as Quadratic Unconstrained Binary Optimization (QUBO) problems. A significant challenge in Adiabatic Quantum Machine Learning (AQML) is the computational demand of the training phase. To mitigate this, approximation techniques inspired by quantum annealing, like Simulated Annealing and Multiple Start Tabu Search (MSTS), have been employed to expedite QUBO-based AQML training. This paper introduces a novel hybrid algorithm that incorporates an "r-flip" strategy. This strategy is aimed at solving large-scale QUBO problems more effectively, offering better solution quality and lower computational costs compared to existing MSTS methods. The r-flip approach has practical applications in diverse fields, including cross-docking, supply chain management, machine scheduling, and fraud detection. The paper details extensive computational experiments comparing this r-flip enhanced hybrid heuristic against a standard MSTS approach. These tests utilize both standard benchmark problems and three particularly large QUBO instances. The results indicate that the r-flip enhanced method consistently produces high-quality solutions efficiently, operating within practical time constraints.

quant-ph↗