Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...

This work introduces a novel method for sentiment analysis in the stock market by combining Deep Neural Networks (DNN) and an improved Firefly Algorithm (FA). It is essential to comprehend investor sentiment in financial markets in order to forecast stock price movements and make wise investment...

This study investigates the impact of competition on credit risk in the Turkish banking system, focusing on Islamic (participation) and conventional banking under the same regulatory conditions regarding credit risk. The credit risk model is trained using CatBoost, extreme gradient boosting...

Explainable Artificial Intelligence (xAI) plays a crucial role in enhancing our understanding of decision-making processes within black-box Machine Learning models. Our objective is to introduce various xAI methodologies, providing risk managers with accessible approaches to model...

Identifying the causal effects of monetary policy is challenging due to the endogeneity of policy decisions. In recent years, high-frequency monetary policy surprises have become a popular identification strategy. To serve as a valid instrument, monetary policy surprises must be correlated with...

The integration of solar photovoltaic systems into power grids has become crucial for sustainable energy development, particularly in extreme climate conditions where accurate power forecasting remains challenging. This study presents a novel multi-architecture machine learning framework with...

This study presents a novel Early Warning System for monitoring the credit risk of commercial customers at a large international bank headquartered in the Netherlands. Traditional early warning methods often rely on backward-looking indicators such as probability of default or loss given...

The escalating pressures of population growth, surging global energy needs, water shortages, reliance on fossil fuels, and urban air pollution underscore the critical demand for sustainable energy alternatives. Biofuels present a viable solution, yet their successful adoption hinges on an...

Current research on evaluating tourism’s ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can...

Current research on evaluating tourism's ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can...

This study investigates how global monetary and oil shocks propagate across advanced and pegged oil economies, focusing on the United States, Germany, the United Kingdom, Saudi Arabia, and the United Arab Emirates over the period 2015-2023. It examines which transmission channels-liquidity,...

This study explores the dynamics of industrial value chains from the perspective of directed graph neural networks (DGNNs). This study focuses on the effects of environmental, social, and governance (ESG) factors on industrial value extension. The industrial value chain, which encompasses a...

Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel...

The purpose of this study is to identify the most effective supervised machine learning models for predicting the financial performance of companies listed on the BIST100 index. In the rapidly evolving field of financial forecasting, machine learning techniques offer robust predictive...

The transition to a zero-emission vehicle fleet represents a pivotal element of Europe’s decarbonization strategy, with Italy’s participation being particularly significant given the size of its automotive market. This study investigates the potential for battery electric cars (BEVs) to...

Green hydrogen, a zero-carbon emission fuel, has become a real competitor to transform the energy market, thanks to improvements in the electrolysis process, decreased costs, and the presence of renewable energy resources. Energy industries have shown considerable progress in hydrogen production...

Background: In the current context of increasing competitiveness and complexity in markets, accurate demand forecasting has become a key element for efficient production planning. Methods: This study implements recurrent neural networks (RNNs) to predict raw material demand using historical...

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine...

Deep learning has transformed numerous areas of data science by achieving outstanding performance in tasks such as image recognition, speech processing, and natural language understanding. Recently, the challenges of financial forecasting-marked by nonlinear dynamics, volatility, and regime...

This study provides a comprehensive evaluation of five deep learning (DL) architectures-TiDE, LSTM, DeepAR, TCN, and Transformer-against the extended Heterogeneous Autoregressive (HAR) model for stock market volatility forecasting. Utilizing 22.5 years of high-frequency data from the S&P 500,...

This study aims to improve the accuracy of transportation delay prediction in supply chains by developing a machine learning-based model. The proposed approach addresses the challenges of parameter tuning and feature selection by integrating the Firefly Algorithm with decision tree regression,...

This work explores the potential of different machine learning (ML) algorithms in the valuation of American options (Aos), contrasting them with the Longstaff-Schwartz (L-S) model. To carry out this research, the algorithms K-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron...

Yield curve extrapolation to unobservable tenors is a key technique for the market-consistent valuation of actuarial liabilities required by Solvency II and forthcoming similar regulations. Since the regulatory method, the Smith-Wilson method, is inconsistent with observable yield curve...

Recent studies highlight the superior performance of non-linear machine learning models, such as neural networks, over traditional linear models in predicting cross-sectional stock returns. These models are capable of capturing complex non-linear interactions between predictive signals and...

Although generative conversational artificial intelligence (AI) can answer questions well and hold conversations as a person, the semantic ambiguity inherent in text-based communication poses challenges to effective use. Effective use reflects the users' utilization of generative conversational...

Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans. It usually appears in locations that are exposed to the sun, but can also appear in areas that are not regularly exposed to the sun. Due to the striking similarities...

Urban functional areas, as an important fundamental component of urban planning, hold significant importance for optimizing urban layouts, resource allocation, and the rational organization of socio-economic activities. Urban functional area division using traditional methods often has...

Predicting the outcome of a sports game is a favourite pastime for sports fans and researchers. The interest has intensified in recent years due to data availability, the development and successful implementation of machine learning algorithms, and the proliferation of internet gaming. This...