This study proposes a novel attention-based deep neural network (AttDNN) model specifically designed for predicting mergers and acquisitions (M&A). The model extends existing deep learning frameworks by incorporating M&A-specific features, regularization layers, and an attention mechanism that...

This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000-2023). Weak signals, conceptualized as subtle anomalies within otherwise...

Accurate wind power forecasting is crucial for the effective scheduling and scientific management of wind energy, enhancing the safety and reliability of power grids. To handle the inherent intermittency of wind energy, forecasting methodologies have evolved from traditional statistical models...

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated...

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...

Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

The natural interest rate is the real rate that would prevail in the long-run. The standard view in macroeconomics is that the natural rate depends exclusively on structural factors such as productivity growth and demographics. This paper challenges this view by discussing three alternative, and...

Environmental, social, and governance (ESG) considerations are becoming increasingly vital in corporate decision-making, especially for global companies, and in evaluating corporate performance and investments. This study examines the ESG tendencies of the companies with the largest market...

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory...

This paper presents a framework to operationalize the multidimensional construct of a bank's business model (BBM). We conceptualize the construct from a structural perspective, defining it as its balance sheet's strategic composition and structure, encompassing asset allocation and funding...

This study revisits the macroeconomic determinants of non-performing loans using a deep neural network (DNN). We present the proposed DNN as a methodological framework that combines deep learning techniques and causal inference methods. We employ a rigorous triple-validation methodology that...

Optimizing solid waste management (SWM) is essential for ensuring a sustainable and healthy environment in a city. This study considers a two-echelon solid waste logistics system (2E-SWLS) in a metropolitan city with a fleet of capacitated heterogeneous vehicles. The problem consists of waste...

In the automotive sector, vehicle data gathered through On-board Diagnostics (OBD) systems offers continuous insights into vehicle health status and performance. Leveraging this data for predictive maintenance can significantly reduce unplanned failures, enhance safety, and extend vehicle...

Imbalanced data is often encountered in scenarios where rare but critical events occur much less frequently than others, and it is particularly prominent in fields such as disease diagnosis, fraud detection, and risk management. The main problem with imbalanced data is that predictive models...

Insurance fraud detection is a significant problem in the insurance industry, producing immeasurable losses. Conventional insurance fraud detection models depend heavily on experts' knowledge, and accurately estimating fraud when the data and the claim data are enormous is a complex and...

Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals,...

This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of...

This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by...

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...

This study explores supervised ML-DL based approaches for enhancing credit card fraud detection and improving financial risk management systems for EU banks. This research proposes an ensemble method based on majority voting (Hard Voting Classifier) of deep learning models to detect fraud...

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,...

Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise prediction intervals that can offer valuable insights. Thus far, we introduced a novel hybrid...

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization,...

Salience theory has been proposed as a new stock trading strategy. To assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational mode decomposition (VMD) model, a bidirectional gated recurrent unit (BiGRU) model, and...

We develop firm-level measures of input and output price changes using textual analysis of earnings calls. We establish five facts: (1) Input prices increase (decrease) at the median firm once every seven (30) months. (2) Input price changes contain an equal blend of aggregate and firm-specific...