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...

We study how consumers form and revise inflation expectations using a unique, highly balanced monthly panel of Dutch households. We develop a Bayesian framework that nests Full-Information Rational Expectations (FIRE) alongside common forecasting heuristics and test it by recovering...

Early adoption of artificial intelligence (AI) reshaped how firms responded to market dynamics by enhancing data collection and analysis. Linking China's universe of customs shipments to millions of online job ads, we tracked AI hiring in sales, marketing, and analytics to build a firm-level...

The resurgence of subsidies and industrial policies has raised concerns about their potential inefficiency and alignment with multilateral principles. Critics warn that such policies may divert resources to less efficient firms and provoke retaliatory measures from other countries, leading to a...

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...

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 -...

Can a recession have a positive, long-term impact on firm innovation? A downturn represents an opportunity for firms to learn from their peers and try to understand the main drivers of resilience. If they deem R&D capital one of them, they may raise innovation in the following years, in order to...

We study how confidence bias affects investment in learning via experimentation, a mechanism critical for technology adoption under uncertainty. We hypothesize that bias direction and strength predict how willingness to experiment diverges from unbiased agents. We measure revealed and stated...

We document systematic parental under-reporting of children's socio-emotional difficulties relative to children's self-reports, using representative data from Luxembourg, the United Kingdom, and Australia. To study the origins of this discrepancy, we develop a simple theoretical framework...

Identifying sensitive periods in which the returns to investments into skills are especially high is challenging, but crucial for an effective and efficient timing of parental or public investments aimed at fostering children's skills. We can detect sensitive periods with a novel design by...

This work highlights a previously overlooked factor that contributes to bias in private inflation forecasts-ignorance of confidential monetary rules. Additionally, it examines how this ignorance indirectly affects policy rate settings. The model proposed reconciles biases in two key forecast...

Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm's optimization with a deep...

Organizational learning theory has long recognized the advantages of sequential repetition, since successive events allow organizations multiple opportunities to iteratively refine their approaches. In many situations, though, events unfold so quickly that they can overlap, becoming more...

This study explores the relationship between multidimensional technology adoption and utilization with exports, focusing on textile and clothing firms in Indonesia. Grounded by self-selection and learning-by-exporting hypotheses, this study uses binomial probit and ordinary least squares (OLS)...

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,...

An adaptive analytics workflow is presented for customer churn prediction, combining Principal Component Analysis for dimensionality reduction, a hybrid Modified Particle Swarm Gravitational Search Optimization (MPSO-GSO) for feature selection and hyperparameter tuning, and an ensemble learning...

Personality prediction has become an increasingly important area in psychological computing and human-centered AI, especially with the rise of user-generated textual data from social media platforms. However, current approaches - primarily based on supervised learning - face major challenges in...

Context: This study uses a social space approach to investigate Virtual Reality (VR)-based workplace learning in the context of apprenticeship training in the electronics industry in Switzerland. It was one part of a project which developed a prototype VR environment that enables apprentices to...