-restructuring performance through a reward-punishment framework and empirically assesses its predictive power using explainable machine learning …

-run adjustments from announcement-driven dynamics, we pair Double Debiased Machine Learning estimates in a fixed-effects panel with a …

This study introduces a novel two-step machine learning methodology to generate high-frequency (daily and weekly … inputs for training machine learning models. In the second step, various feature selection techniques are evaluated and … multiple machine learning algorithms are rigorously fine-tuned via hyperparameter optimization. Through systematic evaluation …

, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear …

sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data …

countries using machine learning models trained on BERT embeddings of historical news articles (1903-2020). The backcasted …

than 50 million municipal bond trades from 2020 to 2024, the empirical approach integrates machine learning estimators with …

data did not previously exist. By training and validating a machine-learning classifier on development finance project … Machine-Learning-Klassifikationsmodell auf Basis der Beschreibungen von 5 Millionen Entwicklungshilfeprojekten aus den Jahren …