We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods …. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using … impact on future flows. Furthermore, our results demonstrate that machine learning-based fund flow predictions can be used to …

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods …. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using … impact on future flows. Furthermore, our results demonstrate that machine learning-based fund flow predictions can be used to …

comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional … machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an …

machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western … for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional …

Statistische Bundesamt hat in einer Machbarkeitsstudie untersucht, ob Machine-Learning-Modelle geeignet sind, um Daten der … much earlier. The Federal Statistical Office conducted a feasibility study to examine whether machine learning models are …

National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost … machine learning. Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as …