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 …

Stock Exchange (‘JSE’). We apply 1- and 3-month style momentum as naïve predictors against three tree-based machine learning … application of machine learning ("ML") for style rotation. The results indicate that ML based rotation signals generate excess …

certifications. We employed a quantitative, cross-sectional methodology using three machine learning algorithms (Decision Tree … Classification, Random Forest, Logistic Regression) and the double machine learning technique to identify causal effects and …