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 interpretable ML methods, we identify past flows and the...

Accurate prediction of customer repurchase behaviour is vital for businesses aiming to boost customer retention. This study introduces an advanced approach that merges classification algorithms with RFM analysis, a widely adopted framework in customer relationship management. The proposed models...

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 interpretable ML methods, we identify past flows and the...

We examine how machine learning models predict stock returns in the Korean market. By analyzing various firm characteristics and macroeconomic variables, we find that tree-based models outperform other machine learning approaches. This finding suggests that, in data-constrained contexts,...

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently,...