Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/337467 
Erscheinungsjahr: 
2026
Schriftenreihe/Nr.: 
CFR Working Paper No. 26-03
Verlag: 
University of Cologne, Centre for Financial Research (CFR), Cologne
Zusammenfassung: 
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 Morningstar rating as the most important predictors for net- flows, while other past performance variables are of minor importance. We find that the importance of Morningstar ratings and expenses has increased over time. In addition, the interaction effect of past flows with the Morningstar rating has a substantial impact on future flows. Furthermore, our results demonstrate that machine learning-based fund flow predictions can be used to ex-ante differentiate between high and low-performing mutual funds. Finally, funds whose flow predictions can be improved the most using ML reveal the worst performance, consistent with the idea that liquidity management is particularly challenging for these funds.
Schlagwörter: 
Machine learning
fund flow prediction
big data
interpretable machine learning
JEL: 
C45
C52
C53
C55
G10
G11
G12
G17
G23
Dokumentart: 
Working Paper

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