Machine learning mutual fund flows

Jürg Fausch, Moreno Frigg, Stefan Ruenzi and Florian Weigert

No 26-03, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

Abstract: 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.

Keywords: Machine learning; fund flow prediction; big data; interpretable machine learning (search for similar items in EconPapers)
JEL-codes: C45 C52 C53 C55 G10 G11 G12 G17 G23 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:337467

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