Note available here.
For stress testing and strategy formulation, it is important for asset managers to forecast their Assets under Management (AUM) accurately. This paper develops statistical methods for forecasting AUM and applies them to data for 2,494 UK, French, German and Italian-domiciled funds. The results allow one to project forward AUM under different assumptions about market conditions and in a way that is customised for the age, size and fund flow and return histories of the funds in question.
Building on an extensive academic literature, we employ an approach that splits the dynamics of AUM into the contributions of returns and of fund flow (defined as the change in AUM less the effects of returns). We use statistical methods to model these two components separately and then combine the models to generate forecasts of AUM itself.
The effects implied by our estimates are broadly intuitive (in a qualitative sense) for the four domiciles of funds that we analyse. Lagged fund flow and lagged returns imply higher (percentage) growth as does being a young or a small fund. However, the (quantitative) sensitivities of fund flow to different effects and, in particular to lagged returns and lagged fund flow, are much greater for UK funds than for those from other domiciles we consider. Italian funds are least sensitive in this regard. We, also, find very pronounced non-linearities in the relations between fund flow and several of its key determinants.