  We propose a novel deep learning architecture suitable for the prediction of
investor interest for a given asset in a given time frame. This architecture
performs both investor clustering and modelling at the same time. We first
verify its superior performance on a synthetic scenario inspired by real data
and then apply it to two real-world databases, a publicly available dataset
about the position of investors in Spanish stock market and proprietary data
from BNP Paribas Corporate and Institutional Banking.
