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.

翻译：我们提出了一个新的深层次学习架构,适合在特定的时间框架内预测特定资产投资者的权益。这一架构同时进行投资者集群和建模。 我们首先根据由真实数据启发的合成情景核实其优异业绩,然后将其应用于两个真实世界数据库,一个公开的关于投资者在西班牙股票市场地位的数据集,另一个是法国巴黎银行PARIBAS公司和机构银行的专有数据。