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arXiv · 1909.05289

Deep Prediction of Investor Interest: a Supervised Clustering Approach

Baptiste Barreau·Laurent Carlier·Damien Challet
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Abstract

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.

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Baptiste Barreau, Laurent Carlier, Damien Challet. 2019-09-11. Deep Prediction of Investor Interest: a Supervised Clustering Approach. https://doi.org/10.3233/af-200296

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