Deep Prediction of Investor Interest: a Supervised Clustering Approach

Baptiste Barreau, Laurent Carlier and Damien Challet

Papers from arXiv.org

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.

Date: 2019-09, Revised 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Published in Algorithmic Finance, vol. 8, no. 3-4, pp. 77-89, 2020

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http://arxiv.org/pdf/1909.05289 Latest version (application/pdf)

Related works:
Working Paper: Deep Prediction Of Investor Interest: a Supervised Clustering Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1909.05289

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