Deep Prediction Of Investor Interest: a Supervised Clustering Approach

Baptiste Barreau (), Laurent Carlier () and Damien Challet
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Baptiste Barreau: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab
Laurent Carlier: BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab

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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 timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking.

Keywords: investor activity prediction; deep learning; neural networks; mixture of experts; clustering (search for similar items in EconPapers)
Date: 2021-01-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-cwa
Note: View the original document on HAL open archive server: https://hal.science/hal-02276055v3
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Published in Algorithmic Finance, 2021, 8 (3-4), pp.77-89. ⟨10.3233/AF-200296⟩

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Working Paper: Deep Prediction of Investor Interest: a Supervised Clustering Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02276055

DOI: 10.3233/AF-200296

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