Deep Prediction of Investor Interest: a Supervised Clustering Approach
Machine Learning
2021-03-01 v3 Computational Finance
Machine Learning
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.
Keywords
Cite
@article{arxiv.1909.05289,
title = {Deep Prediction of Investor Interest: a Supervised Clustering Approach},
author = {Baptiste Barreau and Laurent Carlier and Damien Challet},
journal= {arXiv preprint arXiv:1909.05289},
year = {2021}
}