Deep Prediction of Investor Interest: a Supervised Clustering Approach

TL;DR

This paper introduces a deep learning model that simultaneously clusters investors and predicts their interest in assets, demonstrating superior performance on synthetic and real-world financial datasets.

Contribution

The paper presents a novel deep architecture that combines investor clustering and interest prediction in a unified framework, validated on multiple datasets.

Findings

Superior performance on synthetic data

Effective application to real-world stock market data

Demonstrates practical utility in financial analysis

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.