Supervised Similarity for Firm Linkages

TL;DR

This paper introduces Characteristic Vector Linkages (CVLs) as a new proxy for firm linkages and applies Quantum Cognition Machine Learning (QCML) to improve similarity estimation, resulting in more profitable trading strategies.

Contribution

The paper presents a novel proxy for firm linkages and demonstrates the effectiveness of QCML in improving similarity learning over traditional Euclidean methods.

Findings

QCML outperforms Euclidean similarity in constructing profitable strategies

CVLs effectively estimate firm linkages

Quantum cognition enhances similarity learning

Abstract

We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.