Supervised Similarity for Firm Linkages

ArXiv ID: 2506.19856 “View on arXiv”

Authors: Ryan Samson, Adrian Banner, Luca Candelori, Sebastien Cottrell, Tiziana Di Matteo, Paul Duchnowski, Vahagn Kirakosyan, Jose Marques, Kharen Musaelian, Stefano Pasquali, Ryan Stever, Dario Villani

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.

Keywords: firm linkages, momentum spillover, quantum cognition machine learning, similarity learning, trading strategies

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including quantum cognition machine learning with Hilbert spaces and Hermitian operators, scoring high on complexity. It demonstrates backtest-ready empirical results on constructing profitable momentum spillover strategies, with detailed methodology and performance comparison, indicating strong empirical rigor.

  flowchart TD
    A["Research Goal:<br>Estimate Firm Linkages"] --> B["Data: Firm Characteristics"]
    B --> C["Methodology 1:<br>Euclidean Similarity"]
    B --> D["Methodology 2:<br>QCML Similarity Learning"]
    C --> E["Trading Strategy:<br>Momentum Spillover"]
    D --> E
    E --> F["Outcome:<br>Profitable Strategies"]
    F --> G["Key Finding:<br>QCML Outperforms Euclidean"]