Paper: arXiv 2506.19856

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

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0 of 57 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.