Supervised Similarity for Firm Linkages
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909659697250304 |
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| author | Samson, Ryan Banner, Adrian Candelori, Luca Cottrell, Sebastien Di Matteo, Tiziana Duchnowski, Paul Kirakosyan, Vahagn Marques, Jose Musaelian, Kharen Pasquali, Stefano Stever, Ryan Villani, Dario |
| author_facet | Samson, Ryan Banner, Adrian Candelori, Luca Cottrell, Sebastien Di Matteo, Tiziana Duchnowski, Paul Kirakosyan, Vahagn Marques, Jose Musaelian, Kharen Pasquali, Stefano Stever, Ryan Villani, Dario |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supervised Similarity for Firm Linkages Samson, Ryan Banner, Adrian Candelori, Luca Cottrell, Sebastien Di Matteo, Tiziana Duchnowski, Paul Kirakosyan, Vahagn Marques, Jose Musaelian, Kharen Pasquali, Stefano Stever, Ryan Villani, Dario Statistical Finance Machine Learning Quantum Physics 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. |
| title | Supervised Similarity for Firm Linkages |
| topic | Statistical Finance Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2506.19856 |