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One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation — arXiv:2611.05952

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05952
License (as archived): CC0-1.0
Archived: 2026-11-05T18:29:20.652Z
SHA-256 of the archived text: 18b0f71537b505b8d1e6c93670bceb73f33c5889b7d19a752e96191f8d66ec9a
Stable URL of this page: https://metraipolis.com/documento/dc_2dfefa1eb7eb

How to cite

One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation — arXiv:2611.05952 — arXiv (en), https://arxiv.org/abs/2611.05952 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2dfefa1eb7eb (Metraipolis archive dc_2dfefa1eb7eb, sha256 18b0f71537b505b8d1e6c93670bceb73f33c5889b7d19a752e96191f8d66ec9a)

BibTeX

@misc{dc2dfefa1eb7eb,
  title = {One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation — arXiv:2611.05952},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05952},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2dfefa1eb7eb; sha256 18b0f71537b505b8d1e6c93670bceb73f33c5889b7d19a752e96191f8d66ec9a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05952. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Financial asset recommendation for retail investors is typically optimized for relevance, that is, how well a recommendation matches what each user will buy. From the service provider's point of view, the value of a recommendation also depends on portfolio performance: the risk-adjusted return the recommended assets (the basket) deliver, which drives user engagement and fee revenue. The two goals are rarely achieved jointly, because the recommendations that best match user behavior concentrate on a few popular assets whose returns are highly correlated, which caps the risk-adjusted return a provider can offer. We propose Decorrelated-Satellite Expert Re-ranking (\textbf{DSER}), which retains the most relevant recommendations from an expert-following list and replaces the rest with return-decorrelated alternatives. The number of replaced slots is a single integer~$m$ (a knob) that the provider sets, so the relevance--performance trade-off becomes an explicit, auditable choice rather than a property hidden inside a learned model. In experiments on the public FAR-Trans retail-brokerage dataset, under the primary protocol, replacing a few slots improves risk-adjusted return at a small relevance cost, and the gain survives realistic trading costs at typical retail portfolio sizes. Minimizing correlation would lower basket volatility, but in our data the least-correlated assets are more volatile and higher-return, so their volatility offsets that reduction while their return drives the gain, not lower volatility. The resulting trade-off is asymmetric, with the relevance cost consistent across market phases while the gain is phase-dependent. The provider sets $m$ to read off exactly how much relevance each operating point costs.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.IR, nuovi lavori del giorno).
Categorie arXiv: Information Retrieval (cs.IR) ; Computational Engineering, Finance, and Science (cs.CE).
Pagina del lavoro: https://arxiv.org/abs/2611.05952 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-05T18:19:33.955Z dalla pagina https://arxiv.org/list/cs.IR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).