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Université Paris Nanterre, G614B (bâtiment G)

  Organisation : Remzi Uctum
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date_range 26 NOVEMBER 2026

Words that trade: A performance analysis of AI rebalancing agents

Speaker(s)

Gilles de Truchis (Université d'Orléans)

Additional information

Résumé : Can a large language model add value to a portfolio management by reading the news? We build a controlled experiment to answer this question on the SBF 120 over 2025, a year that post-dates the training of the two open-weight models we test. News are generated from a latent model, so that the information they carry is a parameter we set rather than a quantity we infer. The models read them weekly and tilt an equally weighted portfolio within an explicit, bounded rule. At the reference dose of information (R-squared of 0.17) the LLM agents earn Sharpe ratios of 2.3-2.4, against 1.1-1.4 for 1/N, minimum variance and risk parity, and 1.3 for the same model reading an unstructured digest of the news. The gain is in return at constant risk, 47% against 19% a year. To analyse further what the LLM brings, each AI agent is paired with a deterministic twin that receives the same information and applies the same rule. The twin earns more: the agent sits 0.1-0.2 below it at every dose of information, because it under-trades and reads the texts no better than a regular expression does. The way the rule is written in the prompt moves the result more than which model is used. What the agentic architecture adds is the structure it imposes on the information, not the model’s judgement.

date_range 10 DECEMBER 2026

TBA

Speaker(s)

Jean-Marc Zogheib

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