Thinking Fast, Trading Late: System One Decision Models vs the Market

Amir Dehkordi, Alireza Kargarzadeh, Nariman Khaledian, Navid Parvini, Sid Ghatak and Arman Khaledian
Additional contact information
Amir Dehkordi: Independent Researcher
Alireza Kargarzadeh: TailState Intelligence Ltd
Nariman Khaledian: Independent Researcher
Navid Parvini: Zanista AI Ltd
Sid Ghatak: Increase Alpha, LLC
Arman Khaledian: Zanista AI Ltd

Papers from arXiv.org

Abstract: Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns. We ask how much of that predictability survives a test that rules out look-ahead, memorization and selection. We propose OLIVIA, a protocol for such tests: point-in-time inputs with company identities masked, selection on validation data only, an analysis plan frozen by cryptographic hash, and a test period unblinded once through a logged lock. We apply it to AI decision models, non-generative language models that return probabilities for typed questions, reading news about S&P 500 stocks: an open-weights model we fine-tuned on 2024-2025 data, three commercial models and standard baselines. The fine-tuned model's signal replicated on two validation quarters (daily information coefficients 0.055 and 0.065) but fell to 0.020 (t = 1.7) on the blinded January-July 2026 test. The commercial models' readings line up with the overnight move to the open (information coefficient 0.074 to 0.077) but not with the move after it, so the news is priced by the time a trader can act on it at the open. No model can tell over- from under-reaction, and no pre-registered strategy survives costs and a deflated Sharpe ratio. Commercial models identify masked companies in 62% to 74% of cases against a 28% baseline: masking hides names, not companies. The market reads the news first.

Date: 2026-11
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2611.03985 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2611.03985

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().