arXiv Machine Learning
Say, Echo, Do: Strategic Narratives and Revealed Positioning in Financial Markets
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 17
Buy the Rumor, Sell the News: When Is News Priced In?
arXiv:2608. 14014v1 Announce Type: new Abstract: Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold.
arXiv AI
Jul 24
Frontier Financial Judgement: Can agents tell what might move a stock?
arXiv:2607. 20645v1 Announce Type: cross Abstract: We introduce Frontier Financial Judgement, a challenging new benchmark developed in collaboration with professional equity analysts to assess agents' ability to replicate expert human judgements.
arXiv Machine Learning
Jul 24
Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets
arXiv:2607. 20441v1 Announce Type: cross Abstract: Every information ecosystem produces beliefs that shape strategic decisions.
arXiv Machine Learning
Aug 19
Temporal Leakage in Financial News NLP: A Multi-Architecture Audit with a Regime-Specific M&A Signal
The paper audits the impact of temporal leakage on financial-news direction prediction across 49,799 articles and 16 feature-model combinations, including TF‑IDF, MiniLM, FinBERT, and fine‑tuned RoBERTa‑large / DeBERTa‑v3‑large, as well as zero/few‑shot and LoRA probes of Llama‑3 and Qwen2.5. Random train‑test splits inflate MCC scores by 1.1× to 6.5×, with larger models and richer features showing greater gains, while end‑to‑end FinBERT fine‑tuning actually increases the gap. Only the mergers and acquisitions (M&A) category shows a positive locked‑test signal under near‑temporal chronological evaluation, with the signal localized to 2024‑2025 European‑tilted M&A semantics and not transferring to a 2009‑2020 U.S. corpus.
arXiv AI
Sep 3
Dutch Books for Language Models
The paper investigates the coherence of probabilistic forecasts produced by language models, particularly in the context of life‑decision support. Using a de Finetti‑based method, the authors elicit forecasts for events derived from stock return data and compute the maximum Dutch‑book profit via linear programming, which quantifies incoherence. They find significant incoherence, especially when events have complex logical relationships or when irrelevant context is present, and suggest that alternative training strategies could improve coherence.
arXiv AI
3d ago
Understanding as No-Arbitrage: Bounded Dutch Books as a Definition and Training Objective for Language Models
The paper proposes measuring a language model’s understanding via no‑arbitrage, defining it as the inability of a bounded trader to profit from Dutch books against the model’s probabilities on logically related claims. It shows that full logical coherence is computationally infeasible, that standard next‑token training yields incoherent predictions across formats, and that uncertainty grows predictably along reasoning chains, creating arbitrage opportunities. The authors introduce Arbitr, a training framework that penalizes logical inconsistencies while maintaining accuracy, reducing exploitability by orders of magnitude and revealing a scaling illusion where large models appear coherent yet exhibit extreme unjustified confidence.