The paper introduces Delta-Consistent Scoring (DCS), an annotation‑free method that uses large language model representations to produce continuous monetary‑policy stance scores. DCS jointly learns absolute stance for each FOMC statement and relative shifts between consecutive statements, enforcing consistency through a delta‑consistency objective. The approach outperforms supervised probes and LLM‑as‑judge baselines, achieving up to 71.1% accuracy on sentence‑level hawkish‑dovish classification and producing meeting‑level scores that correlate strongly with inflation indicators and Treasury yield movements.
By Yixuan Tang, Yi Yang
arXiv:2608. 07251v1 Announce Type: cross Abstract: This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements.
By Gabriel de Macedo Santos
Large language models (LLMs) can synthesize financial narratives but may express high confidence when evidence is sparse, stale, or contradictory. This failure is especially consequential in forecasting, where filings, news, prices, volume, and technical signals can disagree.
Large language models are increasingly used as decision aids whose probability judgments shape downstream choices. Whether those judgments carry a systematic directional tilt has been hard to detect: calibration metrics aggregate unsigned errors, and naturalistic uncertainty offers no ground-truth probability.
The paper evaluates twelve financial sentiment models—including dictionary-based methods, finance-specific transformers, and open-source large language models—using linguistic and economic validity criteria. General-purpose LLMs match finance-specific transformers in classification performance but do not yield stronger economic relationships. While several models correlate with earnings surprises, none shows a significant link to next‑day stock returns, and performance is strongest for large earnings beats or misses.
By Arslan Bisharat, Oudom Hean
The paper presents the CDSP (context-conditional deliberation signal pipeline), which transforms investment committee meeting transcripts into structured predictive features. CDSP segments transcripts into topical chunks, assigns asset‑class context labels via a large language model, maps financial keywords to a taxonomy, and adds sentiment polarity and mention frequency features. Using these engineered features on 48 monthly meetings, the best model—combining sentence embeddings with CDSP features—achieves 73% accuracy and a 0.73 F1 score, outperforming a simple stock‑choice baseline, though the improvement is not statistically significant.
By Vivek Batra, Kristin Chen, Sanjiv Das, Samuel Judge, Harshad Khadilkar, Sukrit Mittal, Amir Nasrollahzadeh, Daniel Ostrov, Jacob Sisk
The study investigates which inputs influence a language model’s financial forecasts by creating synthetic earnings announcements that independently reverse 20‑day momentum, commentary tone, and EPS surprise. Six instruction/chat checkpoint conversions from four model families (0.49‑7.61 B parameters) were tested with 2,016 prompts each, covering 384 primary scenarios. Results show that the contrast in primary EPS ranges from –0.19 to 86.80 percentage points, with signed tone and momentum contrasts often exceeding EPS effects; numerical diagnostic accuracy varied from 51.0 % to 95.8 %. The experiment measures observable input reliance and numerical recognition but does not establish internal reasoning mechanisms or return predictability.
By Alina Khaybullina
arXiv:2605. 30363v2 Announce Type: replace-cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration.
By Mingxuan Yi, Vidal Mehra, Jing Chen, John Cartlidge
The study examines whether financial sentiment tools that are validated against human labels also reliably predict market outcomes. Using a large corpus of securities class action messages linked to abnormal stock returns, the authors compare five sentiment instruments—VADER, Loughran‑McDonald, FinBERT, Twitter‑RoBERTa, and an LLM annotator—within a single pipeline. Results show that the alignment between human agreement and sentiment scores varies with sampling strategy and time horizon: conventional sampling favors same‑day associations, while fixed‑n panels yield similar correlations for both same‑day and one‑day‑ahead predictions, yet overall predictive rankings remain weak.
By AS Aravinthkakshan, Laven Srivastava, Harsh Nandwani
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.
By Patara Trirat, Jin Myung Kwak, Jay Heo, Heejun Lee, Sung Ju Hwang
arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.
By Sanggyu Sean Choi
The paper investigates the coherence of probabilistic forecasts produced by language models, particularly when users rely on them for life decisions involving uncertain events. Using a de Finetti-based method, the authors extract forecasts from language models about stock‑return events and compute the maximum Dutch‑book profit via linear programming, a metric of incoherence that does not require observed outcomes. The study finds significant incoherence, especially when events have complex logical relationships or when irrelevant context is present, and suggests that alternative training strategies could improve probabilistic coherence.