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.
arXiv:2608. 04200v1 Announce Type: cross Abstract: Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability.
This paper presents an empirical comparison of lexicon-based and Large Language Model (LLM)-based sentiment analysis for extracting market-relevant signals from social media discourse in highly volatile equity markets. Using Reddit data from r/WallStreetBets and focusing on meme stocks (GME, AMC, NOK), we construct time-aligned sentiment indicators and evaluate their relationship with market returns, with particular attention to extreme positive return events in the upper tail of the return distribution.
arXiv:2608. 09834v1 Announce Type: cross Abstract: Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making.
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.
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.
arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).
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.
arXiv:2609.23703v1 Announce Type: cross Abstract: Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrat...
The paper explores whether ChatGPT can predict stock market movements by analyzing Twitter sentiment. Using tweets about Microsoft and Google, the study finds a positive correlation between ChatGPT’s sentiment assessments and the subsequent stock performance of both companies. The results suggest that ChatGPT’s language understanding can translate social media sentiment into useful financial forecasts.
arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.
arXiv:2608. 19558v1 Announce Type: new Abstract: Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, while standard F1 scores do not indicate which predictions remain safe to automate when the input distribution changes.
arXiv:2607. 00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications.