Purpose: This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across communication domains and market conditions. It di...
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.
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.
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.
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: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 study examines how large language models (ChatGPT, Gemini, and Grok) embed religious bias in AI‑generated financial advice. Using 432 simulated advisor‑client interactions across four religious identities and three financial decisions, the authors find that only 12‑18% of advice is unbiased, with Gemini showing the most bias and ChatGPT comparable to Grok. The research identifies structural biases in model design and discursive mechanisms—such as religious anchoring and tone modulation—that vary by scenario, revealing a tension between personalization and neutrality in AI advisory services.
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: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...
arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.
The paper investigates affective framing in Bengali digital journalism by analyzing 300,000 news headlines with zero‑shot inference using Gemma 3 4B to estimate dominant emotions and overall affective tone. Results reveal frequent negative emotions—especially anger, sadness, disappointment, and fear—across the corpus, and a pilot validation on 200 headlines indicates the model’s estimates are useful yet not definitive benchmarks. The authors suggest a bias‑sensitive news interface that visualizes emotional cues to help readers detect affective framing patterns in daily news.
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.