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 paper introduces a training‑free, alignment‑free method for corporate intelligence that uses deterministic sparse seed vectors to hash word strings into a fixed high‑dimensional basis. By accumulating these seed vectors across sentence contexts, the authors create corpus‑specific semantic signatures that enable rapid document comparison, issuer fingerprinting, vocabulary shift tracking, and thematic sentence extraction—all on standard CPU hardware. Applied to a multi‑year set of SEC filings, the approach reveals distinct semantic profiles for major corporate events such as Boeing’s 737 MAX crisis, Intel’s supply‑chain disruptions, and Bunge’s acquisition of Viterra, with each profile traceable to its source sentences without any domain‑specific training or LLM inference.
By Jean-Fran\c{c}ois Delpech
arXiv:2609.15369v1 Announce Type: new
Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
By Jochen Madler (Sitefire)
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
arXiv:2610.09529v1 Announce Type: new
Abstract: There are more than 2,000 listed companies on the UK's London Stock Exchange, divided into 11 sectors who are required to communicate their financial r...
By Nadhem Zmandar, Mo El-Haj, Paul Rayson
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. 19526v1 Announce Type: cross Abstract: Stock market analysts and investors face a daily challenge: too much financial news, too little time.
By Pranav Chandaliya
arXiv:2412.05655v2 Announce Type: replace
Abstract: This paper presents a case study on the development of Auto-AdvER, a specialised span-labelling schema, along with its corresponding dataset for th...
By Filippos Ventirozos, Ioanna Nteka, Tania Nandy, Jozef Baca, Charlie Roadhouse, Peter A. Appleby, Matthew Shardlow
The paper explores how large language models can detect hidden narratives in social messages without training data. By feeding the models human-written narrative descriptions, performance improves markedly, while automatically generated descriptions or few-shot examples can hurt accuracy. Ensemble techniques, especially majority voting, further boost robustness, and larger models show the best results with less sensitivity to prompts.
By Jes\'us M. Fraile-Hern\'andez, Anselmo Pe\~nas, Patrick Giedemann
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
arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.
By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao
The paper investigates confidence estimation and selective prediction for financial named entity recognition (NER) under domain shift, using a stress test across SEC filings, financial news, and social media. It evaluates BERT and LoRA‑tuned Qwen2.5 models with five inference‑time confidence signals, finding that whole‑output probability is a strong in‑domain error detector but weak out‑of‑domain, while entity‑span probability and self‑consistency remain robust. Abstention can dramatically reduce sentence error on high‑confidence in‑domain data, but offers limited benefit under extreme social‑media shift, suggesting a staged deployment that first detects severe distribution shift before applying confidence gating.
By Zihao Zheng, Baichuan Li, Junyi Yao, Jiayu Long