arXiv Machine Learning August 6, 2026 By Fusheng Luo

From Financial Sentiment Classification to Return Predictability: A QLoRA Benchmark of Large Language Models

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

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llms nlp reinforcement-learning benchmarks

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Can Open-Weight Models Compete on Financial Text Comprehension?

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FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting

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.

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LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks

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.

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Related stories

RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification

How Much of a 10-K Matters? Aggregation-Dependent Value of Full-Text versus Risk-Factor Sentiment

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

Can Open-Weight Models Compete on Financial Text Comprehension?

FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting

LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks