FinRAIN: Reliability-Aware News Ingestion for Sentiment Analysis

LLM-based trading systems often treat financial news ingestion as a sentiment preprocessing task: each article is assigned a directional label, linked to the tickers it mentions, and passed to a downstream trading framework. This design conflates two questions: whether an article's market impact is confined to a single ticker or extends to multiple tickers, and whether the article is sufficiently reliable to support a trading signal. We propose FinRAIN, a model-agnostic news ingestion framework that transforms raw financial news into structured, reliability-aware inputs for LLM-based trading systems, with optional retrieval augmentation from external knowledge sources. FinRAIN produces article-level annotations for Market Relevance, Source Attribution, Information Novelty, Headline Exaggeration, and Stakeholder Bias. Rather than replacing existing trading agents, FinRAIN serves as a modular news processing infrastructure that can power a broader ecosystem of LLM-based trading systems by supplying higher-quality and more interpretable inputs to diverse downstream trading frameworks. Across intrinsic evaluations and downstream trading simulations, FinRAIN improves the quality of sentiment signals and achieves higher risk-adjusted returns across diverse trading settings. These results suggest that reliability-aware ingestion is a reusable interface for making financial-news pipelines more robust, interpretable, and transferable across trading frameworks.