Trading the Story Before the Price: A Reflexivity-Aware Neural Framework for the Narrative Risk Premium

Traditional portfolio allocation often treats market sentiment as noise. That misses how stories now move through markets before they appear in prices. This paper treats social sentiment as a mispriced risk factor, which we call narrative beta, and argues that investors can earn a premium by identifying a story before prices adjust. The central claim isthat financial narratives tend to move through emergence, amplification, and decay, with social sentiment often peaking about 24 to 48 hours before related asset prices respond. That delay may come from how people process market information, including hierarchical spread, confirmation-seeking behavior, weekend discussion cycles, and the gap between emotional reaction and rational adjustment. To estimate this Narrative Risk Premium, we propose a multi-head attention transformer that combines social media, news, geopolitical signals, and market data. The model links three tasks: embedding the narrative, measuring sentiment intensity, and translating that signal into risk-weighted portfolio adjustments. We test the approach in stages, starting with historical backtests on 2018 to 2024 sentiment data, then paper trading on live feeds, then limited live deployment with strict risk controls. The strategy also has to account for reflexivity. If too much capital follows the same signal, the signal weakens. A credible system must estimate its own market impact, not just predict other traders’ reactions. It also has to detect coordinated narrative manipulation, especially AI-generated campaigns built with large language models. The broader goal is a market system that can price narrative risk while identifying stories designed to distort prices.