Media Narratives, Algorithmic Trading, and Energy Market Volatility: Insights from the 2026 U.S.-Iran Conflict

Abstract

Abstract Global energy markets are increasingly driven by perception rather than physical supply, creating a system where information flow is as decisive as the movement of oil itself. This paper examines the interaction between media narratives and algorithmic trading, using the US–Iran conflict as a central case study. The objective is to analyse how narrative framing steers market sentiment and how automated trading systems amplify these effects into rapid price movements. The study draws on Agenda-Setting and Framing Theory to explain how media outlets establish the salience of geopolitical events, while applying Signal Theory to show how algorithms treat headlines as instant commands to buy or sell. Using a desk review methodology, the research correlates documented news reports from January to May 2026 with reported shifts in Brent and WTI crude prices. The findings demonstrate that headlines alone generated massive market swings independently of physical supply, such as the price rise following de-escalation signals in early 2020 and the historic surges triggered by war headlines in March 2026. The evidence suggests that trading algorithms magnified these shifts, creating a feedback loop where perception consistently outweighed reality. The study concludes that this dynamic represents a structural vulnerability in energy security, calling for greater transparency in geopolitical communication, responsible media framing, and tighter regulatory oversight of algorithmic trading to mitigate perception-driven distortions in global markets.