INTEGRATING AI-BASED SENTIMENT ANALYSIS INTO CORPORATE FX RISK MANAGEMENT: EFFECTS ON HEDGING DECISIONS DURING PERIODS OF HIGH VOLATILITY

Abstract

Foreign exchange risk management remains one of the most challenging aspects of international business operations, particularly during periods of heightened market volatility. Traditional hedging strategies rely primarily on historical data and technical indicators, often failing to capture the rapidly shifting market sentiment that drives currency movements during volatile periods. This research examines how AI-based sentiment analysis, applied to news feeds, social media, and financial communications, can enhance corporate foreign exchange hedging decisions. Through analysis of hedging outcomes across multiple corporations during recent high-volatility periods including the 2022 inflation surge and 2023 banking crisis, we demonstrate that sentiment-augmented hedging strategies outperform traditional approaches by 18-27% in terms of hedge effectiveness. The study reveals that AI sentiment analysis provides early warning signals of market shifts approximately 6-12 hours before traditional indicators, enabling more timely hedging adjustments. However, implementation challenges including false signals during extreme volatility and integration complexity with existing treasury systems require careful management. This research contributes practical frameworks for incorporating sentiment analysis into FX risk management processes while identifying conditions under which sentiment-based approaches deliver greatest value.