A novel hybrid decision making framework for FX hedging strategy selection using machine learning and fuzzy TOPSIS

Abstract

FX market is one of the most highly traded and volatile markets in the world with a huge daily trading volume. Corporations, who deal with FX markets on a daily basis, face significant FX risks due to volatile and unpredictable nature of the market. Hence, hedging FX exposures is of paramount importance to avoid losses when the markets turn in unfavourable directions. Previous research has shown that hedging decisions are a multiple criteria problem where several market and firm related characteristics should be considered together. Treasury dealers in a bank, who make these hedging decisions on behalf of a customer, can often make inconsistent decisions when multiple criteria have to be evaluated in a fast-paced and volatile dealing room environment. Hence this study builds a rule-based data-driven decision making framework, using machine learning and fuzzy TOPSIS models, to recommend best hedging strategy under multiple criteria and scenarios. The study trained LSTM and XGBoost with data from January 2020 till July 2025 and generated market signals for the period August 2025 till September 2025. The signals were fed into fuzzy TOPSIS model where a rule-based framework was created for strategy selection. Strategies selected for each case were then back tested and compared with returns from randomly selected strategies and other alternatives. The results showed significance performance of the decision making framework in the 7 day horizon, especially in bearish markets. Performance declined in the longer horizons as forecast accuracy of the machine learning model also declined in longer tenors. Overall, results revealed that our decision making framework can be used to protect the downside risk in short horizon while longer tenors need improvement.