Trading the FX volatility risk premium with machine learning and alternative data

• Published in: The Journal of finance and data science Vol. 8; pp. 162 - 179
• Main Authors: Dierckx, Thomas, Davis, Jesse, Schoutens, Wim
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-11-2022; KeAi Communications Co., Ltd
• Subjects: Alternative data; Financial news; Machine learning; Trading strategy; Volatility risk premium; Volatility risk premium; Alternative data; Financial news; Trading strategy; Machine learning
• ISSN: 2405-9188; 2405-9188
• DOI: 10.1016/j.jfds.2022.07.001

In this study, we show how both machine learning and alternative data can be successfully leveraged to improve and develop trading strategies. Starting from a trading strategy that harvests the EUR/USD volatility risk premium by selling one-week straddles every weekday, we present a machine learning approach to more skillfully time new trades and thus prevent unfavorable ones. To this end, we build probability-calibrated Random Forests on various predictors, extracted from both traditional market data and financial news, to predict the closing Sharpe ratio of short one-week delta-hedged straddles. We then demonstrate how the output of these calibrated machine learning models can be used to engineer intuitive new trading strategies. Ultimately, we show that our proposed strategies outperform the original strategy on risk-based performance measures. Moreover, the features that we derived from financial news articles significantly improve the performance of the approach.
•The future performance of short one-week delta-hedged straddles on EUR/USD can be predicted using Random Forests.•Features extracted from financial news improve prediction performance.•Trading strategies based on probability-calibrated Random Forest predictions significantly outperform the original strategy on risk-based measures.