Machine learning approaches for modelling water futures

Abstract

Modelling water futures is challenging due to the dynamics of several variables and non-linearity. The traditional models are often inefficient to capture such hidden patterns. Thus, utilizing datasets that include daily price and volume information of water futures, this study evaluates the performance of SVM, Random Forest Regressor, LSTM, GBM, and XGBoost models. The findings indicate that XGBoost outperforms other models in accuracy; however, all models tested perform well and provide accurate predictions. Consequently, it makes valuable contributions to the field of modelling financial products based on water-adjacent entities. Furthermore, it underscores the potential for machine learning in enhancing market predictions for emerging and niche sectors that have thus far been overlooked by market participants but will increasingly become more important as climate change progresses. Lastly, as futures markets for water as a commodity mature (with higher trading volume of futures contracts), this study will serve as a benchmark for better modelling of water futures.