ChatGPT and Commodity Return

Abstract

This paper investigates the ability of a ChatGPT-based indicator to forecast excess returns of the commodity futures index. Using ChatGPT to extract information from over 2.5 million articles from nine international newspapers, we demonstrate that our constructed commodity news ratio index significantly predicts future commodity returns, both in-sample and out-of-sample. Furthermore, it outperforms traditional textual analysis methods, including Bidirectional Encoder Representations from Transformers (BERT) and Bag-of-Words (BoW), while indicating economic significance within an asset allocation framework. The results highlight the critical role of ChatGPT in forecasting commodity market dynamics and provide valuable insights for both financial market participants and researchers.