Using ChatGPT to Extract Market Sentiment for Commodity Trading

A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data to understand, generate, and analyze human language. In finance, LLMs are used for tasks like analyzing earnings reports, generating market sentiment analysis, automating financial research, and enhancing algorithmic trading strategies. They can process vast amounts of unstructured data, such as news articles, SEC filings, and analyst reports, to extract insights and identify patterns that may impact markets.

In this context, Reference [1] examines the effectiveness of ChatGPT in predicting commodity returns. Specifically, it extracts commodity news information and forecasts commodity futures returns. The study gathers over 2.5 million articles related to the commodity market from nine international newspapers across three countries, covering a diverse set of 18 commodities.

ChatGPT-3.5 is then used to assess the sentiment of these articles by analyzing their headlines, abstracts, or body content, classifying the news as either good or bad for the commodity market. It then constructs the Commodity News Ratio Index (CNRI) and conducts a comprehensive set of tests to evaluate its forecasting efficacy for commodity futures returns.

The authors pointed out,

This paper develops a text‐based CNRI using ChatGPT, derived from a comprehensive analysis of over 2.5 million articles sourced from nine newspapers. Our empirical results demonstrate that the ChatGPT‐based CNRI is significantly effective in forecasting 1‐ to 12‐month accumulated commodity futures index excess returns, as evidenced by both in‐sample and out‐of‐sample regression analyses. Moreover, we control for business variables and economic indicators to examine the significance of predictability.

Our analysis further reveals that the CNRI exhibits enhanced predictive power during periods of economic expansion, contango markets, and declining inflation. We also confirm that our constructed CNRI contains valuable predictive information regarding future macroeconomic performances. In summary, our study illustrates that ChatGPT can effectively predict trends in the commodity market, enhancing our understanding of the information processing capabilities of LLMs and their implications for investors in the financial market.

In short, ChatGPT proves useful in forecasting commodity market dynamics and provides valuable insights for investors and risk managers.

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References

[1] Shen Gao, Shijie Wang, Yuanzhi Wang, Qunzi Zhang, ChatGPT and Commodity Return, Journal of Futures Markets, 2025; 1–15