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Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures

Author

Listed:
  • Avi Thaker

    (Co-Founder, Tauroi Technologies, Pacifica, CA 94044, USA)

  • Leo H. Chan

    (Department of Finance and Economics, Woodbury School of Business, Utah Valley University, Orem, UT 84058, USA)

  • Daniel Sonner

    (Co-Founder, Tauroi Technologies, Pacifica, CA 94044, USA)

Abstract

In this paper, we utilize a machine learning model (the convolutional neural network) to analyze aerial images of winter hard red wheat planted areas and cloud coverage over the planted areas as a proxy for future yield forecasts. We trained our model to forecast the futures price 20 days ahead and provide recommendations for either a long or short position on wheat futures. Our method shows that achieving positive alpha within a short time window is possible if the algorithm and data choice are unique. However, the model’s performance can deteriorate quickly if the input data become more easily available and/or the trading strategy becomes crowded, as was the case with the aerial imagery we utilized in this paper.

Suggested Citation

  • Avi Thaker & Leo H. Chan & Daniel Sonner, 2024. "Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures," JRFM, MDPI, vol. 17(4), pages 1-15, April.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:4:p:143-:d:1369047
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    References listed on IDEAS

    as
    1. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
    2. Michael K. Adjemian, 2012. "Quantifying the WASDE Announcement Effect," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 238-256.
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