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

Avi Thaker, Leo H. Chan () and Daniel Sonner
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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

JRFM, 2024, vol. 17, issue 4, 1-15

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.

Keywords: machine learning; convolutional neural network; futures price forecasting; commodity futures (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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