JRFM, Vol. 17, Pages 143: Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures

JRFM, Vol. 17, Pages 143: Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures

Journal of Risk and Financial Management doi: 10.3390/jrfm17040143

Authors: Avi Thaker Leo H. Chan Daniel Sonner

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.