We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures.
- Authors
Thaker, Avi; Chan, Leo H.; Sonner, Daniel
- 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.
- Subjects
COMMODITY futures; PRICES; MACHINE learning; AGRICULTURAL forecasts; CONVOLUTIONAL neural networks; COMMODITY exchanges
- Publication
Journal of Risk & Financial Management, 2024, Vol 17, Issue 4, p143
- ISSN
1911-8066
- Publication type
Article
- DOI
10.3390/jrfm17040143