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.

In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would react the day after the election. AI models have been criticized as black-box models and lack the clarity needed for decision-making by different stakeholders. The explainable AI model we utilized in this model provides more clarity for the outcomes of the model. Using features commonly used by previous studies related to this topic, we find that the previous market direction leading up to the election and the incumbency information combined with the political affiliation are larger drivers for a 1-day post-election market return than sentiment and which party wins the election.