Advancing physical commodity trading with artificial intelligence

Abstract

This thesis examines the ways in which price prediction and trading decision processes can be AI-optimized to enhance the physical commodity trading. The research combines results from other studies to develop a hybrid forecasting framework that uses deep neural networks combined with meta learning and genetic algorithm optimization. It combines historical market data and environmental indicators from satellites such as proxies for crop yields to predict commodity prices and crop yields. Traditional forecasting methods are compared against AI-powered models to demonstrate their superior accuracy when predicting complex non-linear dynamics of commodity markets. The benefits of AI-powered models, reliability of predictions, reduced risk, and improved inventory and supply chain management, are further explained through the newly attainable heightened forecast precision. The research reinforces the argument of AI-enhanced trading, providing insight into the possibilities of integrating AI technology into commodity market trading strategies.