Comparing the great recession and COVID-19 using Long Short-Term Memory: A close look into agricultural commodity prices

Abstract

We employ a neural network (NN) approach—Long Short-Term Memory (LSTM)—to study agricultural commodity prices during the 2008 Great Recession and the COVID-19 recession. Our analysis reveals more structural breaks and higher volatility in plant-based commodities like corn and soybeans during recessions compared with animal-based commodities. The price reactions varied among commodities, with corn responding first to both recessions, while milk price, which was found independent of other prices, recovered last from the financial recession and first from the disease-induced recession. This insight into commodity behavior during recessions can aid in trend prediction and recession preparation for investors and researchers.