Estimating the impact of weather on CBOT corn futures prices using machine learning
Date
2022-05
Authors
Singh, Sriramjee
Major Professor
Advisor
Hayes, Dermot
Ganapathysubramanian, Baskar
Crespi, John
Sarkar, Soumik
Hart, Chad
Committee Member
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Altmetrics
Abstract
I apply machine-learning methods to study the impact of hourly changes in county-level weather in major corn-producing US states on the Chicago Board of Trade
(CBOT) corn futures prices. Futures prices respond to shocks in expected production levels, which in turn depend on weather outcomes. The percentage change in
futures prices at daily/weekly/weekend frequency is forecasted using a convolutional
neural network that exploits the spatial characteristic of the data. Additionally, the
direction of changes in futures prices is predicted using a suite of classification models viz. logistic regression, support vector machine, and decision trees. Analytically, I
compare the outcome of machine learning models on big data and test the effectiveness of high-frequency weather data in predicting futures prices. The results suggest
that weather forecast data provides important information that efficiently moves the
market.
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Academic or Administrative Unit
Economics
Type
article