Date of Award
2021
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Agribusiness and Applied Economics
Department
Agribusiness and Applied Economics
College
Agriculture, Food Systems and Natural Resources
Faculty Advisor
Wilson, William
Description
Digitization is influencing commodity trading and agricultural markets and as they transition towards extreme liquidity, agribusiness risk exposures increase, and traditional competitive advantages diminish. In commodity origination, logistics and destination basis comprise the most volatile and determinant influences of margin. To capture a consistently higher margin and represent narrowed interior basis, agribusiness firms must manage these risks by optimizing transformations. To accomplish this, ex-ante decision-making is often necessary as forward price clairvoyance is not always prevalent, is risky, or contains premium. Modeling this spatial equilibrium is difficult through traditional reductionist and essentialist application as the overdetermined and convoluted system presents bidirectional and simultaneous price discoveries. Developments in neurobiology, technology, and Artificial Intelligence expand capabilities to represent brain behavior and unconscious inference in computational modeling. The use of Recurrent Deep Machine Learning could improve ex-ante decision accuracy within commodity trading through its nonlinear, nonlocal, nonstationary, and sequential capabilities.
Recommended Citation
Carlson, Noah Joseph, "Ex-Ante Temporal Optimization in Soybean Origination: An Overdetermined Approach Through Deep Learning" (2021). Agribusiness and Applied Economics. 107.
https://digitalcommons.ndsu.edu/agribusiness-applied-econ/107
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Author ORCID Identifier
0000-0001-6583-4936
Handle Identifier
https://hdl.handle.net/10365/32608