Agribusiness Commons^™

Session 7: Would Ai Stocks Estimate Be As Surprised To Usda Stock Reports As Private Market Analysts?, Asif Mahmud Chowdhury, Matthew Elliott

SDSU Data Science Symposium

Would AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts?

Keywords: Machine Learning, Random Forest, Agricultural Commodities Market, Informational Impact, Efficient Market Hypothesis.

The USDA survey-based Quarterly Grain Stocks reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Previous research has examined the accuracy of the USDA stock reports and their relevancy to the market, given alternative sources of estimates (e.g., Isengildina-Massa et al., 2021). For example, private industry analysts also estimate expected quarterly grain stock reports before USDA releases their reports. …

Deploying Live Dashboard Data Using Usda Data Apis To Inform Farmers/Producers, Indira Fuyal, Paurakh Paudel, David Zeng

SDSU Data Science Symposium

The main objective of this project is to provide the farmers/producers and small business owners with a data intensive information hub to better understand the market trends and patterns in an interactive way to help make informed decisions. This technology is part of a larger project that includes AI powered data services to provide users with decision making support for real time data solutions. Together with Natural Language Generation, there is a huge potential to deploy this technology. In this small demo of this hugely beneficial technology, we make use of the publicly available Open Data Services – ESR Data …