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Estimating the Impact of Weather on CBOT Corn Futures Prices Using Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating the Impact of Weather on CBOT Corn Futures Prices Using Machine Learning./
Author:	Singh, Sriramjee.
Description:	1 online resource (109 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Geographic information science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28969432click for full text (PQDT)
ISBN:	9798819384985

Estimating the Impact of Weather on CBOT Corn Futures Prices Using Machine Learning.
Singh, Sriramjee.

Estimating the Impact of Weather on CBOT Corn Futures Prices Using Machine Learning. - 1 online resource (109 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Iowa State University, 2022.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798819384985Subjects--Topical Terms:

3432445
Geographic information science.
Subjects--Index Terms:

Futures pricesIndex Terms--Genre/Form:

542853
Electronic books.

Estimating the Impact of Weather on CBOT Corn Futures Prices Using Machine Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Hayes, Dermot.
502 $a Thesis (Ph.D.)--Iowa State University, 2022.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Geographic information science. $3 3432445
650 4 $a Artificial intelligence. $3 516317
650 4 $a Meteorology. $3 542822
653 $a Futures prices
653 $a Machine learning
653 $a Weather
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