Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models.
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| Title: | Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models. |
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| Authors: | Wilcoxson, Jordan (AUTHOR), Follett, Lendie (AUTHOR), Severe, Sean (AUTHOR) |
| Source: | Journal of Behavioral Finance. Oct-Dec2020, Vol. 21 Issue 4, p412-422. 11p. |
| Subjects: | Foreign exchange market, Foreign exchange rates, Forecasting |
| Geographic Terms: | United States |
| Abstract: | Foreign exchange markets affect a variety of humans and businesses worldwide and there is a wide array of literature aimed at providing more accurate forecasts of their movement. In an attempt to quantify human expectations, Google query search terms related to foreign exchange markets are used to help explain and predict foreign exchange rates between the United States' dollar and ten other currencies during the time period of January 2004 and August 2018. We find evidence that, while Google Trends can be helpful in prediction, it is necessary to implement some sort of shrinkage or sparsity scheme on the coefficients. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Behavioral Finance is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 146380036 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wilcoxson%2C+Jordan%22">Wilcoxson, Jordan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Follett%2C+Lendie%22">Follett, Lendie</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Severe%2C+Sean%22">Severe, Sean</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Behavioral+Finance%22">Journal of Behavioral Finance</searchLink>. Oct-Dec2020, Vol. 21 Issue 4, p412-422. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Foreign+exchange+market%22">Foreign exchange market</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+exchange+rates%22">Foreign exchange rates</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Foreign exchange markets affect a variety of humans and businesses worldwide and there is a wide array of literature aimed at providing more accurate forecasts of their movement. In an attempt to quantify human expectations, Google query search terms related to foreign exchange markets are used to help explain and predict foreign exchange rates between the United States' dollar and ten other currencies during the time period of January 2004 and August 2018. We find evidence that, while Google Trends can be helpful in prediction, it is necessary to implement some sort of shrinkage or sparsity scheme on the coefficients. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Behavioral Finance is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=146380036 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15427560.2020.1716233 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 412 Subjects: – SubjectFull: Foreign exchange market Type: general – SubjectFull: Foreign exchange rates Type: general – SubjectFull: Forecasting Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wilcoxson, Jordan – PersonEntity: Name: NameFull: Follett, Lendie – PersonEntity: Name: NameFull: Severe, Sean IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct-Dec2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 15427560 Numbering: – Type: volume Value: 21 – Type: issue Value: 4 Titles: – TitleFull: Journal of Behavioral Finance Type: main |
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