How can Google trends be used as a technical indicator for investor interest?

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dc.contributor.authorBozkurt, Gözde
dc.date.accessioned2025-03-09T10:53:17Z
dc.date.available2025-03-09T10:53:17Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractSince investor interest is not a directly measurable concept, search engine and social media data can be used to measure active investor interest. Google search volume data has the potential to help customers, investors, and policymakers make better decisions. When looking for information to make investment decisions, investors consider Google trends as they provide news about changes in prices. Studies examining investor interest in the literature have often been carried out with the applications of linear models. This suggests that possible structural changes are not taken into account in the time series. When we look at the literature, it has been shown that the prediction performance of nonlinear models is better than linear models. In addition, it is seen that the studies conducted to investigate the relationship between the return of the stock markets and the trading volume and the investor interest are frequently included in the international literature. In contrast, the studies are limited in the national literature. In this direction of the study, the relationship between the trade volume and the investor's discovery of information on the stock market by Google is examined through linear and nonlinear econometric techniques in the investor reputation hypothesis. According to the investor reputation hypothesis, investors only invest in stocks they are aware of without adequate research and knowledge. In this context, the study results were realized in a way that supports the investor recognition hypothesis within the scope of 2020. In the context of 2021, it is seen that it does not support the investor reputation hypothesis. In future studies to be carried out in this area, it seems possible to determine the degree of effect of nonlinear regression estimations and the relationship between variables.
dc.identifier.doi10.48146/odusobiad.1233817
dc.identifier.endpage116
dc.identifier.issn1309-9302
dc.identifier.issue1
dc.identifier.startpage99
dc.identifier.trdizinid1227597
dc.identifier.urihttps://doi.org/10.48146/odusobiad.1233817
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1227597
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4782
dc.identifier.volume14
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorBozkurt, Gözde
dc.language.isoen
dc.relation.ispartofOrdu Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Araştırmaları Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250310
dc.subjectInvestor reputation hypothesis
dc.subjectınvestor ınterest
dc.subjectgoogle trend searches
dc.subjectnonlinear causality analysis
dc.titleHow can Google trends be used as a technical indicator for investor interest?
dc.typeArticle

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