Sentimental herding in Borsa Istanbul: informed versus uninformed

dc.citation.epage968en_US
dc.citation.issueNumber14en_US
dc.citation.spage965en_US
dc.citation.volumeNumber21en_US
dc.contributor.authorSolakoglu, M. N.en_US
dc.contributor.authorDemir, N.en_US
dc.date.accessioned2016-02-08T11:00:59Z
dc.date.available2016-02-08T11:00:59Z
dc.date.issued2014en_US
dc.departmentDepartment of Banking and Financeen_US
dc.description.abstractThis study searches for sentimental herding in Borsa Istanbul (BIST) using a state-space model for two distinct groups of investors/traders. We expect to find no sentimental herding in BIST30 as the investors are closely following the market, given their access to maximum amount of information and high quality of guidance from the world-known intermediaries that reduce information asymmetries. However, as the SNM investors are mostly local investors with smaller assets and with higher level of information asymmetry, we expect to find evidence of sentimental herding. As expected, we find no evidence of herding by the BIST30 investors from 2000 to 2013. In contrast, our findings provide evidence that the SNM investors demonstrate sentimental herding persistently and independently from market fundamentals in three stages: evidence of herding in the first stage (2000-2004), a 4-year (2005-2008) no-herding calm period and finally a volatile adverse herding pattern (2009-2013) where fundamentals about the firm values became more important.en_US
dc.identifier.doi10.1080/13504851.2014.902015en_US
dc.identifier.eissn1466-4291
dc.identifier.issn1350-4851
dc.identifier.urihttp://hdl.handle.net/11693/26523
dc.language.isoEnglishen_US
dc.publisherRoutledgeen_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/13504851.2014.902015en_US
dc.source.titleApplied Economics Lettersen_US
dc.subjectCross-sectional volatilityen_US
dc.subjectEmerging marketen_US
dc.subjectHerdingen_US
dc.subjectState-space modelen_US
dc.titleSentimental herding in Borsa Istanbul: informed versus uninformeden_US
dc.typeArticleen_US

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