Can Internet Search Queries Help to Predict Stock Market Volatility?
We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search que...
Gespeichert in:
Veröffentlicht in: | European financial management : the journal of the European Financial Management Association 2016-03, Vol.22 (2), p.171-192 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 192 |
---|---|
container_issue | 2 |
container_start_page | 171 |
container_title | European financial management : the journal of the European Financial Management Association |
container_volume | 22 |
creator | Dimpfl, Thomas Jank, Stephan |
description | We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases. |
doi_str_mv | 10.1111/eufm.12058 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1878796564</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1878796564</sourcerecordid><originalsourceid>FETCH-LOGICAL-c6368-44523c554da32a538a3429614e6d941e1b7a6d955a2a6422ce3f417818f4c0313</originalsourceid><addsrcrecordid>eNp90E1LwzAYB_AiCs7pxU9Q8CJCZ_OenkSne4HNF3Q6dgkxe4rdunYmLbpvb-bUg4flkgfy-4eHfxAco7iF_DmHOl20EI6Z3AkaiHIZxYSPd_1MGI2ESOR-cODcLI5jyphsBNdtXYT9ogJbQBU-grbmLXyowWbgwh7ky7Aqw3sL08z456o083Co7dzb5zLXVZZn1eriMNhLde7g6OduBqPOzVO7Fw3uuv325SAynPhVKGWYGMboVBOsGZGaUJxwRIFPE4oAvQrtJ8Y01pxibICkFAmJZEpNTBBpBqebf5e2fK_BVWqROQN5rgsoa6eQFFIknHHq6ck_OitrW_jtFEowQRRLzLYqwaUgVCZrdbZRxpbOWUjV0mYLbVcKxWpdu1rXrr5r9xht8EeWw2qLVDejzvA3E20ymavg8y_je1ZcEMHUy21XjSftRLCricLkC8yikDk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1768734895</pqid></control><display><type>article</type><title>Can Internet Search Queries Help to Predict Stock Market Volatility?</title><source>EBSCOhost Business Source Complete</source><source>Access via Wiley Online Library</source><creator>Dimpfl, Thomas ; Jank, Stephan</creator><creatorcontrib>Dimpfl, Thomas ; Jank, Stephan</creatorcontrib><description>We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.</description><identifier>ISSN: 1354-7798</identifier><identifier>EISSN: 1468-036X</identifier><identifier>DOI: 10.1111/eufm.12058</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>Dow Jones Indexes ; Forecasting ; Internet ; investor behaviour ; limited attention ; noise trader ; realised volatility ; search engine data ; Search engines ; Securities markets ; Stock market indexes ; Studies ; Volatility</subject><ispartof>European financial management : the journal of the European Financial Management Association, 2016-03, Vol.22 (2), p.171-192</ispartof><rights>2015 John Wiley & Sons Ltd</rights><rights>2016 John Wiley & Sons Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6368-44523c554da32a538a3429614e6d941e1b7a6d955a2a6422ce3f417818f4c0313</citedby><cites>FETCH-LOGICAL-c6368-44523c554da32a538a3429614e6d941e1b7a6d955a2a6422ce3f417818f4c0313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Feufm.12058$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Feufm.12058$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Dimpfl, Thomas</creatorcontrib><creatorcontrib>Jank, Stephan</creatorcontrib><title>Can Internet Search Queries Help to Predict Stock Market Volatility?</title><title>European financial management : the journal of the European Financial Management Association</title><addtitle>Eur Financial Management</addtitle><description>We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.</description><subject>Dow Jones Indexes</subject><subject>Forecasting</subject><subject>Internet</subject><subject>investor behaviour</subject><subject>limited attention</subject><subject>noise trader</subject><subject>realised volatility</subject><subject>search engine data</subject><subject>Search engines</subject><subject>Securities markets</subject><subject>Stock market indexes</subject><subject>Studies</subject><subject>Volatility</subject><issn>1354-7798</issn><issn>1468-036X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp90E1LwzAYB_AiCs7pxU9Q8CJCZ_OenkSne4HNF3Q6dgkxe4rdunYmLbpvb-bUg4flkgfy-4eHfxAco7iF_DmHOl20EI6Z3AkaiHIZxYSPd_1MGI2ESOR-cODcLI5jyphsBNdtXYT9ogJbQBU-grbmLXyowWbgwh7ky7Aqw3sL08z456o083Co7dzb5zLXVZZn1eriMNhLde7g6OduBqPOzVO7Fw3uuv325SAynPhVKGWYGMboVBOsGZGaUJxwRIFPE4oAvQrtJ8Y01pxibICkFAmJZEpNTBBpBqebf5e2fK_BVWqROQN5rgsoa6eQFFIknHHq6ck_OitrW_jtFEowQRRLzLYqwaUgVCZrdbZRxpbOWUjV0mYLbVcKxWpdu1rXrr5r9xht8EeWw2qLVDejzvA3E20ymavg8y_je1ZcEMHUy21XjSftRLCricLkC8yikDk</recordid><startdate>201603</startdate><enddate>201603</enddate><creator>Dimpfl, Thomas</creator><creator>Jank, Stephan</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>201603</creationdate><title>Can Internet Search Queries Help to Predict Stock Market Volatility?</title><author>Dimpfl, Thomas ; Jank, Stephan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6368-44523c554da32a538a3429614e6d941e1b7a6d955a2a6422ce3f417818f4c0313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Dow Jones Indexes</topic><topic>Forecasting</topic><topic>Internet</topic><topic>investor behaviour</topic><topic>limited attention</topic><topic>noise trader</topic><topic>realised volatility</topic><topic>search engine data</topic><topic>Search engines</topic><topic>Securities markets</topic><topic>Stock market indexes</topic><topic>Studies</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dimpfl, Thomas</creatorcontrib><creatorcontrib>Jank, Stephan</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>European financial management : the journal of the European Financial Management Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dimpfl, Thomas</au><au>Jank, Stephan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can Internet Search Queries Help to Predict Stock Market Volatility?</atitle><jtitle>European financial management : the journal of the European Financial Management Association</jtitle><addtitle>Eur Financial Management</addtitle><date>2016-03</date><risdate>2016</risdate><volume>22</volume><issue>2</issue><spage>171</spage><epage>192</epage><pages>171-192</pages><issn>1354-7798</issn><eissn>1468-036X</eissn><abstract>We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/eufm.12058</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1354-7798 |
ispartof | European financial management : the journal of the European Financial Management Association, 2016-03, Vol.22 (2), p.171-192 |
issn | 1354-7798 1468-036X |
language | eng |
recordid | cdi_proquest_miscellaneous_1878796564 |
source | EBSCOhost Business Source Complete; Access via Wiley Online Library |
subjects | Dow Jones Indexes Forecasting Internet investor behaviour limited attention noise trader realised volatility search engine data Search engines Securities markets Stock market indexes Studies Volatility |
title | Can Internet Search Queries Help to Predict Stock Market Volatility? |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T11%3A19%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Can%20Internet%20Search%20Queries%20Help%20to%20Predict%20Stock%20Market%20Volatility?&rft.jtitle=European%20financial%20management%20:%20the%20journal%20of%20the%20European%20Financial%20Management%20Association&rft.au=Dimpfl,%20Thomas&rft.date=2016-03&rft.volume=22&rft.issue=2&rft.spage=171&rft.epage=192&rft.pages=171-192&rft.issn=1354-7798&rft.eissn=1468-036X&rft_id=info:doi/10.1111/eufm.12058&rft_dat=%3Cproquest_cross%3E1878796564%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1768734895&rft_id=info:pmid/&rfr_iscdi=true |