News sentiment and stock market volatility
This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation...
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| Veröffentlicht in: | Review of quantitative finance and accounting 2021-10, Vol.57 (3), p.1093-1122 |
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| container_title | Review of quantitative finance and accounting |
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| creator | Hsu, Yen-Ju Lu, Yang-Cheng Yang, J. Jimmy |
| description | This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified market news sentiment and macroeconomic-related news effects. The evidence suggests that both contemporaneous and lagged news are determinants of market volatility. The effect is especially strong with the market aggregate news sentiment index (
ANSI
) and the negative
ANSI
, particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting. |
| doi_str_mv | 10.1007/s11156-021-00971-8 |
| format | Article |
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ANSI
) and the negative
ANSI
, particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting.</description><identifier>ISSN: 0924-865X</identifier><identifier>EISSN: 1573-7179</identifier><identifier>DOI: 10.1007/s11156-021-00971-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accounting/Auditing ; Corporate Finance ; Econometrics ; Economic crisis ; Economics and Finance ; Finance ; News ; Operations Research/Decision Theory ; Original Research ; Securities markets ; Stochastic models ; Volatility</subject><ispartof>Review of quantitative finance and accounting, 2021-10, Vol.57 (3), p.1093-1122</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-48bed7a3a33bf27062732155c11b9eb3ec6a385fc456f5cf453395d11c69e8dd3</citedby><cites>FETCH-LOGICAL-c409t-48bed7a3a33bf27062732155c11b9eb3ec6a385fc456f5cf453395d11c69e8dd3</cites><orcidid>0000-0003-4840-6926</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11156-021-00971-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11156-021-00971-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Hsu, Yen-Ju</creatorcontrib><creatorcontrib>Lu, Yang-Cheng</creatorcontrib><creatorcontrib>Yang, J. Jimmy</creatorcontrib><title>News sentiment and stock market volatility</title><title>Review of quantitative finance and accounting</title><addtitle>Rev Quant Finan Acc</addtitle><description>This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified market news sentiment and macroeconomic-related news effects. The evidence suggests that both contemporaneous and lagged news are determinants of market volatility. The effect is especially strong with the market aggregate news sentiment index (
ANSI
) and the negative
ANSI
, particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting.</description><subject>Accounting/Auditing</subject><subject>Corporate Finance</subject><subject>Econometrics</subject><subject>Economic crisis</subject><subject>Economics and Finance</subject><subject>Finance</subject><subject>News</subject><subject>Operations Research/Decision Theory</subject><subject>Original Research</subject><subject>Securities markets</subject><subject>Stochastic models</subject><subject>Volatility</subject><issn>0924-865X</issn><issn>1573-7179</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LxDAQhoMouK7-AU8Fb0J0JmmS5iiLX7DoRcFbSNNUuh_tmmSV_fdGK-xNGGYO8z7zDi8h5whXCKCuIyIKSYEhBdAKaXVAJigUpwqVPiQT0KyklRRvx-QkxgVAxoSYkMsn_xWL6PvUrXMrbN8UMQ1uWaxtWPpUfA4rm7pVl3an5Ki1q-jP_uaUvN7dvswe6Pz5_nF2M6euBJ1oWdW-UZZbzuuWKZBMcYZCOMRa-5p7Jy2vROtKIVvh2lJwrkWD6KT2VdPwKbkY727C8LH1MZnFsA19tjRMSA65MjMlbFS5MMQYfGs2ocs_7wyC-cnEjJmYnIn5zcRUGSpGyLuh7-IeUQp0fkpjlvBREvOyf_dh7_7P4W_Clm1w</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Hsu, Yen-Ju</creator><creator>Lu, Yang-Cheng</creator><creator>Yang, J. 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Jimmy</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>News sentiment and stock market volatility</atitle><jtitle>Review of quantitative finance and accounting</jtitle><stitle>Rev Quant Finan Acc</stitle><date>2021-10-01</date><risdate>2021</risdate><volume>57</volume><issue>3</issue><spage>1093</spage><epage>1122</epage><pages>1093-1122</pages><issn>0924-865X</issn><eissn>1573-7179</eissn><abstract>This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified market news sentiment and macroeconomic-related news effects. 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ANSI
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ANSI
, particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11156-021-00971-8</doi><tpages>30</tpages><orcidid>https://orcid.org/0000-0003-4840-6926</orcidid></addata></record> |
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| identifier | ISSN: 0924-865X |
| ispartof | Review of quantitative finance and accounting, 2021-10, Vol.57 (3), p.1093-1122 |
| issn | 0924-865X 1573-7179 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals; Business Source Complete |
| subjects | Accounting/Auditing Corporate Finance Econometrics Economic crisis Economics and Finance Finance News Operations Research/Decision Theory Original Research Securities markets Stochastic models Volatility |
| title | News sentiment and stock market volatility |
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