Big data analytics, firm risk and corporate policies : evidence from China

Gespeichert in:

Autor*in:	Sun, Pengfei [verfasserIn] Yuan, Chunhui - 1963- [verfasserIn] Li, Xiaolong [verfasserIn] Di, Jia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Big data Corporate policy Firm risk Firm value

Übergeordnetes Werk:	Enthalten in: Research in international business and finance - Amsterdam [u.a.] : Elsevier, 2004, 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27
Übergeordnetes Werk:	volume:70 ; year:2024 ; number:2 ; month:06 ; elocationid:102371 ; pages:1-27

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.ribaf.2024.102371

Katalog-ID:	1902256468

Internformat


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allfields	10.1016/j.ribaf.2024.102371 doi (DE-627)1902256468 (DE-599)KXP1902256468 DE-627 ger DE-627 rda eng Sun, Pengfei verfasserin aut Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206 Yuan, Chunhui 1963- verfasserin (DE-588)1245145444 (DE-627)1776246128 aut Li, Xiaolong verfasserin (DE-588)1255011394 (DE-627)1797790609 aut Di, Jia verfasserin aut Enthalten in Research in international business and finance Amsterdam [u.a.] : Elsevier, 2004 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 Online-Ressource (DE-627)470543930 (DE-600)2165501-7 (DE-576)259486434 0275-5319 nnns volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27 https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.ribaf.2024.102371 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 70 2024 2 6 102371 1-27 26 01 0206 4578514188 x1z 10-09-24 2403 01 DE-LFER 4590814870 00 --%%-- --%%-- n --%%-- l01 10-10-24 2403 01 DE-LFER https://doi.org/10.1016/j.ribaf.2024.102371 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf 26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.
spelling	10.1016/j.ribaf.2024.102371 doi (DE-627)1902256468 (DE-599)KXP1902256468 DE-627 ger DE-627 rda eng Sun, Pengfei verfasserin aut Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206 Yuan, Chunhui 1963- verfasserin (DE-588)1245145444 (DE-627)1776246128 aut Li, Xiaolong verfasserin (DE-588)1255011394 (DE-627)1797790609 aut Di, Jia verfasserin aut Enthalten in Research in international business and finance Amsterdam [u.a.] : Elsevier, 2004 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 Online-Ressource (DE-627)470543930 (DE-600)2165501-7 (DE-576)259486434 0275-5319 nnns volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27 https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.ribaf.2024.102371 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 70 2024 2 6 102371 1-27 26 01 0206 4578514188 x1z 10-09-24 2403 01 DE-LFER 4590814870 00 --%%-- --%%-- n --%%-- l01 10-10-24 2403 01 DE-LFER https://doi.org/10.1016/j.ribaf.2024.102371 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf 26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.
allfields_unstemmed	10.1016/j.ribaf.2024.102371 doi (DE-627)1902256468 (DE-599)KXP1902256468 DE-627 ger DE-627 rda eng Sun, Pengfei verfasserin aut Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206 Yuan, Chunhui 1963- verfasserin (DE-588)1245145444 (DE-627)1776246128 aut Li, Xiaolong verfasserin (DE-588)1255011394 (DE-627)1797790609 aut Di, Jia verfasserin aut Enthalten in Research in international business and finance Amsterdam [u.a.] : Elsevier, 2004 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 Online-Ressource (DE-627)470543930 (DE-600)2165501-7 (DE-576)259486434 0275-5319 nnns volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27 https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.ribaf.2024.102371 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 70 2024 2 6 102371 1-27 26 01 0206 4578514188 x1z 10-09-24 2403 01 DE-LFER 4590814870 00 --%%-- --%%-- n --%%-- l01 10-10-24 2403 01 DE-LFER https://doi.org/10.1016/j.ribaf.2024.102371 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf 26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.
allfieldsGer	10.1016/j.ribaf.2024.102371 doi (DE-627)1902256468 (DE-599)KXP1902256468 DE-627 ger DE-627 rda eng Sun, Pengfei verfasserin aut Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206 Yuan, Chunhui 1963- verfasserin (DE-588)1245145444 (DE-627)1776246128 aut Li, Xiaolong verfasserin (DE-588)1255011394 (DE-627)1797790609 aut Di, Jia verfasserin aut Enthalten in Research in international business and finance Amsterdam [u.a.] : Elsevier, 2004 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 Online-Ressource (DE-627)470543930 (DE-600)2165501-7 (DE-576)259486434 0275-5319 nnns volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27 https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.ribaf.2024.102371 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 70 2024 2 6 102371 1-27 26 01 0206 4578514188 x1z 10-09-24 2403 01 DE-LFER 4590814870 00 --%%-- --%%-- n --%%-- l01 10-10-24 2403 01 DE-LFER https://doi.org/10.1016/j.ribaf.2024.102371 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf 26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.
allfieldsSound	10.1016/j.ribaf.2024.102371 doi (DE-627)1902256468 (DE-599)KXP1902256468 DE-627 ger DE-627 rda eng Sun, Pengfei verfasserin aut Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206 Yuan, Chunhui 1963- verfasserin (DE-588)1245145444 (DE-627)1776246128 aut Li, Xiaolong verfasserin (DE-588)1255011394 (DE-627)1797790609 aut Di, Jia verfasserin aut Enthalten in Research in international business and finance Amsterdam [u.a.] : Elsevier, 2004 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 Online-Ressource (DE-627)470543930 (DE-600)2165501-7 (DE-576)259486434 0275-5319 nnns volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27 https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.ribaf.2024.102371 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 70 2024 2 6 102371 1-27 26 01 0206 4578514188 x1z 10-09-24 2403 01 DE-LFER 4590814870 00 --%%-- --%%-- n --%%-- l01 10-10-24 2403 01 DE-LFER https://doi.org/10.1016/j.ribaf.2024.102371 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf 26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.
language	English
source	Enthalten in Research in international business and finance 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27
sourceStr	Enthalten in Research in international business and finance 70(2024), 2 vom: Juni, Artikel-ID 102371, Seite 1-27 volume:70 year:2024 number:2 month:06 elocationid:102371 pages:1-27
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topic_facet	Big data Corporate policy Firm risk Firm value
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container_title	Research in international business and finance
authorswithroles_txt_mv	Sun, Pengfei @@aut@@ Yuan, Chunhui @@aut@@ Li, Xiaolong @@aut@@ Di, Jia @@aut@@
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topic_title	26 00 DE-206 This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust Big data analytics, firm risk and corporate policies evidence from China Pengfei Sun, Chunhui Yuan, Xiaolong Li, Jia Di Big data (dpeaa)DE-206 Corporate policy (dpeaa)DE-206 Firm risk (dpeaa)DE-206 Firm value (dpeaa)DE-206
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code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1016/j.ribaf.2024.102371</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://www.sciencedirect.com/science/article/pii/S0275531924001648/pdfft?md5=117c6bfca1bbe1fd97a3d48a73f404df&pid=1-s2.0-S0275531924001648-main.pdf</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.</subfield></datafield></record></collection>
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