Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States

The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the...
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Autor*in:	Kasztelnik, Karina [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	ROE Equity Valuations Financial Institutions Economic Recession Dividend Yield Price toEarnings Banks

Übergeordnetes Werk:	Enthalten in: Open economics - Warsaw, Poland : De Gruyter Open, 2018, 3(2020), 1 vom: Jan., Seite 98-111
Übergeordnetes Werk:	volume:3 ; year:2020 ; number:1 ; month:01 ; pages:98-111

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1515/openec-2020-0106

Katalog-ID:	1763851796

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allfields	10.1515/openec-2020-0106 doi 10419/236608 hdl (DE-627)1763851796 (DE-599)KXP1763851796 DE-627 ger DE-627 rda eng M21 M41 O51 G24 N20 jelc Kasztelnik, Karina verfasserin aut Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206 Enthalten in Open economics Warsaw, Poland : De Gruyter Open, 2018 3(2020), 1 vom: Jan., Seite 98-111 Online-Ressource (DE-627)894098926 (DE-600)2900479-2 (DE-576)491115032 2451-3458 nnns volume:3 year:2020 number:1 month:01 pages:98-111 https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf Verlag kostenfrei https://doi.org/10.1515/openec-2020-0106 Resolving-System kostenfrei http://hdl.handle.net/10419/236608 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 3 2020 1 1 98-111 26 01 0206 3956507274 x1z 21-07-21 2403 01 DE-LFER 3965087533 00 --%%-- --%%-- n --%%-- l01 11-08-21 2403 01 DE-LFER https://doi.org/10.1515/openec-2020-0106 2403 01 DE-LFER https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf
spelling	10.1515/openec-2020-0106 doi 10419/236608 hdl (DE-627)1763851796 (DE-599)KXP1763851796 DE-627 ger DE-627 rda eng M21 M41 O51 G24 N20 jelc Kasztelnik, Karina verfasserin aut Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206 Enthalten in Open economics Warsaw, Poland : De Gruyter Open, 2018 3(2020), 1 vom: Jan., Seite 98-111 Online-Ressource (DE-627)894098926 (DE-600)2900479-2 (DE-576)491115032 2451-3458 nnns volume:3 year:2020 number:1 month:01 pages:98-111 https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf Verlag kostenfrei https://doi.org/10.1515/openec-2020-0106 Resolving-System kostenfrei http://hdl.handle.net/10419/236608 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 3 2020 1 1 98-111 26 01 0206 3956507274 x1z 21-07-21 2403 01 DE-LFER 3965087533 00 --%%-- --%%-- n --%%-- l01 11-08-21 2403 01 DE-LFER https://doi.org/10.1515/openec-2020-0106 2403 01 DE-LFER https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf
allfields_unstemmed	10.1515/openec-2020-0106 doi 10419/236608 hdl (DE-627)1763851796 (DE-599)KXP1763851796 DE-627 ger DE-627 rda eng M21 M41 O51 G24 N20 jelc Kasztelnik, Karina verfasserin aut Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206 Enthalten in Open economics Warsaw, Poland : De Gruyter Open, 2018 3(2020), 1 vom: Jan., Seite 98-111 Online-Ressource (DE-627)894098926 (DE-600)2900479-2 (DE-576)491115032 2451-3458 nnns volume:3 year:2020 number:1 month:01 pages:98-111 https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf Verlag kostenfrei https://doi.org/10.1515/openec-2020-0106 Resolving-System kostenfrei http://hdl.handle.net/10419/236608 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 3 2020 1 1 98-111 26 01 0206 3956507274 x1z 21-07-21 2403 01 DE-LFER 3965087533 00 --%%-- --%%-- n --%%-- l01 11-08-21 2403 01 DE-LFER https://doi.org/10.1515/openec-2020-0106 2403 01 DE-LFER https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf
allfieldsGer	10.1515/openec-2020-0106 doi 10419/236608 hdl (DE-627)1763851796 (DE-599)KXP1763851796 DE-627 ger DE-627 rda eng M21 M41 O51 G24 N20 jelc Kasztelnik, Karina verfasserin aut Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206 Enthalten in Open economics Warsaw, Poland : De Gruyter Open, 2018 3(2020), 1 vom: Jan., Seite 98-111 Online-Ressource (DE-627)894098926 (DE-600)2900479-2 (DE-576)491115032 2451-3458 nnns volume:3 year:2020 number:1 month:01 pages:98-111 https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf Verlag kostenfrei https://doi.org/10.1515/openec-2020-0106 Resolving-System kostenfrei http://hdl.handle.net/10419/236608 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 3 2020 1 1 98-111 26 01 0206 3956507274 x1z 21-07-21 2403 01 DE-LFER 3965087533 00 --%%-- --%%-- n --%%-- l01 11-08-21 2403 01 DE-LFER https://doi.org/10.1515/openec-2020-0106 2403 01 DE-LFER https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf
allfieldsSound	10.1515/openec-2020-0106 doi 10419/236608 hdl (DE-627)1763851796 (DE-599)KXP1763851796 DE-627 ger DE-627 rda eng M21 M41 O51 G24 N20 jelc Kasztelnik, Karina verfasserin aut Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206 Enthalten in Open economics Warsaw, Poland : De Gruyter Open, 2018 3(2020), 1 vom: Jan., Seite 98-111 Online-Ressource (DE-627)894098926 (DE-600)2900479-2 (DE-576)491115032 2451-3458 nnns volume:3 year:2020 number:1 month:01 pages:98-111 https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf Verlag kostenfrei https://doi.org/10.1515/openec-2020-0106 Resolving-System kostenfrei http://hdl.handle.net/10419/236608 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 3 2020 1 1 98-111 26 01 0206 3956507274 x1z 21-07-21 2403 01 DE-LFER 3965087533 00 --%%-- --%%-- n --%%-- l01 11-08-21 2403 01 DE-LFER https://doi.org/10.1515/openec-2020-0106 2403 01 DE-LFER https://www.degruyter.com/document/doi/10.1515/openec-2020-0106/pdf
language	English
source	Enthalten in Open economics 3(2020), 1 vom: Jan., Seite 98-111 volume:3 year:2020 number:1 month:01 pages:98-111
sourceStr	Enthalten in Open economics 3(2020), 1 vom: Jan., Seite 98-111 volume:3 year:2020 number:1 month:01 pages:98-111
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topic_facet	ROE Equity Valuations Financial Institutions Economic Recession Dividend Yield Price toEarnings Banks
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container_title	Open economics
authorswithroles_txt_mv	Kasztelnik, Karina @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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author	Kasztelnik, Karina
spellingShingle	Kasztelnik, Karina jelc M21 misc ROE misc Equity Valuations misc Financial Institutions misc Economic Recession misc Dividend Yield misc Price toEarnings misc Banks Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States
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topic_title	M21 M41 O51 G24 N20 jelc Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik ROE (dpeaa)DE-206 Equity Valuations (dpeaa)DE-206 Financial Institutions (dpeaa)DE-206 Economic Recession (dpeaa)DE-206 Dividend Yield (dpeaa)DE-206 Price toEarnings (dpeaa)DE-206 Banks (dpeaa)DE-206
topic	jelc M21 misc ROE misc Equity Valuations misc Financial Institutions misc Economic Recession misc Dividend Yield misc Price toEarnings misc Banks
topic_unstemmed	jelc M21 misc ROE misc Equity Valuations misc Financial Institutions misc Economic Recession misc Dividend Yield misc Price toEarnings misc Banks
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title	Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States
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title_full	Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States Karina Kasztelnik
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title_sort	innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the united states
title_auth	Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States
abstract	The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.
abstractGer	The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.
abstract_unstemmed	The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks' equity investment valuations and create an empirical model for predicting national banks' financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks' stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks' value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks' stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.
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title_short	Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States
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Innovative empirical model for predicting national banks' financial failure with artificial intelligence subset data analysis in the United States

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