Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning

Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on...
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Autor*in:	Yang, Lin [verfasserIn] Liu, Junling Fan, Zehao Yang, Dafei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Personal characteristics of executives Corporate governance Corporate performance Embedded system architecture Boosting regression tree

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 14(2022), 7 vom: 21. Jan., Seite 8655-8665
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:7 ; day:21 ; month:01 ; pages:8655-8665

Links:	Volltext

DOI / URN:	10.1007/s12652-021-03623-w

Katalog-ID:	SPR051816008

Internformat


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520			\|a Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance.
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allfields	10.1007/s12652-021-03623-w doi (DE-627)SPR051816008 (SPR)s12652-021-03623-w-e DE-627 ger DE-627 rakwb eng Yang, Lin verfasserin aut Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213 Liu, Junling aut Fan, Zehao aut Yang, Dafei aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 7 vom: 21. Jan., Seite 8655-8665 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665 https://dx.doi.org/10.1007/s12652-021-03623-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 7 21 01 8655-8665
spelling	10.1007/s12652-021-03623-w doi (DE-627)SPR051816008 (SPR)s12652-021-03623-w-e DE-627 ger DE-627 rakwb eng Yang, Lin verfasserin aut Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213 Liu, Junling aut Fan, Zehao aut Yang, Dafei aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 7 vom: 21. Jan., Seite 8655-8665 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665 https://dx.doi.org/10.1007/s12652-021-03623-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 7 21 01 8655-8665
allfields_unstemmed	10.1007/s12652-021-03623-w doi (DE-627)SPR051816008 (SPR)s12652-021-03623-w-e DE-627 ger DE-627 rakwb eng Yang, Lin verfasserin aut Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213 Liu, Junling aut Fan, Zehao aut Yang, Dafei aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 7 vom: 21. Jan., Seite 8655-8665 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665 https://dx.doi.org/10.1007/s12652-021-03623-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 7 21 01 8655-8665
allfieldsGer	10.1007/s12652-021-03623-w doi (DE-627)SPR051816008 (SPR)s12652-021-03623-w-e DE-627 ger DE-627 rakwb eng Yang, Lin verfasserin aut Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213 Liu, Junling aut Fan, Zehao aut Yang, Dafei aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 7 vom: 21. Jan., Seite 8655-8665 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665 https://dx.doi.org/10.1007/s12652-021-03623-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 7 21 01 8655-8665
allfieldsSound	10.1007/s12652-021-03623-w doi (DE-627)SPR051816008 (SPR)s12652-021-03623-w-e DE-627 ger DE-627 rakwb eng Yang, Lin verfasserin aut Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213 Liu, Junling aut Fan, Zehao aut Yang, Dafei aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 7 vom: 21. Jan., Seite 8655-8665 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665 https://dx.doi.org/10.1007/s12652-021-03623-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2022 7 21 01 8655-8665
language	English
source	Enthalten in Journal of ambient intelligence and humanized computing 14(2022), 7 vom: 21. Jan., Seite 8655-8665 volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665
sourceStr	Enthalten in Journal of ambient intelligence and humanized computing 14(2022), 7 vom: 21. Jan., Seite 8655-8665 volume:14 year:2022 number:7 day:21 month:01 pages:8655-8665
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Machine learning Personal characteristics of executives Corporate governance Corporate performance Embedded system architecture Boosting regression tree
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container_title	Journal of ambient intelligence and humanized computing
authorswithroles_txt_mv	Yang, Lin @@aut@@ Liu, Junling @@aut@@ Fan, Zehao @@aut@@ Yang, Dafei @@aut@@
publishDateDaySort_date	2022-01-21T00:00:00Z
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author	Yang, Lin
spellingShingle	Yang, Lin misc Machine learning misc Personal characteristics of executives misc Corporate governance misc Corporate performance misc Embedded system architecture misc Boosting regression tree Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
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topic_title	Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning Machine learning (dpeaa)DE-He213 Personal characteristics of executives (dpeaa)DE-He213 Corporate governance (dpeaa)DE-He213 Corporate performance (dpeaa)DE-He213 Embedded system architecture (dpeaa)DE-He213 Boosting regression tree (dpeaa)DE-He213
topic	misc Machine learning misc Personal characteristics of executives misc Corporate governance misc Corporate performance misc Embedded system architecture misc Boosting regression tree
topic_unstemmed	misc Machine learning misc Personal characteristics of executives misc Corporate governance misc Corporate performance misc Embedded system architecture misc Boosting regression tree
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title	Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
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title_full	Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
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author_browse	Yang, Lin Liu, Junling Fan, Zehao Yang, Dafei
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doi_str_mv	10.1007/s12652-021-03623-w
title_sort	governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
title_auth	Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
abstract	Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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title_short	Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning
url	https://dx.doi.org/10.1007/s12652-021-03623-w
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author2	Liu, Junling Fan, Zehao Yang, Dafei
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up_date	2024-07-03T23:55:08.950Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051816008</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230607064707.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230607s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12652-021-03623-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051816008</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12652-021-03623-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Governance of executive personal characteristics and corporate performance based on empirical evidence based on machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In the current corporate governance literature, most studies on executive characteristics only focus on the relationship between a single executive characteristic and company performance, and lack a comprehensive analysis of executive characteristics; on the other hand, they mainly focus on causal inference. This article uses the Boosting regression tree algorithm in machine learning to thoroughly investigate the relationship between the company's multidimensional performance characteristics and performance, while avoiding the weaknesses of traditional linear models and is more suitable for analyzing nonlinear and interactive relationships between variables. This article uses listed companies from 2015 to 2020 as a sample to empirically evaluate the ability of executives to predict company performance, further dig out the personal characteristics of executives with strong ability to predict company performance, and describe their prediction mechanism. Experiments have shown that the cross-term coefficient of the proportion of senior management’s shareholding and age is − 0.028, showing a significant correlation (P = 0.005 < 0.05), and the cross-term coefficient of the proportion of senior management holdings and education is − 0.003, showing a significant correlation (P = 0.005 < 0.05), which shows that the management analysis of executives' personal characteristics and corporate performance based on machine learning is more accurate than other traditional technical analyzes, which also opens the future corporate governance performance a new direction of research, and the use machine learning and deep learning algorithms for governance decisions can also reduce the involvement of human factors in future corporate performance governance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Personal characteristics of executives</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corporate governance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Corporate performance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Embedded system architecture</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting regression tree</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Junling</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Zehao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Dafei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of ambient intelligence and humanized computing</subfield><subfield code="d">Berlin : Springer, 2010</subfield><subfield code="g">14(2022), 7 vom: 21. 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