Class-imbalanced financial distress prediction with machine learning : incorporating financial, management, textual, and social responsibility features into index system
Gespeichert in:
| Autor*in: |
Song, Yinghua [verfasserIn] Jiang, Minzhe [verfasserIn] Li, Shixuan [verfasserIn] Zhao, Shengzhe [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of forecasting - New York, NY : Wiley Interscience, 1982, 43(2024), 3 vom: Apr., Seite 593-614 |
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| Übergeordnetes Werk: |
volume:43 ; year:2024 ; number:3 ; month:04 ; pages:593-614 |
| Links: |
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| DOI / URN: |
10.1002/for.3050 |
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| Katalog-ID: |
1889737399 |
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| 982 | |2 26 |1 00 |x DE-206 |b Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. | ||
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10.1002/for.3050 doi (DE-627)1889737399 (DE-599)KXP1889737399 DE-627 ger DE-627 rda eng Song, Yinghua verfasserin (DE-588)1252469438 (DE-627)1793805695 aut Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 Jiang, Minzhe verfasserin (DE-588)133791665X (DE-627)1897741693 aut Li, Shixuan verfasserin (DE-588)1337918725 (DE-627)1897742975 aut Zhao, Shengzhe verfasserin (DE-588)1337919829 (DE-627)1897746474 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 3 vom: Apr., Seite 593-614 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:3 month:04 pages:593-614 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 Verlag lizenzpflichtig https://doi.org/10.1002/for.3050 Resolving-System lizenzpflichtig 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_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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_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 43 2024 3 4 593-614 26 01 0206 4528858487 x1z 24-05-24 26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. |
| spelling |
10.1002/for.3050 doi (DE-627)1889737399 (DE-599)KXP1889737399 DE-627 ger DE-627 rda eng Song, Yinghua verfasserin (DE-588)1252469438 (DE-627)1793805695 aut Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 Jiang, Minzhe verfasserin (DE-588)133791665X (DE-627)1897741693 aut Li, Shixuan verfasserin (DE-588)1337918725 (DE-627)1897742975 aut Zhao, Shengzhe verfasserin (DE-588)1337919829 (DE-627)1897746474 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 3 vom: Apr., Seite 593-614 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:3 month:04 pages:593-614 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 Verlag lizenzpflichtig https://doi.org/10.1002/for.3050 Resolving-System lizenzpflichtig 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_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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_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 43 2024 3 4 593-614 26 01 0206 4528858487 x1z 24-05-24 26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. |
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10.1002/for.3050 doi (DE-627)1889737399 (DE-599)KXP1889737399 DE-627 ger DE-627 rda eng Song, Yinghua verfasserin (DE-588)1252469438 (DE-627)1793805695 aut Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 Jiang, Minzhe verfasserin (DE-588)133791665X (DE-627)1897741693 aut Li, Shixuan verfasserin (DE-588)1337918725 (DE-627)1897742975 aut Zhao, Shengzhe verfasserin (DE-588)1337919829 (DE-627)1897746474 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 3 vom: Apr., Seite 593-614 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:3 month:04 pages:593-614 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 Verlag lizenzpflichtig https://doi.org/10.1002/for.3050 Resolving-System lizenzpflichtig 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_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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_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 43 2024 3 4 593-614 26 01 0206 4528858487 x1z 24-05-24 26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. |
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10.1002/for.3050 doi (DE-627)1889737399 (DE-599)KXP1889737399 DE-627 ger DE-627 rda eng Song, Yinghua verfasserin (DE-588)1252469438 (DE-627)1793805695 aut Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 Jiang, Minzhe verfasserin (DE-588)133791665X (DE-627)1897741693 aut Li, Shixuan verfasserin (DE-588)1337918725 (DE-627)1897742975 aut Zhao, Shengzhe verfasserin (DE-588)1337919829 (DE-627)1897746474 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 3 vom: Apr., Seite 593-614 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:3 month:04 pages:593-614 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 Verlag lizenzpflichtig https://doi.org/10.1002/for.3050 Resolving-System lizenzpflichtig 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_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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_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 43 2024 3 4 593-614 26 01 0206 4528858487 x1z 24-05-24 26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. |
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10.1002/for.3050 doi (DE-627)1889737399 (DE-599)KXP1889737399 DE-627 ger DE-627 rda eng Song, Yinghua verfasserin (DE-588)1252469438 (DE-627)1793805695 aut Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 Jiang, Minzhe verfasserin (DE-588)133791665X (DE-627)1897741693 aut Li, Shixuan verfasserin (DE-588)1337918725 (DE-627)1897742975 aut Zhao, Shengzhe verfasserin (DE-588)1337919829 (DE-627)1897746474 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 3 vom: Apr., Seite 593-614 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:3 month:04 pages:593-614 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 Verlag lizenzpflichtig https://doi.org/10.1002/for.3050 Resolving-System lizenzpflichtig 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_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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_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 43 2024 3 4 593-614 26 01 0206 4528858487 x1z 24-05-24 26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators. |
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This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. 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26 00 DE-206 Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system Yinghua Song, Minzhe Jiang, Shixuan Li, Shengzhe Zhao machine learning (dpeaa)DE-206 management indicators (dpeaa)DE-206 SMOTE (dpeaa)DE-206 social responsibility indicators (dpeaa)DE-206 textual indicators (dpeaa)DE-206 |
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Class-imbalanced financial distress prediction with machine learning incorporating financial, management, textual, and social responsibility features into index system |
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| title_short |
Class-imbalanced financial distress prediction with machine learning |
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3050 https://doi.org/10.1002/for.3050 |
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Jiang, Minzhe Li, Shixuan Zhao, Shengzhe |
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Yinghua Song Song, Yinghua Jiang Minzhe Minzhe, Jiang Jiang, Minzhe Shixuan, Li Li Shixuan Li, Shixuan Zhao Shengzhe Shengzhe, Zhao Zhao, Shengzhe |
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Yinghua Song Song, Yinghua Jiang Minzhe Minzhe, Jiang Jiang, Minzhe Shixuan, Li Li Shixuan Li, Shixuan Zhao Shengzhe Shengzhe, Zhao Zhao, Shengzhe |
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Yinghua Song Song, Yinghua Jiang Minzhe Minzhe, Jiang Jiang, Minzhe Shixuan, Li Li Shixuan Li, Shixuan Zhao Shengzhe Shengzhe, Zhao Zhao, Shengzhe |
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10.1002/for.3050 |
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2024-08-06T05:56:42.437Z |
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code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">43</subfield><subfield code="j">2024</subfield><subfield code="e">3</subfield><subfield code="c">4</subfield><subfield code="h">593-614</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4528858487</subfield><subfield code="y">x1z</subfield><subfield code="z">24-05-24</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">Financial distress prediction (FDP) is centrally imported to reduce potential losses of companies and investors. This paper combines social responsibility indicators with financial, management, and textual indicators to construct a multi-dimensional FDP index system. To increase prediction accuracy, the difference in the number of samples between special treatment and health companies is actively considered, and the synthetic minority oversampling technique is adopted to deal with class-imbalanced datasets. Moreover, a feature extraction method based on the genetic algorithm is employed to select features as input of machine learning models for predicting financial distress in Chinese listed companies. Experimental results show that ensemble classifiers are the better choice for predicting financial distress, of which gradient boosting decision tree outperforms other classifiers. Textual indicators play the most significant role in complementing traditional financial indicators of FDP, and their financial distress signals emerge earlier compared with management and social responsibility indicators.</subfield></datafield></record></collection>
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| score |
7.166627 |