FinBERT : a large language model for extracting information from financial text

Gespeichert in:

Autor*in:	Huang, Allen - 1979- [verfasserIn] Wang, Hui [verfasserIn] Yang, Yi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	and governance (ESG) deep learning environment interpretable machine learning large language model sentiment classification social transfer learning

Übergeordnetes Werk:	Enthalten in: Contemporary accounting research - Malden, MA : Wiley Periodicals, Inc, 1984, 40(2023), 2 vom: Sommer, Seite 806-841
Übergeordnetes Werk:	volume:40 ; year:2023 ; number:2 ; month:22 ; pages:806-841

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1111/1911-3846.12832

Katalog-ID:	1848977247

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981			\|2 2403 \|1 01 \|x DE-LFER \|r https://doi.org/10.1111/1911-3846.12832
982			\|2 26 \|1 00 \|x DE-206 \|b We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.

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allfields	10.1111/1911-3846.12832 doi (DE-627)1848977247 (DE-599)KXP1848977247 DE-627 ger DE-627 rda eng Huang, Allen 1979- verfasserin (DE-588)1275407595 (DE-627)1826850082 aut FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206 Wang, Hui verfasserin (DE-588)1292792744 (DE-627)1849768900 aut Yang, Yi verfasserin (DE-588)1263862462 (DE-627)1811959547 aut Enthalten in Contemporary accounting research Malden, MA : Wiley Periodicals, Inc, 1984 40(2023), 2 vom: Sommer, Seite 806-841 Online-Ressource (DE-627)341359521 (DE-600)2068682-1 (DE-576)106890905 1911-3846 nnns volume:40 year:2023 number:2 month:22 pages:806-841 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1911-3846.12832 Verlag kostenfrei https://doi.org/10.1111/1911-3846.12832 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_32 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_224 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER 40 2023 2 22 806-841 AR 40 2023 2 22 806-841 26 01 0206 4337137637 x1z 14-06-23 2403 01 DE-LFER 4346981380 00 --%%-- --%%-- n --%%-- l01 03-07-23 2403 01 DE-LFER https://doi.org/10.1111/1911-3846.12832 26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
spelling	10.1111/1911-3846.12832 doi (DE-627)1848977247 (DE-599)KXP1848977247 DE-627 ger DE-627 rda eng Huang, Allen 1979- verfasserin (DE-588)1275407595 (DE-627)1826850082 aut FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206 Wang, Hui verfasserin (DE-588)1292792744 (DE-627)1849768900 aut Yang, Yi verfasserin (DE-588)1263862462 (DE-627)1811959547 aut Enthalten in Contemporary accounting research Malden, MA : Wiley Periodicals, Inc, 1984 40(2023), 2 vom: Sommer, Seite 806-841 Online-Ressource (DE-627)341359521 (DE-600)2068682-1 (DE-576)106890905 1911-3846 nnns volume:40 year:2023 number:2 month:22 pages:806-841 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1911-3846.12832 Verlag kostenfrei https://doi.org/10.1111/1911-3846.12832 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_32 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_224 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER 40 2023 2 22 806-841 AR 40 2023 2 22 806-841 26 01 0206 4337137637 x1z 14-06-23 2403 01 DE-LFER 4346981380 00 --%%-- --%%-- n --%%-- l01 03-07-23 2403 01 DE-LFER https://doi.org/10.1111/1911-3846.12832 26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
allfields_unstemmed	10.1111/1911-3846.12832 doi (DE-627)1848977247 (DE-599)KXP1848977247 DE-627 ger DE-627 rda eng Huang, Allen 1979- verfasserin (DE-588)1275407595 (DE-627)1826850082 aut FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206 Wang, Hui verfasserin (DE-588)1292792744 (DE-627)1849768900 aut Yang, Yi verfasserin (DE-588)1263862462 (DE-627)1811959547 aut Enthalten in Contemporary accounting research Malden, MA : Wiley Periodicals, Inc, 1984 40(2023), 2 vom: Sommer, Seite 806-841 Online-Ressource (DE-627)341359521 (DE-600)2068682-1 (DE-576)106890905 1911-3846 nnns volume:40 year:2023 number:2 month:22 pages:806-841 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1911-3846.12832 Verlag kostenfrei https://doi.org/10.1111/1911-3846.12832 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_32 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_224 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER 40 2023 2 22 806-841 AR 40 2023 2 22 806-841 26 01 0206 4337137637 x1z 14-06-23 2403 01 DE-LFER 4346981380 00 --%%-- --%%-- n --%%-- l01 03-07-23 2403 01 DE-LFER https://doi.org/10.1111/1911-3846.12832 26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
allfieldsGer	10.1111/1911-3846.12832 doi (DE-627)1848977247 (DE-599)KXP1848977247 DE-627 ger DE-627 rda eng Huang, Allen 1979- verfasserin (DE-588)1275407595 (DE-627)1826850082 aut FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206 Wang, Hui verfasserin (DE-588)1292792744 (DE-627)1849768900 aut Yang, Yi verfasserin (DE-588)1263862462 (DE-627)1811959547 aut Enthalten in Contemporary accounting research Malden, MA : Wiley Periodicals, Inc, 1984 40(2023), 2 vom: Sommer, Seite 806-841 Online-Ressource (DE-627)341359521 (DE-600)2068682-1 (DE-576)106890905 1911-3846 nnns volume:40 year:2023 number:2 month:22 pages:806-841 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1911-3846.12832 Verlag kostenfrei https://doi.org/10.1111/1911-3846.12832 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_32 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_224 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER 40 2023 2 22 806-841 AR 40 2023 2 22 806-841 26 01 0206 4337137637 x1z 14-06-23 2403 01 DE-LFER 4346981380 00 --%%-- --%%-- n --%%-- l01 03-07-23 2403 01 DE-LFER https://doi.org/10.1111/1911-3846.12832 26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
allfieldsSound	10.1111/1911-3846.12832 doi (DE-627)1848977247 (DE-599)KXP1848977247 DE-627 ger DE-627 rda eng Huang, Allen 1979- verfasserin (DE-588)1275407595 (DE-627)1826850082 aut FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206 Wang, Hui verfasserin (DE-588)1292792744 (DE-627)1849768900 aut Yang, Yi verfasserin (DE-588)1263862462 (DE-627)1811959547 aut Enthalten in Contemporary accounting research Malden, MA : Wiley Periodicals, Inc, 1984 40(2023), 2 vom: Sommer, Seite 806-841 Online-Ressource (DE-627)341359521 (DE-600)2068682-1 (DE-576)106890905 1911-3846 nnns volume:40 year:2023 number:2 month:22 pages:806-841 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1911-3846.12832 Verlag kostenfrei https://doi.org/10.1111/1911-3846.12832 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_32 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_224 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER 40 2023 2 22 806-841 AR 40 2023 2 22 806-841 26 01 0206 4337137637 x1z 14-06-23 2403 01 DE-LFER 4346981380 00 --%%-- --%%-- n --%%-- l01 03-07-23 2403 01 DE-LFER https://doi.org/10.1111/1911-3846.12832 26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
language	English
source	Enthalten in Contemporary accounting research 40(2023), 2 vom: Sommer, Seite 806-841 volume:40 year:2023 number:2 month:22 pages:806-841
sourceStr	Enthalten in Contemporary accounting research 40(2023), 2 vom: Sommer, Seite 806-841 volume:40 year:2023 number:2 month:22 pages:806-841
format_phy_str_mv	Article
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topic_facet	and governance (ESG) deep learning environment interpretable machine learning large language model sentiment classification social transfer learning
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container_title	Contemporary accounting research
authorswithroles_txt_mv	Huang, Allen @@aut@@ Wang, Hui @@aut@@ Yang, Yi @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	341359521
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language_de	englisch
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author	Huang, Allen 1979-
spellingShingle	Huang, Allen 1979- misc and governance (ESG) misc deep learning misc environment misc interpretable machine learning misc large language model misc sentiment classification misc social misc transfer learning FinBERT a large language model for extracting information from financial text
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topic_title	26 00 DE-206 We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang and governance (ESG) (dpeaa)DE-206 deep learning (dpeaa)DE-206 environment (dpeaa)DE-206 interpretable machine learning (dpeaa)DE-206 large language model (dpeaa)DE-206 sentiment classification (dpeaa)DE-206 social (dpeaa)DE-206 transfer learning (dpeaa)DE-206
topic	misc and governance (ESG) misc deep learning misc environment misc interpretable machine learning misc large language model misc sentiment classification misc social misc transfer learning
topic_unstemmed	misc and governance (ESG) misc deep learning misc environment misc interpretable machine learning misc large language model misc sentiment classification misc social misc transfer learning
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title	FinBERT a large language model for extracting information from financial text
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title_full	FinBERT a large language model for extracting information from financial text Allen H. Huang, Hui Wang, Yi Yang
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up_date	2024-07-19T08:17:46.210Z
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We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.</subfield></datafield></record></collection>
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