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題名 應用遷移式自然語言結合 ESG 報章媒體情緒建構企業違約預警模型
Applying Transfer Learning of National Language Processing Combined with ESG News Sentiment to Construct Corporate Default Warning Mode作者 蘇于翔
SU, YU-SIANG貢獻者 江彌修
Chiang, Mi-Hsiu
蘇于翔
SU,YU-SIANG關鍵詞 企業違約預警
深度學習
文字探勘
責任投資
Corporate Default Prediction
Deep Learning
Text Mining
responsible investment日期 2023 上傳時間 1-Sep-2023 14:47:33 (UTC+8) 摘要 企業信用風險相關研究一直都是學術界關注的議題，過去已經有不少研究指出信用風險與傳統財務數據相關，例如：帳市比、公司槓桿、股價波動度等等。而近年來各界永續議題的關注度不斷提升，愈來愈多投資者認為環境、社會和公司治理（Environmental, Social, and Governance，簡稱 ESG）議題的表現會影響到企業整體營運狀況，應將企業的 ESG 表現納入投資決策中。然而在信用風險的研究方面，過去的研究主要專注在傳統財務數據等結構化資料上，且較少關注 ESG 因素對信用風險的影響，本研究嘗試結合結構化資料以及非結構化資料，建立機器學習的模型，對信用風險進行預測。結構化資料方面，本研究除了傳統財務數據外，額外加入碳排放量等與 ESG 相關的指標數據；非結構化資料方面，將利用BERT（Bidirectional Encoder Representations from Transformers）模型以及 FinBERT (BERT for Financial Text Mining) 模型，對新聞媒體進行語意分析，從媒體文本中萃取出財務情緒以及 ESG 情緒，最終建立隨機森林模型。本次研究發現，ESG 因子對於信用風險能夠提供有用的資訊，ESG 整體表現愈好的企業，有助於降低信用風險。
Corporate credit risk has been a prominent topic in academia, with previous studies emphasizing the correlation between credit risk and traditional financial data. However, the growing focus on sustainability, particularly Environmental, Social, and Governance (ESG) factors, has led to the need for their inclusion in credit risk research. This study aims to combine structured and unstructured data, incorporating ESG indicators alongside traditional financial metrics. By leveraging machine learning techniques and sentiment analysis on news media using BERT and FinBERT models, a random forest model is developed. The findings reveal that ESG factors provide valuable information, as companies with better ESG performance tend to exhibit reduced credit risk.參考文獻 Albuquerque, R., Koskinen, Y., & Zhang, C. (2019). Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science, 65(10), 4451-4469. Chava, S. (2014). Environmental externalities and cost of capital. Management science, 60(9),2223-2247.Alsentzer, E., Murphy, J. R., Boag, W., Weng, W., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323.Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models.arXiv preprint arXiv:1908.10063.B.F. Shi, X. Zhao, B. Wu, Y.Z. Dong. Credit rating and microfinance lending decisions based on loss given default (LGD). Financ. Res. Lett., 30 (2019), pp. 124-129Bauer, R., & Hann, D. (2010). Corporate environmental management and credit risk.Available at SSRN 1660470.Beltagy, I., Cohan, A., & Lo, K. (2019). SciBERT: Pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676.Collin-Dufresn, P, Goldstein, R. S., and Martin, J. S. (2001). The determinants of credit spread changes. The Journal of Finance, 56(6):2177-2207.Cutler, B. L., Penrod, S. D., & Dexter, H. R. (1989). The eyewitness, the expert psychologist, and the jury. Law and Human Behavior, 13(3), 311-332.Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.El Ghoul，S.，Guedhami，O.，Kwok，C.C.，& Mishra，D. R. （2011）. Does cor-porate social responsibility affect the cost of capital?. Journal of Banking & Finance, 35(9), 2388-2406.Ericsson, J., Jacobs, K., & Oviedo, R. (2009). The determinants of credit default swap premia. Journal of financial and quantitative analysis, 44(1), 109-132.Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233.H. Ogut, M.M. Doganay, N.B. Ceylan, R. Aktas. Prediction of bank financial strength ratings: the case of Turkey. Econ. Modell., 29 (3) (2012), pp. 632-640Huang, A. H., Wang, H., & Yang, Y. (2022). FinBERT: A Large Language Model for Extracting Information from Financial Text. Contemporary Accounting Research.Li, Z., Crook, J., Andreeva, G., & Tang, Y. (2021). Predicting the risk of financial distress using corporate governance measures. Pacific-Basin Finance Journal, 68, 101334.Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2019). BioBERT:A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.Lins, K. V., Servaes, H., & Tamayo, A. (2017. Social capital, trust, and firm performance:The value of corporate social responsibility during the financial crisis. The Journal of Finance, 72(4), 1785-1824.Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.Luo，X.，& Bhattacharya，C.B. （2006）. Corporate social responsibility, customer satisfaction, and market value. Journal of marketing, 70(4), 1-18.Luo，X.，& Bhattacharya，C.B. （2009）. The debate over doing good: Corporate social performance, strategic marketing levers, and firm-idiosyncratic risk. Journal of marketing, 73(6), 198-213.Michalis Doumpos & José Rui Figueira (2019). A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method. Omega, 82 (2019), Pages 166-180.N. Benbouzid, S.K. Mallick, R.M. Sousa. An international forensic perspective of the determinants of bank CDS spreads. J. Financ. Stab., 33 (2017), pp. 60-70Norden, L. (2017). Information in cds spreads. Journal of Banking & Finance, 75:118135.Norden, L. and Weber, M. (2004). Informational efficiency of credit default swap and stock markets: The impact of credit rating announcements. Journal of Banking & Finance, 28(11):2813-2843.Pedrosa, M. (1998). Systematic risk in corporate bond credit spreads. Journal of Fixed Income, 8(3):7–26.Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60-70.Switzer, L. N., Tu, Q, & Wang, J. (2018). Corporate governance and default risk in financial firms over the post-financial crisis period: International evidence. Journal of International Financial Markets, Institutions and Money, 52, 196-210.Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. The journal of finance, 63(3), 14371467. 描述 碩士
國立政治大學
金融學系
110352013資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110352013 資料類型 thesis dc.contributor.advisor 江彌修 zh_TW dc.contributor.advisor Chiang, Mi-Hsiu en_US dc.contributor.author (Authors) 蘇于翔 zh_TW dc.contributor.author (Authors) SU,YU-SIANG en_US dc.creator (作者) 蘇于翔 zh_TW dc.creator (作者) SU, YU-SIANG en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 14:47:33 (UTC+8) - dc.date.available 1-Sep-2023 14:47:33 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 14:47:33 (UTC+8) - dc.identifier (Other Identifiers) G0110352013 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146860 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 110352013 zh_TW dc.description.abstract (摘要) 企業信用風險相關研究一直都是學術界關注的議題，過去已經有不少研究指出信用風險與傳統財務數據相關，例如：帳市比、公司槓桿、股價波動度等等。而近年來各界永續議題的關注度不斷提升，愈來愈多投資者認為環境、社會和公司治理（Environmental, Social, and Governance，簡稱 ESG）議題的表現會影響到企業整體營運狀況，應將企業的 ESG 表現納入投資決策中。然而在信用風險的研究方面，過去的研究主要專注在傳統財務數據等結構化資料上，且較少關注 ESG 因素對信用風險的影響，本研究嘗試結合結構化資料以及非結構化資料，建立機器學習的模型，對信用風險進行預測。結構化資料方面，本研究除了傳統財務數據外，額外加入碳排放量等與 ESG 相關的指標數據；非結構化資料方面，將利用BERT（Bidirectional Encoder Representations from Transformers）模型以及 FinBERT (BERT for Financial Text Mining) 模型，對新聞媒體進行語意分析，從媒體文本中萃取出財務情緒以及 ESG 情緒，最終建立隨機森林模型。本次研究發現，ESG 因子對於信用風險能夠提供有用的資訊，ESG 整體表現愈好的企業，有助於降低信用風險。 zh_TW dc.description.abstract (摘要) Corporate credit risk has been a prominent topic in academia, with previous studies emphasizing the correlation between credit risk and traditional financial data. However, the growing focus on sustainability, particularly Environmental, Social, and Governance (ESG) factors, has led to the need for their inclusion in credit risk research. This study aims to combine structured and unstructured data, incorporating ESG indicators alongside traditional financial metrics. By leveraging machine learning techniques and sentiment analysis on news media using BERT and FinBERT models, a random forest model is developed. The findings reveal that ESG factors provide valuable information, as companies with better ESG performance tend to exhibit reduced credit risk. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究動機與背景 11.2 研究目的 2第二章 文獻回顧 32.1 衡量企業信用風險 32.1.1 信用評級 32.1.2 信用違約交換 42.2 ESG 對企業的影響 5第三章 研究方法 73.1 自然語言模型 73.1.1 BERT 模型 73.1.2 FinBERT 113.2 隨機森林 123.3 模型績效衡量指標 153.3.1 模型預測能力指標 153.3.2 單個特徵的貢獻程度 16第四章 實證分析 184.1 資料處理 184.1.1 新聞媒體資料 184.1.2 市場及財務變數 194.1.3 Refinitiv ESG 指標變數 204.2 特徵生成 214.3 建構 CDS Spread 預測模型 224.3.1 訓練集與測試集 224.3.2 模型訓練與超參數設置 254.4 各模型預測成效 274.4.1 模型預測能力衡量 274.4.2 特徵重要性 30第五章 結論與建議 36參考文獻 37 zh_TW dc.format.extent 1851729 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110352013 en_US dc.subject (關鍵詞) 企業違約預警 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 文字探勘 zh_TW dc.subject (關鍵詞) 責任投資 zh_TW dc.subject (關鍵詞) Corporate Default Prediction en_US dc.subject (關鍵詞) Deep Learning en_US dc.subject (關鍵詞) Text Mining en_US dc.subject (關鍵詞) responsible investment en_US dc.title (題名) 應用遷移式自然語言結合 ESG 報章媒體情緒建構企業違約預警模型 zh_TW dc.title (題名) Applying Transfer Learning of National Language Processing Combined with ESG News Sentiment to Construct Corporate Default Warning Mode en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Albuquerque, R., Koskinen, Y., & Zhang, C. (2019). Corporate social responsibility and firm risk: Theory and empirical evidence. Management Science, 65(10), 4451-4469. Chava, S. (2014). Environmental externalities and cost of capital. Management science, 60(9),2223-2247.Alsentzer, E., Murphy, J. R., Boag, W., Weng, W., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323.Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models.arXiv preprint arXiv:1908.10063.B.F. Shi, X. Zhao, B. Wu, Y.Z. Dong. Credit rating and microfinance lending decisions based on loss given default (LGD). Financ. Res. Lett., 30 (2019), pp. 124-129Bauer, R., & Hann, D. (2010). Corporate environmental management and credit risk.Available at SSRN 1660470.Beltagy, I., Cohan, A., & Lo, K. (2019). SciBERT: Pretrained contextualized embeddings for scientific text. arXiv preprint arXiv:1903.10676.Collin-Dufresn, P, Goldstein, R. S., and Martin, J. S. (2001). The determinants of credit spread changes. The Journal of Finance, 56(6):2177-2207.Cutler, B. L., Penrod, S. D., & Dexter, H. R. (1989). The eyewitness, the expert psychologist, and the jury. Law and Human Behavior, 13(3), 311-332.Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.El Ghoul，S.，Guedhami，O.，Kwok，C.C.，& Mishra，D. R. （2011）. Does cor-porate social responsibility affect the cost of capital?. Journal of Banking & Finance, 35(9), 2388-2406.Ericsson, J., Jacobs, K., & Oviedo, R. (2009). The determinants of credit default swap premia. Journal of financial and quantitative analysis, 44(1), 109-132.Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233.H. Ogut, M.M. Doganay, N.B. Ceylan, R. Aktas. Prediction of bank financial strength ratings: the case of Turkey. Econ. Modell., 29 (3) (2012), pp. 632-640Huang, A. H., Wang, H., & Yang, Y. (2022). FinBERT: A Large Language Model for Extracting Information from Financial Text. Contemporary Accounting Research.Li, Z., Crook, J., Andreeva, G., & Tang, Y. (2021). Predicting the risk of financial distress using corporate governance measures. Pacific-Basin Finance Journal, 68, 101334.Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2019). BioBERT:A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.Lins, K. V., Servaes, H., & Tamayo, A. (2017. Social capital, trust, and firm performance:The value of corporate social responsibility during the financial crisis. The Journal of Finance, 72(4), 1785-1824.Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.Luo，X.，& Bhattacharya，C.B. （2006）. Corporate social responsibility, customer satisfaction, and market value. Journal of marketing, 70(4), 1-18.Luo，X.，& Bhattacharya，C.B. （2009）. The debate over doing good: Corporate social performance, strategic marketing levers, and firm-idiosyncratic risk. Journal of marketing, 73(6), 198-213.Michalis Doumpos & José Rui Figueira (2019). A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method. Omega, 82 (2019), Pages 166-180.N. Benbouzid, S.K. Mallick, R.M. Sousa. An international forensic perspective of the determinants of bank CDS spreads. J. Financ. Stab., 33 (2017), pp. 60-70Norden, L. (2017). Information in cds spreads. Journal of Banking & Finance, 75:118135.Norden, L. and Weber, M. (2004). Informational efficiency of credit default swap and stock markets: The impact of credit rating announcements. Journal of Banking & Finance, 28(11):2813-2843.Pedrosa, M. (1998). Systematic risk in corporate bond credit spreads. Journal of Fixed Income, 8(3):7–26.Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60-70.Switzer, L. N., Tu, Q, & Wang, J. (2018). Corporate governance and default risk in financial firms over the post-financial crisis period: International evidence. Journal of International Financial Markets, Institutions and Money, 52, 196-210.Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. The journal of finance, 63(3), 14371467. zh_TW
