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題名 延伸LDA主題模型於企業破產預測
Extending the Latent Dirichlet Allocation Model for Corporate Default Prediction作者 彭昱齊
Peng, Yu-Chi貢獻者 江彌修
Chiang, Mi-Hsiu
彭昱齊
Peng, Yu-Chi關鍵詞 主題模型
企業破產預警
10-K報告
Topic modeling
LDA
JST
Corporate bankruptcy prediction
10-K日期 2020 上傳時間 3-Aug-2020 17:38:59 (UTC+8) 摘要 近年來，文字分析(textual analysis)的技術越來越成熟，主題模型(topic model)為其中一種文字分析方式，用於萃取文本的潛在主題(latent topic)。本研究使用潛在狄利克雷分布(latent Dirichlet allocation, LDA)主題模型及其延伸的情感主題混合模型(joint sentiment-topic model, JST)與反向情感主題混合模型(reverse joint sentiment-topic model, Reverse-JST)從10-K報吿文本中生成主題變數，結合財務比率變數，以羅吉斯迴歸模型(logistic regression model)方式，建構破產預測模型。根據實證結果顯示，納入主題變數的破產預測模型能夠有效提升模型分類績效，且結合情感分析之主題變數更能助於優化預測模型，因而可以從 10-K 報告中的用詞觀察到是否企業破產的跡象。
In recent years, the technique of textual analysis has been well-developed. Topic modeling is part of a class of textual analysis methods, which extracts latent topics from documents. This paper uses LDA topic modeling and its extensions, JST and Reverse-JST, to generate topic-related variables from 10-K filings, and constructs corporate default prediction model in the form of logistic regression with topic-related variables and financial variables as independent variables.According to the empirical results, when topic-related variables are included in the prediction model, the performance of classification is enhanced. In addition, considering sentiment analysis, topic-related variables are useful to optimize the prediction model. Therefore, by looking at the word usage of 10-K filings, investors can be aware of the sign of corporate bankruptcy.參考文獻 Altman, E. I. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities, The Journal of Political Economy, 81(3),637-654.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.De Finetti, B. (1990). Theory of probability. Vol. 1-2. Chichester: John Wiley & Sons Ltd.Deerwester, S., Dumais, S., Landauer, T., Furnas, G., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6), 391-407.Duan, J. C., Sun, J. and Wang, T. (2012). Multiperiod corporate default prediction- A forward intensity approach, Journal of Econometrics, 170, 191-209.Heinrich, G. (2005). Parameter estimation for text analysis. Web: http://www.arbylon.net/publications/text-est/pdf.Hofmann, T. (1999). Probabilistic latent semantic analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 289-296.Lin, C., He, Y., Everson, R., & Rüger, S. (2012). Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6), 1134-1145.Lopatta, K., Gloger, M. A., & Jaeschke, R. (2017). Can language predict bankruptcy? The explanatory power of tone in 10-K filings. Accounting Perspectives, 16(4), 315-343.Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: a survey, Journal of Accounting Research, 54(4),1187-1230.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.Merton, R. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29, 449-470.Minka, T., & Lafferty, J. (2002). Expectation-propagation for the generative aspect model. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, 352-359.Nguyen, T. H., & Shirai, K. (2015). Topic modeling based sentiment analysis on social media for stock market. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, 1354-1364.Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131. 描述 碩士
國立政治大學
金融學系
107352028資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352028 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) 彭昱齊 zh_TW dc.contributor.author (Authors) Peng, Yu-Chi en_US dc.creator (作者) 彭昱齊 zh_TW dc.creator (作者) Peng, Yu-Chi en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 17:38:59 (UTC+8) - dc.date.available 3-Aug-2020 17:38:59 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 17:38:59 (UTC+8) - dc.identifier (Other Identifiers) G0107352028 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130994 - 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) 107352028 zh_TW dc.description.abstract (摘要) 近年來，文字分析(textual analysis)的技術越來越成熟，主題模型(topic model)為其中一種文字分析方式，用於萃取文本的潛在主題(latent topic)。本研究使用潛在狄利克雷分布(latent Dirichlet allocation, LDA)主題模型及其延伸的情感主題混合模型(joint sentiment-topic model, JST)與反向情感主題混合模型(reverse joint sentiment-topic model, Reverse-JST)從10-K報吿文本中生成主題變數，結合財務比率變數，以羅吉斯迴歸模型(logistic regression model)方式，建構破產預測模型。根據實證結果顯示，納入主題變數的破產預測模型能夠有效提升模型分類績效，且結合情感分析之主題變數更能助於優化預測模型，因而可以從 10-K 報告中的用詞觀察到是否企業破產的跡象。 zh_TW dc.description.abstract (摘要) In recent years, the technique of textual analysis has been well-developed. Topic modeling is part of a class of textual analysis methods, which extracts latent topics from documents. This paper uses LDA topic modeling and its extensions, JST and Reverse-JST, to generate topic-related variables from 10-K filings, and constructs corporate default prediction model in the form of logistic regression with topic-related variables and financial variables as independent variables.According to the empirical results, when topic-related variables are included in the prediction model, the performance of classification is enhanced. In addition, considering sentiment analysis, topic-related variables are useful to optimize the prediction model. Therefore, by looking at the word usage of 10-K filings, investors can be aware of the sign of corporate bankruptcy. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與背景 1第二節 研究目的 2第二章 文獻探討 5第一節 破產預測研究 5第二節 主題模型 6第三章 研究方法 9第一節 主題模型 9第二節 破產預測模型 21第三節 模型績效衡量 22第四章 資料來源與處理 26第一節 10-K報告 26第二節 情感詞典 26第三節 財務變數 27第五章 實證分析 29第一節 破產預測模型建構 29第二節 模型績效評估 42第六章 結論與建議 58參考文獻 60 zh_TW dc.format.extent 2618698 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352028 en_US dc.subject (關鍵詞) 主題模型 zh_TW dc.subject (關鍵詞) 企業破產預警 zh_TW dc.subject (關鍵詞) 10-K報告 zh_TW dc.subject (關鍵詞) Topic modeling en_US dc.subject (關鍵詞) LDA en_US dc.subject (關鍵詞) JST en_US dc.subject (關鍵詞) Corporate bankruptcy prediction en_US dc.subject (關鍵詞) 10-K en_US dc.title (題名) 延伸LDA主題模型於企業破產預測 zh_TW dc.title (題名) Extending the Latent Dirichlet Allocation Model for Corporate Default Prediction en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Altman, E. I. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities, The Journal of Political Economy, 81(3),637-654.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.De Finetti, B. (1990). Theory of probability. Vol. 1-2. Chichester: John Wiley & Sons Ltd.Deerwester, S., Dumais, S., Landauer, T., Furnas, G., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6), 391-407.Duan, J. C., Sun, J. and Wang, T. (2012). Multiperiod corporate default prediction- A forward intensity approach, Journal of Econometrics, 170, 191-209.Heinrich, G. (2005). Parameter estimation for text analysis. Web: http://www.arbylon.net/publications/text-est/pdf.Hofmann, T. (1999). Probabilistic latent semantic analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 289-296.Lin, C., He, Y., Everson, R., & Rüger, S. (2012). Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6), 1134-1145.Lopatta, K., Gloger, M. A., & Jaeschke, R. (2017). Can language predict bankruptcy? The explanatory power of tone in 10-K filings. Accounting Perspectives, 16(4), 315-343.Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: a survey, Journal of Accounting Research, 54(4),1187-1230.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.Merton, R. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29, 449-470.Minka, T., & Lafferty, J. (2002). Expectation-propagation for the generative aspect model. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, 352-359.Nguyen, T. H., & Shirai, K. (2015). Topic modeling based sentiment analysis on social media for stock market. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, 1354-1364.Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202000746 en_US
