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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/130994


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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: 2020-08-03 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.
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    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
    DOI: 10.6814/NCCU202000746
    顯示於類別:[金融學系] 學位論文

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