政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130994
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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130994


    Title: 延伸LDA主題模型於企業破產預測
    Extending the Latent Dirichlet Allocation Model for Corporate Default Prediction
    Authors: 彭昱齊
    Peng, Yu-Chi
    Contributors: 江彌修
    Chiang, Mi-Hsiu
    彭昱齊
    Peng, Yu-Chi
    Keywords: 主題模型
    企業破產預警
    10-K報告
    Topic modeling
    LDA
    JST
    Corporate bankruptcy prediction
    10-K
    Date: 2020
    Issue Date: 2020-08-03 17:38:59 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    金融學系
    107352028
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107352028
    Data Type: thesis
    DOI: 10.6814/NCCU202000746
    Appears in Collections:[Department of Money and Banking] Theses

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