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| Title: | 應用LDA主題模型於美國企業破產預測之研究
Applying LDA Topic Modeling to U.S. Corporate Bankruptcy Prediction |
| Authors: | 許哲維
Hsu, Che-Wei |
| Contributors: | 江彌修
Chiang, Mi-Hsiu
許哲維
Hsu, Che-Wei |
| Keywords: | LDA
主題模型
企業破產預警
10-K財報
LDA
Topic modeling
Corporate bankruptcy prediction
10-K |
| Date: | 2019 |
| Issue Date: | 2019-12-06 09:25:54 (UTC+8) |
| Abstract: | 近年來，利用文字探勘進行文本資訊的特徵提取愈來愈便捷，許多研究逐漸運用文字探勘的技術，結合企業相關的新聞內容或公司發布的消息等文本資料，應用於金融與會計領域的研究，希望透過文字本身隱含的情緒萃取出更精準且即時的訊息，以增強模型的解釋能力、預測能力及結果的穩定程度。本研究以主題模型中的隱含狄利克雷分布LDA（Latent Dirichlet Allocation），將10-K財報的文本資訊透過模型進行主題的分類，觀察和風險有關的主題之下的字詞經由標準化後形成的變數是否能有效增加破產模型預測的準確度。根據實證結果，以10-K財報建立LDA主題分類後，選取和風險攸關的字詞之頻率進行檢驗，並標準化形成風險攸關主題變數後，發現無論是使用Logit模型或是Probit模型，納入風險攸關主題變數皆能夠提升美國企業破產預測的結果。
In recent years, as it is way less time-consuming to apply text mining techniques, more researchers have made efforts to extract certain characteristics from soft data by combining text mining techniques with their own field of expertise to further capture real-time text information and improve their research as well. However, there is little research focusing on topic modeling and consideration of latent topics existing in every document in the field of finance. In this research, LDA topic modeling, a fashion to perform latent semantic analysis, is applied to categorize soft information from 10-K financial reports into several topics. The ultimate goal in this research is to analyze whether the standardization of word frequencies of the words under risk-related topics could improve corporate bankruptcy predicting accuracy. According to the empirical results, when using risk-related topic variable after enforcing LDA topic modeling and further transforming the outcome to a standardized variable in the model, the U.S. corporate bankruptcy predicting accuracy during the time period from 1998 to 2017 is improved under both Logit and Probit models. |
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[22] Timmermans, M., & Finance, M. (2014). US Corporate Bankruptcy Predicting Models (Doctoral Dissertation, Master’s thesis.[online]. Tilburg University, Tilburg. Available from: http://arno. uvt. nl/show. cgi).
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| Description: | 碩士
國立政治大學
金融學系
106352002 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106352002 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201901264 |
| Appears in Collections: | [金融學系] 學位論文
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