政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119203

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119203

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/119203

Title:	結合文字探勘與財務指標建置財務預警模型之研究 Prediction of financial distress with text mining and financial indicators
Authors:	賴士詮
Contributors:	諶家蘭林我聰賴士詮
Keywords:	文字探勘情緒分析財務預警企業失敗 Text mining Sentimental analysis Financial distress Business failure
Date:	2018
Issue Date:	2018-08-06 18:09:50 (UTC+8)
Abstract:	上市櫃公司若是發生財務問題，不僅會影響企業內部的員工與利益，更是會影響外部眾多投資者的利益，造成投資者重大的財物損失，更嚴重也會引起金融秩序的混亂造成金融危機，所以建立一個能提早預警公司之財務狀況的系統能提早察覺公司的財務惡化、發覺公司可能發生問題的癥兆，對投資人發出警訊是非常重要的，也對國際與國外的金融市場中，預防與降低其造成的傷害。現今的財務年報與財經新聞當中都是非結構化的文本資料，然而這些文本資料也蘊藏著許多有關於企業財務狀況的資訊，而這些公開的文本資料雖然豐富且完整，過往之研究卻較少探討財經新聞之文本資料是否會反映出公司內部的財務營運狀況，因此本研究也考慮到非結構化的文本資料做情緒分析，根據過往一年的新聞評論來預警公司是否面臨著倒閉危機。本研究採用KNN、Naive Bayes、支援向量機(SVM)三種演算法對CMoney財經新聞進行情緒分析將新聞分類成正向與負向之情緒，並觀察其準確度比較三種演算法之好壞，而在財務比率指標的部分，本研究採用Altman(2000)之ZETA模型中的七大類財務比率指標。而建立財務預警模型的部分，本研究採用台灣證劵交易所所提供2015到2017年終止上市櫃及變更交易方法之公司的統計資料，並蒐集最近(2015至2017)的財務弊案之新聞公司，加入分析樣本(財務惡化之企業)之中，選擇共21家財務有問題的企業，並依規模選取42家財務狀況良好的企業進行比較且訓練模型，並利用邏輯式回歸、隨機森林與隱藏馬可夫演算法建立模型並比較其準確度。本研究為預警台灣上市公司之財務狀況提供了一套完整的研究流程與方法，並結合文本情緒指標與財務指標的分析流程，可供未來之研究參考。 The financial crisis of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss, and that could also lead to the chaos of financial environment. It is important to establish an effective early warning system for prediction of financial crisis. The early warning system can detect the financial deterioration of the company earlier and find the company which have potential crisis. It also can prevent and decrease the harm in the international financial markets. Financial annual reports and financial news are unstructured text data, however, these unstructured text data also contain a lot of information about the financial status of the business. Although these public text data are plentiful and complete, past studies seldom explore the financial news which could reflect the company`s internal financial operating conditions. Therefore, this study takes into account the unstructured text data to the early warning system for sentimental analysis. According to financial news of the past year to warn the company whether it is facing a crisis of collapse. We adopt three algorithms (KNN, Naive Bayes, SVM) to classify sentiment of the financial news and observe the accuracy of the three algorithms. According to research result, SVM have the best accuracy among these three algorithms. In the section of financial ratio indicators, this study uses the seven major categories of financial ratios in the ZETA model of Altman (2000). This study uses the statistics provided by the Taiwan Stock Exchange for companies which have terminated the listing of listed stocks from 2015 to 2017. We select 21 financial distress companies and other 42 normal companies without financial distress to train financial early warning model. We adopt logistic regression and random forest two data mining techniques to establish the model. However, the weakness of ZETA model is that the prediction accuracy will be greatly dropped over two years. This study introduces a hidden Markov model to improve the long-term prediction accuracy of the model. In the financial early warning model established in this study, it can be found that the sentimental indicators of textual data are significantly affect the model and verify that textual data can reveal the internal financial status of the company. This paper provides a hybrid method which integrates text mining and hidden Markov model for prediction of financial distress for listed companies in Taiwan
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Description:	碩士國立政治大學資訊管理學系 105356021
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0105356021
Data Type:	thesis
DOI:	10.6814/THE.NCCU.MIS.012.2018.A05
Appears in Collections:	[Department of MIS] Theses

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