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| 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 |
| Reference: | 一、 中文部分
王憶紅. (2017). 興航下市走入歷史. 自由時報.
《各報要聞》中電爆掏空案，董座周麗真遭境管. (2016). 時報資訊.
吳娟娟. (2012). 公司年報文字探勘與財務預警資訊內涵. 逢甲大學商學博士學位學程博士論文, 1-124.
呂淑美. (2016a). MSH-DR第4季將下市. 工商時報.
呂淑美. (2016b). 中泰山-DR 11／29終止上市. 工商時報.
杨翰林, 王开骏, & 谢幽篁. (2014). 基于随机森林的上市公司财务危机预警分析. 中央财经大学.
林宜萱. (2013). 財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件. 臺灣大學會計學研究所學位論文, 1-60.
盂杰. (2014). 随机森林模型在财务失败预警中的应用. 统计与决策(4),179-181.
涉環電內線交易 日月光發言人2家族成員收押. (2017). 東森新聞.
袁延壽. (2017). 冠德掏空案起訴3人 根基、冠德：尊重司法調查. 工商時報.
袁顥庭. (2016). 介面淨值為負 8月3日下市. 工商時報.
莊丙農. (2017). 《證交所》台一財報不實，10月中旬前恐下市. 時報資訊.
陳志賢. (2017). 必翔又傳弊案 負責人再遭搜索. 工商時報.
黃鳳丹. (2017). 新焦點-DR 6／27下市. 工商時報
楊佩琪. (2016). 驚爆！中信金重大弊案 辜仲諒恐涉近百億資金掏空. 東森新聞.
楊政郡. (2015). 上市成霖公司董事及顧問 涉內線交易被起訴. 自由時報.
劉妍辰. (2010). 企業年報文字資訊影響資金成本, 股價報酬波動性與分析師盈餘預測準確度之研究. 臺灣大學會計學研究所學位論文, 1-88.
賴佩璇. (2017). 聯發科收購立錡爆內線交易 檢方今起訴兩人. 聯合報.
謝君臨. (2017a). 涉內線交易避損 益航財務長謝妙龍起訴. 自由時報.
謝君臨. (2017b). 涉掏空7000萬 宏正科前法務長被押. 自由時報.
56
二、 英文部分
Akhtar, S., Faff, R., Oliver, B., & Subrahmanyam, A. (2011). The power of bad: The negativity bias in Australian consumer sentiment announcements on stock returns. Journal of Banking & Finance, 35(5), 1239-1249.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
Altman, E. I. (2000). Predicting financial distress of companies: revisiting the Z-score and ZETA models. Stern School of Business, New York University, 9-12.
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman`s Z‐Score Model. Journal of International Financial Management & Accounting, 28(2), 131-171.
Batal, I., & Hauskrecht, M. (2009). Boosting KNN text classification accuracy by using supervised term weighting schemes. Paper presented at the Proceedings of the 18th ACM conference on Information and knowledge management.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
Bijalwan, V., Kumar, V., Kumari, P., & Pascual, J. (2014). KNN based machine learning approach for text and document mining. International Journal of Database Theory and Application, 7(1), 61-70.
Blunsom, P. (2004). Hidden markov models. Lecture notes, August, 15, 18-19.
Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Making words work: Using financial text as a predictor of financial events. Decision support systems, 50(1), 164-175.
Chen, W.-S., & Du, Y.-K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert systems with applications, 36(2), 4075-4086.
Clatworthy, M. A., & Jones, M. J. (2006). Differential patterns of textual characteristics and company performance in the chairman`s statement. Accounting, Auditing & Accountability Journal, 19(4), 493-511.
Dhar, S., Mukherjee, T., & Ghoshal, A. K. (2010). Performance evaluation of Neural Network approach in financial prediction: Evidence from Indian Market. Paper presented at the Communication and Computational Intelligence (INCOCCI), 2010 International Conference on.
Eddy, S. R. (1996). Hidden markov models. Current opinion in structural biology, 6(3), 361-365.
Fan, A., & Palaniswami, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. Paper presented at the Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on.
Frazier, K. B., Ingram, R. W., & Tennyson, B. M. (1984). A methodology for the analysis of narrative accounting disclosures. Journal of accounting research, 318-331.
Frydman, H., Altman, E. I., & KAO, D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. The journal of finance, 40(1), 269-291.
Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European journal of operational research, 241(1), 236-247.
Hassan, M. R., & Nath, B. (2005). Stock market forecasting using hidden Markov model: a new approach. Paper presented at the Intelligent Systems Design and Applications, 2005. ISDA`05. Proceedings. 5th International Conference on.
Hooda, N., Bawa, S., & Rana, P. S. (2018). Fraudulent Firm Classification: A Case Study of an External Audit. Applied Artificial Intelligence, 32(1), 48-64.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Paper presented at the European conference on machine learning.
Li, Q., Wang, T., Gong, Q., Chen, Y., Lin, Z., & Song, S.-k. (2014). Media-aware quantitative trading based on public Web information. Decision support systems, 61, 93-105.
Li, Z., Crook, J., & Andreeva, G. (2014). Chinese companies distress prediction: an application of data envelopment analysis. Journal of the Operational Research Society, 65(3), 466-479.
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.
Magnusson, C., Arppe, A., Eklund, T., Back, B., Vanharanta, H., & Visa, A. (2005). The language of quarterly reports as an indicator of change in the company’s financial status. Information & Management, 42(4), 561-574.
Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The journal of finance, 25(4), 853-868.
Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. Paper presented at the Neural Networks, 1990., 1990 IJCNN International Joint Conference on.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy.Journal of accounting research, 109-131.
Petersen, M. A. (2004). Information: Hard and soft. Northwestern University.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
Srivastava, Kundu, Sural, & Majumdar. (2008). Credit card fraud detection using hidden Markov model. . IEEE Transactions on dependable and secure computing, 5(1), 37-48.
Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: the case of bank failure predictions. Management science, 38(7), 926-947.
Tongshui, X., & Yanli, Z. (2015). The Research on Financial Early Warning of China’s New Energy Listed Company. International Journal of Business and Management, 10(9), 136.
Tsai, M.-F., Wang, C.-J., & Chien, P.-C. (2016). Discovering finance keywords via continuous-space language models. ACM Transactions on Management Information Systems (TMIS), 7(3), 7.
Van Gestel, T., Baesens, B., Suykens, J. A., Van den Poel, D., Baestaens, D.-E., & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European journal of operational research, 172(3), 979-1003.
Wanke, P., Barros, C. P., & Faria, J. R. (2015). Financial distress drivers in Brazilian banks: A dynamic slacks approach. European journal of operational research, 240(1), 258-268.
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision support systems, 55(4), 919-926.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of accounting research, 59-82. |
| 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: | [資訊管理學系] 學位論文
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