系統識別號 U0026-1408201819462900
論文名稱(中文) 應用網路輿情輔助預測上市公司信用評等
論文名稱(英文) Using the Internet Sentiment to Help Predict Credit Ratings:Evidence from Publicly Listed Firms in Taiwan
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 106
學期 2
出版年 107
研究生(中文) 林玲筠
研究生(英文) Ling-Yun Lin
學號 R16051179
學位類別 碩士
語文別 中文
論文頁數 29頁
口試委員 指導教授-顏盟峯
中文關鍵字 信用評等  文本分析  網路輿情 
英文關鍵字 Credit Rating  Textual Analysis  Internet Sentiment 
中文摘要 目前企業信用評等主要以財務變數為主,然而隨著網際網路的普及與大數據時代的來臨,可以發現有越來越多消費者會在社群網站或各大論壇發表自身對企業某項產品或服務的評價,而網路輿論效果與擴散程度很容易影響大眾對該企業的看法,也會動搖投資者的信心。
因此本研究探討相關輿情能否有增額預測能力來預測受評對象下一季之信用評等,提供債權人和股東更佳的決策資訊。本文利用文本分析技術取得台灣2013年至2017年第一季間上市公司每季的輿情分數累加數值,來輔助預測台灣經濟新報(TEJ)所發佈之台灣企業風險指標(Taiwan Corporate Credit Risk Index,又稱 TCRI)。本研究發現季累計之總體情緒向性(Valence)對該企業下一季信用評等具有相關的預測效果,以及激發水準(Arousal)對情緒向性預測該企業下一季信用評等也具有調節效果。
英文摘要 At present, corporate credit rating is mainly based on financial variables. However, with the popularity of the Internet and the era of big data, it can be found that more and more consumers will publish their own opinions on social networking sites or major forums about a product or service. The extent and spread of Internet public opinion can easily affect the views of the rest of the public on the company, and will also shake the confidence of investors.
Therefore, this study explores whether the relevant public opinion can increase the forecasting ability to show the risk of credit default of the respondents and provide investors with better decision-making information. The textual analysis technology was used to obtain the accumulated value of the public opinion scores of listed companies in Taiwan from 2013 to the first quarter of 2017 to estimate the Taiwan Corporate Credit Risk Index (TCRI). This study found that the overall sentiment has a relevant predictive effect on the company's next quarter credit rating, and the arousal has an adjustment effect on the emotional orientation predictive of the company's next quarter credit rating.
論文目次 目 錄
中文摘要 i
英文延伸摘要 ii
目 錄 v
表目錄 vii
圖目錄 viii
第一章、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 章節架構 2
第二章、文獻探討 4
2.1 信用風險 4
2.2 信用評等 5
2.3 文本分析應用於會計和財務 7
第三章、研究方法 10
3.1 資料來源與蒐集 10
3.2 研究技術 11
3.3 變數定義與衡量 13
3.3.1 信用評等之衡量 13
3.3.2 輿情分數之衡量 14
3.4 模型設計 17
3.4.1 假說一模型建立 17
3.4.2 假說二模型建立 17
第四章、實證結果 18
4.1 敘述性統計 18
4.2 相關係數分析 19
4.3 變異數膨脹因素檢測 21
4.4 實證結果 22
4.4.1 假說一之迴歸結果 22
4.4.2 假說二之迴歸結果 22
第五章、結論與建議 24
5.1 研究結果 24
5.2 研究限制與建議 24
參考文獻 26

參考文獻 一、中文文獻
3.林秀玫. (2002). 選擇權基礎企業信用風險評估-以臺灣地區上市公司實證研究, 淡江大學財務金融學系碩士在職專班, 碩士論文.
4.張大成, 薛人瑞, & 黃建隆. (2003). 財務危機模型之變數選取研究.
1.Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
2.Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of finance, 59(3), 1259-1294.
3.Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
4.Calvo, R. A., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on affective computing, 1(1), 18-37.
5.De Choudhury, M., Counts, S., & Gamon, M. (2012, May). Not all moods are created equal! exploring human emotional states in social media. In Sixth international AAAI conference on weblogs and social media.
6.Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200.
7.Erdogan, B. E. (2013). Prediction of bankruptcy using support vector machines: an application to bank bankruptcy. Journal of Statistical Computation and Simulation, 83(8), 1543-1555.
8.Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the Association for Computing Machinery, 56(4), 82-89.
9.Feldman, R., Govindaraj, S., Livnat, J., & Segal, B. (2010). Management’s tone change, post earnings announcement drift and accruals. Review of Accounting Studies, 15(4), 915-953.
10.Henry, E. (2008). Are investors influenced by how earnings press releases are written?. The Journal of Business Communication (1973), 45(4), 363-407.
11.Jiang, F., Lee, J. A., Martin, X., & Zhou, G. (2017). Manager sentiment and stock returns. Journal of Financial Economics (forthcoming).
12.Lang, P. J. (1980). Behavioral treatment and bio-behavioral assessment: computer applications, In Technology in mental health care delivery systems, edited by J. B. Sidowski, J. H. Johnson, T. A. Williams, 119-l37.
13.Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45(2-3), 221-247.
14.Li, J., Ott, M., Cardie, C., & Hovy, E. (2014). Towards a general rule for identifying deceptive opinion spam. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. 1, 1566-1576).
15.Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
16.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.
17.Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.
18.Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
19.Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
20.Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic inquiry and word count: LIWC [Computer software]. Austin, TX: liwc. net.
21.Preoţiuc-Pietro, D., Eichstaedt, J., Park, G., Sap, M., Smith, L., Tobolsky, V., Schwartz, H. A., & Ungar, L. (2015). The role of personality, age, and gender in tweeting about mental illness. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality, , 21-30.
22.Price, S. M., Doran, J. S., Peterson, D. R., & Bliss, B. A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 36(4), 992-1011.
23.Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S., Ritter, A., & Stoyanov, V. (2015). "Semeval-2015 task 10: Sentiment analysis in twitter." Proceedings of the 9th international workshop on semantic evaluation, 451-463.
24.Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.
25.Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.
26.Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The journal of business, 74(1), 101-124.
27.Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168.
28.Wang, J., Yu, L. C., Lai, K. R., & Zhang, X. (2016). Community-based weighted graph model for valence-arousal prediction of affective words. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(11), 1957-1968.
29.Wei, W. L., Wu, C. H., & Lin, J. C. (2011). A regression approach to affective rating of Chinese words from ANEW. In Affective Computing and Intelligent Interaction (121-131). Springer, Berlin, Heidelberg.
30.Yu, L. C., Lee, L. H., Hao, S., Wang, J., He, Y., Hu, J., Lai, K. R., & Zhang, X. (2016). Building Chinese affective resources in valence-arousal dimensions. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 540-545.
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