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Author: 何珮瑜
Pei-Yu Ho
Thesis Title: 運用文字探勘技術探討臺灣三次油品事件新聞 對股價報酬之影響
An In-depth exploration about influence of Taiwan’s three food safety events’news on stock return through text mining technique.
Advisor: 徐中琦
Jon-Chi Shyu
Committee: 陳俊男
Chun-Nan Chen
Pang-Tien Lieu
Yi-Tai Seih
Degree: 碩士
Department: 管理學院 - 企業管理系
Department of Business Administration
Thesis Publication Year: 2015
Graduation Academic Year: 103
Language: 中文
Pages: 67
Keywords (in Chinese): 文字探勘Panel Data食安事件事件研究法公開資訊股價
Keywords (in other languages): Text Mining, Panel Data, food safety, Event Study, public information, stock price
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  • 本研究主要探討新聞內容與股價間之關係,以臺灣三次油品事件爆發後一個月、共三個月之研究期間,探討21間食品類股,共1386筆樣本,在食品安全風波中所遭受的影響。與過往研究不同之處在於,本研究以文字探勘軟體分析後,將新聞內容依發言角色之不同分為五種類型:公司、政府、記者、民意、競爭對手。而後分別以Panel Data及事件分析法分析油品事件中,股價報酬率所受到的影響。

    This study focuses on the relationship between news and the stock return of certain firms. The news content was classified by text mining program into five categories: firm, government, journalist, public opinion and trade rival. I used panel data and the event study method to analyze the effect of Taiwan’s three oil events on stock return.
    The result shows that, it's the trade rival news that has the most influence on stock return; however, it's determined by the overall environment and context. If the news is positive, it has positive effect on the stock return, while if the news is negative, it's more likely for the stock return to go downward.

    摘要 I Abstract II 目錄 III 表目錄 IV 第壹章 緒論 1 第一節 研究背景與動機 1 第二節研究目的 3 第二章 文獻探討 4 第一節 效率市場假說 4 第二節 股價報酬影響因素 7 第三節 文字探勘 12 第三章 研究方法 15 第一節 變數說明與研究模型 15 第二節 文字探勘 (Text Mining) 18 第三節 縱橫資料 (Panel Data) 23 第四節 事件研究法 (Panel Data) 28 第四章 實證結果分析 31 第一節 三次油品事件綜合分析 31 第二節 個別油品事件分析 37 第三節 異常報酬率分析 46 第五章 結論與建議 50 參考文獻 53 附錄 食品安全事件列表 59

    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.
    Bae, K. H., & Karolyi, G. A. (1994). Good news, bad news and international spillovers of stock return volatility between Japan and the US. Pacific-Basin Finance Journal,2(4), 405-438.
    Ball, R. (2009). The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned? Journal of Applied Corporate Finance, 21(4), 8-16. doi: 10.1111/j.1745-6622.2009.00246.x
    Ball, R., & Brown, P. (1968).An empirical evaluation of accounting income numbers.Journal of accounting research, 159-178.
    Baltagi, B. H. (2001). Econometric Analysis of Panel Data, 2nd ed., John Wiley & Sons Ltd, England.
    Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18.
    Beaver, W. H. (1968). The information content of annual earnings announcements. Journal of accounting research, 67-92
    Blume, L., Easley, D., & O'hara, M. (1994). Market statistics and technical analysis: The role of volume. The Journal of Finance, 49(1), 153-181.
    Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
    Boudoukh, J., Feldman, R., Kogan, S., & Richardson, M. (2013). Which news moves stock prices? A textual analysis: National Bureau of Economic Research.
    Boyd, J. H., Hu, J., & Jagannathan, R. (2005). The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks. The Journal of Finance, 60(2), 649-672. doi: 10.1111/j.1540-6261.2005.00742.x
    Braggion, F., & Giannetti, M. (2014). Public Debate and Stock Prices: Evidence from the Voting Premium. ECGI-Finance Working Paper(375).
    Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 239-253.
    Brown, G. W., & Cliff, M. T. (2004).Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
    Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading Volume and Serial Correlation in Stock Returns. The Quarterly Journal of Economics, 108(4), 905-939.
    Conrad, J., Cornell, B., & Landsman, W. R. (2002). When is bad news really bad news?. The Journal of Finance, 57(6), 2507-2532.
    Cortes, C., & Vapnik, V. (1995).Support-vector networks. Machine learning, 20(3), 273-297
    De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets.Journal of political Economy, 703-738.
    Deak, Z., Karali, B., Bosch, D., Marchant, M., McKenzie, A. M., & Paudel, K. P. (2014). Stock market reactions to environmental news in the food industry. Journal of Agricultural and Applied Economics, 46(02), 209-225.
    Elberse, A., & Verleun, J. (2012).The economic value of celebrity endorsements.Journal of advertising Research, 52(2), 149.
    Fama, E. F. (1965). Random Walks In Stock Market Prices. Financial Analysts Journal, 21 (5): 55–59.
    Fama, E. F. (1965). The behavior of stock-market prices. Journal of business, 34-105.
    Fama, E. F. (1970). Efficient Capital Markets: A Review Of Theory And Empirical Work. The Journal of Finance, 25(2), 383-417.
    Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969).The adjustment of stock prices to new information. International economic review, 10(1), 1-21.
    Gaunt, C. (2004). Size and book to market effects and the Fama French three factor asset pricing model: evidence from the Australian stockmarket. Accounting & Finance, 44(1), 27-44.
    Gidófalvi, G., & Elkan, C. (2001).Using news articles to predict stock price movements.Department of Computer Science and Engineering, University of California, San Diego.
    Givoly, D., & Lakonishok, J. (1979). The information content of financial analysts' forecasts of earnings: Some evidence on semi-strong inefficiency. Journal of Accounting and Economics, 1(3), 165-185.
    Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 1251-1271.
    Hiemstra, C., & Jones, J. D. (1994).Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639-1664.
    Holthausen, R. W., & Larcker, D. F. (1992). The prediction of stock returns using financial statement information. Journal of Accounting and Economics, 15(2–3), 373-411.
    Hsiao, C. (1986). Analysis of panel data: Cambridge university press.
    Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
    Johnson, W. B., Magee, R. P., Nagarajan, N. J., & Newman, H. A. (1985). An analysis of the stock price reaction to sudden executive deaths: Implications for the managerial labor market. Journal of Accounting and Economics, 7(1), 151-174.
    Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(01), 109-126.
    Koppel, M., & Shtrimberg, I. (2006). Good News or Bad News? Let the Market Decide. Computing Attitude and Affect in Text: Theory and Applications, 20, 297.
    Koutmos, G., & Booth, G. G. (1995).Asymmetric volatility transmission in international stock markets. Journal of international Money and Finance, 14(6), 747-762.
    Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014).Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.
    Lee, B.-S., & Rui, O. M. (2002). The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence. Journal of Banking & Finance, 26(1), 51-78. doi:
    Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 59-82.
    McQueen, G., & Roley, V. V. (1993). Stock prices, news, and business conditions. Review of financial studies, 6(3), 683-707.
    Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance, 25(7), 1339-1366. doi:
    Ormos, M., & Vázsonyi, M. (2011). Impacts of Public News on Stock Market Prices: Evidence from S&P500. Interdisciplinary Journal of Research in Business, 1(2), 01-17.
    Porta, R. L., Lakonishok, J., Shleifer, A., & Vishny, R. (1997). Good news for value stocks: Further evidence on market efficiency. The Journal of Finance,52(2), 859-874.
    Pozo, V. F. (2014). Effects of meat and poultry recalls on firms' stock prices. (Doctor of Philosophy ), Kansas State University, Manhattan, Kansas.
    Salin, V. and Hooker, N. H. (2001) Stock market reaction to food recalls, Review of Agricultural Economics, 23(1), 33-46.
    Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12.
    Smirlock, M., & Starks, L. (1988).An empirical analysis of the stock price-volume relationship. Journal of Banking & Finance, 12(1), 31-41. doi:
    Sullivan, D. (2001). Document warehousing and text mining: techniques for improving business operations, marketing, and sales: John Wiley & Sons, Inc.
    Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals. The Journal of Finance, 63(3), 1437-1467
    Veronesi, P. (1999). Stock market overreactions to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies, 12(5), 975-1007.
    Wang, Z., Salin, V., Hooker, N. H., & Leatham, D(2002). Stock market reaction to food recalls: a GARCH application. Applied Economics Letters, 9(15), 979-987.
    Wuthrich, B., Cho, V., Leung, S., Permunetilleke, D., Sankaran, K., Zhang, J., & Lam, W. (1998). Daily stock market forecast from textual web data. In IEEE International Conference on Systems, Man & Cybernetics.
    Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences, 26(0), 55-62.
    Zhao, X., Yang, J., Zhao, L., & Li, Q. (2011). The Impact of News on Stock Market: Quantifying the Content of Internet-based Financial News.The 11th International DSI and the 16th APDSI Joint Meeting, Taipei, Taiwan, July 12 – 16, 2011.

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