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題名 VIX與財務預警 – 數據分析觀點
VIX and Financial Warning – A Data Analytics Perspective
作者 呂樂憫
Leu, Lok-Man
貢獻者 諶家蘭
Seng, Jia-Lang
呂樂憫
Leu, Lok-Man
關鍵詞 恐慌指數
新聞
文字探勘
情緒分析
財務預警
Volatility index
Financial news
Text mining
Sentiment analysis
Financial warning
日期 2018
上傳時間 12-Jul-2018 13:31:58 (UTC+8)
摘要 新聞資訊能幫助投資人快速瞭解金融市場及總體經濟環境所發生之事情。從新聞內容中，投資人可以判斷整體市場的走勢。因此，新聞內容將影響投資人的投資決策，台灣投資人之情緒主要受本地新聞媒體所撰寫之報導所影響。本研究之樣本期間為2007年至2017年，新聞來源為全曜財經資訊股份有限公司(CMoney)資料庫。本研究使用文字探勘技術，研究財務預警新聞與台灣投資人情緒之關聯性。本研究使用台灣恐慌指數(VIXTWN)作為衡量整體台灣投資人情緒之變數，觀察本地產業新聞及國際主要股市新聞與市場恐慌指數之關聯性。\r\n\r\n本研究之結果顯示，台灣投資人之整體情緒受本地產業新聞及全球股市新聞內容所影響。投資人情緒波動將反映在當日及明後兩日之恐慌指數上。新聞中所使用的字詞及語調，將影響投資人之情緒及對市場未來走勢之看法，並進一步影響投資人之投資決策。
Mass media communicates with readers, investors can understand issues of the financial market through reading news articles. Information provided in the news articles plays an important role in affecting investors’ perspective on the future trend and opportunities of the financial market. Financial news are extracted from CMoney and the research period is 2007 to 2017. In this study, we the text mining technique to analyze the association between financial warning news and investors’ sentiment. The market volatility index (VIXTWN) will be used to quantify Taiwanese investors’ sentiment, models are established to observe how local industrial news and global stock market news affect market volatility.\r\n\r\nThe empirical result of this study proves the relationship between local industrial and global stock market news and market volatility. Wordings and tone of news affect investors’ sentiment and their perspective on future market return. Therefore, changes in investors’ sentiment affect their investment decision and further affect market volatility. Moreover, the study proves that market volatility reaction consist of two parts, immediate reaction and delayed reaction.
參考文獻 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。\r\n\r\n張溢晃，2009，財經新聞語料中所隱含之樂悲觀情緒在企業財務危機預警模型構建上的應用，銘傳大學財務金融學系碩士論文。\r\n\r\n經濟日報，2017.9.12，陸股漲勢俏 消費股可望成為領頭羊。\r\n\r\n諶家蘭，2017，創新行動金融商務科技之跨領域整合研究結案報告，科技部研究結案報告。\r\n\r\nAgarwal, V., Arisoy, Y. E., & Naik, N. Y. (2017). Volatility of aggregate volatility and hedge fund returns. Journal of Financial Economics, 125(3), 491-510.\r\n\r\nAkhtar, 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.\r\n\r\nBaba, N., & Sakurai, Y. (2011). Predicting regime switches in the VIX index with macroeconomic variables. Applied Economics Letters, 18(15), 1415-1419.\r\n\r\nBansal, R., Kiku, D., Shaliastovich, I., & Yaron, A. (2014). Volatility, the Macroeconomy, and asset prices. The Journal of Finance, 69(6), 2471-2511.\r\n\r\nBaker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104(2), 272-287.\r\n\r\nBecker, R., Clements, A. E., & McClelland, A. (2009). The jump component of S&P 500 volatility and the VIX index. Journal of Banking & Finance, 33(6), 1033-1038.\r\n\r\nBenhabib, J., Liu, X., & Wang, P. (2016). Sentiments, financial markets, and macroeconomic fluctuations. Journal of Financial Economics, 120(2), 420-443.\r\n\r\nBrière, M., & Drut, B. (2009). The revenge of purchasing power parity on carry trades during crises (No. 09-013. RS). Universite Libre de Bruxelles.\r\n\r\nCarretta, A., Farina, V., Martelli, D., Fiordelisi, F., & Schwizer, P. (2011). The impact of corporate governance press news on stock market returns. European financial management, 17(1), 100-119.\r\n\r\nChen, Y. J., Wu, C. H., Chen, Y. M., Li, H. Y., & Chen, H. K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45.\r\n\r\nChung, K. H., & Chuwonganant, C. (2014). Uncertainty, market structure, and liquidity. Journal of Financial Economics, 113(3), 476-499.\r\n\r\nCorrado, C. J., & Miller Jr, T. W. (2005). The forecast quality of CBOE implied volatility indexes. Journal of Futures Markets, 25(4), 339-373.\r\n\r\nDellaVigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. The Journal of Finance, 64(2), 709-749.\r\n\r\nFerguson, N., Philip, D., Lam, H., & Guo, J. M. (2013). Media content and stock returns: The predictive power of press”, Midwest Finance Association 2013 Annual Meeting Papers.\r\n\r\nFernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10.\r\n\r\nFleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: A new measure. Journal of Futures Markets, 15(3), 265-302.\r\n\r\nGray, G. L., & Debreceny, R. S. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380.\r\n\r\nGilbert, T. (2011). Information aggregation around macroeconomic announcements: Revisions matter. Journal of Financial Economics, 101(1), 114-131.\r\n\r\nGoodell, J. W., & Vähämaa, S. (2013). US presidential elections and implied volatility: The role of political uncertainty. Journal of Banking & Finance, 37(3), 1108-1117.\r\n\r\nHammer, S., & Russo, C. J. (2012). Tax-Advantaged Investing for an Uncertain Economy: These Seven Strategies May Mitigate Risk and Enhance After-Tax Returns. Journal of Accountancy, 213(5), 28-33.\r\n\r\nKaplanski, G., & Levy, H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics, 95(2), 174-201.\r\n\r\nKelly, B., Pástor, Ľ., & Veronesi, P. (2016). The price of political uncertainty: Theory and evidence from the option market. The Journal of Finance, 71(5), 2417-2480.\r\n\r\nKim, K., Pandit, S., & Wasley, C. E. (2016). Macroeconomic uncertainty and management earnings forecasts. Accounting Horizons, 30(1), 157-172.\r\n\r\nKurihara, Y. (2006). The relationship between exchange rate and stock prices during the quantitative easing policy in Japan. International Journal of Business, 11(4), 375.\r\n\r\nLi, 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.\r\n\r\nLiu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.\r\n\r\nLiu, L. X., Shu, H., & Wei, K. J. (2017). The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China. Journal of Financial Economics, 286-310.\r\n\r\nMitchell, M. L., & Mulherin, J. H. (1994). The impact of public information on the stock market. The Journal of Finance, 49(3), 923-950.\r\n\r\nPastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219-1264.\r\n\r\nSavor, P., & Wilson, M. (2013). How much do investors care about macroeconomic risk? Evidence from scheduled economic announcements. Journal of Financial and Quantitative Analysis, 48(2), 343-375.\r\n\r\nSimon, D. P., & Wiggins, R. A. (2001). S&P futures returns and contrary sentiment indicators. Journal of futures markets, 21(5), 447-462.\r\n\r\nSolomon, D. H. (2012). Selective publicity and stock prices. The Journal of Finance, 67(2), 599-638.\r\n\r\nTetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.\r\n\r\nYang, R., Yu, Y., Liu, M., & Wu, K. (2017). Corporate Risk Disclosure and Audit Fee: A Text Mining Approach. European Accounting Review, 1-12.\r\n\r\nYin, S., Mazouz, K., Benamraoui, A., & Saadouni, B. (2018). Stock price reaction to profit warnings: the role of time-varying betas. Review of Quantitative Finance and Accounting, 50(1), 67-93.\r\n\r\nZhang, W., & Skiena, S. (2010). Trading Strategies to Exploit Blog and News Sentiment. In ICWSM.
描述 碩士
國立政治大學
會計學系
105353043
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105353043
資料類型 thesis
dc.contributor.advisor 諶家蘭zh_TW
dc.contributor.advisor Seng, Jia-Langen_US
dc.contributor.author (Authors) 呂樂憫zh_TW
dc.contributor.author (Authors) Leu, Lok-Manen_US
dc.creator (作者) 呂樂憫zh_TW
dc.creator (作者) Leu, Lok-Manen_US
dc.date (日期) 2018en_US
dc.date.accessioned 12-Jul-2018 13:31:58 (UTC+8)-
dc.date.available 12-Jul-2018 13:31:58 (UTC+8)-
dc.date.issued (上傳時間) 12-Jul-2018 13:31:58 (UTC+8)-
dc.identifier (Other Identifiers) G0105353043en_US
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=136414-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 會計學系zh_TW
dc.description (描述) 105353043zh_TW
dc.description.abstract (摘要) 新聞資訊能幫助投資人快速瞭解金融市場及總體經濟環境所發生之事情。從新聞內容中，投資人可以判斷整體市場的走勢。因此，新聞內容將影響投資人的投資決策，台灣投資人之情緒主要受本地新聞媒體所撰寫之報導所影響。本研究之樣本期間為2007年至2017年，新聞來源為全曜財經資訊股份有限公司(CMoney)資料庫。本研究使用文字探勘技術，研究財務預警新聞與台灣投資人情緒之關聯性。本研究使用台灣恐慌指數(VIXTWN)作為衡量整體台灣投資人情緒之變數，觀察本地產業新聞及國際主要股市新聞與市場恐慌指數之關聯性。\r\n\r\n本研究之結果顯示，台灣投資人之整體情緒受本地產業新聞及全球股市新聞內容所影響。投資人情緒波動將反映在當日及明後兩日之恐慌指數上。新聞中所使用的字詞及語調，將影響投資人之情緒及對市場未來走勢之看法，並進一步影響投資人之投資決策。zh_TW
dc.description.abstract (摘要) Mass media communicates with readers, investors can understand issues of the financial market through reading news articles. Information provided in the news articles plays an important role in affecting investors’ perspective on the future trend and opportunities of the financial market. Financial news are extracted from CMoney and the research period is 2007 to 2017. In this study, we the text mining technique to analyze the association between financial warning news and investors’ sentiment. The market volatility index (VIXTWN) will be used to quantify Taiwanese investors’ sentiment, models are established to observe how local industrial news and global stock market news affect market volatility.\r\n\r\nThe empirical result of this study proves the relationship between local industrial and global stock market news and market volatility. Wordings and tone of news affect investors’ sentiment and their perspective on future market return. Therefore, changes in investors’ sentiment affect their investment decision and further affect market volatility. Moreover, the study proves that market volatility reaction consist of two parts, immediate reaction and delayed reaction.en_US
dc.description.tableofcontents 1. Introduction 1\r\n1.1 Research Purpose and Motivation 1\r\n1.2 Research Problem 6\r\n1.3 Research Process 7\r\n2. Literature Review 8\r\n2.1 Volatility Index 8\r\n2.2 Macroeconomic Events 13\r\n2.3 Financial Warning 16\r\n2.4 Text Mining and Sentiment Analysis 18\r\n3. Research Method 23\r\n3.1 Hypothesis Development 23\r\n3.1.1 The Relationship between Mood of News and VIXTWN\r\n24\r\n3.1.2 The Relationship between News Tone and VIXTWN 26\r\n3.2 Data Collection 27\r\n3.3 Text Analytic Approach on News 30\r\n3.4 Regression Model 34\r\n3.4.1 Dependent Variables 36\r\n3.4.2 Independent Variables 36\r\n3.4.3 Control Variables 38\r\n4. Empirical Result 41\r\n4.1 Descriptive Statistics 41\r\n4.2 Correlation Analysis 44\r\n4.3 Regression Analysis 49\r\n4.3.1 Mood of Local Industrial News and VIXTWN 49\r\n4.3.2 Mood of Global Stock Market News and VIXTWN 52\r\n4.3.3 Tone of Local Industrial News and VIXTWN 54\r\n4.3.4 Tone of Global Stock Market News and VIXTWN 56\r\n4.3.5 Regression Result with Fixed Effect 58\r\n5. Conclusion and Discussion 63\r\n5.1 Research Discussion and Contribution 63\r\n5.2 Limitation and Future Research Work 65\r\nAppendix 67\r\nReferences 78\r\n zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105353043en_US
dc.subject (關鍵詞) 恐慌指數zh_TW
dc.subject (關鍵詞) 新聞zh_TW
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) 情緒分析zh_TW
dc.subject (關鍵詞) 財務預警zh_TW
dc.subject (關鍵詞) Volatility indexen_US
dc.subject (關鍵詞) Financial newsen_US
dc.subject (關鍵詞) Text miningen_US
dc.subject (關鍵詞) Sentiment analysisen_US
dc.subject (關鍵詞) Financial warningen_US
dc.title (題名) VIX與財務預警 – 數據分析觀點zh_TW
dc.title (題名) VIX and Financial Warning – A Data Analytics Perspectiveen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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。\r\n\r\n張溢晃，2009，財經新聞語料中所隱含之樂悲觀情緒在企業財務危機預警模型構建上的應用，銘傳大學財務金融學系碩士論文。\r\n\r\n經濟日報，2017.9.12，陸股漲勢俏 消費股可望成為領頭羊。\r\n\r\n諶家蘭，2017，創新行動金融商務科技之跨領域整合研究結案報告，科技部研究結案報告。\r\n\r\nAgarwal, V., Arisoy, Y. E., & Naik, N. Y. (2017). Volatility of aggregate volatility and hedge fund returns. Journal of Financial Economics, 125(3), 491-510.\r\n\r\nAkhtar, 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.\r\n\r\nBaba, N., & Sakurai, Y. (2011). Predicting regime switches in the VIX index with macroeconomic variables. Applied Economics Letters, 18(15), 1415-1419.\r\n\r\nBansal, R., Kiku, D., Shaliastovich, I., & Yaron, A. (2014). Volatility, the Macroeconomy, and asset prices. The Journal of Finance, 69(6), 2471-2511.\r\n\r\nBaker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104(2), 272-287.\r\n\r\nBecker, R., Clements, A. E., & McClelland, A. (2009). The jump component of S&P 500 volatility and the VIX index. Journal of Banking & Finance, 33(6), 1033-1038.\r\n\r\nBenhabib, J., Liu, X., & Wang, P. (2016). Sentiments, financial markets, and macroeconomic fluctuations. Journal of Financial Economics, 120(2), 420-443.\r\n\r\nBrière, M., & Drut, B. (2009). The revenge of purchasing power parity on carry trades during crises (No. 09-013. RS). Universite Libre de Bruxelles.\r\n\r\nCarretta, A., Farina, V., Martelli, D., Fiordelisi, F., & Schwizer, P. (2011). The impact of corporate governance press news on stock market returns. European financial management, 17(1), 100-119.\r\n\r\nChen, Y. J., Wu, C. H., Chen, Y. M., Li, H. Y., & Chen, H. K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45.\r\n\r\nChung, K. H., & Chuwonganant, C. (2014). Uncertainty, market structure, and liquidity. Journal of Financial Economics, 113(3), 476-499.\r\n\r\nCorrado, C. J., & Miller Jr, T. W. (2005). The forecast quality of CBOE implied volatility indexes. Journal of Futures Markets, 25(4), 339-373.\r\n\r\nDellaVigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. The Journal of Finance, 64(2), 709-749.\r\n\r\nFerguson, N., Philip, D., Lam, H., & Guo, J. M. (2013). Media content and stock returns: The predictive power of press”, Midwest Finance Association 2013 Annual Meeting Papers.\r\n\r\nFernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10.\r\n\r\nFleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: A new measure. Journal of Futures Markets, 15(3), 265-302.\r\n\r\nGray, G. L., & Debreceny, R. S. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380.\r\n\r\nGilbert, T. (2011). Information aggregation around macroeconomic announcements: Revisions matter. Journal of Financial Economics, 101(1), 114-131.\r\n\r\nGoodell, J. W., & Vähämaa, S. (2013). US presidential elections and implied volatility: The role of political uncertainty. Journal of Banking & Finance, 37(3), 1108-1117.\r\n\r\nHammer, S., & Russo, C. J. (2012). Tax-Advantaged Investing for an Uncertain Economy: These Seven Strategies May Mitigate Risk and Enhance After-Tax Returns. Journal of Accountancy, 213(5), 28-33.\r\n\r\nKaplanski, G., & Levy, H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics, 95(2), 174-201.\r\n\r\nKelly, B., Pástor, Ľ., & Veronesi, P. (2016). The price of political uncertainty: Theory and evidence from the option market. The Journal of Finance, 71(5), 2417-2480.\r\n\r\nKim, K., Pandit, S., & Wasley, C. E. (2016). Macroeconomic uncertainty and management earnings forecasts. Accounting Horizons, 30(1), 157-172.\r\n\r\nKurihara, Y. (2006). The relationship between exchange rate and stock prices during the quantitative easing policy in Japan. International Journal of Business, 11(4), 375.\r\n\r\nLi, 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.\r\n\r\nLiu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.\r\n\r\nLiu, L. X., Shu, H., & Wei, K. J. (2017). The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China. Journal of Financial Economics, 286-310.\r\n\r\nMitchell, M. L., & Mulherin, J. H. (1994). The impact of public information on the stock market. The Journal of Finance, 49(3), 923-950.\r\n\r\nPastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance, 67(4), 1219-1264.\r\n\r\nSavor, P., & Wilson, M. (2013). How much do investors care about macroeconomic risk? Evidence from scheduled economic announcements. Journal of Financial and Quantitative Analysis, 48(2), 343-375.\r\n\r\nSimon, D. P., & Wiggins, R. A. (2001). S&P futures returns and contrary sentiment indicators. Journal of futures markets, 21(5), 447-462.\r\n\r\nSolomon, D. H. (2012). Selective publicity and stock prices. The Journal of Finance, 67(2), 599-638.\r\n\r\nTetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.\r\n\r\nYang, R., Yu, Y., Liu, M., & Wu, K. (2017). Corporate Risk Disclosure and Audit Fee: A Text Mining Approach. European Accounting Review, 1-12.\r\n\r\nYin, S., Mazouz, K., Benamraoui, A., & Saadouni, B. (2018). Stock price reaction to profit warnings: the role of time-varying betas. Review of Quantitative Finance and Accounting, 50(1), 67-93.\r\n\r\nZhang, W., & Skiena, S. (2010). Trading Strategies to Exploit Blog and News Sentiment. In ICWSM.zh_TW
dc.identifier.doi (DOI) 10.6814/THE.NCCU.ACCT.025.2018.F07-