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題名 財經新聞和VIX之關聯性研究 – 以文字分析為例
Financial News and VIX - A Text Analysis Approach
作者 蔡駿廷
Tsai, Jyun Ting
貢獻者 諶家蘭
Seng, Jia Lang
蔡駿廷
Tsai, Jyun Ting
關鍵詞 恐慌指數
財經新聞
文字分析
Volatility Index
Financial News
Text analysis
日期 2015
上傳時間 27-Jul-2015 11:19:37 (UTC+8)
摘要 本研究針對台灣地區投資人情緒程度與財經新聞之間的關聯進行研究。本研究以台灣地區發布之恐慌指數(TWVIX)做為投資人情緒程度代理變數。研究樣本期間為2006年12月至2014年12月，新聞資料來源為知識贏家(KMW)。本研究使用文字分析法針對財經新聞內容進行分析，透過計算詞頻和建立語調評分方法來衡量新聞內涵。
本研究之主要發現有：單日媒體發布之新聞數量與恐慌指數呈正相關、單篇新聞平均長度與恐慌指數呈負相關。在文字內容分析的迴歸結果方面，實證結果指出當新聞內的正向字詞使用的越多，投資人情緒程度會降低，相反地，當負向字詞使用的越多，將會提升投資人情緒，產生恐慌狀態。另外，結果也發現當新聞的語調越接近樂觀情緒時，投資人的情緒程度會降低。
本文嘗試了一個新的切入角度來討論金融指標，也就是由新聞媒體效果來探討與恐慌指數之關聯性。本研究結果提供了實證證據證明台灣地區媒體可有效地透過控制財經新聞的內涵，來影響市場上投資人的情緒程度。
This study examines the relation between investor sentiment and financial news released in Taiwan for the period from Dec. 2006 to Dec. 2014. This study adopts the TAIEX Options Volatility Index (TWVIX) as a proxy for investor sentiment. The study employs the text analysis approach to measure the tone of financial news. The news data is collected from Knowledge Management Winner (KMW) of China Times.
The empirical results show that the number of news released on a day has a positive effect on TWVIX; otherwise the length of financial news affects TWVIX negatively. By the text analysis, this study finds that with more positive wording, the degree of investor sentiment is decreased. On the contrary, the quantity of negative words provokes the investor sentiment. Lastly, this research also finds that when the tone of financial news is closer to optimism, it help ease the level of investor sentiment.
This research explores a new cut point to discuss the VIX, connecting the issue between a principal financial index and news media in Taiwan. This study believes that the results provide the evidence that Taiwan media actually communicate messages to investors effectively. Thus, news media has the ability to influence investor sentiment no matter by news coverage, words usage and the tone of financial news.
參考文獻 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.
Birz, G., & Lott, J. R. (2011). The effect of macroeconomic news on stock returns: New evidence from newspaper coverage. Journal of Banking & Finance 35(11):2791-2800.
Carretta, 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.
Chan, S. W., & Franklin, J. (2011). A text-based decision support system for financial sequence prediction. Decision Support Systems 52(1):189-198.
Chen, K. T., Lu, H. M., Chen, T. J., Li, S. H., Lian, J. S., & Chen, H. (2011). Giving context to accounting numbers: The role of news coverage. Decision Support Systems 50(4):673-679.
Copeland, M. M., & Copeland, T. E. (1999). Market timing: Style and size rotation using the VIX. Financial Analysts Journal 55(2):73-81.
Davis, A. K., Piger, J. M., & Sedor, L. M. (2012). Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language*. Contemporary Accounting Research 29(3):845-868.
Ederington, L. H., & Lee, J. H. (1993). How markets process information: News releases and volatility. The Journal of Finance 48(4):1161-1191.
Fang, L., & Peress, J. (2009). Media Coverage and the Cross‐section of Stock Returns. The Journal of Finance 64(5):2023-2052.
Fernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance 40:1-10.
Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: a new measure. Journal of Futures Markets 15(3):265-302.
Garcia, D. (2013). Sentiment during recessions. The Journal of Finance 68(3):1267-1300.
Geva, T., & Zahavi, J. (2014). Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems 57:212-223.
Goodell, J. W., & Vähämaa, S. (2013). US presidential elections and implied volatility: The role of political uncertainty. Journal of Banking & Finance 37(3):1108-1117.
Groth, S. S., & Muntermann, J. (2011). An intraday market risk management approach based on textual analysis. Decision Support Systems 50(4):680-691.
Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55(3):685-697.
Huang, X., Teoh, S. H., & Zhang, Y. (2013). Tone management. The Accounting Review 89(3):1083-1113.
Huang, A. H., Zang, A. Y., & Zheng, R. (2014). Evidence on the Information Content of Text in Analyst Reports. The Accounting Review 89(6):2151-2180.
Jubinski, D., & Lipton, A. F. (2013). VIX, Gold, Silver, and Oil: How do Commodities React to Financial Market Volatility?. Journal of Accounting and Finance 13(1):70-88.
LaakkOnen, H., & Lanne, M. (2013). The relevance of accuracy for the impact of macroeconomic news on exchange rate volatility. International Journal of Finance & Economics 18(4):339-351.
Mitchell, M. L., & Mulherin, J. H. (1994). The impact of public information on the stock market. The Journal of Finance 49(3):923-950.
Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance 25(7):1339-1366.
Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems 53(3):458-464.
Simon, D. P., & Wiggins, R. A. (2001). S&P futures returns and contrary sentiment indicators. Journal of Futures Markets 21(5):447-462.
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.
Traub, H., Ferreira, L., & McArdle, M. (2000). Fear and greed in global asset allocation. Journal Of Investing 9(1):27-31.
Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management 26(3):12-17.
描述 碩士
國立政治大學
會計研究所
102353027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102353027
資料類型 thesis
dc.contributor.advisor 諶家蘭zh_TW
dc.contributor.advisor Seng, Jia Langen_US
dc.contributor.author (Authors) 蔡駿廷zh_TW
dc.contributor.author (Authors) Tsai, Jyun Tingen_US
dc.creator (作者) 蔡駿廷zh_TW
dc.creator (作者) Tsai, Jyun Tingen_US
dc.date (日期) 2015en_US
dc.date.accessioned 27-Jul-2015 11:19:37 (UTC+8)-
dc.date.available 27-Jul-2015 11:19:37 (UTC+8)-
dc.date.issued (上傳時間) 27-Jul-2015 11:19:37 (UTC+8)-
dc.identifier (Other Identifiers) G0102353027en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76848-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 會計研究所zh_TW
dc.description (描述) 102353027zh_TW
dc.description.abstract (摘要) 本研究針對台灣地區投資人情緒程度與財經新聞之間的關聯進行研究。本研究以台灣地區發布之恐慌指數(TWVIX)做為投資人情緒程度代理變數。研究樣本期間為2006年12月至2014年12月，新聞資料來源為知識贏家(KMW)。本研究使用文字分析法針對財經新聞內容進行分析，透過計算詞頻和建立語調評分方法來衡量新聞內涵。
本研究之主要發現有：單日媒體發布之新聞數量與恐慌指數呈正相關、單篇新聞平均長度與恐慌指數呈負相關。在文字內容分析的迴歸結果方面，實證結果指出當新聞內的正向字詞使用的越多，投資人情緒程度會降低，相反地，當負向字詞使用的越多，將會提升投資人情緒，產生恐慌狀態。另外，結果也發現當新聞的語調越接近樂觀情緒時，投資人的情緒程度會降低。
本文嘗試了一個新的切入角度來討論金融指標，也就是由新聞媒體效果來探討與恐慌指數之關聯性。本研究結果提供了實證證據證明台灣地區媒體可有效地透過控制財經新聞的內涵，來影響市場上投資人的情緒程度。		zh_TW
dc.description.abstract (摘要) This study examines the relation between investor sentiment and financial news released in Taiwan for the period from Dec. 2006 to Dec. 2014. This study adopts the TAIEX Options Volatility Index (TWVIX) as a proxy for investor sentiment. The study employs the text analysis approach to measure the tone of financial news. The news data is collected from Knowledge Management Winner (KMW) of China Times.
The empirical results show that the number of news released on a day has a positive effect on TWVIX; otherwise the length of financial news affects TWVIX negatively. By the text analysis, this study finds that with more positive wording, the degree of investor sentiment is decreased. On the contrary, the quantity of negative words provokes the investor sentiment. Lastly, this research also finds that when the tone of financial news is closer to optimism, it help ease the level of investor sentiment.
This research explores a new cut point to discuss the VIX, connecting the issue between a principal financial index and news media in Taiwan. This study believes that the results provide the evidence that Taiwan media actually communicate messages to investors effectively. Thus, news media has the ability to influence investor sentiment no matter by news coverage, words usage and the tone of financial news.		en_US
dc.description.tableofcontents 1. INTRODUCTION 1
1.1 RESEARCH PURPOSE AND MOTIVATION 1
1.2 RESEARCH ISSUE 10
1.3 RESEARCH PROCESS 11
2. LITERERTURE REVIEW 13
2.1 VOLATILITY INDEX 13
2.2 PUBLIC INFORMATION, PRESS MEDIA and MARKET BEHAVIOR 15
2.3 TEXTUAL DATA AND CONTENT ANAYLSIS 20
3. RESEARCH METHOD AND DESIGN 27
3.1 HYPOTHESIS DEVELOPMENT 27
3.1.1 The Relation between TWVIX and Number of News Releases 27
3.1.2 The Relation between TWVIX and Length of a News Article 29
3.1.3 The Relation between TWVIX and Word Usage in Financial News Media 30
3.1.4 The Relation between TWVIX and Tone of Financial News 31
3.2 DATA COLLECTION 32
3.3 TEXT ANALYSIS APPROACHES ON FINANCIAL NEWS 37
3.4 REGRESSION MODEL 41
3.4.1 Dependent Variable 43
3.4.2 Independent Variable 43
3.4.3 Control Variable 44
4. EMPIRICAL RESULTS 48
4.1 DESCRIPTIVE STATISTICS 48
4.2 CORRELATION ANALYSIS 51
4.3 REGRESSION ANALYSIS 54
4.3.1 Number of News Releases and TWVIX 54
4.3.2 Length of a News Article and TWVIX 55
4.3.3 Word Usage of News and TWVIX 57
4.3.4 Tone of Financial News and TWVIX 59
5. CONCLUSION AND DISCUSSION 61
5.1 RESEARCH DISCUSSION AND CONTRIBUTION 61
5.2 LIMITATION AND FUTURE RESEARCH WORK 63
REFERENCES 65		zh_TW
dc.format.extent 1296376 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102353027en_US
dc.subject (關鍵詞) 恐慌指數zh_TW
dc.subject (關鍵詞) 財經新聞zh_TW
dc.subject (關鍵詞) 文字分析zh_TW
dc.subject (關鍵詞) Volatility Indexen_US
dc.subject (關鍵詞) Financial Newsen_US
dc.subject (關鍵詞) Text analysisen_US
dc.title (題名) 財經新聞和VIX之關聯性研究 – 以文字分析為例zh_TW
dc.title (題名) Financial News and VIX - A Text Analysis Approachen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 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.
Birz, G., & Lott, J. R. (2011). The effect of macroeconomic news on stock returns: New evidence from newspaper coverage. Journal of Banking & Finance 35(11):2791-2800.
Carretta, 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.
Chan, S. W., & Franklin, J. (2011). A text-based decision support system for financial sequence prediction. Decision Support Systems 52(1):189-198.
Chen, K. T., Lu, H. M., Chen, T. J., Li, S. H., Lian, J. S., & Chen, H. (2011). Giving context to accounting numbers: The role of news coverage. Decision Support Systems 50(4):673-679.
Copeland, M. M., & Copeland, T. E. (1999). Market timing: Style and size rotation using the VIX. Financial Analysts Journal 55(2):73-81.
Davis, A. K., Piger, J. M., & Sedor, L. M. (2012). Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language*. Contemporary Accounting Research 29(3):845-868.
Ederington, L. H., & Lee, J. H. (1993). How markets process information: News releases and volatility. The Journal of Finance 48(4):1161-1191.
Fang, L., & Peress, J. (2009). Media Coverage and the Cross‐section of Stock Returns. The Journal of Finance 64(5):2023-2052.
Fernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance 40:1-10.
Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: a new measure. Journal of Futures Markets 15(3):265-302.
Garcia, D. (2013). Sentiment during recessions. The Journal of Finance 68(3):1267-1300.
Geva, T., & Zahavi, J. (2014). Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems 57:212-223.
Goodell, J. W., & Vähämaa, S. (2013). US presidential elections and implied volatility: The role of political uncertainty. Journal of Banking & Finance 37(3):1108-1117.
Groth, S. S., & Muntermann, J. (2011). An intraday market risk management approach based on textual analysis. Decision Support Systems 50(4):680-691.
Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55(3):685-697.
Huang, X., Teoh, S. H., & Zhang, Y. (2013). Tone management. The Accounting Review 89(3):1083-1113.
Huang, A. H., Zang, A. Y., & Zheng, R. (2014). Evidence on the Information Content of Text in Analyst Reports. The Accounting Review 89(6):2151-2180.
Jubinski, D., & Lipton, A. F. (2013). VIX, Gold, Silver, and Oil: How do Commodities React to Financial Market Volatility?. Journal of Accounting and Finance 13(1):70-88.
LaakkOnen, H., & Lanne, M. (2013). The relevance of accuracy for the impact of macroeconomic news on exchange rate volatility. International Journal of Finance & Economics 18(4):339-351.
Mitchell, M. L., & Mulherin, J. H. (1994). The impact of public information on the stock market. The Journal of Finance 49(3):923-950.
Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance 25(7):1339-1366.
Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems 53(3):458-464.
Simon, D. P., & Wiggins, R. A. (2001). S&P futures returns and contrary sentiment indicators. Journal of Futures Markets 21(5):447-462.
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.
Traub, H., Ferreira, L., & McArdle, M. (2000). Fear and greed in global asset allocation. Journal Of Investing 9(1):27-31.
Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management 26(3):12-17.		zh_TW