Publications-Theses
Article View/Open
Publication Export
-
題名 新聞情緒分析與系統風險之關聯性研究
The association between news sentiment analytics and system risk作者 湯明勳
Tang, Ming-Hsun貢獻者 諶家蘭
湯明勳
Tang, Ming-Hsun關鍵詞 新聞情緒分析
系統風險
情緒字典
文本分析
News sentiment analysis
System risk
Sentiment dictionary
Textual analysis日期 2019 上傳時間 7-Aug-2019 15:55:45 (UTC+8) 摘要 隨著資訊科技的日新月異，網路已成為投資人取得資訊時最迅速便捷的管道，有別於以往投資人係根據投資報告或財務報告來作決策，現在投資人也會根據「財經新聞」的報導進一步做出投資決策，財經新聞能提供投資人即時獲取公司內外部之特定消息，因此，在向投資人傳遞訊息時扮演著重要的角色。本研究最主要之目的，係探討「財經新聞」報導對投資人產生之「情緒」影響，與「系統風險」間是否有著緊密的關聯，本研究假設「新聞情緒分析」與系統風險間具有關聯，使用2018年第四季「台灣50指數成分股」51家上市公司相關之財經新聞，利用本研究所建置之「財經正負向情緒字典」統計財經新聞中之「正負面情緒字詞」，用以計算與各模型相關之「情緒語調」及「新聞數量」，最後以SAS統計軟體執行「多元線性回歸」，用以檢驗財經新聞情緒分析與系統風險間之關聯。實證之結果發現，「情緒語調」及「新聞數量」與系統風險間具有關聯性，且新聞情緒分析與系統風險間具有「三日窗期」之關聯，本研究還發現，「負面情緒語調」及「負面新聞」之情緒分析皆與系統風險間具有顯著關聯。
With the rapid development of information technology, the Internet has become the fastest and most convenient channel for investors to obtain information. Different from previous investors who made decisions based on investment reports or financial reports. Nowadays investors also based on the "Financial News" further makes investment decisions. Financial news can provide investors with instant information, therefore, plays an important role in delivering information to investors. The main purpose of this study is to explore whether the "sentiment" impact of "Financial News" on investors is closely related to "System Risk". This study assumes that "News Sentiment Analysis" is related to System Risk. Using the financial news related to 51 listed companies in the "Taiwan 50 Index constituents" in the fourth quarter of 2018, with the "Financial Sentiment Dictionary" to collect "Positive and Negative sentimental words" in financial news, it is used to calculate the variables of "Sentimental Tone" and "Quantity of News" related to each model. Finally, the "Multiple Linear Regression" is performed by SAS statistical software to test the relationship between financial News Sentiment Analysis and System Risk. The empirical results show that there is a correlation between "Sentimental Tone" and " Quantity of News" and System Risk, and there is a "Three-day periods" relation between News Sentiment Analysis and System Risk. This study also found that "Negative Sentimental Tone" and the sentiment analysis of "Negative News" has a significant correlation with System Risk.參考文獻 一、中文參考文獻林宜萱，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，國立臺灣大學會計學系碩士論文。吳昱萱，2017，股價波動與財務預警-數據分析觀點，國立政治大學會計學系碩士論文。許馨予，2016，股票價格波動性與新聞情緖分析之關聯性研究，國立政治大學會計學系碩士論文。二、英文參考文獻Astutik, E., Surachman, S., & Djazuli, A. (2014). The effect of fundamental and technical variables on stock price (Study on manufacturing companies listed in Indonesia Stock Exchange). Journal of Economics, 17(3), 345-352.Barber, B., & Odean , T. (2008). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. The Review of Financial Studies, 21(2), 785-818.Booth, L., Cleary, W., & Rakita, I. (2016). Introduction to Corporate Finance ISBN: 978-1-119-17128-7. Wiley.Chan, W. (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics, 70, 223-260.Engelberg, J., & Parsons, C. (2011). The Causal Impact of Media in Financial Markets. The Journal of Finance, 66(1), 67-97.Ferguson, N., Philip, D., Lam, H., & Guo, J. (2015). Media Content and Stock Returns: The Predictive Power of Press. Multinational Finance Journal, 19(1), 1-31.Hussainey, K., Mgbame, C., & Chijoke-Mgbame, A. (2011). Dividend policy and share price volatility: UK evidence. The Journal of Risk Finance, 12(1), 57-68.Kim, Y., & Meschke, F. (2011). CEO Interviews on CNBC . Fifth Singapore International Conference on Finance 2011.Kurihara , Y. (2006). The Relationship between Exchange Rate and Stock Prices during the Quantitative Easing Policy in Japan. International Journal of Business, 11(4), 375-386.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.Lintner, J. (1965). The Valuation of Risk Assets and the Selection of RiskyInvestments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47, 13-37.Liu, B. (2012). Sentiment Analysis and Opinion Mining. Human Language Technologies, 5(1), 1-167.Liu, B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. Springer, Boston, MA.Mgbame, C., & Ikhatua, O. (2013). Accounting Information and Stock Volatility in the Nigerian. International Review of Management and Business Research, 2(1), 265-281.Mossin, J. (1966). Equilibrium in Capital Asset Market. Econometrica, 34, 768-83.Patton, A., & Verardo, M. (2012). Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability. Review of Financial Studies, 25(9), 2789-2839.Sadique, S., Ina, F., & Veeraraghavan, M. (2008). The Impact of Spin and Tone on Stock Returns and Volatility: Evidence from Firm-issued Earnings Announcements and the Related Press Coverage. Available at SSRN-id1121231.Schwert, G. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance, 44(5), 1115-1154.Sharpe, W. ( 1964). Capital Asset Price: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19, 425-42.Stefano, D., & Pollet, J. (2009). Investor Inattention and Friday Earnings Announcements. The Journal of Finance, 64(2), 709-750.Tetlock, P., Saar-Tsechansky, M., & Macskassy, S. (2008). More Than Words: Quantifying Language to Measure Firms’ Fundamentals. The Journal of Finance, 63(3), 1437-1467.Tetlock, P. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168.Treynor, J. (1961). Towards a Theory of Market Value of Risky Asset. Unpublished paper.Yu, Y., Cao, Q., & Duan, W. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926.Zhang, W., & Skiena, S. (2010). Trading Strategies To Exploit Blog and News Sentiment. Proceedings of the International Conference on Weblogs and Social Media. In ICWSM. 描述 碩士
國立政治大學
會計學系
106353117資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106353117 資料類型 thesis dc.contributor.advisor 諶家蘭 zh_TW dc.contributor.author (Authors) 湯明勳 zh_TW dc.contributor.author (Authors) Tang, Ming-Hsun en_US dc.creator (作者) 湯明勳 zh_TW dc.creator (作者) Tang, Ming-Hsun en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 15:55:45 (UTC+8) - dc.date.available 7-Aug-2019 15:55:45 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 15:55:45 (UTC+8) - dc.identifier (Other Identifiers) G0106353117 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124674 - 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) 106353117 zh_TW dc.description.abstract (摘要) 隨著資訊科技的日新月異，網路已成為投資人取得資訊時最迅速便捷的管道，有別於以往投資人係根據投資報告或財務報告來作決策，現在投資人也會根據「財經新聞」的報導進一步做出投資決策，財經新聞能提供投資人即時獲取公司內外部之特定消息，因此，在向投資人傳遞訊息時扮演著重要的角色。本研究最主要之目的，係探討「財經新聞」報導對投資人產生之「情緒」影響，與「系統風險」間是否有著緊密的關聯，本研究假設「新聞情緒分析」與系統風險間具有關聯，使用2018年第四季「台灣50指數成分股」51家上市公司相關之財經新聞，利用本研究所建置之「財經正負向情緒字典」統計財經新聞中之「正負面情緒字詞」，用以計算與各模型相關之「情緒語調」及「新聞數量」，最後以SAS統計軟體執行「多元線性回歸」，用以檢驗財經新聞情緒分析與系統風險間之關聯。實證之結果發現，「情緒語調」及「新聞數量」與系統風險間具有關聯性，且新聞情緒分析與系統風險間具有「三日窗期」之關聯，本研究還發現，「負面情緒語調」及「負面新聞」之情緒分析皆與系統風險間具有顯著關聯。 zh_TW dc.description.abstract (摘要) With the rapid development of information technology, the Internet has become the fastest and most convenient channel for investors to obtain information. Different from previous investors who made decisions based on investment reports or financial reports. Nowadays investors also based on the "Financial News" further makes investment decisions. Financial news can provide investors with instant information, therefore, plays an important role in delivering information to investors. The main purpose of this study is to explore whether the "sentiment" impact of "Financial News" on investors is closely related to "System Risk". This study assumes that "News Sentiment Analysis" is related to System Risk. Using the financial news related to 51 listed companies in the "Taiwan 50 Index constituents" in the fourth quarter of 2018, with the "Financial Sentiment Dictionary" to collect "Positive and Negative sentimental words" in financial news, it is used to calculate the variables of "Sentimental Tone" and "Quantity of News" related to each model. Finally, the "Multiple Linear Regression" is performed by SAS statistical software to test the relationship between financial News Sentiment Analysis and System Risk. The empirical results show that there is a correlation between "Sentimental Tone" and " Quantity of News" and System Risk, and there is a "Three-day periods" relation between News Sentiment Analysis and System Risk. This study also found that "Negative Sentimental Tone" and the sentiment analysis of "Negative News" has a significant correlation with System Risk. en_US dc.description.tableofcontents 目錄 i圖目錄 ii表目錄 iii壹、緒論 1第一節 研究背景 2第二節 研究問題 4第三節 研究流程 8貳、文獻探討 11第一節 新聞情緒 12第二節 系統風險 16參、研究方法 19第一節 研究假說 20第二節 資料蒐集 27第三節 研究設計 29第四節 研究模型 39肆、研究結果 49第一節 敘述統計 50第二節 相關檢定 59第三節 實證結果 64伍、研究結論 72第一節 研究討論 73第二節 研究限制 75附錄一 財經正向情緒字典 77附錄二 財經負向情緒字典 83參考文獻 90 zh_TW dc.format.extent 2207525 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106353117 en_US dc.subject (關鍵詞) 新聞情緒分析 zh_TW dc.subject (關鍵詞) 系統風險 zh_TW dc.subject (關鍵詞) 情緒字典 zh_TW dc.subject (關鍵詞) 文本分析 zh_TW dc.subject (關鍵詞) News sentiment analysis en_US dc.subject (關鍵詞) System risk en_US dc.subject (關鍵詞) Sentiment dictionary en_US dc.subject (關鍵詞) Textual analysis en_US dc.title (題名) 新聞情緒分析與系統風險之關聯性研究 zh_TW dc.title (題名) The association between news sentiment analytics and system risk en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文參考文獻林宜萱，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，國立臺灣大學會計學系碩士論文。吳昱萱，2017，股價波動與財務預警-數據分析觀點，國立政治大學會計學系碩士論文。許馨予，2016，股票價格波動性與新聞情緖分析之關聯性研究，國立政治大學會計學系碩士論文。二、英文參考文獻Astutik, E., Surachman, S., & Djazuli, A. (2014). The effect of fundamental and technical variables on stock price (Study on manufacturing companies listed in Indonesia Stock Exchange). Journal of Economics, 17(3), 345-352.Barber, B., & Odean , T. (2008). All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. The Review of Financial Studies, 21(2), 785-818.Booth, L., Cleary, W., & Rakita, I. (2016). Introduction to Corporate Finance ISBN: 978-1-119-17128-7. Wiley.Chan, W. (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics, 70, 223-260.Engelberg, J., & Parsons, C. (2011). The Causal Impact of Media in Financial Markets. The Journal of Finance, 66(1), 67-97.Ferguson, N., Philip, D., Lam, H., & Guo, J. (2015). Media Content and Stock Returns: The Predictive Power of Press. Multinational Finance Journal, 19(1), 1-31.Hussainey, K., Mgbame, C., & Chijoke-Mgbame, A. (2011). Dividend policy and share price volatility: UK evidence. The Journal of Risk Finance, 12(1), 57-68.Kim, Y., & Meschke, F. (2011). CEO Interviews on CNBC . Fifth Singapore International Conference on Finance 2011.Kurihara , Y. (2006). The Relationship between Exchange Rate and Stock Prices during the Quantitative Easing Policy in Japan. International Journal of Business, 11(4), 375-386.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.Lintner, J. (1965). The Valuation of Risk Assets and the Selection of RiskyInvestments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47, 13-37.Liu, B. (2012). Sentiment Analysis and Opinion Mining. Human Language Technologies, 5(1), 1-167.Liu, B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. Springer, Boston, MA.Mgbame, C., & Ikhatua, O. (2013). Accounting Information and Stock Volatility in the Nigerian. International Review of Management and Business Research, 2(1), 265-281.Mossin, J. (1966). Equilibrium in Capital Asset Market. Econometrica, 34, 768-83.Patton, A., & Verardo, M. (2012). Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability. Review of Financial Studies, 25(9), 2789-2839.Sadique, S., Ina, F., & Veeraraghavan, M. (2008). The Impact of Spin and Tone on Stock Returns and Volatility: Evidence from Firm-issued Earnings Announcements and the Related Press Coverage. Available at SSRN-id1121231.Schwert, G. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance, 44(5), 1115-1154.Sharpe, W. ( 1964). Capital Asset Price: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19, 425-42.Stefano, D., & Pollet, J. (2009). Investor Inattention and Friday Earnings Announcements. The Journal of Finance, 64(2), 709-750.Tetlock, P., Saar-Tsechansky, M., & Macskassy, S. (2008). More Than Words: Quantifying Language to Measure Firms’ Fundamentals. The Journal of Finance, 63(3), 1437-1467.Tetlock, P. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168.Treynor, J. (1961). Towards a Theory of Market Value of Risky Asset. Unpublished paper.Yu, Y., Cao, Q., & Duan, W. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926.Zhang, W., & Skiena, S. (2010). Trading Strategies To Exploit Blog and News Sentiment. Proceedings of the International Conference on Weblogs and Social Media. In ICWSM. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900410 en_US
