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題名 極端事件下企業法說會與財報之情緒對報酬之影響：以美國股票市場為例
The Impact of the Sentiment in Earnings Call and Financial Reports on Return under Extreme Events: Evidence from U.S. Stock Market作者 姚詠馨
Yao, Yung-Hsin貢獻者 林士貴
Lin, Shih-Kuei
姚詠馨
Yao, Yung-Hsin關鍵詞 自然語言處理
情感分析
主觀性分析
Earnings Call
財務報表
Natural language processing
Sentiment analysis
Subjectivity analysis
Earnings call
Financial reports日期 2022 上傳時間 10-Feb-2022 12:55:16 (UTC+8) 摘要 2020年在COVID-19疫情下，企業營運模式產生巨變，企業在紛紛在Earnings Call與財報中表現出不同情緒。鑑往知來，本研究透過建立關注程度、情緒與風險三種測量，衡量過去歷史上不同極端事件下，上述三種測量對於報酬的影響，並進一步使用TextBlob測量主觀性，探討Earnings Call與10-Q、10-K財報主觀性差異是否可以解釋兩者之間的情緒差異。實證結果發現，Earnings Call逐字稿與10-Q、10-K文稿之關注程度對於報酬有負向影響，但依事件性質有所不同，對於2008金融危機而言，企業長期關注經濟議題，將有較高的報酬。Earnings Call逐字稿情緒對於長短期報酬皆有正向顯著關係，並由負向情緒貢獻，顯示極端事件下情緒越負面、報酬越低，但此結果在10-Q、10-K文稿中較不明顯。相較於Earnings Call逐字稿，10-Q、10-K文本之風險對於報酬有較強烈的負向顯著關係，說明因為企業充分在財報中揭露營運各面向的風險，故10-Q、10-K文本所表現出的風險對於長短期報酬有顯著負面的影響。同時我們發現，911恐怖攻擊對於風險有最強烈的負向顯著關係，凸顯企業對於危及國家安全的事件風險敏感性高。最後，當Earnings Call比10-Q、10-K越主觀，Earnings Call的情緒就比10-Q、10-K的情緒越負面，且主觀性差異能確實解釋Earnings Call與10-Q、10-K財報樣本的情緒差異。
Under the impact of COVID-19, companies express different sentiments in earnings calls and financial reports. In this thesis, I construct the concern measure, sentiment measure, and risk measure to explore how text information in financial documents affects stock returns under different extreme events. Furthermore, I construct the subjectivity measure to explain the sentiment difference between earnings calls and financial reports. Empirical results show that the text information plays an important role. First, concern measures of earnings call and financial reports are both significantly negatively related to returns, but still different in specular extreme events. Second, companies with more depressed sentiment in earnings call will have lower returns. However, this relationship is not significant enough in financial reports. Third, the risk measure of financial reports is negatively related to returns, which is stronger than that of earnings call, since companies expose more aspects of risk in financial reports. Last, when earnings call is more subjective than financial reports, the sentiment of earnings call is more negative. I also find that the subjectivity difference between earnings call and financial reports can explain sentiment difference indeed.參考文獻 1. Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M., & Viratyosin, T. (2020). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10(4), 742-758.2. Bretscher, L., Hsu, A., Simasek, P., & Tamoni, A. (2020). COVID-19 and the cross-section of equity returns: Impact and transmission. The Review of Asset Pricing Studies, 10(4), 705-741.3. Brockman, P., Li, X., & Price, S. M. (2015). Differences in conference call tones: Managers vs. analysts. Financial Analysts Journal, 71(4), 24-42.4. Brounen, D., & Derwall, J. (2010). The impact of terrorist attacks on international stock markets. European Financial Management, 16(4), 585-598.5. Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (2020). The structure of economic news (No. w26648). National Bureau of Economic Research (NBER).6. Davis, S. J., Hansen, S., & Seminario-Amez, C. (2020). Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19 (No. w27867). National Bureau of Economic Research (NBER).7. Davis, A. K., Piger, J. M., & Sedor, L. M. (2012). Beyond the numbers Measuring the information content of Earningss press release language. Contemporary Accounting Research, 29(3), 845-868.8. Drakos, K. (2010). Terrorism activity, investor sentiment, and stock returns. Review of Financial Economics, 19(3), 128-135.9. Gandhi, P., Loughran, T., & McDonald, B. (2019). Using annual report sentiment as a proxy for financial distress in US banks. Journal of Behavioral Finance, 20(4), 424-436.10. Hassan, T. A., Hollander, S., Van Lent, L., Schwedeler, M., & Tahoun, A. (2021). Firm-level exposure to epidemic diseases: Covid-19, SARS, and H1N1 (No. w26971). National Bureau of Economic Research (NBER).11. Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14550.12. International Monetary Fund. (2001). World Economic Outlook: The Global Economy After September 11, Washington, DC: IMF.13. Li, F. (2006). Do stock market investors understand the risk sentiment of corporate annual reports? Available at SSRN 898181.14. Loria, S. (2020). textblob Documentation. Release 0.16.0. Retrieved from https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf15. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.16. Loughran, T., & McDonald, B. (2014). Measuring readability in financial disclosures. The Journal of Finance, 69(4), 1643-1671.17. Reinhart, C. M., & Rogoff, K. S. (2009). The Aftermath of Financial Crises. American Economic Review, 99(2), 466–472.18. Schwert, G. W. (2011). Stock volatility during the recent financial crisis. European Financial Management, 17(5), 789- 805.19. Stone, P. J., Dunphy, D. C., & Smith, M. S. (1966). The general inquirer: A computer approach to content analysis. M.I.T. Press.20. 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. 描述 碩士
國立政治大學
金融學系
109352016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109352016 資料類型 thesis dc.contributor.advisor 林士貴 zh_TW dc.contributor.advisor Lin, Shih-Kuei en_US dc.contributor.author (Authors) 姚詠馨 zh_TW dc.contributor.author (Authors) Yao, Yung-Hsin en_US dc.creator (作者) 姚詠馨 zh_TW dc.creator (作者) Yao, Yung-Hsin en_US dc.date (日期) 2022 en_US dc.date.accessioned 10-Feb-2022 12:55:16 (UTC+8) - dc.date.available 10-Feb-2022 12:55:16 (UTC+8) - dc.date.issued (上傳時間) 10-Feb-2022 12:55:16 (UTC+8) - dc.identifier (Other Identifiers) G0109352016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138890 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 109352016 zh_TW dc.description.abstract (摘要) 2020年在COVID-19疫情下，企業營運模式產生巨變，企業在紛紛在Earnings Call與財報中表現出不同情緒。鑑往知來，本研究透過建立關注程度、情緒與風險三種測量，衡量過去歷史上不同極端事件下，上述三種測量對於報酬的影響，並進一步使用TextBlob測量主觀性，探討Earnings Call與10-Q、10-K財報主觀性差異是否可以解釋兩者之間的情緒差異。實證結果發現，Earnings Call逐字稿與10-Q、10-K文稿之關注程度對於報酬有負向影響，但依事件性質有所不同，對於2008金融危機而言，企業長期關注經濟議題，將有較高的報酬。Earnings Call逐字稿情緒對於長短期報酬皆有正向顯著關係，並由負向情緒貢獻，顯示極端事件下情緒越負面、報酬越低，但此結果在10-Q、10-K文稿中較不明顯。相較於Earnings Call逐字稿，10-Q、10-K文本之風險對於報酬有較強烈的負向顯著關係，說明因為企業充分在財報中揭露營運各面向的風險，故10-Q、10-K文本所表現出的風險對於長短期報酬有顯著負面的影響。同時我們發現，911恐怖攻擊對於風險有最強烈的負向顯著關係，凸顯企業對於危及國家安全的事件風險敏感性高。最後，當Earnings Call比10-Q、10-K越主觀，Earnings Call的情緒就比10-Q、10-K的情緒越負面，且主觀性差異能確實解釋Earnings Call與10-Q、10-K財報樣本的情緒差異。 zh_TW dc.description.abstract (摘要) Under the impact of COVID-19, companies express different sentiments in earnings calls and financial reports. In this thesis, I construct the concern measure, sentiment measure, and risk measure to explore how text information in financial documents affects stock returns under different extreme events. Furthermore, I construct the subjectivity measure to explain the sentiment difference between earnings calls and financial reports. Empirical results show that the text information plays an important role. First, concern measures of earnings call and financial reports are both significantly negatively related to returns, but still different in specular extreme events. Second, companies with more depressed sentiment in earnings call will have lower returns. However, this relationship is not significant enough in financial reports. Third, the risk measure of financial reports is negatively related to returns, which is stronger than that of earnings call, since companies expose more aspects of risk in financial reports. Last, when earnings call is more subjective than financial reports, the sentiment of earnings call is more negative. I also find that the subjectivity difference between earnings call and financial reports can explain sentiment difference indeed. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機 1第二節 研究目的 3第三節 研究貢獻 3第二章 文獻回顧 4第一節 極端事件下的報酬表現相關研究 4第二節 文本情緒與主觀性測量 6第三節 Earnings Call逐字稿情緒分析 8第四節 10-Q與10-K財報情緒分析 9第三章 研究方法 10第一節 自然語言處理 11第二節 關注程度、情緒與風險測量 13第三節 主觀性測量 18第四節 實證分析 20第四章 資料來源與處理方法 22第一節 資料來源與期間 22第二節 10-Q、10-K資料處理方法 24第三節 Earnings Call逐字稿資料處理方法 25第五章 實證分析結果 26第一節 敘述統計 26第二節 實證結果 34第六章 結論與展望 52第一節 研究結論 52第二節 未來展望 53參考文獻 54附錄 57 zh_TW dc.format.extent 3804206 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109352016 en_US dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 情感分析 zh_TW dc.subject (關鍵詞) 主觀性分析 zh_TW dc.subject (關鍵詞) Earnings Call zh_TW dc.subject (關鍵詞) 財務報表 zh_TW dc.subject (關鍵詞) Natural language processing en_US dc.subject (關鍵詞) Sentiment analysis en_US dc.subject (關鍵詞) Subjectivity analysis en_US dc.subject (關鍵詞) Earnings call en_US dc.subject (關鍵詞) Financial reports en_US dc.title (題名) 極端事件下企業法說會與財報之情緒對報酬之影響：以美國股票市場為例 zh_TW dc.title (題名) The Impact of the Sentiment in Earnings Call and Financial Reports on Return under Extreme Events: Evidence from U.S. Stock Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M., & Viratyosin, T. (2020). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10(4), 742-758.2. Bretscher, L., Hsu, A., Simasek, P., & Tamoni, A. (2020). COVID-19 and the cross-section of equity returns: Impact and transmission. The Review of Asset Pricing Studies, 10(4), 705-741.3. Brockman, P., Li, X., & Price, S. M. (2015). Differences in conference call tones: Managers vs. analysts. Financial Analysts Journal, 71(4), 24-42.4. Brounen, D., & Derwall, J. (2010). The impact of terrorist attacks on international stock markets. European Financial Management, 16(4), 585-598.5. Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (2020). The structure of economic news (No. w26648). National Bureau of Economic Research (NBER).6. Davis, S. J., Hansen, S., & Seminario-Amez, C. (2020). Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19 (No. w27867). National Bureau of Economic Research (NBER).7. Davis, A. K., Piger, J. M., & Sedor, L. M. (2012). Beyond the numbers Measuring the information content of Earningss press release language. Contemporary Accounting Research, 29(3), 845-868.8. Drakos, K. (2010). Terrorism activity, investor sentiment, and stock returns. Review of Financial Economics, 19(3), 128-135.9. Gandhi, P., Loughran, T., & McDonald, B. (2019). Using annual report sentiment as a proxy for financial distress in US banks. Journal of Behavioral Finance, 20(4), 424-436.10. Hassan, T. A., Hollander, S., Van Lent, L., Schwedeler, M., & Tahoun, A. (2021). Firm-level exposure to epidemic diseases: Covid-19, SARS, and H1N1 (No. w26971). National Bureau of Economic Research (NBER).11. Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14550.12. International Monetary Fund. (2001). World Economic Outlook: The Global Economy After September 11, Washington, DC: IMF.13. Li, F. (2006). Do stock market investors understand the risk sentiment of corporate annual reports? Available at SSRN 898181.14. Loria, S. (2020). textblob Documentation. Release 0.16.0. Retrieved from https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf15. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.16. Loughran, T., & McDonald, B. (2014). Measuring readability in financial disclosures. The Journal of Finance, 69(4), 1643-1671.17. Reinhart, C. M., & Rogoff, K. S. (2009). The Aftermath of Financial Crises. American Economic Review, 99(2), 466–472.18. Schwert, G. W. (2011). Stock volatility during the recent financial crisis. European Financial Management, 17(5), 789- 805.19. Stone, P. J., Dunphy, D. C., & Smith, M. S. (1966). The general inquirer: A computer approach to content analysis. M.I.T. Press.20. 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200101 en_US
