博碩士論文 etd-0726118-004832 詳細資訊

[回到前頁查詢結果 | 重新搜尋]

姓名 程迺雯(Nai-Wen Cheng ) 電子郵件信箱 E-mail 資料不公開
畢業系所 財務管理學系研究所(Department of Finance)
畢業學位 碩士(Master) 畢業時期 107學年第1學期
論文名稱(中) 媒體情緒對於波動率指數期貨市場之影響
論文名稱(英) The Effect of Media-based Sentiment on VIX Futures Market
  • etd-0726118-004832.pdf
  • 本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。

    紙本論文:5 年後公開 (2023-08-26 公開)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 英文/98
    統計 本論文已被瀏覽 5561 次,被下載 0 次
    摘要(中) 近年來,愈來愈多文獻指出流通於廣泛媒體間的公開資訊對於金融市場有莫大的影響性,例如:資產價格的變動間接反映了資訊內涵。本研究首先探討以媒體資訊為基準的情緒衡量是否能有效預測波動率指數期貨的價格變化。根據Webhose.io提供規模龐大且各式型態的媒體資料,該情緒指數的建立是利用文字探勘的技術來分析並量化在線新聞報導、部落格文章以及論壇留言中所涵蓋的資訊及情緒語調。實證結果表明,媒體所發布的資訊介於前一日收盤後至當日開盤前,該區間的情緒指數對於當日波動率指數期貨的報酬具有顯著預測力。接著探討在週頻率基礎下,波動率指數期貨報酬的預測力能持續多久。根據結果顯示,新聞報導下的週平均情緒指數僅能預測一週後的波動率指數期貨週報酬。此外,絕大多數的總經宣告衝擊並未提升情緒指標對於波動率指數期貨報酬的解釋力,反而削弱情緒指數的預測力。依據市場及資訊流之特性來逐一分組,發現情緒指數對於波動率指數期貨的預測及影響性在以下四種情況較為強烈:(1)大量資訊發布、(2)交易活動頻繁、(3)價格波動性大、(4)市場較不具流動性。最後,從投資角度來看,無論是避險抑或是未避險部位,根據情緒指數來放空或買進波動率指數期貨的獲利性皆非常可觀。相較於其他類型的媒體,新聞報導下所計算的情緒指數,其預測性和獲利能力皆具有強烈的顯著性。整體研究結果意味著媒體情緒不僅具有統計上顯著的波動率指數期貨報酬之可預測性,同時在交易實務上具有高獲利性的經濟價值。
    摘要(英) With the circulation of information in a wider variety of social media, this study first explores the predictive relation between measurements of media-based sentiment extracted from large-scale web data feeds on Webhose.io and the VIX futures returns. The media-based sentiment measure is constructed based on textual analysis to quantify tone of information included in the online news stories, blog posts and discussions messages. The substantial evidence show that daily VIX futures returns are predictable using sentiment index calculated during overnight hours after closing of previous day and before the opening of the day. On the basis of longer horizon, the weekly average sentiment index of news articles can only predict future return after one week. Moreover, most macroeconomic announcements shocks weaken the predictability of sentiment index. According to analyzing the different characteristics, the sentiment effect is stronger during large number of postings are released, high trading volume, high volatility, and high illiquidity days. From an investment perspective, simulated trading on VIX futures in accordance with media-based sentiment measure is highly profitable. Both predictive power and profitability of sentiment measure is strongly significant in news articles. While the overall findings demonstrate that media-based sentiment appears to carries not only statistically significant return predictability of VIX futures but economic value while evaluated with the simulated trading strategies.
  • 波動率指數期貨
  • 資訊流
  • 媒體情緒
  • 文字探勘
  • 投資人行為
  • 關鍵字(英)
  • VIX futures
  • Information flow
  • Text mining
  • Investor Sentiment
  • Media
  • 論文目次 論文審定書 i
    摘要 ii
    ABSTRACT iii
    I. Introduction 1
    II. Literature Review 6
    III. Data and Methodology 17
    3.1 Implied Volatility Index Futures 17
    3.2 Media Data 19
    3.3 Sentiment Index of Media Data Feed 21
    IV. Empirical Analysis 22
    4.1 Return Predictability of Sentiment index on VIX Futures 22
    4.2 The Effect of Macroeconomic Announcements Surprises 25
    4.3 Day of the Week 28
    4.4 Information Flow and Market Characteristics 30
    4.5 How Long does the Return Predictability Last? 34
    V. Profitability of Sentiment based on Trading Strategies 36
    5.1 Performance of Unhedged VIX Futures Positions 36
    5.2 Performance of Hedged VIX Futures Positions 41
    5.3 Jensen's alpha 45
    VI. Conclusions 49
    Reference 54
    參考文獻 Abraham, A., & Ikenberry, D. L. (1994). The individual investor and the weekend effect. Journal of Financial and Quantitative Analysis, 29(2), 263-277.
    Alizadeh, S., Brandt, M. W., & Diebold, F. X. (2002). Range‐based estimation of stochastic volatility models. The Journal of Finance, 57(3), 1047-1091.
    Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial markets, 5(1), 31-56.
    Ammann, M., & Schaub, N. (2017). The Impact of Internet Postings on Individual Investors. Paper presented at the proceedings American Finance Association 2018 Annual Meeting.
    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.
    Azuma, T., Okada, K., & Hamuro, Y. (2016). Is no news good news? The streaming news effect on investor behavior surrounding analyst stock revision announcement Behavioral Interactions, Markets, and Economic Dynamics (pp. 567-593): Springer.
    Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
    Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance, 55(2), 773-806.
    Barber, B. M., & Odean, T. (2007). 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.
    Blau, B. M., DeLisle, J. R., & Price, S. M. (2015). Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales. Journal of Corporate Finance, 31, 203-219.
    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. Retrieved from
    Chan, W. S. (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics, 70(2), 223-260.
    Chen, H., De, P., Hu, Y. J., & Hwang, B.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367-1403.
    Chen, J. V., & Li, F. (2015). Discussion of “Textual analysis and international financial reporting: Large sample evidence”. Journal of Accounting and Economics, 60(2-3), 181-186.
    Chen, L. H., Jiang, G. J., & Wang, Q. (2013). Market Reaction to Information Shocks—Does the Bloomberg and Briefing. com Survey Matter? Journal of Futures Markets, 33(10), 939-964.
    Cutler, D. M., Poterba, J. M., & Summers, L. H. (1988). What moves stock prices? : National Bureau of Economic Research Cambridge, Mass., USA.
    Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
    Da, Z., Engelberg, J., & Gao, P. (2014). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32.
    Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management science, 53(9), 1375-1388.
    Dougal, C., Engelberg, J., Garcia, D., & Parsons, C. A. (2012). Journalists and the stock market. The Review of Financial Studies, 25(3), 639-679.
    Dzielinski, M., & Hasseltoft, H. (2013). Aggregate News Tone, Stock Returns, and Volatility. Unpublished Working Paper.
    Ederington, L. H., & Lee, J. H. (1993). How markets process information: News releases and volatility. The Journal of Finance, 48(4), 1161-1191.
    Engelberg, J. (2008). Costly information processing: Evidence from earnings announcements.
    Engelberg, J. E., & Parsons, C. A. (2011). The causal impact of media in financial markets. The Journal of Finance, 66(1), 67-97.
    Erdemlioglu, D., Gillet, R. L., & Renault, T. (2017). Market reaction to news and investor attention in real time.
    Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
    Fang, L., & Peress, J. (2009). Media coverage and the cross‐section of stock returns. The Journal of Finance, 64(5), 2023-2052.
    Fedyk, A. (2017). Front Page News: The Effect of News Positioning on Financial Markets. Retrieved from
    Feldman, R., Govindaraj, S., Livnat, J., & Segal, B. (2010). Management’s tone change, post earnings announcement drift and accruals. Review of Accounting Studies, 15(4), 915-953.
    French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics, 8(1), 55-69.
    Garcia, D. (2013). Sentiment during recessions. The Journal of Finance, 68(3), 1267-1300.
    Groß-Klußmann, A., & Hautsch, N. (2011). When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. Journal of Empirical Finance, 18(2), 321-340.
    Heston, S. L., & Sinha, N. R. (2015). News versus sentiment: Predicting stock returns from news stories.
    Kaniel, R., Liu, S., Saar, G., & Titman, S. (2012). Individual investor trading and return patterns around earnings announcements. The Journal of Finance, 67(2), 639-680.
    Kaniel, R., & Parham, R. (2017). WSJ Category Kings–The impact of media attention on consumer and mutual fund investment decisions. Journal of Financial Economics, 123(2), 337-356.
    Keim, D. B., & Stambaugh, R. F. (1984). A further investigation of the weekend effect in stock returns. The Journal of Finance, 39(3), 819-835.
    Kim, S.-H., & Kim, D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior & Organization, 107, 708-729.
    Leinweber, D., & Sisk, J. (2011). Event Driven Trading and the'New News'.
    Lerman, A., & Livnat, J. (2010). The new Form 8-K disclosures. Review of Accounting Studies, 15(4), 752-778.
    Li, Q., Wang, T., Li, P., Liu, L., Gong, Q., & Chen, Y. (2014). The effect of news and public mood on stock movements. Information Sciences, 278, 826-840.
    Liu, L., Wu, J., Li, P., & Li, Q. (2015). A social-media-based approach to predicting stock comovement. Expert Systems with Applications, 42(8), 3893-3901.
    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.
    Luo, X., & Zhang, J. (2013). How do consumer buzz and traffic in social media marketing predict the value of the firm? Journal of Management Information Systems, 30(2), 213-238.
    Manela, A., & Moreira, A. (2017). News implied volatility and disaster concerns. Journal of Financial Economics, 123(1), 137-162.
    Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The Journal of Finance, 42(3), 483-510.
    Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance, 25(7), 1339-1366.
    Peress, J. (2014). The media and the diffusion of information in financial markets: Evidence from newspaper strikes. The Journal of Finance, 69(5), 2007-2043.
    Price, S. M., Doran, J. S., Peterson, D. R., & Bliss, B. A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 36(4), 992-1011.
    Riordan, R., Storkenmaier, A., Wagener, M., & Zhang, S. S. (2013). Public information arrival: Price discovery and liquidity in electronic limit order markets. Journal of Banking & Finance, 37(4), 1148-1159.
    Rogers, J. L., Van Buskirk, A., & Zechman, S. L. (2011). Disclosure tone and shareholder litigation. The Accounting Review, 86(6), 2155-2183.
    Simon, D. P., & Campasano, J. (2014). The vix futures basis: Evidence and trading strategies. Journal of Derivatives, 21(3), 54.
    Sinha, N. R. (2016). Underreaction to news in the US stock market. Quarterly Journal of Finance, 6(02), 1650005.
    Smales, L. A. (2014a). News sentiment and the investor fear gauge. Finance Research Letters, 11(2), 122-130.
    Smales, L. A. (2014b). News sentiment in the gold futures market. Journal of Banking & Finance, 49, 275-286.
    Smales, L. A. (2014c). Non-scheduled news arrival and high-frequency stock market dynamics: Evidence from the Australian Securities Exchange. Research in International Business and Finance, 32, 122-138.
    Smales, L. A. (2015). Time-variation in the impact of news sentiment. International Review of Financial Analysis, 37, 40-50.
    Sul, H. K., Dennis, A. R., & Yuan, L. I. (2014). Trading on Twitter: The financial information content of emotion in social media. Paper presented at the System Sciences (HICSS), 2014 47th Hawaii International Conference on.
    Sun, L., Najand, M., & Shen, J. (2016). Stock return predictability and investor sentiment: A high-frequency perspective. Journal of Banking & Finance, 73, 147-164.
    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.
    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.
    Tumarkin, R., & Whitelaw, R. F. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41-51.
    Veronesi, P. (1999). Stock market overreactions to bad news in good times: a rational expectations equilibrium model. The Review of Financial Studies, 12(5), 975-1007.
    Wu, C.-H., & Lin, C.-J. (2017). The impact of media coverage on investor trading behavior and stock returns. Pacific-Basin Finance Journal, 43, 151-172.
    Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926.
  • 戴天時 - 召集委員
  • 吳庭斌 - 委員
  • 翁培師 - 委員
  • 蔡維哲 - 指導教授
  • 口試日期 2018-06-25 繳交日期 2018-08-26

    [回到前頁查詢結果 | 重新搜尋]