博碩士論文 104421037 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:14 、訪客IP:52.23.219.12
姓名 高延豪(Yan-hao Gao)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 調查情感指標與指數期貨價格之間關係
(Investigating the Relationship Between the Emotion of Blogs and the Price of Index Futures)
相關論文
★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究★ 太陽能光電產業經營績效評估-應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 利用資料探勘技術探討北台灣地區機動車輛稅費繳納模式★ 以資料挖礦方法發掘臍帶血品質診斷規則
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-6-20以後開放)
摘要(中) 在台灣金融活動眾多且交易方面日漸成熟,在衍生性金融商品更是發展迅速,尤其是期貨商品,原因在於期貨商品具有避險、套利與投機等功用且操作槓桿大,在市場上十分受到投資人的喜愛。另一方面,在網際網路快速發展下,人們習慣在網路上傳遞、 分享訊息,使得許多社群媒體因此蓬勃發展,而財務與經濟學中認為,投資者情緒與經濟市場會有相關性。因此越來越多的研究尋找金融市場波動與社群媒體情緒的關係。在過去研究顯示情緒確實能影響股市價格波動。情感相關研究則需要透過議題長時間觀察情感變化才能找出市場關係。
本研究將使用中文情感詞彙與萃取社群媒體上的情感,藉由每日討論文章進一步分析出每日投資者情感,確認情感是否能與市場價格有相關性。結果顯示,情感「懼」的 強度與市場跌幅有顯著相關性;主要情感「好」與「哀」時則情感強度與市場價格漲跌 幅度有顯著相關性。
摘要(英) As the financial derivatives tradable market developed quickly in Taiwan, the trading volumes in futures grow quickly in recent years. At the same time, many people post and shared opinion on social media. Many research in economics and behavioral finance have posited and confirmed that investors‟ mood is correlated with the performance of financial market. Several research have been devoted to study the relationship between the volatility of financial market and sentiments expressed in social media. On the other hand, even though emotion can describe the feeling of people more precisely than sentiment, to the best of our knowledge, only one research has tried to discover the relationship between futures performance and emotion fluctuation. The research tracked the evolvement of specific events and the changes of emotion as the time elapsed and observed that emotion changes are related to stock performance.
Instead of tracking long term emotional fluctuation, this study strived to predict price change of derivatives with emotion expressed in social media in previous day. The result confirmed emotions such as “Good”, “Fear” and “Disgust” are highly correlated with Taiwan index futures price change.
關鍵字(中) ★ 情感分析
★ 社群媒體
★ 台指期貨
關鍵字(英) ★ emotion analysis
★ social media
★ Taiwan index futures
論文目次
中文摘要 ...................................................................................v Abstract....................................................................................vi
目錄 .........................................................................................vii
表目錄 .....................................................................................viii
圖目錄 ......................................................................................ix
第一章 緒論...............................................................................1
1-1 研究動機.............................................................................1
1-2 研究目的.............................................................................2
1-3 研究架構.............................................................................3
第二章 文獻探討........................................................................5
2-1 有關情緒應用的相關文獻.....................................................5
2-2 情緒影響投資者行為相關文獻..............................................6
2-3 中文情緒字典相關文獻.........................................................8
第三章 研究流程與資料分析.......................................................9
3-1 社群選擇..............................................................................9
3-2 萃取文章與中文斷詞............................................................11
3-3 情感分數計算.......................................................................11
3-4 統計分析..............................................................................13
3-4-1 探討情感的強度與期指價格漲跌幅度的關聯 ......................13
3-4-2 單一情感強度與期指價格情況變化之關係 ..........................14
3-4-3 探究不同的情感出現對期指價格影響是否有差異 ................16
第四章 結論與未來研究建議.........................................................23
4-1 研究結論與實務意涵..............................................................23
4-2 研究限制及未來研究建議.......................................................24
參考文獻 .................................................................................... 25
附錄一:中文情感詞彙(部分示例) ...................................................28
參考文獻 1. Abbasi, A., Chen, H. (2008), ” Cyber Gate: a design framework and system for text analysis of computer-mediated communication.” MIS Q. 32, 811-837.
2. Baumeister, R. F., Vohs, K. D., DeWall, C. N., & Zhang, L. (2007),” How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation.” Personality and Social Psychology Review, 11(2), 167-203.
3. Bollen, J., Mao, H., Zeng, X. (2010),”Twitter mood predicts the stock market.” Journal of Computational Science 2(1),1-8.
4. Brown, Gregory W. and Michael T. Cliff. (2002), “Investor sentiment and near term stock market” Journal of Empirical Finance, 1-27.
5. Dolan, R.J. (2002), “Emotion, cognition, and behavior.” Science 298(5596), 1191–1194.
6. Ekman, P., & Friesen, W. V. (1976),” Measuring facial movement.” Environmental
Psychology and Nonverbal Behavior, 56-75.
7. F. Yang, Y. Liu, X. Yu and M. Yang. (2012), “Automatic detection of rumor on Sina
Weibo” ACM SIGKDD Workshop on Mining Data Semantics.
8. Fama, E. F. (1970), “Efficient Capital Markets: A review of theory and empirical work.”
Journal of Finance 25, 383-417.
9. Gilbert, E., Karahalio, E. (2010),”Widespread worry and the stock market.” International
AAAI Conference on Weblogs and Social Media.
10. Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins A. (2005),” The predictive power of
online chatter.” Proceedings of the Eleventh ACM SIGKDD International Conference on
Knowledge Discovery in Data Mining, 78-87.
11. Kahneman, D., and Tversky, A. (1979), “Prospect theory: An analysis of decision under
risk” Econometrica 47, 263-291.
12. Ku, L.-W., & Chen, H.-H. (2007),”Mining opinions from the Web: Beyond relevance
retrieval.” Journal of the American Society for Information Science and Technology,
58(12), 1838-1850.
13. L. H. Xu, H.F. Lin, Y. Pan. (2008), ”Constructing the Affective Lexicon Ontology.”
China Society for Scientific and Technical Information, 180-185.
14. Liang,H.,Tsai,F.S.,Kwee, A.T. (2009),”Detecting novel business blogs.” Proceedings of
the 7th International Conference on Information, Communications and Signal
Processing.
15. Liu,A.Y.,Gu,B.,Konana,P.,Ghosh,J. (2002), ”Predicting stock price from financial
message boards with a mixture of experts framework.” Intelligent Data Exploration &
Analysis Laboratory, 1-14.
16. Mishne, G., Glance N. (2006),” Predicting movie sales from blogger sentiment.”
Proceedings of AAAI-CAAW , The Spring Symposia on Computational Approaches to
25
Analyzing Weblogs.
17. Nofsinger,J.R. (2005),” Social mood and financial economics. ”Journal of Behavioral
Finance.6(3),144-160.
18. O‟Leary, D.E. (2011),” blog mining-review and extensions: „from each according to his
opinion‟.” Support Syst. 54, 821-830.
19. O′Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010), “From
Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series.” Proceedings of
the Fourth International AAAI Conference on Weblogs and Social Media,122-129.
20. Q. Gao, F. Abel, G.J. Houben and Y. Yu. (2012), ”A comparative study of users‟ microblogging behavior on Sina Weibo and Twitter, in: User Modeling” Adaptation, and Personalization, 88-101.
21. Rystrom, D. S., & Benson, E. D. (1989),” Investor psychology and the day-of-the-week effect.” Financial Analysts Journal, 45(5), 75-78.
22. Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012),” Evaluating sentiment in financial news articles.” Decision Support Systems, 53(3), 458-464.
23. Schumaker, R.P., Chen, H. (2009),” Textual analysis of stock market prediction using breaking financial news: the AZF in text system”. Journal ACM Transactions on Information Systems, 1-19.
24. Shiller, Robert J., Fumiko, Kon-Ya and Yoshiro Tsutsui. (1996),”Why did the Nikkei Crash? Expanding the scope of expectations data collection.”, Economics and Statistics 78 (1), 156-64.
25. Shleifer,A. (2000), “The inefficient markets : an introduction to behavioral finance. ”.
26. Thomas R. Gruber.(1993),A Translation Approach to Portable Ontology Specifications,
Knowledge Acquisition, 199-220.
27. Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010),” Predicting
elections with twitter: What 140 characters reveal about political sentiment.” Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 178-185.
28. Wen Hao Chen, Yi Cai, and Kin Keung Lai. (2016),” Weibo Mood Towards Stock Market.” DASFAA 2016 Workshops, 3-14.
29. Zhang,X.,Fuehres,H.,Gloor,P.A.(2011), ”Predicting stock market indicator through twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences,55-62.
30. 中研院知網 (2003), http://ckip.iis.sinica.edu.tw/CKIP/conceptnet.htm
31. Ptt 網站介紹 (2017), https://zh.wikipedia.org/wiki/批踢踢
32. 社群媒體使用者統計(2016), http://expandedramblings.com/index.php/resource-how-many-people-use-the-top-social-media
33. 聯合新聞網(2016), https://udn.com/news/story/7239/1764949
指導教授 許秉瑜(Ping-Yu Hsu) 審核日期 2017-6-21
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明