BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ

Yıl: 2019 Cilt: 74 Sayı: 1 Sayfa Aralığı: 1 - 34 Metin Dili: Türkçe İndeks Tarihi: 04-12-2019

BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ

Öz:
Şirketlere ilişkin haber metinleri ile finansal değerler arasındaki ilişkilerin nesnel bir temelde analizedilebilmesini sağlamak için, öncelikle ilişkili haberlerin ve bu haberlerde yer alan olumlu ya da olumsuzifadelerin sayısal değerlere dönüştürülmesi gerekmektedir. Bu amaçla literatürde "metin madenciliği" ve“Duygu (Sentiment) Analizi” yaklaşım ve yöntemleri kullanılmaktadır. Bu çalışmada da, Borsa İstanbul’daişlem gören BIST30 şirketlerine ilişkin 2014 yılında farklı kaynaklarda yayınlanmış 14.108 haber metinmadenciliği teknikleri ile alınarak, yıllık ve çeyreklik bazda haber sayıları tespit edilmiştir. Haber içeriklerindeyer alan ifadeler de, Türkçe diline çevrilerek oluşturulmuş bir “Duygu Sözlüğü” yardımıyla, sayısal değerleredönüştürülmüştür. Daha sonra, bu sayısal skorlar ile aynı dönemde piyasada oluşan şirket değerleri arasındakiilişkiler analiz edilmiştir. Ortaya çıkan temel sonuç, finansal piyasalarla yayınlanan haberler ve bunların duygutonları ile finansal değerler arasında anlamlı ilişkilerin var olduğudur. Bu sonuç Türk finansal piyasalarınındeğerlendirilmesinde önemli bir araç olarak Türkçe haber kaynaklarının da kullanılabileceğini ortayakoymaktadır.
Anahtar Kelime:

Konular: İletişim

Relations Between Financial News and Market Capitalizations of Companies in BIST30 Index: Text Mining and Sentiment Analysis Methods

Öz:
In order to analyze the relations between financial values and the textual news related with the companies, there is a need for a conversion of textual content to quantitative values. For this aim, “text mining” approaches and “sentiment analysis” tools are used. In this framework, this study evaluates the relations between market capitalization of companies in Borsa Istanbul (BIST30) and published financial news about them. For this aim, published 14.108 news in the year 2014, have been extracted from 313 different news content providers and have been quantified by using text mining approaches. Then, for the statements in the news, a sentiment analysis has been performed by using a sentiment dictionary which is translated into Turkish in this study. Finally, the relations between these quantities and market capitalization values of the companies have been examined. The result of the analyses is the existence of relations between financial values of and published financial news about the companies. This study, via providing a methodological framework may help and show that the sources of financial news in Turkish also can be used as a novel tool for the analysis of Turkish financial markets.
Anahtar Kelime:

Konular: İletişim
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Agarwal, Basant, Namita Mittal (2015), Prominent Feature Extraction for Sentiment Analysis. (Springer).
  • Bajo, Emanuele ve Carlo Raimondo (2017) "Media sentiment and IPO underpricing." Journal of Corporate Finance.
  • Barber, Brad M. ve Terrance Odean (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.
  • Brun, Wibecke ve Karl Halvor Teigen (1988), "Verbal probabilities: ambiguous, context-dependent, or both?" Organizational Behavior and Human Decision Processes, 41(3), 390-404.
  • Colm Kearney ve Sha Liu. (2013), "Textual Sentiment in Finance: A Survey of Methods and Models". International Review of Financial Analysis, 33, 171-185.
  • Da, Zhi, Joseph Engelberg ve Pengjie Gao (2011), In search of attention. The Journal of Finance, 66(5), 1461-1499.
  • Edwards, Jeny Kevin McCurley ve John Tomlin. (2001). "An Adaptive Model for Optimizing Performance of an Incremental Web Crawler", Proceedings of the 10th International Conference on World Wide Web (New York, United States of America, ACM)
  • Engelberg, Joseph (2008), Costly Information Processing: Evidence from Earnings Announcements (SSRN Scholarly Paper No: ID 1107998). Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=1107998. (03.03.2016)
  • Feldman, Ronen (2013). "Techniques and applications for sentiment analysis". Communications of the ACM, 56(4), 82–89.
  • Ferguson, Nicky J, Dennis Philip, Herbert Y. T. Lam, Jie Guo (2014), “Media Content and Stock Returns: The Predictive Power of Press” (SSRN Scholarly Paper No: ID 2111352).
  • Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract= 2111352 (03.03.2016)
  • García, Diego (2013), ""Sentiment during Recession"s. The Journal of Finance, 68(3), 1267–1300.
  • Gidofalvi, Gyozo ve Charles Elkan (2001), "Using news articles to predict stock price movement"s. Department of Computer Science and Engineering.
  • Gigerenzer, Gerd, Peter M. Todd (1999), Fast and frugal heuristics: The adaptive toolbox. Simple heuristics that make us smart içinde, Evolution and cognition. (ss. 3–34), (Oxford University Press).
  • Hu, Minqing, Bing Liu. (2004), Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining içinde (ss. 168–177).
  • Li, Xiaodong, Haoran Xie, Li Chen, Jianping Wang, Xiaotie Deng (2014), "News impact on stock price return via sentiment analysis". Knowledge-Based Systems, 69, 14–23.
  • Liu, Laura Xiaolei, Ann E. Sherman, and Yong Zhang (2014), "The long-run role of the media: Evidence from initial public offerings" Management Science, 60(8), 1945-1964.
  • Lo, Andrew W. (2005). Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis (SSRN Scholarly Paper No: ID 1702447). Rochester, NY: Social Science Research Network.
  • Loughran, Tim. ve Bill McDonald, (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65.
  • Miner, Gary, John Elder IV, Thomas Hill (2012), Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. (Academic Press)
  • Mitra, Gautam, Leela Mitra (Ed.) (2011), The handbook of news analytics in finance (Wiley).
  • Nassirtoussi, Arman Khadjeh, Saeed Aghabozorgi, Teh Ying Wah ve David Chek Ling Ngo. (2014), "Text mining for market prediction: A systematic review". Expert Systems with Applications, 41(16)
  • Ryan, Paul ve Richard Taffler (2002), "What Firm-Specific News Releases Drive Economically Significant Stock Returns and Trading Volumes" (SSRN Scholarly Paper No: ID 314880). Rochester, NY
  • Schumaker, Robert P., Yulei Zhang, Chun-Neng Huang, Hsinchun Chen (2012), Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458–464.
  • Shang, Zilu, Chris Brooks, and Rachel McCloy (2014), "Are investors guided by the news disclosed by companies or by journalists?" Journal of behavioral and experimental finance, 1, 45-60.
  • Sinha, Nitish Ranjan (2015), Underreaction to News in the US Stock Market. Quarterly Journal of Finance, 6.02 (2016): 1650005.
  • Steve Skovran. (2001). System and method for influencing a position on a search result list generated by a computer network search engine. United States of America Patent No:6, 269, 361,
  • http://www.google.com/patents/US6269361 adresinden erişildi. (03.03.2016)
  • Taşcı, Şerafettin, Tunga Güngör. (2013). Comparison of text feature selection policies and using an adaptive framework. Expert Systems with Applications, 40(12), 4871–4886.
  • Tetlock, Paul C. (2007), Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139–1168.
  • Tutar, Kadir, Murat Osman Ünalır, Levent Toker (2015), Sosyal Ağlar Üzerinde Ontoloji Tabanlı Sezgi Analizi için bir Uygulama Çerçevesinin Geliştirilmesi. Pamukkale University Journal of Engineering Sciences, 21(5).
  • Tversky, Amos. ve Daniel Kahneman, (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131.
  • Uçan, Alaettin (2014), Otomatik Duygu Sözlüğü Çevrimi ve Duygu Analizinde Kullanımı. (Yayımlanmamış yüksek lisans tezi). (Hacettepe Üniversitesi)
  • Urquhart, Andrew Robert Hudson (2013), Efficient or adaptive markets? Evidence from major stock markets using very long run historic data. International Review of Financial Analysis, 28, 130–142.
  • Van Nieuwerburgh, Stijn, and Laura Veldkamp (2009), "Information immobility and the home bias puzzle." The Journal of Finance, 64(3), 1187-1215
  • Wisniewski, Tomasz Piotr ve Brendan Lambe. (2013), "The role of media in the credit crunch: The case of the banking sector" Journal of Economic Behavior & Organization, Financial Sector Performance and Risk, 85, 163–175.
APA ATAN S, ÇINAR Y (2019). BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. , 1 - 34.
Chicago ATAN Suat,ÇINAR YETKİN BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. (2019): 1 - 34.
MLA ATAN Suat,ÇINAR YETKİN BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. , 2019, ss.1 - 34.
AMA ATAN S,ÇINAR Y BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. . 2019; 1 - 34.
Vancouver ATAN S,ÇINAR Y BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. . 2019; 1 - 34.
IEEE ATAN S,ÇINAR Y "BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ." , ss.1 - 34, 2019.
ISNAD ATAN, Suat - ÇINAR, YETKİN. "BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ". (2019), 1-34.
APA ATAN S, ÇINAR Y (2019). BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. Ankara Üniversitesi SBF Dergisi, 74(1), 1 - 34.
Chicago ATAN Suat,ÇINAR YETKİN BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. Ankara Üniversitesi SBF Dergisi 74, no.1 (2019): 1 - 34.
MLA ATAN Suat,ÇINAR YETKİN BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. Ankara Üniversitesi SBF Dergisi, vol.74, no.1, 2019, ss.1 - 34.
AMA ATAN S,ÇINAR Y BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. Ankara Üniversitesi SBF Dergisi. 2019; 74(1): 1 - 34.
Vancouver ATAN S,ÇINAR Y BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ. Ankara Üniversitesi SBF Dergisi. 2019; 74(1): 1 - 34.
IEEE ATAN S,ÇINAR Y "BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ." Ankara Üniversitesi SBF Dergisi, 74, ss.1 - 34, 2019.
ISNAD ATAN, Suat - ÇINAR, YETKİN. "BORSA İSTANBUL’DA FİNANSAL HABERLER İLE PİYASA DEĞERİ İLİŞKİSİNİN METİN MADENCİLİĞİ VE DUYGU (SENTİMENT) ANALİZİ İLE İNCELENMESİ". Ankara Üniversitesi SBF Dergisi 74/1 (2019), 1-34.