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Analysing Effect of Financial News on Bitcoin Price with Artificial Neural Networks

Yıl 2021, Cilt: 14 Sayı: 1, 65 - 78, 30.01.2021
https://doi.org/10.17671/gazibtd.703688

Öz

Developments on computer and technology cannot be denied in economical dimension as well, as it takes effects on every field of our lives. These innovations which is accepted as economical reform, is a part of FinTech (Financial Technology). Bitcoin which has started a decentralised movement with the Fintech innovation is now has an important position in the world economy. As for Blockchain technology that has been stepped in our lives with Bitcoin, obviously will be an essential part of our lives in the near future.
In considerations of these developments, the impacts of the news, which is covered in newspapers that is described as mass media, concerned by people and the society, and one of the most important necessities of the society, on Bitcoin that is seen as the currency of the future, have been wanted to be presented. In this sense, 5 national finance newspapers was designated by digitising according to both positive and negative comment contents as from the first year of public offering of Bitcoin, taking the provision of USA dollars with the blockchain data which is the basis technology of Bitcoin and the Ethereum which is the second most popular crypto, were associated in the network that was created with the artificial neural nets. After the results of study, it was deduced that Bitcoin thematic news was not a strong impact on the Bitcoin price forecasting in the artificial neural nets that included 99% percent success. In addition to this, it has been detected that The Wall Street newspaper has a relatively impact on Bitcoin price forecasting, compared to other financial newspapers.

Kaynakça

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  • A. Rubini, Fintech in a Flash Financial Technology Made Easy, Simtac, Londra, 2017.
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Finansal Haberlerin Bitcoin Fiyatlarına Etkisinin Yapay Sinir Ağları İle Analizi

Yıl 2021, Cilt: 14 Sayı: 1, 65 - 78, 30.01.2021
https://doi.org/10.17671/gazibtd.703688

Öz

Bilgisayar ve internet teknolojilerindeki gelişmeler, hayatımızın her alanında etkisini gösterdiği gibi ekonomi boyutunda da etkileri yadsınamaz. Ekonomi alanındaki yeni ekonomik reform mahiyetinde kabul gören bu yenilikler FinTek (Finans ve Teknoloji) kapsamı içerisinde yer almaktadır. FinTech inovasyonu ile ekonomide ademi merkeziyetçi bir akım başlatan Bitcoin, şu an dünya ekonomisinde önemli bir yere sahiptir. Bitcoin ile hayatımıza giren Blockchain (Blokzincir) teknolojisinin ise yakın gelecekte hayatımızın vazgeçilmez bir parçası haline geleceği öngörülmektedir.
Tüm bu gelişmeler ışığında günümüzün popüler teknolojilerinden olan yapay zeka yöntemlerinden yararlanılarak, kitle haberleşme aracı olarak tanımlanan gazetelerde yer alan, insan ve toplumu ilgilendiren ve toplumun en önemli ihtiyaçlarından olan haberlerin, geleceğin para birimi olarak görülen Bitcoin üzerindeki etkileri ortaya konmak istenmiştir. Bu bağlamda 5 ulusal finans gazetesi belirlenip, Bitcoin’in ilk halka arz yılından itibaren yayınlanan haberleri olumlu ve olumsuz yorum içeriklerine göre sayısallaştırarak, Bitcoin altyapı teknolojisi olan blokzinciri verileri ile ikinci en popüler kripto para olan Ethereum’un ABD dolar karşılığı alınarak, yapay sinir ağları teknikleri ile oluşturulan ağ içerisinde ilişkilendirilmiştir. Çalışma sonucunda %99’luk tahminsel başarı içeren yapay sinir ağında, finansal gazetelerin yayınlamış olduğu Bitcoin içerikli haberlerin, Bitcoin fiyat tahminine güçlü bir etkisinin olmadığı sonucuna varılmıştır. Bunulan birlikte seçilen finansal gazetelerden The Wall Street gazetesinin diğer finansal gazetelere oranla Bitcoin fiyat tahminde nispeten etkisinin olduğu saptanmıştır. 

Kaynakça

  • P. Donaldson, “The Augmented Investment Management Industry”, The WealthTech Book The FinTech Handbook for Investors, Entrepreneurs and Finance Visionaire, Editor: Chishti S. & Puschmann T., WILEY, İngiltere, 7-10, 2018.
  • M. Belli, Bankıng and Fıntech Developıng a Fıntech Ecosystem in Istanbul, BKM, İstanbul, 2018.
  • A. Rubini, Fintech in a Flash Financial Technology Made Easy, Simtac, Londra, 2017.
  • Bankalararası Kart Merkezi, Türkiye FinTech Ekosisteminin Sürdürülebilir Gelişimi için 23 Öneri, Türkiye, 2018.
  • B. Nicolletti, The Future of FinTech Integrating Finance and Technology, Palgrave Macmillan, İsveç, 2018.
  • G. Dorfleitner, L. Hornuf, M. Schmitt, M. Weber, FinTech in Germany, Springer, Almanya, 2017.
  • D. L. Kuo Chuen, L. Low, Inclusive Fintech Blockchain, Cryptocurrency and ICO, World Scientific, USA, 2018.
  • V. Tiberius, C. Rasche, FinTechs Disruptive Geschäftsmodelle im Finanzsektor, Editör: Bankmagazin, Springer Gabler, Almanya, 2017.
  • İnternet: P. Schueffel, What is Fintech? [Attempting] A Definition, https://www.linkedin.com/pulse/what-fintech-attempting-definitionpatrick-schueffel/, 16.01.2019.
  • Y. Kitao, Learning Practical FinTech from, Nikkei Publishing, Japonya, 2018.
  • B. E. Juengerkes, “FinTechs and Banks – Collaboration is Key”, The FinTech Book The Financial Technology Handbook for Investors, Entrepreneurs and Visionaries, Editör: Chishti S. & Barberis J., Wiley, İngiltere, 179 – 182, 2016.
  • P. Vigna, M. J. Casey, Kriptopara Çağı, Ankara: Buzdağı, 2015.
  • B. Singhal, G. Dhameja, P. S. Panda, Beginning Blockchain a Beginner’s Guide to Building Blockchain Solutions, Apress, 2018.
  • T. Laurence, Blockchain For Dummies (2.b.), John Wiley & Sons, Canada: (2019).
  • Z. Zhang, L. Zhao, “Blockchain – ICBC 2018”, Blockchain – ICBC 2018, Editör: Chen S., Wang H. & Zhang L.J., Springer, Seattle, 32 – 47, 2018.
  • M. Di Pierro, “What Is the Blockchain”, Computing in Science & Engineering, 92 – 95, 2017.
  • İnternet: V. Altıntaş, Veri Tabanı Yönetim Sistemleri, http://volkanaltintas.com/wp-content/uploads/2016/09/1_veri_tabani_giris.pdf, 12.05.2019.
  • J. L. Massey, “An Introduction to Contemporary Cryptology”, IEEE, 533 – 549, 1988.
  • R. Klima, N. Sigmon, Cryptology Classical and Modern, CRC Press, London, 2019.
  • B. Láng, Real Life Cryptology Ciphers and Secrets in Early Modern Hungar, Atlantis Press, Amsterdam, 2018.
  • G. Brassard, Lecture Notes in Computer Science, Editör: Goos G., Hartmanis J., Springer-Verlag, New York, 1988.
  • S. Braman, “The Meta-Technologies of İnformation”, Biotechnology and Communication: The Meta-Technologies of Information Editör: Braman S., 3 – 36, 2004.
  • S. Kamble, A. Gunasekaran, H. Arha, “Understanding The Blockchain Technology Adoption in Supply Chains-Indian Context”, International Journal of Production Research, 2009 – 2033, 2019.
  • V. Rakovic, J. Karamachoski, V. Atanasovski, L. Gavrilovska, “Blockchain Paradigm and Internet of Things”, Wireless Personal Communications, 219 – 235, 2019.
  • K. R. Ozyılmaz, A. Yurdakul, “Designing a Blockchain-Based IoT With Ethereum, Swarm, and LoRa The Software Solution to Create High Availability With Minimal Security Risks”, IEEE Consumer Electronics Magazine, 28 – 34, 2019.
  • Y. Yu, Y. N. Li, J. F. Tian, J. W. Liu, “Blockchain-Based Solutions to Security and Privacy Issues in the Internet of Things”, IEEE Wireless Communications, 12 – 18, 2018.
  • D. Ayberkin, M. Beştaş, Ü. Özen, “Blok Zinciri ile Gerçek Zamanlı Doğrulanabilir Eğitim Belgeleri”, Uluslararası Uygulamalı İşletme Yönetim ve Ekonomi Araştırmaları İktisadi Yenilik Dergisi, 75 – 82, 2018.
  • A. Srivastava, P. Bhattacharya, A. Singh, A. Mathur, “A Systematic Review on Evolution of Blockchain Generations”, ITEE Journal, 1 – 8, 2018.
  • M. B. Hoy, “An Introduction to the Blockchain and Its Implications for Libraries and Medicine”, Medıcal Reference Servıces Quarterly, 273 – 279, 2017.
  • M. Swan, Blockchain Blueprint for a New Economy, O'reilly, America, 2015.
  • İnternet: LDR A FAIRFAX Company, Blockchain Education, https://ldrinvest.com/wp-content/uploads/2018/02/LDR-Blockchain-1.0- Final.pdf, 29.05.2019.
  • G. Chen, B. Xu, M. Lu, N. S. Chen, “Exploring Blockchain Technology and Its Potential Applications for Education”, Smart Learning Environments, 2 – 10, 2018.
  • J. Ackermann, M. Meier, “Blockchain 3.0 - The Next Generation of Blockchain Systems”, Advanced Seminar Blockchain Technologies, 2018.
  • İnternet: Unibright.io, Blockchain evolution: from 1.0 to 4.0, https://medium.com/@UnibrightIO/blockchain-evolution-from-1-0-to-4- 0-3fbdbccfc666, 1.06.2019.
  • E. Karaarslan, M. F. Akbaş, “Blok Zinciri Tabanlı Siber Güvenlik Sistemleri”, Uluslararası Bilgi Güvenliği ve Kriptoloji Konferansı, Ankara, 2017.
  • A. Usta, S. Doğantekin, Blockchain 101, BKM.
  • M. Dodgson, D. M. Gann, “Managing Digital Money”, The Academy of Managemen Journal, 325 – 333, 2015.
  • E. Dumlu, Kripto Para Birimi Olarak Bitcoin ve Ceza Hukuku, Yüksek Lisans Tezi, Galatasaray Üniversitesi, Sosyal Bilimler Enstitüsü, 2018.
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  • A. M. Antonopoulos, Mastering Bitcoin Programming the Open Blockchain, O'REILLY, America, 2017.
  • P. Champegne, The Book of Satoshi The Collected Writings of Bitcoin Creator Satoshi Nakamoto, LLC, A.B.D, 2014.
  • H. Halaburda, M. Sarvary, Beyond Bıtcoın the Economıcs of Dıgıtal Currencıes, Palgrave Macmillan Londra, 2016.
  • D. Birch, Kimlik: Yeni Para, Çeviri: Usta A., Digitalage, İstanbul, 2016.
  • S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, 2008.
  • D. Golumbia, The Politics of Bitcoin, University of Minnesota Press, Minneapolis.
  • D. Frisby, Bitcoin: The Future of Money?, Unbound, 2013.
  • A. Çarkacıoğlu, Kripto-Para Bitcoin, Sermaye Piyasası Kurulu Araştırma Dairesi, Ankara, 2016.
  • R. Caetano, Learning Bitcoin, PACKT, İngiltere, 2015.
  • F. Azman, “Kripto Para”, Kripto Para Ekonomisi, Editör: Alptekin V., Metin İ., Akcan A. T., Eğitim Yayınevi, Konya, 59 – 74, 2018.
  • S. Ammous, Bitcoin Standardı. Çeviri: Serbest E., Liber Plus Yayınları, İstanbul, 2018.
  • İnternet: CoinmarketCap, Tüm Coinler, https://coinmarketcap.com/all/views/all/, 5.06.2019.
  • İnternet: E. Dönmezgel, Bitcoin Tarihi, https://bitcoinlerim.com/bitcoin-tarihi/, 01.06.2019.
  • V. V. Nabiyev, Yapay Zeka, Seçkin Yayıncılık, Ankara, 2016.
  • A. Yılmaz, Yapay Zeka, Kodlab, İstanbul, 2018.
  • Ç. Çağlar, “Ölçeklenebilir ve Paylaşılabilir Yapay Zeka Ortamları”, HPE Üniversiteler Teknolojik Zirvesi, Kıbrıs, 2019.
  • R. Rojas, Theorie der Neuronalen Netze, Springer, Berlin, 1993.
  • S. Haykin, Neural Networks and Learning Machines, Pearson Education, A.B.D, 2009.
  • J. Yang, J. Ma, S. K. Howard, “A Strucure Optimization Algorithm of Neural Networks for Pattern Learning from Educational Data”, Artificial Neural Network Modelling, Editör: Shanmuganathan S., Samarasinge S., Springer, İsviçre, 67 – 82, 2016.
  • B. Bhosale, “Curvelet Interaction with Artificial Neural Networks”, Editör: Shanmuganathan S., Samarasinghe S., Artificial Neural Network Modelling, Springer, İsviçre, 109 – 125, 2016.
  • K. S. Kasiviswanathan, K. P. Sudheer, J. He, “Quantification of Prediction Uncertainty in Artificial Neural Network Models”, Editör: Shanmuganathan S., Samarasinghe S., Artifical Neural Network Modelling, Springer, İsviçre, 145 – 159, 2016.
  • M. Karahan, İstatistiksel Tahmin Yöntemleri: Yapay Sinir Ağları Metodu ile Ürün Talep Tahmini Uygulaması, Doktora Tezi, Selçuk Üniversitesi, Sosyal Bilimler Enstitüsü, 2011.
  • P. D. McNelis, Neural Networks in Finance: Gaining Predictive Edge in the Market, Elsevier, Amsterdam, 2005.
  • İnternet: A. Öztürk Birge, Sinir Sistemi Anatomisi, https://acikders.ankara.edu.tr/pluginfile.php/12069/mod_resource/content/0/S% C4%B0N%C4%B0R%20S%C4%B0STEM%C4%B0%20ANATOM%C4%B0S %C4%B0.pdf, 8.06.2019.
  • V. S. Arıkan Kargı, Yapay Sinir Ağ Modelleri ve Bir Tekstil Firmasında Uygulama, Ekin, Bursa, 2015.
  • V. Sharma, S. Rai, A. Dev, “A Comprehensive Study of Artificial Neural Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, 278 – 284, 2012.
  • E. Ersoy, Ö. Karal, “Yapay Sinir Ağları ve İnsan Beyni”, İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 188 – 205, 2012.
  • S. Shanmuganathan, “Artificial Neural Network Modelling: An Introduction”, Artificial Neural Network Modelling, Editör: Shamuganathan S., Samarasinghe S., Springer, İsviçre, 1 – 14, 2016.
  • E. Öztemel, Yapay Sinir Ağları, Papatya Bilim Üniversite Yayıncılığı, İstanbul, 2016.
  • E. Çetin, Yapay Zeka Uygulamaları, Seçkin, Ankara, 2016.
  • İnternet: RobMcZag, Role of Bias in Neural Networks, https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks, 03.06.2019.
  • İnternet: F. Grün, What Is the Transfer Function in Artificial Neural Networks?, https://www.quora.com/What-is-the-transfer-function-inArtificial-Neural-Networks, 02.06.2019.
  • K. L. Priddy, P. E. Keller, Artificial neural networks:an introduction, Spie Press, Bellingham, 2005.
  • C. Nwankpa, W. Ijomah, A. Gachagan, S. Marshall, “Activation Functions: Comparison of Trends in Practice and Research for Deep Learning”, arXiv preprint arXiv:1811.03378, 2018.
  • B. Ataseven, “Yapay Sinir Ağları ile Öngürü Modellemesi”, Öneri, 101 – 115, 2013.
  • World Association of Newspapers and News Publishers., WORLD PRESS TRENDS 2017. World Association of Newspapers and News Publishers, 2017.
  • Development Indicators database, Gross Domestic Product 2017. World Bank World, 2019.
  • İnternet: Quandl, Blockchain, https://www.quandl.com/data/BCHAIN-Blockchain, 27.12.2018.
  • E. N. Güven, H. Onur, Ş. Sağıroğlu, “Yapay Sinir Ağları ile Web İçeriklerinin Sınıflandırma”, Bilgi Dünyası, 158 – 178, 2008.
  • B. G. Kermani, S. S. Schiffman, T. H. Nagle, “Performance of the LevenbergMarquardt neural network training method in electronic nose applications”, Elsevier, 13 – 22, 2005.
  • H. Yu, B. M. Wilamowski, “Levenberg-Marquardt Training” Intelligent Systems, 2011.
  • S. Asadi, E. Hadavandi, F. Mehmanpazir, M. M. Nakhostin, “Hybridization of Evolutionary Levenber-Marquardt Neural Networks and Data Pre-Processing for Stock Market Prediction”, Knowledge-Based Systems, 245 – 258, 2012.
  • S. H. Ngia, J. Sjöberg, “Efficient Training of Neural Nets for Nonlinear Adaptive Filtering Using a Recursive Levenberg-Marquardt Algorithm”, IEEE, 1915 – 1927, 2000.
  • D. Aşkın, İ. İskender, A. Mamızadeh, “Farklı Yapay Sinir Ağları Yöntemlerini Kullanarak Kuru Tipi Transformatör Sargısının Termal Analizi”. Gazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 905 – 913, 2011.
  • C. M. Çolak, C. Çolak, H. Kocatürk, Ş. Sağıroğlu, İ. Barutçu, “Predicting Coronary Artery Disease Using Different Artificial Neural Network Models”, Anadolu Karyol Dergisi, 249 – 254, 2008.
  • U. Okkan, “Applivation of Levenberg-Marquardt Optimization Algorithm Based Multilayer Neural Netwoks for Hydrological Time Series Modelling”, An İnternational Journal of Optimization and Control, 53 – 63, 2011.
  • O. Arkoç, T. Ç. Akıncı, S. H. Nogay, “Yapay Sinir Ağları Yardımıy ile Yeraltı Suyunda Sodyum Absorbsiyon Oranı (SAR) Tahmini: Ergene Havzası Doğu Akiferi Örneği”, Jeoloji Mühendisliği Dergisi, 177 – 188, 2016.
  • İnternet: H. Çizmeci, Ü. Atilla, İ.R. Karaş, Yapay Sinir Ağları Kullanılarak Yükseköğretimde Öğrenci Adaylarının Başarı Durumlarının Tahmin Edilmesi, web.karabuk.edu.tr/ismail.karas/files/YSA_Ogrenci_Cizmeci_Atilla_Karas.pdf, 24.04.2019.
  • C. Lv, Y. Xing, J. Zhang, X. Na, Y. Li, T. Liu, E. Y. Wang, “LevenbergMarquardt Backpropagation Training of Multilayer Neural Networks for Satate Estimation of a Sfetty-Critical Cyber-Physical System”. IEEE Transcastıons on Industrial Informatics, 3496 – 3446, 2018.
  • S. Sapna, A. Tamilarasi, & M. P. Kumar, “Backpropagatıon Learnıng Algorıthm Based on Levenberg Marquardt Algorıthm”, Computer Science & Information Technology, 393 – 398, 2012.
  • D. J. Hopkins, E. Kim, S. Kim, “Does Newspaper Coverage Influence or Reflect Public Perceptions of the Economy?”, Research and Politics, 1 – 7, 2017.
  • M. Matta, I. Lunesu, M. Marchesi, Bitcoin Spread Prediction Using Social and Web Search Media, UMAP Workshops, 2015.
  • E. E. Şahin, “Kripto Para Bitcoin: ARIMA ve Yapay Sinir Ağları ile Fiyat Tahmini”, Fiscaoeconomia 2(2), 74 – 92, 2018.
  • K. Ceyhan, E. Kurtulmaz, O.C. Sert, T. Özyer, “Bitcoin Movement Prediction with Text Mining”, 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir Katip Çelebi University, İzmir, 2018.
  • Y. B. Kim, J. Lee, N. Park, J. Choo, J. H. Kim, Chang, “When Bitcoin Encounters İnformation in an Online Forum: Using Text Mining to Analyse User Opinions and Predict Value Fluctuation”, PloS one, 12(5), 2017.
  • N. I. Indera, I. M. Yassin, A. Zabidi, Z. I. Rizman, “Non-Linear Autoregressive With Exogenous Input (Narx) Bitcoin Price Prediction Model Using Pso-Optımızed Parameters and Movıng Average Technical Indicators”, Journal of Fundamental and Applied Sciences, 791 – 808, 2017.
  • B. Sakız, E. Kutlugün, “Bitcoin Price Forecast Via Blockchain Technology and Artificial İntelligence Algorithms”, 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir Katip Çelebi University, İzmir, 2018.
Toplam 103 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Berat Çağlar 0000-0003-2945-5677

Uğur Yavuz

Yayımlanma Tarihi 30 Ocak 2021
Gönderilme Tarihi 14 Mart 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 14 Sayı: 1

Kaynak Göster

APA Çağlar, B., & Yavuz, U. (2021). Finansal Haberlerin Bitcoin Fiyatlarına Etkisinin Yapay Sinir Ağları İle Analizi. Bilişim Teknolojileri Dergisi, 14(1), 65-78. https://doi.org/10.17671/gazibtd.703688