Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 20 Sayı: 1, 219 - 232, 07.03.2020
https://doi.org/10.18037/ausbd.700349

Öz

Kaynakça

  • Akay, D., ve Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
  • Akcora, C.G., Dey, A.K., Gel, Y.R. & Kantarcioglu, M. (2018, June). Forecasting Bitcoin price with graph chainlets. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, 765-776.
  • Almeida, J., Tata, S., Moser, A. & Smit, V. (2015). Bitcoin prediciton using ANN. Neural Networks, 1-12.
  • Altıner, M. (2017). Kripto para: Bitcoin ve uluslararası ilişkiler. Cyberpolitik Journal, 2(4), 130-147.
  • Amjad, M. & Shah, D. (2017, February). Trading Bitcoin and online time series prediction. Proceedings of the NIPS 2016 Time Series Workshop, 1-15.
  • Bakar, N.A. & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of Bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130-137.
  • Baur, D., Lucey, B. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Finance Reviews, 45(2), 217–229.
  • Brière, M., Oosterlinck, K. & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with Bitcoin. Journal of Asset Management, 16(6), 365-373.
  • Cabanilla, K.I.M. (2016). The future of cryptocurrency: Forecasting the Bitcoin-philippine peso exchange rate using SARIMA through TRAMO-SEATS.
  • Catania, L., Grassi, S. & Ravazzolo, F. (2018). Forecasting cryptocurrencies financial time series.
  • Deng, J. L. (1982). Control problems of grey systems. Systems & Control. Letters., 1(5), 288-294.
  • Detzel, A., Liu, H., Strauss, J., Zhou, G. & Zhu, Y. (2018). Bitcoin: Predictability and profitability via technical analysis. SSRN Electronic Journal.
  • Dinges, C. (2018). Forecast of Bitcoin–can it become a major currency or is it just another bubble?. Available at SSRN 3110445. http://dx.doi.org/10.2139/ssrn.3110445.
  • Gencer, A.H. (2017). Yapay sinir ağları ile Bitcoin fiyatını tahminleme forecasting the bitcoin price via artificial neural networks. http://www.avekon.org/papers/2070.pdf . Halaburda, H. & Gandal, N. (2014). Competition in the cryptocurrency market. https://www.econstor.eu/bitstream/10419/103022/1/791932281.pdf.
  • Indera, N.I., Yassin, I.M., Zabidi, A. & Rizman, Z.I. (2017). Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators. Journal of Fundamental and Applied Sciences, 9(3S), 791-808.
  • Jang, H. & Lee, J. (2017). An empirical study on modeling and prediction of Bitcoin prices with bayesian neural networks based on Blockchain information. Ieee Access, 6, 5427-5437.
  • Karasu, S., Altan, A., Saraç, Z. & Hacioğlu, R. (2018, May). Prediction of Bitcoin prices with machine learning methods using time series data. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4.
  • Kayacan, E., Ulutas, B. & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems With Applications, 37(2), 1784-1789.
  • Kodama, O., Pichl, L. & Kaizoji, T. (2017, September). Regime change and trend prediction for Bitcoin time series data. Proceedings of the CBU International Conference Proceedings, 5,384-388). https://doi.org/10.12955/cbup.v5.954
  • Lahmiri, S. & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35-40. https://doi.org/10.1016/j.chaos.2018.11.014.
  • Liu, S.F., Yang, Y.J., Wu, L.F. & Xie, N.M. (2014). Grey system theory and its application, 6.
  • McNally, S., Roche, J. & Caton, S. (2018, March). Predicting the price of Bitcoin using machinelearning. Proceedings of the 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), IEEE, 339-343.
  • Nakamoto, S. (2008). “Bitcoin: A peer to peer Electronic Cash System”, https://bitcoin.org/bitcoin.pdf.
  • Öztürk, M.B., Arslan, H., Kayhan, T., Uysal, M. (2018). Yeni bir hedge enstrumanı olarak Bitcoin: Bitconomi. Ömer Halisdemir Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi Academıc Revıew Of Economıcs And Admınıstratıve Scıences, 11(2), ISSN: 2564-6931308–4216, 217.
  • Sin, E., Wang, L. (2017, July). Bitcoin price prediction using ensembles of neural networks. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 666-671.
  • Sutiksno, D.U., Ahmar, A.S., Kurniasih, N., Susanto, E. & Leiwakabessy, A. (2018, June). Forecasting historical data of Bitcoin using ARIMA and α-Sutte indicator. Proceedings of the Journal of Physics: Conference Series, IOP Publishing, 1028(1), p. 012194.
  • Şahin, E.E. (2018). Kripto para Bitcoin: ARIMA ve yapay sinir ağları ile fiyat tahmini. Fiscaoeconomia, 2(2), 74-92.
  • Tseng, F.M., Yu, H.C. & Tzeng, G.H. (2001). Applied hybrid grey model to forecast seasonal time series. Technological Forecasting And Social Change, 67(2-3), 291-302.
  • Usta A. & Doğantekin S. (2017). Blockchain 101, (1. baskı), İstanbul, İnkilap Kitapevi.
  • Üzer, B. (2017). Sanal para birimleri, (Uzmanlık Tezi), Ankara.
  • Vassiliadis, S., Papadopoulos, P., Rangoussi, M., Konieczny, T. & Gralewski, J. (2017). Bitcoin value analysis based on cross-correlations. Journal of Internet Banking and Commerce, 22(S7), 1-12.
  • Velankar, S., Valecha, S. & Maji, S. (2018, February). Bitcoin price prediction using machine learning. Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT), IEEE, 144-147.
  • Vigna P. & Casey J. (2015). Kriptopara çağı, (2. baskı), Buzdağı Yayın Evi,
  • Wen, K.L. (2004). Grey systems. Yang’s Scientific Press.
  • Yang, S.Y. & Kim, J. (2015, December). Bitcoin market return and volatility forecasting using transaction network flow properties. Proceedings of the 2015 IEEE Symposium Series onComputational Intelligence, 1778-1785. https://coinmarketcap.com/currencies https://trends.google.com/trends/?geo=US

Kripto Para Fiyatlarının Tahmininde Gri Sistem Teorisi: Yöntemsel Karşılaştırma

Yıl 2020, Cilt: 20 Sayı: 1, 219 - 232, 07.03.2020
https://doi.org/10.18037/ausbd.700349

Öz

2008 yılında temelleri atılmış olan Kiripto para kavramı, 2017 yılı Aralık ayı itibari ile 19.060
ABD dolarına ulaşmış ve tanınırlığını arttırmıştır. Bitcoin ve sayıları 2700’ü bulan diğer kripto
paralar hızlı kazanç elde etmek isteyen yatırımcıların dikkatini çekmeyi başarmıştır. Bu
kapsamda kripto paraların fiyatının nasıl ve ne yönde değişeceği birçok kesim tarafından
araştırma konusu olmuştur. Bu çalışmanın amacı, Bitcoin, Ethereum, IOTA ve Ripple gibi farklı
altyapısal özellikleri olan kripto paraların gelecek fiyatını geçmişte gerçekleşen fiyatlardan
hareketle tahmin etmektir. Çalışmada Deng Ju-Long tarafından 1980’li yıllarda ortaya atılan gri
sistem teorisi ile fiyat tahminlemesi yapılmıştır. Çalışmada kullanılan geçmiş fiyatlar 11 günlük
süreci kapsamaktadır. Literatüre göre kısa sayılabilecek bu süre modelin diğer modellere görece
üstünlüğünü göstermektedir. Elde edilen sonuçlara göre GM(1,1) model ve Rolling-GM(1,1)
model sonuçlarının birbirine çok yakın hata oranlarıyla tahmin yaptıkları ve yapılan tahminlere
ait hata oranlarının çok düşük olduğu görülmüştür.

Kaynakça

  • Akay, D., ve Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
  • Akcora, C.G., Dey, A.K., Gel, Y.R. & Kantarcioglu, M. (2018, June). Forecasting Bitcoin price with graph chainlets. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, 765-776.
  • Almeida, J., Tata, S., Moser, A. & Smit, V. (2015). Bitcoin prediciton using ANN. Neural Networks, 1-12.
  • Altıner, M. (2017). Kripto para: Bitcoin ve uluslararası ilişkiler. Cyberpolitik Journal, 2(4), 130-147.
  • Amjad, M. & Shah, D. (2017, February). Trading Bitcoin and online time series prediction. Proceedings of the NIPS 2016 Time Series Workshop, 1-15.
  • Bakar, N.A. & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of Bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130-137.
  • Baur, D., Lucey, B. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Finance Reviews, 45(2), 217–229.
  • Brière, M., Oosterlinck, K. & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with Bitcoin. Journal of Asset Management, 16(6), 365-373.
  • Cabanilla, K.I.M. (2016). The future of cryptocurrency: Forecasting the Bitcoin-philippine peso exchange rate using SARIMA through TRAMO-SEATS.
  • Catania, L., Grassi, S. & Ravazzolo, F. (2018). Forecasting cryptocurrencies financial time series.
  • Deng, J. L. (1982). Control problems of grey systems. Systems & Control. Letters., 1(5), 288-294.
  • Detzel, A., Liu, H., Strauss, J., Zhou, G. & Zhu, Y. (2018). Bitcoin: Predictability and profitability via technical analysis. SSRN Electronic Journal.
  • Dinges, C. (2018). Forecast of Bitcoin–can it become a major currency or is it just another bubble?. Available at SSRN 3110445. http://dx.doi.org/10.2139/ssrn.3110445.
  • Gencer, A.H. (2017). Yapay sinir ağları ile Bitcoin fiyatını tahminleme forecasting the bitcoin price via artificial neural networks. http://www.avekon.org/papers/2070.pdf . Halaburda, H. & Gandal, N. (2014). Competition in the cryptocurrency market. https://www.econstor.eu/bitstream/10419/103022/1/791932281.pdf.
  • Indera, N.I., Yassin, I.M., Zabidi, A. & Rizman, Z.I. (2017). Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators. Journal of Fundamental and Applied Sciences, 9(3S), 791-808.
  • Jang, H. & Lee, J. (2017). An empirical study on modeling and prediction of Bitcoin prices with bayesian neural networks based on Blockchain information. Ieee Access, 6, 5427-5437.
  • Karasu, S., Altan, A., Saraç, Z. & Hacioğlu, R. (2018, May). Prediction of Bitcoin prices with machine learning methods using time series data. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4.
  • Kayacan, E., Ulutas, B. & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems With Applications, 37(2), 1784-1789.
  • Kodama, O., Pichl, L. & Kaizoji, T. (2017, September). Regime change and trend prediction for Bitcoin time series data. Proceedings of the CBU International Conference Proceedings, 5,384-388). https://doi.org/10.12955/cbup.v5.954
  • Lahmiri, S. & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, Solitons & Fractals, 118, 35-40. https://doi.org/10.1016/j.chaos.2018.11.014.
  • Liu, S.F., Yang, Y.J., Wu, L.F. & Xie, N.M. (2014). Grey system theory and its application, 6.
  • McNally, S., Roche, J. & Caton, S. (2018, March). Predicting the price of Bitcoin using machinelearning. Proceedings of the 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), IEEE, 339-343.
  • Nakamoto, S. (2008). “Bitcoin: A peer to peer Electronic Cash System”, https://bitcoin.org/bitcoin.pdf.
  • Öztürk, M.B., Arslan, H., Kayhan, T., Uysal, M. (2018). Yeni bir hedge enstrumanı olarak Bitcoin: Bitconomi. Ömer Halisdemir Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi Academıc Revıew Of Economıcs And Admınıstratıve Scıences, 11(2), ISSN: 2564-6931308–4216, 217.
  • Sin, E., Wang, L. (2017, July). Bitcoin price prediction using ensembles of neural networks. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 666-671.
  • Sutiksno, D.U., Ahmar, A.S., Kurniasih, N., Susanto, E. & Leiwakabessy, A. (2018, June). Forecasting historical data of Bitcoin using ARIMA and α-Sutte indicator. Proceedings of the Journal of Physics: Conference Series, IOP Publishing, 1028(1), p. 012194.
  • Şahin, E.E. (2018). Kripto para Bitcoin: ARIMA ve yapay sinir ağları ile fiyat tahmini. Fiscaoeconomia, 2(2), 74-92.
  • Tseng, F.M., Yu, H.C. & Tzeng, G.H. (2001). Applied hybrid grey model to forecast seasonal time series. Technological Forecasting And Social Change, 67(2-3), 291-302.
  • Usta A. & Doğantekin S. (2017). Blockchain 101, (1. baskı), İstanbul, İnkilap Kitapevi.
  • Üzer, B. (2017). Sanal para birimleri, (Uzmanlık Tezi), Ankara.
  • Vassiliadis, S., Papadopoulos, P., Rangoussi, M., Konieczny, T. & Gralewski, J. (2017). Bitcoin value analysis based on cross-correlations. Journal of Internet Banking and Commerce, 22(S7), 1-12.
  • Velankar, S., Valecha, S. & Maji, S. (2018, February). Bitcoin price prediction using machine learning. Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT), IEEE, 144-147.
  • Vigna P. & Casey J. (2015). Kriptopara çağı, (2. baskı), Buzdağı Yayın Evi,
  • Wen, K.L. (2004). Grey systems. Yang’s Scientific Press.
  • Yang, S.Y. & Kim, J. (2015, December). Bitcoin market return and volatility forecasting using transaction network flow properties. Proceedings of the 2015 IEEE Symposium Series onComputational Intelligence, 1778-1785. https://coinmarketcap.com/currencies https://trends.google.com/trends/?geo=US
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Eyyüp Ensari Şahin

Buğra Bağcı Bu kişi benim

Yayımlanma Tarihi 7 Mart 2020
Gönderilme Tarihi 21 Aralık 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 20 Sayı: 1

Kaynak Göster

APA Şahin, E. E., & Bağcı, B. (2020). Kripto Para Fiyatlarının Tahmininde Gri Sistem Teorisi: Yöntemsel Karşılaştırma. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 20(1), 219-232. https://doi.org/10.18037/ausbd.700349