Fiscaoeconomia

Fiscaoeconomia

Crypto Money Bitcoin: Price Estimation With ARIMA and Artificial Neural Networks

Yazarlar: Eyyüp Ensari ŞAHİN

Cilt 2 , Sayı 2 , 2018 , Sayfalar 74 - 92

Konular:İşletme

DOI:10.25295/fsecon.2018.02.005

Anahtar Kelimeler:Blockchain,Bitcoin,ARIMA,Artifical Neural Networks

Özet: In the world finance and technological development in finance, along with innovative financial instruments, have attracted investors. The most popular of these developments is undoubtedly Bitcoin, which is an output of the blockchain infrastructure .Bitcoin that is not connected to a central authority and contains cryptographic features, is one of the crypto moneys. The fact that Bitcoin does not depend on Central Authority and disclose the factors affecting its price by supply and demand have resulted in high volatility. In this study, firstly blockchain technology will be explained briefly and time-dependent price estimates for Bitcoin which is one of the important outputs of this technology, will be made. Artificial Neural Networks (YSA), which has become increasingly popular among estimation methods in recent years, has been used in the study and compared with ARIMA in traditional estimation methods. The sample of the study was created using daily closing prices between 02.02.2012 - 09.01.2018 dates. As a result of this study, both directions and values of estimated prices by artificial neural networks MPL (6-3-1) model between 10.01.2018 - 18.01.2018 have been more successful than ARIMA (1.1.6) model.

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@article{2018, title={Crypto Money Bitcoin: Price Estimation With ARIMA and Artificial Neural Networks}, volume={2}, number={2}, publisher={Fiscaoeconomia}, author={Eyyüp Ensari ŞAHİN}, year={2018}, pages={74–92} }
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Eyyüp Ensari ŞAHİN. (2018). Crypto Money Bitcoin: Price Estimation With ARIMA and Artificial Neural Networks (Vol. 2, pp. 74–92). Vol. 2, pp. 74–92. Fiscaoeconomia.
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Eyyüp Ensari ŞAHİN. Crypto Money Bitcoin: Price Estimation With ARIMA and Artificial Neural Networks. no. 2, Fiscaoeconomia, 2018, pp. 74–92.
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