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STOK AKIŞ MODELİ VE FACEBOOK PROPHET ALGORİTMASI İLE BİTCOİN FİYATI TAHMİNİ / Prediction of Bitcoin Price with Stock to Flow Model and Facebook Prophet Algorithm

Year 2021, , 16 - 30, 30.04.2021
https://doi.org/10.29216/ueip.878925

Abstract

Bir paranın sağlam olup olmadığı iki değere bakılarak anlaşılabilmektedir. İlki arzını gösteren stok durumu, ikincisi ise devam eden süreçte üretilecek olan birimi gösteren akış değeridir. Stok ve akış arasındaki oran, para olarak tanımlanan malın sağlamlığının göstergesi olarak ifade edilebilmektedir. Bitcoin, toplam arzı 21.000.000 adet ile sınırlı olan bir kripto paradır. Arzının sınırlı olması, fiyatını yükseltecek bir etmen olarak düşünülmektedir. Stok Akış Modeli de arzı sınırlı olan varlıklar için kullanılabilir. Bu çalışmada zaman serisi analiz modellerinden Facebook Prophet algoritması kullanılarak Bitcoin fiyat tahmini yapılmıştır. 2013-2020 yılları arasındaki günlük verilerin kullanıldığı çalışmada diğer çalışmalardan farklı olarak Stok Akış Modeli’nden elde edilen Stok Akış Oranı da modele eklenmiştir. Doğruluk ölçüleri ile desteklenen çalışma sonuçlarına göre Stok Akış Oranı’nın modele dâhil edilmesi ile Facebook Prophet algoritması kullanıldığında modelin performansının arttığı sonucuna ulaşılmıştır. Son olarak, Prophet yöntemi, ARIMA yöntemine göre daha etkin sonuçlar verdiği elde edilen bulgular arasındadır.

Thanks

Bu çalışmada ifade edilen görüşler yazarlara ait olup, çalıştıkları kurumların görüşleri olarak yorumlanmamalıdır. Stok Akış Modeli uygulamalarına ilham veren PlanB (@100trillionUSD) ile veri setinin elde edilmesinde yardımcı olan Rob Wolfram’a (@hamal03) saygılar sunarız.

References

  • Akdağ, M. (2019). Kripto Paralizasyon ve Türkiye Ekonomisi için bir Uygulama. (Yayınlanmamış Doktora Tezi). Atatürk Üniversitesi Sosyal Bilimler Enstitüsü. Erzurum.
  • Ali, M., & Shatabda, S. (2020). A Data Selection Methodology to Train Linear Regression Model to Predict Bitcoin Price. In 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT) (pp. 330-335). IEEE.
  • Ammous, S. (2018). The Bitcoin Standard: The Decentralized Alternative To Central Banking. John Wiley & Sons.
  • Awoke, T., Rout, M., Mohanty, L., & Satapathy, S. C. (2021). Bitcoin Price Prediction And Analysis Using Deep Learning Models. In Communication Software and Networks (pp. 631-640). Springer, Singapore.
  • Baek, C., & Elbeck, M. (2015). Bitcoins As An Investment Or Speculative Vehicle? A First Look. Applied Economics Letters, 22(1), 30-34.
  • Bouoiyour, J., & Selmi, R. (2015). What does Bitcoin look like?. Annals of Economics and Finance, 16(2), 449-492.
  • Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, Technology, And Governance. Journal Of Economic Perspectives, 29(2), 213-38.
  • Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits And Bets, Information, Price Volatility, And Demand For Bitcoin. Economics, 312, 2-48.
  • Cavalli, S., & Amoretti, M. (2021). CNN-Based Multivariate Data Analysis For Bitcoin Trend Prediction. Applied Soft Computing, 101, 107065.
  • Cheung, A., Roca, E., & Su, J. J. (2015). Crypto-Currency Bubbles: An Application Of The Phillips–Shi–Yu (2013) Methodology On Mt. Gox Bitcoin Prices. Applied Economics, 47(23), 2348-2358.
  • Çolak, Y, Sandalcılar, A. (2019). Türkiye’de Sanal Para Değerinin Belirleyicileri: Bitcoin Üzerine Bir Uygulama. Recep Tayyip Erdoğan Üniversitesi Sosyal Bilimler Dergisi, 5 (10), 205-232.
  • Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The Digital Traces Of Bubbles: Feedback Cycles Between Socio-Economic Signals In The Bitcoin Economy. Journal of the Royal Society Interface, 11(99).
  • Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin-Asset Or Currency? Revealing Users' Hidden Intentions. Revealing Users' Hidden Intentions (April 15, 2014). ECIS.
  • Gourieroux, C., & Hencic, A. (2014). Noncausal Autoregressive Model In Application To Bitcoin/USD Exchange Rate. Econometrics Of Risk, Series: Studies in Computational Intelligence, Springer.
  • Gupta, A., & Nain, H. (2021). Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques. In Machine Learning for Predictive Analysis (pp. 551-560). Springer, Singapore.
  • Hua, Y. (2020). Bitcoin Price Prediction Using ARIMA and LSTM. In E3S Web of Conferences (Vol. 218). EDP Sciences.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles And Practice. OTexts.
  • Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do The Rich Get Richer? An Empirical Analysis Of The Bitcoin Transaction Network. PloS One, 9(2).
  • Kristoufek, L. (2013). BitCoin Meets Google Trends And Wikipedia: Quantifying The Relationship Between Phenomena Of The Internet Era. Scientific Reports, 3(1), 1-7.
  • Kristoufek, L. (2015). What Are The Main Drivers Of The Bitcoin Price? Evidence From Wavelet Coherence Analysis. PloS One, 10(4).
  • Moore, T., & Christin, N. (2013). Beware The Middleman: Empirical Analysis Of Bitcoin-Exchange Risk. In International Conference On Financial Cryptography And Data Security (pp. 25-33). Springer, Berlin, Heidelberg.
  • Nakamoto, S., & Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf, 4.
  • Oo, Z. Z., & Sabai, P. H. Y. U. (2020). Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina. International Journal of Applied Mathematics Electronics and Computers, 8(4), 263-267.
  • Sapuric, S., & Kokkinaki, A. (2014). Bitcoin is volatile! Isn’t that right?. In International Conference on Business Information Systems (pp. 255-265). Springer, Cham.
  • Selgin, G. (2015). Synthetic Commodity Money. Journal of Financial Stability, 17, 92-99.
  • Shen, J., Valagolam, D., & McCalla, S. (2020). Prophet Forecasting Model: A Machine Learning Approach To Predict The Concentration Of Air Pollutants (PM2. 5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. PeerJ, 8, e9961.
  • Swamidass, P. M. (Ed.). (2000). Encyclopedia Of Production And Manufacturing Management. Springer Science & Business Media.
  • Van Wijk, D. (2013). What Can Be Expected From The BitCoin. Erasmus Universiteit Rotterdam.
  • Velankar, S., Valecha, S., & Maji, S. (2018). Bitcoin Price Prediction Using Machine Learning. In 2018 20th International Conference On Advanced Communication Technology (ICACT) (pp. 144-147). IEEE.
  • Wirawan, I. M., Widiyaningtyas, T., & Hasan, M. M. (2019). Short Term Prediction on Bitcoin Price Using ARIMA Method. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 260-265). IEEE.
  • Yenidoğan, I., Çayir, A., Kozan, O., Dağ, T., & Arslan, Ç. (2018). Bitcoin Forecasting Using ARIMA And Prophet. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 621-624). IEEE.

PREDICTION OF BITCOIN PRICE WITH STOCK TO FLOW MODEL AND FACEBOOK PROPHET ALGORITHM / Stok Akış Modeli Ve Facebook Prophet Algoritması İle Bitcoin Fiyatı Tahmini

Year 2021, , 16 - 30, 30.04.2021
https://doi.org/10.29216/ueip.878925

Abstract

Whether money is solid or not can be understood by looking at two values. The first is the stock status indicating the supply and the second is the flow value indicating the unit to be produced in the ongoing process. The ratio between stock and flow can be expressed as an indicator of the strength of the good defined as money. Bitcoin is a cryptocurrency whose total supply is limited to 21,000,000 units. The limited supply is considered as a factor that will increase its price. The Stock Flow Model can also be used for assets with limited supply. In this study, Bitcoin price prediction is made using the Facebook Prophet algorithm which is one of the time series analysis models. Bitcoin data between 2013-2020 were used, the Stock to Flow Rate obtained from the Stock Flow Model was also added to the model, unlike other studies. According to the results of the study supported by the accuracy measures, it was concluded that the performance of the model increased when the Facebook Prophet algorithm was used by including the Stock Flow Ratio in the model.

References

  • Akdağ, M. (2019). Kripto Paralizasyon ve Türkiye Ekonomisi için bir Uygulama. (Yayınlanmamış Doktora Tezi). Atatürk Üniversitesi Sosyal Bilimler Enstitüsü. Erzurum.
  • Ali, M., & Shatabda, S. (2020). A Data Selection Methodology to Train Linear Regression Model to Predict Bitcoin Price. In 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT) (pp. 330-335). IEEE.
  • Ammous, S. (2018). The Bitcoin Standard: The Decentralized Alternative To Central Banking. John Wiley & Sons.
  • Awoke, T., Rout, M., Mohanty, L., & Satapathy, S. C. (2021). Bitcoin Price Prediction And Analysis Using Deep Learning Models. In Communication Software and Networks (pp. 631-640). Springer, Singapore.
  • Baek, C., & Elbeck, M. (2015). Bitcoins As An Investment Or Speculative Vehicle? A First Look. Applied Economics Letters, 22(1), 30-34.
  • Bouoiyour, J., & Selmi, R. (2015). What does Bitcoin look like?. Annals of Economics and Finance, 16(2), 449-492.
  • Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, Technology, And Governance. Journal Of Economic Perspectives, 29(2), 213-38.
  • Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits And Bets, Information, Price Volatility, And Demand For Bitcoin. Economics, 312, 2-48.
  • Cavalli, S., & Amoretti, M. (2021). CNN-Based Multivariate Data Analysis For Bitcoin Trend Prediction. Applied Soft Computing, 101, 107065.
  • Cheung, A., Roca, E., & Su, J. J. (2015). Crypto-Currency Bubbles: An Application Of The Phillips–Shi–Yu (2013) Methodology On Mt. Gox Bitcoin Prices. Applied Economics, 47(23), 2348-2358.
  • Çolak, Y, Sandalcılar, A. (2019). Türkiye’de Sanal Para Değerinin Belirleyicileri: Bitcoin Üzerine Bir Uygulama. Recep Tayyip Erdoğan Üniversitesi Sosyal Bilimler Dergisi, 5 (10), 205-232.
  • Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The Digital Traces Of Bubbles: Feedback Cycles Between Socio-Economic Signals In The Bitcoin Economy. Journal of the Royal Society Interface, 11(99).
  • Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin-Asset Or Currency? Revealing Users' Hidden Intentions. Revealing Users' Hidden Intentions (April 15, 2014). ECIS.
  • Gourieroux, C., & Hencic, A. (2014). Noncausal Autoregressive Model In Application To Bitcoin/USD Exchange Rate. Econometrics Of Risk, Series: Studies in Computational Intelligence, Springer.
  • Gupta, A., & Nain, H. (2021). Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques. In Machine Learning for Predictive Analysis (pp. 551-560). Springer, Singapore.
  • Hua, Y. (2020). Bitcoin Price Prediction Using ARIMA and LSTM. In E3S Web of Conferences (Vol. 218). EDP Sciences.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles And Practice. OTexts.
  • Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do The Rich Get Richer? An Empirical Analysis Of The Bitcoin Transaction Network. PloS One, 9(2).
  • Kristoufek, L. (2013). BitCoin Meets Google Trends And Wikipedia: Quantifying The Relationship Between Phenomena Of The Internet Era. Scientific Reports, 3(1), 1-7.
  • Kristoufek, L. (2015). What Are The Main Drivers Of The Bitcoin Price? Evidence From Wavelet Coherence Analysis. PloS One, 10(4).
  • Moore, T., & Christin, N. (2013). Beware The Middleman: Empirical Analysis Of Bitcoin-Exchange Risk. In International Conference On Financial Cryptography And Data Security (pp. 25-33). Springer, Berlin, Heidelberg.
  • Nakamoto, S., & Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf, 4.
  • Oo, Z. Z., & Sabai, P. H. Y. U. (2020). Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina. International Journal of Applied Mathematics Electronics and Computers, 8(4), 263-267.
  • Sapuric, S., & Kokkinaki, A. (2014). Bitcoin is volatile! Isn’t that right?. In International Conference on Business Information Systems (pp. 255-265). Springer, Cham.
  • Selgin, G. (2015). Synthetic Commodity Money. Journal of Financial Stability, 17, 92-99.
  • Shen, J., Valagolam, D., & McCalla, S. (2020). Prophet Forecasting Model: A Machine Learning Approach To Predict The Concentration Of Air Pollutants (PM2. 5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. PeerJ, 8, e9961.
  • Swamidass, P. M. (Ed.). (2000). Encyclopedia Of Production And Manufacturing Management. Springer Science & Business Media.
  • Van Wijk, D. (2013). What Can Be Expected From The BitCoin. Erasmus Universiteit Rotterdam.
  • Velankar, S., Valecha, S., & Maji, S. (2018). Bitcoin Price Prediction Using Machine Learning. In 2018 20th International Conference On Advanced Communication Technology (ICACT) (pp. 144-147). IEEE.
  • Wirawan, I. M., Widiyaningtyas, T., & Hasan, M. M. (2019). Short Term Prediction on Bitcoin Price Using ARIMA Method. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 260-265). IEEE.
  • Yenidoğan, I., Çayir, A., Kozan, O., Dağ, T., & Arslan, Ç. (2018). Bitcoin Forecasting Using ARIMA And Prophet. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 621-624). IEEE.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Economics
Journal Section RESEARCH ARTICLES
Authors

Murat Akdağ 0000-0003-3559-6177

Gürkan Bozma 0000-0003-4047-9012

Publication Date April 30, 2021
Published in Issue Year 2021

Cite

APA Akdağ, M., & Bozma, G. (2021). STOK AKIŞ MODELİ VE FACEBOOK PROPHET ALGORİTMASI İLE BİTCOİN FİYATI TAHMİNİ / Prediction of Bitcoin Price with Stock to Flow Model and Facebook Prophet Algorithm. Uluslararası Ekonomi İşletme Ve Politika Dergisi, 5(1), 16-30. https://doi.org/10.29216/ueip.878925

Uluslararası Ekonomi, İşletme ve Politika Dergisi

Recep Tayyip Erdoğan Üniversitesi
İktisadi ve İdari Bilimler Fakültesi
İktisat Bölümü
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