Journals / İktisat Politikası Araştırmaları Dergisi / 2021 / Cilt: 8 - Sayı: 2

On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA

Bitcoin Fiyat Hareketleri Üzerine: ARIMA ile Kısa Vadeli Bir Fiyat Tahmini

Pages
293–309
Publication Date
2021-08-04
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Daily transactions in cryptocurrencies have long been following an ascending tendency, with Bitcoin leading the charge.
📊
Key Findings & Data:Daily transactions recorded in the system increased from 7000 trade per day in 2012to more than 1 million nowadays.
💡
Conclusion & Impact:Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Volatilite Tahmini · Kripto Piyasası · Piyasa Mikro Yapısı

Abstract

Daily transactions in cryptocurrencies have long been following an ascending tendency, with Bitcoin leading the charge. Daily transactions recorded in the system increased from 7000 trade per day in 2012to more than 1 million nowadays. The study aims to examine the utility of cryptocurrencies specific to Bitcoin and diagnose how predictable its price fluctuations and the volatility of the crypto market. Because the dilemma between risk aversion and return maximization became evident for investors with high yielded digital assets in a zero-lower bound environment. Hence the predictability of its price movements in the short run may shed some light on the price formation of Bitcoin. Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19.

Özet

Kripto paraların günlük işlem hacmi Bitcoin öncülüğündeki artış eğilimini uzun zamandır sürdürürken 2012’den bugüne, günlük işlem miktarı 7 binden 1 milyona ulaşmıştır. Bu çalışma Bitcoin özelinde kripto paralardan beklenen faydanın ne olduğunu inceleyip, fiyat dalgalanmaları ve volatilitenin öngörülebilirlik derecesini tespit etmeyi amaçlamaktadır. Çünkü yüksek getiri vaadeden bu piyasaya dönük risk iştahı ve getiri maksimizasyonu ikilemi, kurumsal yatırımcılar için özellikle sıfır alt sınırı ve negatif getiri ortamında belirginleşmiştir. Dolayısıyla Bitcoin’in kısa vadeli fiyat hareketlerinin öngörülebilirliği, fiyat formasyonuna da ışık tutacaktır. Çalışmada ARIMA yaklaşımının tercih edilme nedeni kısa vadeli tahminlerdeki duyarlılığıdır. ARIMA (1,1,0) Bitcoin’in 2020Q3 ve 2020Q4 dönemlerindeki fiyat hareketlerini istatistiki olarak isabetli tahmin etmiş ve modelde görülen sapmalar, yatırımcıların Bitcoin için dijital bir varlık olması yönünde değişen, yatırım değeri algıları ile ilişkilendirilmiştir.

🔖

Cite This Article

BENZEKRİ, M. K., ÖZÜTLER, H. Ş. (2021). On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. İktisat Politikası Araştırmaları Dergisi, Cilt: 8 - Sayı: 2, 293–309. https://ajindex.com/articles/243908

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Aalborg, H. A., Molnar, P., & Erik de Vries, J. (2019). What can explain the price, volatility and trading volume of bitcoin?. FinanceResearchLetters, Vol. 29, 255-265. google scholar Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting. 8(1), 69-80. google scholar Androulaki E., Karame G.O., Roeschlin M., Scherer T., & Capkun S. (2013) Evaluating User Privacy in Bitcoin. In: Sadeghi AR. (ed) Financial cryptography and data security (pp 34-51. FC 2013. Lecture Notes in Computer Science, vol 7859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39884-1_4. google scholar Azari, A. (2019). Bitcoin price prediction: an ARIMA approach. Social and Information Networks, arXiv:1904.05315. google scholar Baig, A., Blau, B. M., & Sabah, N. (2019). Price clustering and sentiment in bitcoin. Finance Research Letters, Vol. 29, 111-116. google scholar Balcilar, M., Bourid, E., Guptac, R., & Roubaud, D. (2017). Can volume predict bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, No. 64, 74-81. google scholar Bhutoria, R. (2021). 2020 Bitcoin Retrospective. Fidelity Digital Assets Researches. Retrieved from https://www. fidelitydigitalassets.com/articles/2020-bitcoin-retrospective. google scholar Bhutoria, R. (2020). Bitcoin investment thesis: bitcoin’s role as an alternative investment. Fidelity Digital Assets Researches. Retrieved from https://www.fidelitydigitalassets.com/bin-public/060_www_fidelity_com/ documents/FDAS/bitcoin-alternative-investment.pdf. google scholar Bhutoria, R., & McCurdy, T. (2020). Why corporate treasurers may consider bitcoin. Fidelity Digital Assets Researches. Retrieved from https://www.fidelitydigitalassets.com/articles/corporate-treasurer-bitcoin?ccmedia =owned&ccchannel=email&cccampaign=newsletter&cctactics=newsletter_dec. google scholar Box, G. E. P., & Jenkins, M. G. (1976). Time series analysis forecasting and control, 2nd edition. San Francisco: Holden-Day. google scholar Bouri, E., Azzi, Georges, & Dyhrberg, A. H. (2017). On the return-volatility relationship in the bitcoin market around the price crash of 2013. Economics: The Open-Access, Open-Assessment E-Journal, 11 (2017-2), 1-16. google scholar Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238. google scholar Buchholz, M., Del