Etkin Piyasalar Hipotezi Kapsamında Kripto Paraların Zayıf Form Bilgisel Etkinliklerinin Karşılaştırılması

Satoshi Nakamoto (2008) tarafından blok zinciri temelli üretilen Bitcoin’in elde ettiği popülerlik sayesinde ortaya çıkan ve altcoin olarak tanımlanan binlerce kripto para bulunmaktadır. Bitcoin ve diğer altcoinler yapısı itibari ile herhangi bir merkezi otoriteye bağlı olmadanbilgisayar kaydının şifrelenmiş halinin parasal değerini taşımaktadır. Bu özelliği itibari ile fiyat tahmini yapmak oldukça zordur. Kripto paralar yapısı gereği makroekonomik faktörler ve diğer para politikası araçları ile oldukça düşük düzeyde korelasyona sahiptir. Özellikle hızlı ve kolay para kazanmak isteyen yatırımcıların ilgisi Bitcoin ve diğer kripto paralar üzerine odaklanmıştır. Bu odaklanma durumu kripto paraların fiyat tahminlemesi konusunu gündeme getirmiş ve kripto paralar geçmiş fiyatlardan hareketle çeşitli analizler yoluyla gelecek fiyat tahminlemesi çalışmasına konu olmuştur. Bu çalışma kripto paralar zayıf form bilgisel etkinliğe sahip midir? Hangi tarihlerde kripto paralarda zayıf form bilgisel etkinlik/etkinsizlik söz konusudur? Geçmiş fiyatları kullanarak kripto paralara yatırım gerçekleştirenler hangi kripto paraya/paralara yatırım gerçekleştirmesi başarı şansını artıracaktır? şeklindekisorulara cevap aramaktadır. Bu kapsamda çalışmada kripto paraların (Bitcoin, Ethereum, Litecoin ve Ripple) fiyat tahmini ve yatırım stratejisi için 24.08.2016 ile 28.02.2020 tarihleri arasındaki Dolar cinsi fiyatlarına ait günlük veriler kullanılarak Escanciano ve Lobato (2009) tarafından geliştirilen otomatik portmanteau testi ile analizler yapılmıştır.Yapılan analizler sonucunda çalışma kapsamındaki kripto paraların zayıf form bilgisel etkinliklerinin zaman içerisinde değişim gösterdiği ve ayrıca zayıf form bilgisel etkinliğin olduğu gün sayılarına bakıldığında en fazla zayıf form bilgisel etkinliğe sahip olan kripto paranın Bitcoin olduğu belirlenmiştir.

Comparison of Weak Form Informational Efficiency of Cryptocurrencies within the Scope of the Efficient Markets Hypothesis

There are thousands of cryptocurrencies that are defined as subcoin, which emerged thanks to the popularity of Bitcoin produced by Satoshi Nakamoto (2008) based on blockchain. Bitcoin and other subcoins have the monetary value of the encrypted version of the computer record, regardless of any central authority. It is very difficult to estimate the price with this feature. Cryptocurrencies have very low correlations with macroeconomic factors and other monetary policy instruments due to their nature. In particular, the interest of investors who want to make money quickly and easily is focused on Bitcoin and other cryptocurrencies. This focusing situation brought up the issue of price estimation of cryptocurrencies and cryptocurrencies havebeen the subject of future price estimation study through various analyzes based on past prices. This study seeks to answer questions such as Do cryptocurrencies have weak form informational efficiency?, On which dates do cryptocurrencies have weak form informational efficiency/inefficiency?, and Which cryptocurrencies/currency will increase the chances of success for those who invest in cryptocurrencies using past price movements?In this context, in the study, analyses were carried out using the automatic portmanteau test developed by Escanciano and Lobato (2009) using the daily data of Dollar prices of cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) between 24.08.2016 and 28.02.2020 for the price prediction and investment strategy. As a result of the analyses, it was determined that the weak form informational efficiency of the cryptocurrencies within the scope of the study varies over time and also the cryptocurrency that has the most weak form informational efficiency is Bitcoin.

Kaynakça

Avrupa Merkez Bankası (2012). https://www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemes201210en.pdf, Erişim Tarihi: 04.03.2020.

Avrupa Merkez Bankası (2015). https://www.ecb.europa.eu/pub/pdf/other/virtualcurrencyschemesen.pdf,Erişim Tarihi: 04.03.2020.

Baek, C. & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30-34.https://doi.org/10.1080/13504851.2014.916379

Balcilar, M.,Elie, B., Rangan, G. & David, R. (2017). Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64(1), 74-81.https://doi.org/10.1016/j.econmod.2017.03.019

Bariviera, A.F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.https://doi.org/10.1016/j.econlet.2017.09.013

Bitfinex. https://www.bitfinex.comErişim Tarihi: 29.02.2020.Charles, A., Darné, O. & Kim, J.H. (2011). Small sample properties of alternative tests for martingale difference hypothesis. Economics Letters, 110, 151-154.https://doi.org/10.1016/j.econlet.2010.11.018

Charles, A., Darné, O. & Kim, J.H. (2015). Will precious metals shine? A market efficiency perspective. International Review of Financial Analysis, 41, 284-291.https://doi.org/10.1016/j.irfa.2015.01.018

Charles, A., Darné, O. & Kim, J.H. (2017). Adaptive markets hypothesis for Islamic stock indices: Evidence from Dow Jones size and sector-indices. International Economics, 151, 100-112.https://doi.org/10.1016/j.inteco.2017.05.002

Cheah, E. & John, F. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130(1), 32-36. https://doi.org/10.1016/j.econlet.2015.02.029

Demartino, I. (2018). Bitcoin Rehberi (The Bitcoin Guidbook), Çev. Kübra Tenekeci, Epsilon Yayınevi.

Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations. Econometrica, 50(4), 987-1007.https://doi.org/10.2307/1912773

Escanciano, J.C. & Lobato, I.N. (2009). An automatic portmanteau test for serial correlation. Journal of Econometrics, 151, 140-149.https://doi.org/10.1016/j.jeconom.2009.03.001

Gandal, N. & Halaburda, H. (2016). Can we predict the winner in a market with network effects? Competition in cryptocurrency market. Games, 7(3), 16.https://doi.org/10.3390/g7030016

Güven, V. & Şahinöz, E. (2018). Blokzinciri, Kripto Paralar, Bitcoin Satoshi Dünya’yı Değiştiriyor. Kronik Kitap.

Iwamura, M., Kitamura, Y., Matsumoto, T. & Saito, K. (2014). Can we stabilize the price of a Cryptocurrency?: Understanding the design of Bitcoin and its potential to compete with Central Bank money.Hitotsubashi Journal of Economics, 60(1), 41-60.https://doi.org/10.2139/ssrn.2519367

Jarque, C.M. & Bera, A.K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259.https://doi.org/10.1016/0165-1765(80)90024-5

Karasu, S., Altan, A., Saraç, Z. & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data. 26th Signal Processing and Communications Applications Conference (SIU),Izmir, 1-4.https://doi.org/10.1109/siu.2018.8404760

Khuntia, S. & Pattanayak, J.K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, 26-28.https://doi.org/10.1016/j.econlet.2018.03.005

Kristoufek, L. (2018). On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257-262.https://doi.org/10.1016/j.physa.2018.02.161

Kristoufek. L. (2015). What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923

Kurihara, Y. & Fukushima, A. (2017). The market efficiency of Bitcoin: a weekly anomaly perspective. Journal of Applied Finance and Banking, 7(3), 57.

Lazar, D., Todea, A. & Filip, D. (2012). Martingale Difference Hypothesis and Financial Crisis: Empirical Evidence from European Emerging Foreign Exchange Markets. Economic Systems, 36, 338-350.https://doi.org/10.1016/j.ecosys.2012.02.002

Ljung, G.M. & Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303.https://doi.org/10.1093/biomet/65.2.297

Lobato, I.N.,Nankervis, J.C. & Savin, N.E. (2001). Testing for autocorrelation using a modified Box–Pierce Q test. International Economic Review, 42, 187-205.https://doi.org/10.1111/1468-2354.00106

Nadarajah, S. & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6-9.https://doi.org/10.1016/j.econlet.2016.10.033

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf,Erişim Tarihi: 07.03.2020.

Said, S.E. & Dickey, D.A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.https://doi.org/10.1093/biomet/71.3.599

Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73.https://doi.org/10.1016/j.frl.2018.04.002

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

Şahin, E.E. & Özkan, O. (2018). Asimetrik Volatilitenin Tahmini: Kripto Para Bitcoin Uygulaması. Bilecik Şeyh Edebali Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(2), 240-247. https://doi.org/10.33905/bseusbed.450018

Şahin, E.E. (2018). Kripto para Bitcoin: ARIMA ve yapay sinir ağları ile fiyat tahmini. Fiscaoeconomia, 2(2), 74-92.https://doi.org/10.21657/topraksu.654778

Tiwari, A.K., Jana, R.K., Das, D. & Roubaud, D. (2018). Informational efficiency of Bitcoin-An extension. Economics Letters, 163, 106-109.https://doi.org/10.1016/j.econlet.2017.12.006

Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82. https://doi.org/10.1016/j.econlet.2016.09.019

Verheyden, T., Moor, L.D. & Bossche, F.V.D. (2015). Towards a new framework on efficient markets. Research in International Business and Finance, 34, 294-308.https://doi.org/10.1016/j.ribaf.2015.02.007

Vidal-Tomás, D. & Ibañez, A. (2018). Semi-strong efficiency of Bitcoin. Finance Research Letters, 27, 259-265.https://doi.org/10.1016/j.frl.2018.03.013

Wei, W.C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168, 21-24.https://doi.org/10.1016/j.econlet.2018.04.003

Kaynak Göster