Non-random walk in cryptocurrency: An empirical analysis of bitcoin

Main Article Content

Ahmad Fraz
Arshad Hassan
Sumayya Chughtai

Abstract

The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there.  Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.

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How to Cite
Fraz, A., Arshad Hassan, & Sumayya Chughtai. (2022). Non-random walk in cryptocurrency: An empirical analysis of bitcoin. Journal of Public Value and Administrative Insight, 4(4), 425–435. https://doi.org/10.31580/jpvai.v4i4.2106
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References

Bachelier, L. (1900). Théorie de la spéculation. Gauthier-Villars.

Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 30-34.

Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: a dynamic approach. Economics Letters, 1-4.

Bradbury , D. (2013). The problem with Bitcoin. Computer Fraud & Security, 5-8.

Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 365–373.

Buchholz, M., Delaney, J., Warren, J., & Parker, J. (2012). Bits and Bets, Information, Price Volatility, and Demand for Bitcoin. Economics.

Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A Statistical Analysis of Cryptocurrencies. Journal of Risk and Financial Management, 1-23.

Cheah, E.-T., & Fry, J. . (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 32-36.

Cheung, A. W.-K., 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, 2348-2358.

Chow, V. K., & Denning, K. C. (1993). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of Econometrics, 385-401.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American statistical association, 427-431.

Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 85-92.

Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 139-144.

Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 383-417.

Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of political Economy, 246-273.

Fischer , D. E., & Jordan, R. J. (1991). Security Analysis and Portfolio Management. Prentice Hall.

Fraz, A., & Hassan, A. (2016). Testing Information Efficiency using Random Walk Model: Empirical evidence from Karachi stock exchange. Journal of Managerial Sciences, 249-266.

Fraz, A., Hassan, A., & Chughtai, S. (2019). Seasonality in Bitcoin Market. NICE Research Journal, 1-11.

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 , 1-8.

Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users' Hidden Intentions.

Gujrati, D. (2008). Basic Econometrics. New york: McGraw Hill.

Hencic, A., & Gouriéroux, C. (2015). Noncausal autoregressive model in application to bitcoin/usd exchange rates. Econometrics of Risk, 17-39.

Jarque, C. M., & Bera, A. K. (1987). A Test for Normality of Observations and Regression Residuals. International Statistical Review / Revue Internationale de Statistique, 163-172.

Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 3-6.

Kolmogorov, A. N. (1933). Sulla determinazione empirica di una leggi di distribuzione. Giorn. 1st it lit о Ital. Attuari.

Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network. PLoS ONE, e86197.

Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific reports, 3415.

Kristoufek, L. (2015). What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PloS one, e0123923.

Little, E. M. (2014). "Bitcoin". The Investment Lawyer, 22-26.

Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series. Biometrika, 297–303.

Lo, A. W., & MacKinlay, C. A. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The review of financial studies, 41-66.

Moore, T., & Christin, N. (2013). Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk. Financial Cryptography and Data Security. Berlin, Heidelberg: Springer.

Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 6-9.

Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series. Biometrika, 335–346.

Selgin, G. (2015). Synthetic commodity money. Journal of Financial Stability, 92-99.

Smirnov, N. (1948). Table for Estimating the Goodness of Fit of Empirical Distributions. The Annals of Mathematical Statistics, 279-281.

Turpin, J. B. (2014). Bitcoin: The Economic Case for a Global, Virtual Currency Operating in an Unexplored Legal Framework. Indiana Journal of Global Legal Studies, 335-368.

Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 80-82.

Vranken, H. (2017). Sustainability of bitcoin and blockchains. Current Opinion in Environmental Sustainability, 1-9.

Wallis, A. W., & Roberts, H. V. (1956). Statistics a new approach. Illinois: The Free Press, Glencoe.