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Bitcoin and market-(in)efficiency: a systematic time series approach

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Abstract

Recently, cryptocurrencies have received substantial attention by investors given their innovative features, simplicity and transparency. We here analyze the increasingly popular Bitcoin and verify pertinence of the efficient market hypothesis. Recent research suggests that Bitcoin markets, while inefficient in their early days, transitioned into efficient markets recently. We challenge this claim by proposing simple trading strategies based on moving average filters, on classic time series models as well as on non-linear neural nets. Our findings suggest that trading performances of our designs are significantly positive; moreover, linear and non-linear approaches perform similarly except at singular time periods of the Bitcoin; finally, our results suggest that markets are becoming less rather than more efficient towards the sample end of the data.

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Notes

  1. Quandl is a general data market place that collects and makes available public as well as commercial data sets through a unified API. For more information, visit https://quandl.com.

  2. The test statistic is based on \(t{\text {-test}}:=\overline{d_t}/(\sigma _{d_t}/\sqrt{T-L-1})\) where \(d_t,t=L+1,\ldots ,T\) are first differences of the performance curve (which starts in \(t=L\) where \(L=6\) is the filter length), \(\overline{d_t}\) and \(\sigma _{d_t}\) denote the arithmetic mean and the empirical standard deviation of \(d_t\), so that \((\sigma _{d_t}/\sqrt{T-L-1})\) is the standard deviation of the mean, assuming independence of \(d_t\) (null hypothesis). We rely on the t.test function in the R-package for deriving empirical significance levels of the statistic where we selected the one-sided test of the null-hypothesis of a vanishing drift against the alternative of a positive drift.

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Authors and Affiliations

  1. School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA

    Nils Bundi

  2. School of Engineering, Zurich University of Applied Sciences, Technikumstrasse 9, 8400, Winterthur, Switzerland

    Marc Wildi

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  1. Nils Bundi
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  2. Marc Wildi
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Correspondence to Nils Bundi.

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Bundi, N., Wildi, M. Bitcoin and market-(in)efficiency: a systematic time series approach. Digit Finance 1, 47–65 (2019). https://doi.org/10.1007/s42521-019-00004-z

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