Profitability of technical trading rules among cryptocurrencies with privacy function

Ahmed, Shaker; Grobys, Klaus; Sapkota, Niranjan

doi:10.1016/j.frl.2020.101495

Cited by 20 publications

(15 citation statements)

References 14 publications

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“…The rank correlation is estimated at 0.4496 with a t-statistic of 7.24 statistically significant on any level. This result confirms the findings of Ahmed et al (2020) who explore the profitability of technical trading implemented among ten large cap cryptocurrencies that exhibit the so-called 'privacy function'. The authors find that the rank correlation of their set of cryptocurrencies is 0.77 and statistically significant over the 2016-2018 period.…”

Section: Other Robustness Checkssupporting

confidence: 87%

“…The authors find that the rank correlation of their set of cryptocurrencies is 0.77 and statistically significant over the 2016-2018 period. Hence, in line with Ahmed et al (2020) we infer that even though there is variation in market capitalizations across time, the rank among the large cap cryptocurrencies is, on average, fairly stable confirming the reliability of our results.…”

Section: Other Robustness Checkssupporting

confidence: 87%

“…Furthermore, as pointed out by Ahmed, Grobys, and Sapkota (2020), one could argue that the rank of market capitalizations of our set of cryptocurrencies could be too volatile during the sample period, which could cast doubt on the reliability of our results. To investigate this issue, we report in Table A6 the rank of our cryptocurrencies in terms of market capitalization in the beginning and at the end of the sample.…”

Section: Other Robustness Checksmentioning

confidence: 85%

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Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns appear to be short-memory processes, we modify Bali, Cakici and Whitelaw's (2011) and Bali, Brown, Murray, and Tang's (2017) MAX measure and employ a weekly forecast horizon and daily log-returns from the previous week to calculate the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk-adjusted return differences between cryptocurrencies in the lowest and highest MAX quintiles exceed 1.50% per week. These results are robust after controlling for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.

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Section: Other Robustness Checkssupporting

confidence: 87%

Section: Other Robustness Checkssupporting

confidence: 87%

Section: Other Robustness Checksmentioning

confidence: 85%

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Speculation and Lottery-Like Demand in Cryptocurrency Markets

Grobys

Junttila

2020

SSRN Journal

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“…This is a surprising finding because earlier literature suggests that technical trading rules are profitable for cryptocurrency markets (Grobys et al, 2020;Gerritsen et al, 2020;Corbet et al, 2019;Miller et al, 2019). Ahmed et al's (2020) study does not include any fully elaborated dynamic general equilibrium asset-pricing models to assess whether the reported payoffs are merely the equilibrium rents that accrue to investors willing to carry the risks associated with such strategies (Lo, Mamaysky, and Wang, 2000) and, hence, encourages studies to discern the economic sources of return differentials among cryptocurrency submarkets. Our study contributes to this strand of literature by taking an important next step by exploring whether statistical equilibriums in cryptocurrency submarkets do exist.…”

Section: Introductionmentioning

confidence: 95%

Asset Market Equilibria in Cryptocurrency Markets: Evidence from a Study of Privacy and Non-Privacy Coins

Sapkota

Grobys

2019

SSRN Journal

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This paper explores whether asset market equilibria in cryptocurrency markets do exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users' demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each submarket in the 2016-2018 periods, we find that privacy coins exhibit a distinct market equilibrium. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to originate from non-privacy coins with highest market capitalizations. We argue that the reason for this finding could be that non-privacy coins may be the first choice for criminals who might prefer cryptocurrencies exhibiting both a high level of anonymity and liquidity.

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“…Yahyaee et al, 2018;Ahmed et al, 2020). Moreover, these markets exhibit positive disposition effects in bearish conditions and reverse disposition effects during bullish times(Haryanto et al, 2020).…”

mentioning

confidence: 99%

Pairs trading in cryptocurrency market: A long-short story

Nair

2021

Investment Management and Financial Innovations

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Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.

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Profitability of technical trading rules among cryptocurrencies with privacy function

Cited by 20 publications

References 14 publications

Speculation and Lottery-Like Demand in Cryptocurrency Markets

Speculation and Lottery-Like Demand in Cryptocurrency Markets

Asset Market Equilibria in Cryptocurrency Markets: Evidence from a Study of Privacy and Non-Privacy Coins

Pairs trading in cryptocurrency market: A long-short story

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