On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks?

Apopo, Natalya; Phiri, Andrew

doi:10.1016/j.heliyon.2021.e06685

Cited by 14 publications

(14 citation statements)

References 62 publications

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“…2, the stock return series, R t , is considered a random walk only if ρ = 1 , whereas if |ρ| < 1 , then the series is a stationary and predictable process, which violates the weak-form EMH. We apply the Kapetanios, Shin, and Snell (KSS) nonlinear unit root test (Kapetanios et al 2003), which is more robust when there are market frictions (i.e., transaction costs) as it is proposed in Apopo and Phiri (2021) for cryptocurrency markets. We use the test implementation in the R package by Guris (2021).…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Lorenzo

Arroyo

2023

Financ Innov

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Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index . The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Lorenzo

Arroyo

2023

Financ Innov

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“…Zargar and Kumar (2019) found that higher frequencies of Bitcoin prices indicate that prices deviate from their random nature, and stated that the market is far from efficient. Apopo and Phiri (2021), in a study using data from five different cryptocurrencies for the years 2009-2019, found evidence of weak-form market efficiency in daily returns but revealed that market efficiency was not valid in weekly returns.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Positive and Negative Events on Cryptocurrency Prices

Öget¹

2022

Ekonomi, Politika &Amp; Finans Araştırmaları Dergisi

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In recent years, cryptocurrencies have become a new topic for financial studies. In this study, the effects of positive and negative events related to cryptocurrencies on the prices of related cryptocurrencies were researched using the event study. These events include major listing, delisting and airdrop announcements, and SEC enforcements. As a result of the analysis, 22 significant abnormal return values related to negative events and eight significant abnormal return values related to positive events were determined at 1% significance level within the event window (-5, +10). Therefore, it has been determined that negative events have more effect on cryptocurrencies than positive events. The number of significant cumulative abnormal return values obtained (13 for negative events, three for positive events) also supports these results. The results of the study have crucial implications for investors, centralized cryptocurrency exchanges, and cryptocurrency CEOs. Even after the negative events were announced publicly, pull out of the market will prevent investors from making more losses. In addition, it is recommended that investors sell for profits in case of a rapid high return on the day of the listing announcement. Because it was determined that the prices returned to the equilibrium prices at the closing.

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“…One of the most useful barometers for measurement of market liquidity of cryptocurrencies is the extent and rapidity of trading volumes [83]. In terms of efficiency, other than Bitcoin, most cryptocurrencies have been generally found to be market inefficient [51,84,85]. However, the levels of efficiency or inefficiency has varied with different time horizons with more recent timelines showing improving efficiency with increased usage of cryptocurrencies [86].…”

Section: Cryptocurrencies and Market Liquiditymentioning

confidence: 99%

Cryptocurrency and Global Practices: Lessons for Nigeria

Ogunode

Iwala

Awoniyi

et al. 2022

SAJSSE

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This paper examined cryptocurrency and its global practices with particular reference to salient lessons for the Nigerian economy. The desk review methodology anchored on content analysis was used for the study. The paper identified distrust in political systems, weak domestic currency and high inflation rates as key factors fueling the growth of cryptocurrency usage in Nigeria thus motivating individuals to resort to cryptocurrencies as a tool for wealth preservation and inflation hedge. The study also found that the existence of trust deficit and challenges associated with privacy concerns, system uptime and stringent onboarding requirements were capable of derailing the success of the newly launched digital currency(‘e-naira’) issued by government to curtail cryptocurrency usage in Nigeria. The study concluded that cryptocurrencies and central bank issued digital currencies (CBDCs) are now part and parcel of the new economic order and represents the future of finance. It therefore recommended that nation states should work assiduously to develop uniformly agreed regulatory framework and global standards for the usage of cryptocurrencies.

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On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks?

Cited by 14 publications

References 62 publications

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

The Effect of Positive and Negative Events on Cryptocurrency Prices

Cryptocurrency and Global Practices: Lessons for Nigeria

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