Up or down? Short-term reversal, momentum, and liquidity effects in cryptocurrency markets

Zaremba, Adam; Bilgin, Mehmet Hüseyin; Long, Huaigang; Mercik, Aleksander; Szczygielski, Jan

doi:10.1016/j.irfa.2021.101908

Cited by 29 publications

(5 citation statements)

References 66 publications

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“…Recently, Safdar (2020) has proposed mispricing-based explanation and described momentum as either underreaction or overreaction to information, but eventually these trends should be reversed in the longer run when prices are corrected. Consistently, Zaremba et al (2021) reveal findings that support the overreaction explanation for long-term reversal across 71 countries.…”

Section: Introductionsupporting

confidence: 68%

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Market frictions and momentum premium: does stock mispricing matter? Evidence from China

Tarek,

Ali,

Mohamed

2023

J Corp Accounting Finance

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This study examines if both market frictions and stock mispricing provide better explanation of the momentum premium, compared to the conventional asset pricing models. Using a large sample of 3727 companies listed on the Chinese stock market, we show that winner stocks are associated with larger market frictions and stock mispricing. Our findings reveal new empirical evidence that momentum premium can be attributed to market friction risk‐factor but additionally explained by a mispricing component.

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Section: Introductionsupporting

confidence: 68%

“…Consistently, Zaremba et al. (2021) reveal findings that support the overreaction explanation for long‐term reversal across 71 countries.…”

Section: Introductionmentioning

confidence: 52%

Market frictions and momentum premium: does stock mispricing matter? Evidence from China

Tarek,

Ali,

Mohamed

2023

J Corp Accounting Finance

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“…Dong et al (2022) argue that the illiquidity of cryptocurrencies generates anomalous returns, which prevents the development of an efficient cryptocurrency market. Zaremba et al (2021) find that illiquid cryptocurrencies exhibit daily short-term price reversals, whereas liquid ones display daily momentum. Mensi et al (2019) present evidence that the persistence levels of both returns and volatility decrease when controlling for the long memories of cryptocurrencies and switching states.…”

Section: Introductionmentioning

confidence: 84%

Price Delay and Market Efficiency of Cryptocurrencies: The Impact of Liquidity and Volatility during the COVID-19 Pandemic

Abou Tanos,

Badr

2024

JRFM

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The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets.

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“…However, for this to occur, the momentum and mean reversion should be known. In particular, Zaremba et al (2021) found a powerful one-day reversal in cryptocurrencies. Cryptocurrencies with low returns on the previous day strongly outperform those with high last day's returns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Overall, these studies show that accounting for structural breaks is crucial to avoid bias in results. With regards to mean reversion and momentum (persistence), Zaremba et al (2021) and Pavlov (2022) explain that for traders, mean reversion and persistence, if notable, can be exploited for profit-making. These traders believe in buying the upward momentum and selling the downward momentum.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial modelling of cryptocurrency: a case study of Bitcoin, Ethereum, and Dogecoin in comparison with JSE stock returns.

Kaseke

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The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.

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Up or down? Short-term reversal, momentum, and liquidity effects in cryptocurrency markets

Cited by 29 publications

References 66 publications

Market frictions and momentum premium: does stock mispricing matter? Evidence from China

Market frictions and momentum premium: does stock mispricing matter? Evidence from China

Price Delay and Market Efficiency of Cryptocurrencies: The Impact of Liquidity and Volatility during the COVID-19 Pandemic

Financial modelling of cryptocurrency: a case study of Bitcoin, Ethereum, and Dogecoin in comparison with JSE stock returns.

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