Feedback trading in the cryptocurrency market

Mohamed Shaker Ahmed, Adel Alsamman and Kaouther Chebbi

Studies in Economics and Finance, 2023, vol. 41, issue 1, 46-63

Abstract: Purpose - This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market. Design/methodology/approach - It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market. Findings - The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only. Originality/value - This paper contributes to the literature by extendingSentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

Keywords: Feedback trading; Herding behavior; Cryptocurrencies; Bitcoin; Trading volume; Autocorrelation; GJR-GARCH model; C43; C54; C58; D80; G01; G41 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eme:sefpps:sef-02-2023-0096

DOI: 10.1108/SEF-02-2023-0096

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