Feedback trading in the cryptocurrency market

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 extending Sentana 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.

Financial Behaviour, Financial Self Efficacy and Intention to Invest in Cryptocurrency

“…Individuals who are more open to new experiences tend to be more willing to try new things, including investing in digital assets such as cryptocurrencies. This trait can increase financial self-efficacy as one feels more confident in exploring new and rapidly evolving financial domains (Ahmed et al, 2023). And last but not least, personality traits can also influence how much someone is interested in improving their knowledge of finance and investment.…”