Industry herding in crypto assets

Zhao, Yuan; Liu, Nan; Li, Wanpeng

doi:10.1016/j.irfa.2022.102335

Cited by 10 publications

(4 citation statements)

References 92 publications

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“…Fundamental information drives herd behavior in 75 cryptocurrencies during cyber-attacks. Since herding at the industry level is widely found in stock markets (see Sias, 2004;Yao et al, 2014;Zheng et al, 2017), Zhao et al (2022) recently studied the time-varying and asymmetric herd behavior of investors in the cryptocurrency market at the industry level. They also observed the factors influencing herd behavior during the COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

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Herd behavior in cryptocurrency market: evidence of network effect

Wanidwaranan,

Termprasertsakul

2023

RBF

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PurposeThis study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.Design/methodology/approachThe authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.FindingsThe empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.Practical implicationsThese findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.Originality/valueMost previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

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Section: Literature Reviewmentioning

confidence: 99%

“…, 2014; Zheng et al. , 2017), Zhao et al. (2022) recently studied the time-varying and asymmetric herd behavior of investors in the cryptocurrency market at the industry level.…”

Section: Literature Reviewmentioning

confidence: 99%

Herd behavior in cryptocurrency market: evidence of network effect

Wanidwaranan,

Termprasertsakul

2023

RBF

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“…Filtering the cryptocurrencies with price data available since the beginning of 2018 results in 15 currencies: Bitcoin (BTC), Ethereum (ETH), Tether (USDT), BNB, XRP, Cardano (ADA), Dogecoin (DOGE), Litecoin (LTC), Chainlink (LINK), Tron (TRX), Bitcoin Cash (BCH), Decentraland (MANA), Stellar (XLM), Ethereum Classic (ETC), and Filecoin (FIL). Since the crypto market contains many more cryptocurrencies, we also used the CCi30 index in our analysis, which is an index of the top 30 cryptocurrencies (the CCi30 index has been used as a representative index of the crypto market for analyzing liquidity [ 36 ], herding behavior [ 37 , 38 ], and dynamics of cryptocurrencies [ 39 ]).…”

Section: Datamentioning

confidence: 99%

Observing Cryptocurrencies through Robust Anomaly Scores

Bae

Kim²

2022

Entropy

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The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.

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“…The findings suggest that cryptocurrencies have a role to play as a hedging tool, providing diversification benefits to investors looking to reduce portfolio risk (Kyriazis et al 2023). In addition, there is emerging work on the relationship between investor sentiment and crypto investment returns and volatility (Kyriazis et al 2022), including works on herding behaviour (Rubbaniy et al 2022;Zhao et al 2022) and related analyses of bubble formation (Ghosh et al 2022). Looking at the investment landscape, Coinbase (2022) reports an average growth rate of 32% in 2022 and indicates further growth opportunities for the crypto market in the coming years.…”

Section: Introductionmentioning

confidence: 99%

Insights on Crypto Investors from a German Personal Finance Management App

Nemeczek

Weiss

2023

JRFM

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This study investigates the socio-economic characteristics, behavioral preferences, and consumption of individuals who own crypto-assets. Our empirical analysis utilizes data from a German personal finance management app where users connect their bank accounts and depots. We conducted a survey and elicited behavioral factors for financial decision-making. By combining survey with account and security account data, we identify crypto investors’ preferences for financial decision-making and financial advice. Our results suggest that, in particular, students or self-employed, young, and male individuals who are risk-seeking and impatient are more likely to have invested in crypto-assets. Most crypto owners have less experience with financial advisory. They see it as too time-consuming and qualitatively poor, and instead, they prefer to decide on their own as they have self-reported high financial literacy. Investigating their consumption in more detail we conclude that crypto investors more often spend on travelling, electronics, food delivery and investmentsnd less aboutealth Our findings can assist policymakers in identifying high-risk consumers and investors, and help financial institutions develop appropriate products

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Industry herding in crypto assets

Cited by 10 publications

References 92 publications

Herd behavior in cryptocurrency market: evidence of network effect

Herd behavior in cryptocurrency market: evidence of network effect

Observing Cryptocurrencies through Robust Anomaly Scores

Insights on Crypto Investors from a German Personal Finance Management App

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