Herding and feedback trading in cryptocurrency markets

King, Timothy; Koutmos, Dimitrios

doi:10.1007/s10479-020-03874-4

Cited by 69 publications

(33 citation statements)

References 56 publications

(67 reference statements)

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“…The overheated hype and the legend of overnight wealth have caused "herd behavior" [30][31][32][33][34][35] in the virtual cryptocurrency market, which has caused many individual investors to blindly enter [36], and also provided opportunities for some criminals to take advantage of blind individual investors' ignorance of virtual cryptocurrency. The launch of "unstable value virtual cryptocurrencies" has ruined the fortunes of many people who want to get rich quick.…”

Section: The Concept Of Virtual Cryptocurrency Popularity Valuementioning

confidence: 99%

Fast Popularity Value Calculation of Virtual Cryptocurrency Trading Stage Based on Machine Learning

et al. 2022

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This paper proposes a new definition method of currency, which further divides the current hot digital currency according to its legitimacy, encryption, centralization, and other characteristics. Among these, we are mainly interested in virtual cryptocurrencies. Virtual cryptocurrency is one of the application directions of blockchain technology. Its essence is a distributed shared ledger database, which generally has the characteristics of decentralization and non-tampering. The technologies supporting the practical application of virtual cryptocurrencies involve multiple scientific and technological fields such as mathematical algorithms, cryptography, Internet communication, and computer software. Since the launch of the first virtual cryptocurrency bitcoin in 2009, it has developed rapidly worldwide. As of August 1, 2021, more than 11,570 virtual cryptocurrencies have been publicly issued and traded globally, with a total value of over $1.68 trillion. This paper proposes the virtual cryptocurrency trading popularity value system as a standardized index for quantitative analysis of virtual cryptocurrency trading, and the virtual cryptocurrency trading index system as a barometer of the virtual cryptocurrency trading market. It has contributed schemes to the analysis of the market rules of virtual cryptocurrency transactions and the realization and early warning of abnormal virtual cryptocurrency transactions, which are the two main hot research directions of virtual cryptocurrency. To be specific, the popularity value of virtual cryptocurrency transactions provides parameters for analyzing individual virtual cryptocurrencies, and the popularity index of virtual cryptocurrency transactions provides parameters for analyzing the virtual cryptocurrency trading market, so as to prevent major risks of virtual cryptocurrency transactions.

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Section: The Concept Of Virtual Cryptocurrency Popularity Valuementioning

confidence: 99%

Fast Popularity Value Calculation of Virtual Cryptocurrency Trading Stage Based on Machine Learning

et al. 2022

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“…Broadly speaking, there are three budding strands of academic literature on this subject. The first strand attempts to statistically explain variations or shocks in cryptocurrency prices (Bouri et al, 2018, 2021; Brauneis & Mestel, 2018; Cheah et al, 2018; Gkillas & Katsiampa, 2018; Khuntia & Pattanayak, 2018; King & Koutmos, 2021; Koutmos, 2018; Li & Wang, 2017; Phillip et al, 2018; Urquhart, 2017; Zhang et al, 2021). Studies in this strand generally find that cryptocurrency price changes are statistically detached from economic fundamentals, at least in the center of their distributions, and exhibit greater degrees of unexplainable volatility in relation to conventional assets.…”

Section: Introductionmentioning

confidence: 99%

Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet?

Koutmos

King

Zopounidis

2021

J of Financial Research

Self Cite

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Are cryptocurrencies useful minimum‐variance hedging instruments? This paper develops a two‐step analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation approach of Engle. Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty. Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID‐19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two‐step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.

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“…Bitcoin (BTC) attracts special attention from market participants, academicians and policymakers, alike. BTC is the most popular cryptocurrency among more than 3,000 JEL classification -G14 Dynamic frequency relationships cryptocurrencies (Coinmarketcap, 2021;King and Koutmos, 2021). It is the largest cryptocurrency in terms of market capitalization as it accounts for more than 50% of the total market capitalization of global cryptocurrency markets (Al-Yahyaee et al, 2020;Cabarcos et al, 2021;Coinmarketcap, 2021;King and Koutmos, 2021).…”

Section: Introductionmentioning

confidence: 99%

Dynamic frequency relationships between bitcoin, oil, gold and economic policy uncertainty index

Hazgui¹,

Sebai²,

Mensi

2021

SEF

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Purpose This paper aims to examine the frequency of co-movements and asymmetric dependencies between bitcoin (BTC), gold, Brent crude oil and the US economic policy uncertainty (EPU) index. Design/methodology/approach The authors use a wavelet approach and a quantile-on-quantile regression (QQR) method. Findings The results show a positive interdependence between BTC and commodity price returns at both medium and low frequencies over the sample period. In contrast, the dependence is negative between BTC and EPU index at both medium and low frequencies. Furthermore, the co-movements between markets are more pronounced during crises. The results show that strategic commodities and EPU index have the ability to predict BTC price returns at both medium- and long-terms. The QQR method reveals that higher gold returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. Moreover, lower gold returns tend to predict lower (higher) BTC returns when the market is in a bearish (bullish) state (positive (negative) relationship). The lower Brent returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. High Brent quantiles tend to predict the lower BTC returns in its extremely bearish states. Finally, higher and lower EPU changes tend to predict lower and higher BTC returns when the market is in a bearish/bullish state (negative relationship). Originality/value There is generally a lack of understanding of the linkages between BTC, gold, oil and uncertainty index across multiple frequencies. This is, as far as the authors know, the first attempt to apply both the wavelet approach and a QQR method to examine the multiscale linkages among markets under study. The findings should encourage the relevant policymakers to consider these co-movements which vary over time and in duration when setting up regulations that deem to enhance the market efficiency.

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Herding and feedback trading in cryptocurrency markets

Cited by 69 publications

References 56 publications

Fast Popularity Value Calculation of Virtual Cryptocurrency Trading Stage Based on Machine Learning

Fast Popularity Value Calculation of Virtual Cryptocurrency Trading Stage Based on Machine Learning

Hedging uncertainty with cryptocurrencies: Is bitcoin your best bet?

Dynamic frequency relationships between bitcoin, oil, gold and economic policy uncertainty index

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