An adoption model of cryptocurrencies

The network effect, measured by users’ adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovators’ component is the primary driver of the association between user adoption and cryptocurrency returns, both in-sample and out-of-sample. Next, we show that innovators’ adoption improves price efficiency, while imitators’ adoption contributes to noisier prices. Furthermore, we demonstrate that the adoption model captures significant cryptocurrency market phenomena, such as herding behaviour, more effectively, making it better suited for forecasting models in cryptocurrency pricing. These results suggest that our methodology for linking early and late adopters to market dynamics can be applied to various domains, offering a framework for future research at the intersection of operational research and financial markets. • This study examines the role of adoption timing in cryptocurrency markets. • We find that the innovators’ component is the primary driver. • We show that innovators’ adoption improves price efficiency. • Imitators’ adoption contributes to noisier prices.

Authors

Publication Details

Journal
European Journal of Operational Research
Published
2024-11-23
DOI
https://doi.org/10.1016/j.ejor.2024.11.024
Citations
6
Primary Topic
Innovation Diffusion and Forecasting
Type
article
Field-Weighted Citation Impact
2.94

Funders

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article

An adoption model of cryptocurrencies

Khaladdin Rzayev, Andrew Urquhart, Αθανάσιος Σάκκας
6 citations
European Journal of Operational Research
Innovation Diffusion and Forecasting
2.94
article

An adoption model of cryptocurrencies

Khaladdin Rzayev, Andrew Urquhart, Αθανάσιος Σάκκας
article en
6 citations

Abstract

The network effect, measured by users’ adoption, is considered an important driver of cryptocurrency market dynamics. This study examines the role of adoption timing in cryptocurrency markets by decomposing total adoption into two components: innovators (early adopters) and imitators (late adopters). We find that the innovators’ component is the primary driver of the association between user adoption and cryptocurrency returns, both in-sample and out-of-sample. Next, we show that innovators’ adoption improves price efficiency, while imitators’ adoption contributes to noisier prices. Furthermore, we demonstrate that the adoption model captures significant cryptocurrency market phenomena, such as herding behaviour, more effectively, making it better suited for forecasting models in cryptocurrency pricing. These results suggest that our methodology for linking early and late adopters to market dynamics can be applied to various domains, offering a framework for future research at the intersection of operational research and financial markets. • This study examines the role of adoption timing in cryptocurrency markets. • We find that the innovators’ component is the primary driver. • We show that innovators’ adoption improves price efficiency. • Imitators’ adoption contributes to noisier prices.

European Journal of Operational ResearchVol. 323(1)
Economic and Social Research Council
Gender equality
Openalex Percentile: Top 8%
Innovation Diffusion and Forecasting
2.94
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An adoption model of cryptocurrencies — Khaladdin Rzayev, Andrew Urquhart, et al. · European Journal of Operational Research (2024) | TGRS Research Map | TGRS