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Title: deciphering the blockchain: a comprehensive analysis of bitcoin's evolution, adoption, and future implications.

Abstract: This research paper provides a comprehensive analysis of Bitcoin, delving into its evolution, adoption, and potential future implications. As the pioneering cryptocurrency, Bitcoin has sparked significant interest and debate in recent years, challenging traditional financial systems and introducing the world to the power of blockchain technology. This paper aims to offer a thorough understanding of Bitcoin's underlying cryptographic principles, network architecture, and consensus mechanisms, primarily focusing on the Proof-of-Work model. We also explore the economic aspects of Bitcoin, examining price fluctuations, market trends, and factors influencing its value. A detailed investigation of the regulatory landscape, including global regulatory approaches, taxation policies, and legal challenges, offers insights into the hurdles and opportunities faced by the cryptocurrency. Furthermore, we discuss the adoption of Bitcoin in various use cases, its impact on traditional finance, and its role in the growing decentralized finance (DeFi) sector. Finally, the paper addresses the future of Bitcoin and cryptocurrencies, identifying emerging trends, technological innovations, and environmental concerns. We evaluate the potential impact of central bank digital currencies (CBDCs) on Bitcoin's future, as well as the broader implications of this technology on global finance. By providing a holistic understanding of Bitcoin's past, present, and potential future, this paper aims to serve as a valuable resource for scholars, policymakers, and enthusiasts alike.

Subjects:	Cryptography and Security (cs.CR)
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A systematic literature review on the determinants of cryptocurrency pricing

China Accounting and Finance Review

ISSN : 1029-807X

Article publication date: 15 September 2023

Issue publication date: 5 March 2024

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

Design/methodology/approach

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

Originality/value

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

• Cryptocurrency
• Systematic literature review
• Influential factors

Peng, S. , Prentice, C. , Shams, S. and Sarker, T. (2024), "A systematic literature review on the determinants of cryptocurrency pricing", China Accounting and Finance Review , Vol. 26 No. 1, pp. 1-30. https://doi.org/10.1108/CAFR-05-2023-0053

Emerald Publishing Limited

Copyright © 2023, Sanshao Peng, Catherine Prentice, Syed Shams and Tapan Sarker

Published in China Accounting and Finance Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

In recent years, cryptocurrencies have attracted more attention in the wider community, with market capitalization reaching a high level ( Bouri, Shahzad, & Roubaud, 2019 ; Fry, 2018 ). Cryptocurrency refers to a digital payment system that operates similarly to the standard monetary currency system and allows users to send and receive virtual payments outside of traditional financial institutions. These virtual payments offer low transaction costs and a peer-to-peer system ( Kim, Bock, & Lee, 2021 ). The decentralization of cryptocurrencies has been a key factor in the enhancement of user privacy and provides various levels of anonymity ( Sarwar, Nisar, & Khan, 2019 ). Bitcoin was the first decentralized blockchain-based cryptocurrency and continues to be the most well-known and widely used cryptocurrency in the market ( Li & Wang, 2017 ). A blockchain is a distributed ledger technology that allows data to be recorded and shared across a network of computers or nodes. Each block in the blockchain contains a list of transactions, and once a block is added to the chain, it cannot be altered. The immutability of records is a key feature of blockchain technology and provides a high level of trust and security ( Ferguson, 2018 ). Blockchain provides users with the promise of transaction trust and transparency. Blockchain technology, as demonstrated by cryptocurrency, is also widely considered to be a significant innovation with profound implications for the future of finance ( Liu, Tsyvinski, & Wu, 2022 ).

While cryptocurrency innovation brings benefits and potential advantages, it also poses significant challenges and issues for traditional financial systems. This is because cryptocurrencies diverge from traditional financial assets in their value determination. Instead of being reliant on tangible assets or governments, the value of cryptocurrencies is based on specific algorithms that record transactions within the underlying blockchain networks ( Corbet, Lucey, Urquhart, & Yarovaya, 2019 ). Yermack (2015) highlighted the prevalence of speculative price bubbles in the cryptocurrency market. These bubbles arise from swift and sometimes irrational increases in cryptocurrency prices, often not supported by underlying fundamentals. Thus, the unique nature of cryptocurrencies, their decentralized structure and the influence of speculative factors pose distinct challenges for investors and policymakers. Understanding these characteristics is crucial when assessing the value and potential risks associated with cryptocurrency market investment.

Studies have shed light on the factors influencing the price of Bitcoin and other more notable cryptocurrencies. In the case of Bitcoin, its decentralized system and a unique combination of anonymous miners and profit-driven incentives have been the primary drivers of innovation. This innovation has encouraged investors to participate freely in the Bitcoin market and has motivated researchers to identify the various factors that affect returns ( Leshno & Strack, 2020 ). Van Wijk (2013) investigated the influence of macroeconomic factors on bitcoin price and suggested that factors such as the stock market index, exchange rates and oil prices impacted Bitcoin’s value. Polasik, Piotrowska, Wisniewski, Kotkowski, and Lightfoot (2015) observed that the Bitcoin price experienced exponential growth in July 2010, which was attributed to increased trading against the US dollar. Bouoiyour and Selmi (2015) found that the long-term price increase in Bitcoin was influenced by a growing demand for Bitcoin trading and exchange transactions. Kristoufek (2013) indicated that the increased interest, as measured by the number of Google searches for Bitcoin, had a positive impact on Bitcoin’s price. The prices of common cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin and Monero were significantly affected by factors related to the overall crypto market, the attractiveness of individual cryptocurrencies and movement in the S&P 500 Index ( Sovbetov, 2018 ). Technological factors were also an important determinant influencing Bitcoin price in the early market ( Li & Wang, 2017 ).

Studies have provided many determinants of cryptocurrency pricing within the existing financial market; however, research on cryptocurrency pricing is rather fragmented. This study systematically reviews the literature and identifies and synthesizes the factors that influence cryptocurrency pricing. This review contributes to the literature by providing a consolidated view of cryptocurrency pricing and systematically maps significant influential factors. This review also highlights the different research methods used in cryptocurrency pricing studies and identifies those commonly applied. This review provides a depth of understanding and a more comprehensive discussion of the determinants of cryptocurrency prices. This consolidation of the literature will inform investors and investment managers about the market dynamics of cryptocurrencies. Thus, it will guide the construction of more comprehensive cryptocurrency price prediction models and trading decisions within the cryptocurrency market.

The following presents the methodology, including the procedure used to conduct the systematic literature review, followed by the results of the review. The study highlights research gaps and offers direction for future research. The conclusion presents the implications of the study, and limitations are acknowledged.

To identify the influential factors of cryptocurrency pricing, this systematic literature review utilized the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) approach. PRISMA is an evidence-based approach for reporting and evaluating the literature ( Saeed, Paolo, & Sarah NR, 2019 ) and is regarded as an appropriate methodology for reproducing data, especially when compared to narrative literature reviews ( Rother, 2007 ).

Keywords and databases

This review followed a predetermined search strategy using the terms (“cryptocurrency” OR “encryption currency” OR “digital money” OR “digital currency”) AND (“factor” OR “determine”) AND “(price)”. Three databases, Scopus, Web of Science and EBSCOhost, were used as most relevant studies can be sourced from these databases ( Akyildirim, Aysan, Cepni, & Darendeli, 2021 ; Liu et al. , 2022 ; Mohamed, 2021 ). To maintain a consistent standard for analysis and to ensure high-quality findings, this review only considered peer-reviewed journal articles which provided reliable and accurate data ( Li et al. , 2019 ). Articles published in English were chosen. This review included all relevant studies published before August 2022 when the search was conducted. The review followed the procedure described in the PRISMA checklist ( Tricco et al. , 2018 ).

Figure 1 presents the flow chart of the systematic literature review using the PRISMA approach. The initial search yielded a total of 563 articles: Scopus (313), Web of Science (72) and EBSCOhost (178). EndNote X9 software was utilized to screen the articles for duplication, with 185 articles discarded as duplicates. A further 213 articles were taken out after initial screening based on a comprehensive review of titles and abstracts. The remaining 165 articles were assessed for eligibility. In this assessment, 76 articles did not explicitly examine the factors of cryptocurrency pricing and were excluded. A further 18 peer-reviewed journal articles were removed as they were conference papers, and 15 articles were excluded as they were not in English. A total of 56 articles met the eligibility criteria for final analysis. The review conducted a thorough examination of the reference lists, which resulted in the inclusion of an additional 32 articles. This resulted in 88 articles being selected for the review. This approach ensured the inclusion of a diverse and relevant body of literature for the review.

Publishing trends and currency focus

Much of the literature focused on Bitcoin, suggesting that it remains the most popular and widely researched cryptocurrency. As a pioneer and the first cryptocurrency, Bitcoin has received significant attention from researchers, investors and the general public ( Wang & Vergne, 2017 ). The earliest article on cryptocurrency pricing was published in 2014, indicating that research remains in the early stages of development. As cryptocurrencies gained traction and public attention over the last decade, academic interest in pricing dynamics also grew. The upward trend in the number of published studies on cryptocurrency pricing reflects increasing interest and recognition of the importance of this research topic. The development of the research is presented in Figure 2 .

Journal outlets

Studies of cryptocurrency pricing have been published in journals across a wide range of disciplines, with a primary focus on finance. Table 1 highlights the 54 different journals that have published cryptocurrency pricing studies. The spread of interest indicates recognition of the importance of this research area. Finance Research Letters published a total of 27 articles, followed by the PLoS One journal (4), Financial Innovation (2), Journal of Risk and Financial Management (2), Journal of Behavioural Finance (2), Studies in Economics and Finance (2) and International Review of Financial Analysis (2). The distribution of the remaining 47 articles across journals from various disciplines highlights the wide-ranging interest and the multi-faceted nature of cryptocurrencies. The journals covered disciplines such as electrical energy, technological innovation, social media, investor sentiment and macroeconomic policy.

Geographic analysis considered the location of data collection of the studies included in the review. An understanding of the geographic distribution of research and how different regions or countries contribute to the body of knowledge of cryptocurrency pricing is also included. The 88 studies were conducted in 18 different regions, with Europe accounting for 29 studies; followed by the United Kingdom (12), China (12), the United States (9), United Arab Emirates (4), Russia (3), India (3), Canada (3), Australia (3) and South Korea (2) (see Table 2 ). The imposition of restrictions on cryptocurrency trading by the Chinese government in September 2017 had an impact on cryptocurrency pricing research ( Chen & Liu, 2022 ). However, despite the regulatory challenges, 12 studies were conducted in China and contributed to the literature.

Research methods

Table 3 presents the research methods used to analyze the determinants of cryptocurrency pricing. The most used model was the vector autoregression model (9), followed by the autoregressive distributed lag model (6), generalized autoregressive conditional heteroskedasticity model (5), three-factor model (4), the fixed-effect model (3), the wavelet coherence analysis (3), the ordinary least squares (L.S.) regression (2), the vector error correlation (2), the asset pricing model (2), the cost of production model (2) and the text analytic approach (2). The vector autoregression model is a statistical model used to reveal correlations between variables as they change over time ( Garcia, Tessone, Mavrodiev, & Perony, 2014 ) and generates a vector error correction model ( Hakim das Neves, 2020 ). This model has achieved better performance in simulating past Bitcoin trading prices, in contrast to traditional autoregression models and Bayesian regression models ( Ibrahim, Kashef, Li, Valencia, & Huang, 2020 ).

Cryptocurrency pricing factors

The current review identified and categorized the factors that influence cryptocurrency pricing. These factors include (i) supply and demand, (ii) technology, (iii) economics, (iv) market volatility, (v) investors’ attributes and (vi) social media, where the categories are not mutually exclusive. The following subsections present a discussion of each category.

Supply and demand

Studies in Table 4 have shown that the basic principles of supply and demand are fundamental factors which play a crucial role in determining cryptocurrency prices ( Ciaian, Rajcaniova, & Kancs, 2016 ; Lamothe-Fernández, Alaminos, Lamothe-López, & Fernández-Gámez, 2020 ). Bitcoin was the most cited currency. The supply of Bitcoins has been asymptotically capped at 21 million ( Polasik et al. , 2015 ) and is governed by a special cryptographic algorithm that determines the frequency, time and amount of Bitcoin supply ( Ibrahim et al. , 2020 ; Sauer, 2016 ). While the supply of Bitcoin works as a standard supply, the growth of supply leads to downtrend pressures being exerted on its price. This means that a negative relationship exists between the supply of Bitcoin and its price ( Ciaian et al. , 2016 ; Dubey, 2022 ; Kristoufek, 2015 ). However, it has been argued that growth in the cryptocurrency supply can drive up the price, based on a random-effect and fixed-effect analysis ( Wang & Vergne, 2017 ), the rationale being that new cryptocurrencies appear to be more attractive than older competitors.

Although the literature provides evidence that the supply of cryptocurrency has a significant effect on the price, demand-side drivers have a stronger impact on cryptocurrency prices ( Ciaian et al. , 2016 , Ciaian, Rajcaniova, & Kancs, 2016 ). An increase in the number of Bitcoins available for transactions may result in Bitcoin price volatility and a massive speculative price bubble ( Ciaian et al. , 2016 ). The growth of a transactional need for Bitcoin leads to an increase in price ( KaraÖMer, 2022 ). For example, Bitcoin trading against the US dollar has increased exponentially since July 2010 ( Polasik et al. , 2015 ). Additionally, Bitcoin as a payment method has had a positive effect on Bitcoin price ( Polasik et al. , 2015 ) as many people in developing countries have limited access to traditional bank transfer systems ( Schuh & Stavins, 2011 ). Network factors including wallet users, payment accounts and transaction accounts were the main demand for cryptocurrencies and contributed to the volatility of their returns ( Liu & Tsyvinski, 2021 ; Nakagawa & Sakemoto, 2022 ). Bouri, Vo, and Saeed (2021) highlighted the importance of trading volume in shaping the dynamics of the cryptocurrency market and its impact on returns and correlations. A Garman–Klass analysis also demonstrated that the emergence of other cryptocurrencies positively affected Bitcoin returns ( Będowska-Sójka, Kliber, & Rutkowska, 2021 ). Although Bitcoin is governed by a cryptographic algorithm, its usage in transactions, supply and price level are consistent with standard economic theory, especially the quantity theory of money ( Kristoufek, 2015 ).

As can be seen in Table 5 , the literature suggests that Bitcoin mining is one of the main factors driving the supply and pricing of Bitcoin ( Bouoiyour & Selmi, 2016 ; Garcia et al. , 2014 ; Ibrahim et al. , 2020 ). Bitcoin supply is determined by a mathematical algorithm for blockchain hashing ( Ibrahim et al. , 2020 ), where any attempt to modify the amount of issuance is rejected ( Nelson, 2018 ). The term hash rate refers to the speed of computer processing power in the Bitcoin network ( Lopatin, 2019 ). There are indications that growth in the hash rate has a significant and positive effect on Bitcoin returns ( KaraÖMer, 2022 ). However, Kjaerland, Khazal, Krogstad, Nordstrom, and Oust (2018) argued that the hash rate is an irrelevant technological factor for modeling Bitcoin return dynamics, the reason being that the underlying code makes the supply of Bitcoins deterministic, which contrasts with previous studies. This finding was supported by Fantazzini and Kolodin (2020) who demonstrated that the hash rate had no direct effect on the Bitcoin price from the energy efficiency effect of Bitcoin mining equipment, based on the cost of production model.

Mining difficulty is also an important determinant influencing the supply and pricing of Bitcoin ( Kristoufek, 2015 ). The term “mining difficulty” refers to a measurement unit used in the process of Bitcoin mining to maintain the speed of block generation and the hash rate criterion ( Zhang, Qin, Yuan, & Wang, 2018 ). The unique Bitcoin mining process has a significant effect on the Bitcoin price ( Kristoufek, 2015 ). In other words, an increase in mining difficulty leads to an increase in the Bitcoin price ( Guizani & Nafti, 2019 ). This is in line with Li and Wang (2017) who used the autoregressive distributed lag model to confirm that the growth of mining difficulty would increase the Bitcoin price in the early market. The rationale for this is that the short-term adjustment in the Bitcoin price is the response to the growth of mining difficulty, although mining difficulty has a weak impact on the Bitcoin price in the long term ( Guizani & Nafti, 2019 ).

Halving is another technical factor that influences the supply and pricing of Bitcoin ( Ibrahim et al. , 2020 ; Meynkhard, 2019 ). The term Bitcoin halving refers to a process in which the reward for mining Bitcoin transactions is reduced by half ( Ramos & Zanko, 2020 ). Miners can earn new Bitcoins as remuneration for their work, but the block subsidy will decrease by 50% every four years. Reducing the supply of Bitcoins every four years leads to the growth of Bitcoin capitalization ( Fantazzini & Kolodin, 2020 ). Ramos and Zanko (2020) demonstrated that the first halving occurrence caused increases in the Bitcoin price, market capitalization and average transaction fees. Meynkhard (2019) utilized comparative analysis to show that halving positively affected the cryptocurrency price.

The theoretical literature has considered the cost of cryptocurrency mining as a crucial factor that influences cryptocurrency pricing. Sapkota and Grobys (2020) employed portfolio analysis to explore the relationship between mining cost and cryptocurrency pricing. Results indicated that the mining cost from an energy aspect positively impacted cryptocurrency pricing. Chico-Frias (2021) confirmed this impact by demonstrating that mining costs were positively related to cryptocurrency pricing, as Bitcoin mining consumes electricity ( Lamothe-Fernández et al. , 2020 ). Nevertheless, Baldan and Zen (2020) argued that profits and costs were not the factors driving Bitcoin pricing. One possible explanation is that there is insufficient evidence to support the association between Bitcoin price and mining costs. Liu and Tsyvinski (2021) confirmed that electricity and computing costs (mining costs) did not drive cryptocurrency returns. However, transaction costs can be an important determinant driving cryptocurrency pricing ( Crettez & Morhaim, 2022 ) because the impact of volatility in cryptocurrency pricing can be driven by the transaction costs that individuals incur when purchasing cryptocurrency.

Empirical studies indicate that other technologies may also contribute to the volatility of the cryptocurrency price. Chen (2021) argued that blockchain technology factors only demonstrated a small impact on the Bitcoin price. Kim et al. (2021) showed that blockchain information was an important determinant influencing Ethereum prices. Wang and Vergne (2017) found that the drivers of cryptocurrency returns were the number of unique collaborators and proposals emerging. Chowdhury, Damianov, and Elsayed (2022) indicated that the price dynamics of cryptocurrencies, particularly Rapple, were influenced by the technologies related to the consensus protocol used in these cryptocurrencies. However, Vo et al. (2022) showed that cryptocurrency pricing, while changeable in the short term, may be less sensitive to technological factors and more responsive to underlying economic factors in the long term.

Economic factors

This study shows that economic factors significantly affect cryptocurrency pricing. For example, Van Wijk (2013) examined the impact of Bitcoin price on macroeconomic factors, such as the stock market index, exchange rates and oil prices. Polasik et al. (2015) showed an exponential increase in the Bitcoin price due to increased trading against the US dollar in July 2010. Similarly, Bouoiyour and Selmi (2015) found that demand for Bitcoin trading and exchange transactions will drive up prices. The correlation between variables is shown in Table 6 . The economic factors most commonly examined in this research are now discussed.

Exchange rates

Exchange rates appear to have a significant effect on cryptocurrency pricing. Previous studies have demonstrated that the exchange rate has a significant and negative relationship with the Bitcoin price ( KaraÖMer, 2022 ; Zhu, Dickinson, & Li, 2017 ). Polasik et al. (2015) demonstrated that both the US dollar and the Euro had a strong negative relationship with the Bitcoin price. These findings were consistent with Poyser (2019) who suggested that the exchange rate of the Chinese yuan was negatively associated with the Bitcoin price. Panagiotidis, Stengos, and Vravosinos (2018) , through a Least Absolute Shrinkage and Selection Operator (LASSO) approach, revealed that the exchange rates including JPY/USD, CNY/USD, USD/EUR, and GBP/USD positively affected Bitcoin returns in order to have a positive impact. This was supported by Huang, Gau, and Wu (2022) who found that the exchange rates of EUR/USD, GBP/USD and JPY/USD affected Bitcoin returns. However, it has also been argued that Bitcoin returns are not significantly affected by exchange rates USD/JPY, USD/GBP, USD/GBP and USD/AU when confidence was measured at a 95% level ( Almansour, Almansour, & In’airat, 2020 ). When the confidence level was 90%, however, the exchange rate of the GBP was found to be significant.

Interest rates

Studies indicate that interest rates are also an important determinant of cryptocurrency pricing. Nguyen, Nguyen, Nguyen, Pham, and Nguyen (2022) investigated the Federal rate of the US and the Chinese interbank rate on the stablecoins and cryptocurrencies, based on the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), EGARCH and the fixed-effect model. The results suggested that higher federal fund rates and Chinese interbank rates had a significant impact on both stablecoins and cryptocurrencies, leading to increased price volatility in these markets. Havidz, Karman, and Mambea (2021) also found that the Federal Reserve interest rate negatively affected the price of Bitcoin, with the negative relationship being that a higher Federal Reserve interest rate discouraged investors from investing in Bitcoin as a speculative asset. This finding was consistent with Zhu et al. (2017) who stated that an increased interest rate may result in reduced speculative investment by investors. In addition, an increase in interest rates was found to reduce the demand for Bitcoin as well as its returns ( Jareño, González, Tolentino, & Sierra, 2020 ). However, Panagiotidis et al. (2018) found a positive effect on Bitcoin returns from interest rates through a LASSO approach.

Consumer price index (CPI)

Studies have indicated that the consumer price index (CPI) is an important determinant influencing the Bitcoin price. Empirical results have suggested that the CPI had a long-term negative influence on the Bitcoin price ( Zhu et al. , 2017 ). In contrast with previous findings, Wang, Sarker, and Bouri (2022) argued that the CPI had a positive correlation with Bitcoin in the short term as Bitcoin can be a hedging asset. However, Corbet, Larkin, Lucey, Meegan, and Yarovaya (2020) utilized a sentiment index to explore the relationship between macroeconomic news regarding the CPI and Bitcoin pricing. The results indicated that CPI news had no significant relationship with the Bitcoin price.

Gold and oil

Several studies have demonstrated that gold, as a macro-financial factor, has a significant and positive effect on the Bitcoin price. Based on deep learning methods, Lamothe-Fernández et al. (2020) showed that gold positively affected Bitcoin pricing. This finding was supported by Ciaian et al. (2016) and Pogudin, Chakrabati, and Di Matteo (2019) where it was found that gold and oil were positively correlated with the Bitcoin price. Panagiotidis et al. (2018) utilizing a LASSO framework, also supported that Bitcoin returns were positively affected by gold and oil. Nevertheless, Jareño et al. (2020) used the asymmetric nonlinear cointegration approach and Ciaian et al. (2016) utilized the vector autoregressive model to reveal a negative relationship between oil price and the Bitcoin price. It was considered that as oil prices increase, available budgets (consumer and company) decrease, resulting in less expenditure on investment assets, including Bitcoin.

Stock market

Many studies in Table 6 suggest that economic indicators have a significant impact on cryptocurrency pricing. For example, the Dow Jones Index was found to be positively associated with the Bitcoin price ( Ciaian et al. , 2016 ; Lamothe-Fernández et al. , 2020 ). However, Zhu et al. (2017) demonstrated that the Dow Jones Index had a long-term negative effect on the price of Bitcoin. The S&P 500 Index was found to have a significant and positive effect on the price of Bitcoin ( Bakas, Magkonis, & Oh, 2022 ; Francisco. Jareño et al. , 2020 ; Nguyen, 2022 ), while it also moved in tandem with Bitcoin returns ( Vo et al. , 2022 ). The Chinese Stock Market Index also had a positive and significant effect on the Bitcoin price ( Bouoiyour & Selmi, 2015 ). This was also consistent with Panagiotidis et al. (2018) , who showed that the Nikkei index emerged as a determinant that positively affected Bitcoin returns. Anamika, Chakraborty, and Subramaniam (2021) also indicated that fear in the equity market had a positive correlation with Bitcoin, Ethereum and Litecoin returns. When the equity market was experiencing bearish sentiment, this may lead investors to consider cryptocurrency as an alternative asset as a result of the increase in cryptocurrency prices. These findings were supported by Dyhrberg (2016) who studied which stock markets had an impact on the Bitcoin price. However, Havidz et al. (2021) argued that the Stock Market Index had a negative but insignificant effect on the Bitcoin price, which contrasted with previous findings. Other factors such as government bond indices and small company stock returns significantly impacted the cryptocurrency returns ( Ciner, Lucey, & Yarovaya, 2022 ).

Empirical studies have provided evidence that the cryptocurrency price may also be affected by the Economic Uncertainty Index. A number of studies conducted by Hasan, Hassan, Karim, and Rashid (2022) and Wu, Ho, and Wu (2022) showed a negative relationship between the Cryptocurrency Policy Uncertainty Index and the Bitcoin price. This means that when the cryptocurrency policy uncertainty increases, the Bitcoin price will decrease, when all other variables are kept constant ( KaraÖMer, 2022 ). Similarly, the Economic Uncertainty Index displayed the same negative and significant association with the Bitcoin price ( Kalyvas, Papakyriakou, Sakkas, & Urquhart, 2020 ; Wang, Sarker, & Bouri, 2022 ). These results were consistent with Jareño et al. (2020) , who demonstrated that fear in the Financial Market Index and the St Louis Fed’s Financial Stress Index had a negative and significant effect on Bitcoin returns. European economic policy uncertainty was the most important variable for Bitcoin returns ( Panagiotidis et al. , 2018 ). The possible explanation is that when the economy has suffered a crisis or was under stress, cryptocurrency was more likely to be considered by investors as a hedging asset ( Nakagawa & Sakemoto, 2022 ). Scharnowski (2022) indicated that economic policies related to central bank digital currencies (CBDC) have had a positive effect on cryptocurrency prices, the rationale being that the introduction and development of CBDC can be perceived as a favorable signal for other forms of digital currencies, including cryptocurrencies.

Market volatility

Table 7 presents that the systematic risk of cryptocurrencies is an important factor driving returns. Zhang, Li, Xiong, and Wang (2021) showed a positive cross-sectional relationship existed between downside risk and future returns in the cryptocurrency market. Liu, Liang, and Cui (2020) demonstrated that cryptocurrency returns were driven by three common risk factors: cryptocurrency market returns, market capitalization (size) and the momentum of cryptocurrencies. These findings were supported by Liu et al. (2022) who found that cryptocurrency returns were captured by the cryptocurrency market, size and momentum. Similarly, size, momentum and the value to the growth of cryptocurrency also affected cryptocurrency returns ( Wang & Chong, 2021 ). The combined effect of size and momentum factors can effectively capture the cross-sectional variation observed in cryptocurrency returns ( Liu et al. , 2020 ). Other factors specific to the cryptocurrency market, such as MAX momentum ( Li, Urquhart, Wang, & Zhang, 2021 ), reversal factors ( Jia, Goodell, & Shen, 2022 ), idiosyncratic volatility ( Leirvik, 2022 ; Liu & Tsyvinski, 2021 ) and liquidity ( Zhang & Li, 2020 ), were also important for predicting cryptocurrency returns. Furthermore, Ciaian et al. (2016) showed that risk and uncertainty related to the Bitcoin system negatively affected the Bitcoin price. Nadler and Guo (2020) added that specific risk associated with blockchain had a stronger effect on cryptocurrency pricing.

Studies have also provided evidence that unsystematic risk can be a determinant of cryptocurrency price. Koutmos (2020) , unitizing the Markov regime switching model, stated that other asset pricing risk factors were important determinants of Bitcoin returns. Corbet et al. (2019) found that hacking events are drivers of price volatility in cryptocurrencies. Almaqableh et al. (2022) indicated that terrorist attacks positively affected cryptocurrency returns, while these attacks also resulted in short-term risk shifting behavior for different cryptocurrencies. The COVID-19 pandemic has had a positive and significant effect on the Bitcoin price in the short term ( ÇElik, Yilmaz, Emir, & Sak, 2020 ; Lee, Vo, & Chapman, 2022 ). The pandemic had a notable impact on the conditional volatility of cryptocurrency returns ( Apergis, 2022 ; Nguyen, 2022 ; Sarkodie, Ahmed, & Owusu, 2022 ). The heightened uncertainty and market disruptions caused by the pandemic have led to increased cryptocurrency price fluctuations and volatility. Additionally, increased COVID-19 cases/deaths were positively linked to cryptocurrency returns. Demiralay and Golitsis (2021) also found that cryptocurrency returns exhibit time-varying patterns and were highly correlated with major events such as hacker attacks and the COVID-19 pandemic. These events can significantly affect investor sentiment and market dynamics as a result of cryptocurrency price fluctuation ( Corbet et al. , 2022 ). Zhu et al. (2017) further indicated that cryptocurrency exchange platforms are a potential risk that could influence cryptocurrency pricing. For example, Mt. Gox, a Bitcoin exchange platform, saw both the website and trading engine disappear without official comment, leading to a decline in the Bitcoin price.

Investors’ attributes

Investors’ attention has been argued to be an important determinant of cryptocurrency pricing. Smales (2021) showed that investors’ attention had a positive relationship with the cryptocurrency price. Similarly, others have highlighted that investors’ attention had the potential to improve prediction accuracy for Bitcoin returns. Zhu, Zhang, Wu, Zheng, and Zhang (2021) and Mohamed (2021) also confirmed that investor attention predicts future cryptocurrency volatility through a vector autoregression framework. Attractiveness indicators were also found to be important determinants of Bitcoin pricing, with variations over time ( Guizani & Nafti, 2019 ). These findings suggest that a strong relationship exists between investors’ interest and the Bitcoin price ( Hakim das Neves, 2020 ). Cryptocurrency popularity is one of the main factors that determine returns. KaraÖMer (2022) demonstrated that popularity had a significant and positive relationship with Bitcoin in the short term. The growth of Bitcoin’s popularity has been predicted to exert upward pressure on the Bitcoin price ( Garcia et al. , 2014 ; Nepp & Karpeko, 2022 ). With cryptocurrency’s growing popularity leading to higher search volume and social media activity, the implications are that there is increasing investor interest in cryptocurrencies, which drives higher prices.

The literature has demonstrated evidence of a wide range of volatility within cryptocurrency prices (see Table 8 ), which is significantly affected by investors’ sentiment. Positive investor opinion or sentiment has a positive correlation with pricing ( Kjaerland et al. , 2018 ; Patel, Tanwar, Gupta, & Kumar, 2020 ). Social media as a platform where investors can express psychological and financial sentiments plays a significant role in Bitcoin volatility ( Gurrib & Kamalov, 2022 ; Sapkota, 2022 ). These findings were consistent with those of Garcia  et al. (2014) who stated that positive word of mouth contributes to Bitcoin price bubbles. Positive feedback associated with Bitcoin trading behavior also increased its volatility ( Wang, Lee, Liu, & Lee, 2022 ). Huynh (2021) also showed that negative sentiment had a significant impact on Bitcoin return and trading volume. This was supported by Wang and Vergne (2017) who demonstrated that the “buzz” surrounding cryptocurrencies was negatively associated with returns. Shahzad, Anas, and Bouri (2022) emphasized the influential role of key individuals, such as Elon Musk, and social media tweets that led to the formation of bubbles, which significantly affected cryptocurrency prices. Similarly, Gerritsen, Lugtigheid, and Walther (2022) revealed that crypto experts have had a significant effect on Bitcoin returns. Barth, Herath, Herath, and Xu (2020) highlighted a negative association between the frequency of discussions of unethical practices related to Bitcoin and its price. Bartolucci et al. (2020) showed that developers’ emotions were also the drivers of the price volatility within Bitcoin and Ethereum. Ahn and Kim (2021) agreed that emotional factors played a significant role in predicting Bitcoin trading volume and return volatility. Rubbaniy, Tee, Iren, and Abdennadher (2022) also supported the notion that investors’ mood is linked to the volatility of the cryptomarket.

Social media

Empirical evidence has demonstrated that cryptocurrency pricing was significantly affected by online activities (see Table 9 ). Wikipedia views, which represented online information queries, had a positive and statistically significant effect on the Bitcoin price ( Figà-Talamanca & Patacca, 2020 ). Ciaian et al. (2016) also suggested that Wikipedia exercised a strong impact on the Bitcoin price. Growth in the volume of Google Trends or Google Search also led to high Bitcoin returns ( Polasik et al. , 2015 ). Aslanidis, Bariviera and López (2022) suggested a positive relationship between cryptocurrency returns and the attention received on Google Trends, particularly when measuring attention specific to the cryptomarket. Additionally, Panagiotidis et al. (2018) identified Google Search as the most important variable for explaining Bitcoin returns, and it was found to be a good predictor of cryptocurrency prices ( Chuffart, 2022 ). This indicated that increased interest and search volume for cryptocurrencies on Google can be associated with higher cryptocurrency returns ( Bakas et al. , 2022 ). Increased investors’ curiosity and attention imply that demand for Bitcoin will also likely increase ( Kjaerland et al. , 2018 ). Online factors, such as online activities, social media, Google Search and Wikipedia, have had a long-term positive relationship with the cryptocurrency price ( Phillips & Gorse, 2018 ). However, it has also been reported that Bitcoin and Ethereum price movements were negatively affected by search volume obtained via Google Trends ( Smuts, 2019 ).

This study employs a systematic literature review to identify the influential factors of cryptocurrency pricing and to determine the major gaps for future research. This review included all peer-reviewed journal articles that met the selection criteria and were published before September 2022. The final analysis included a total of 88 articles, 56 articles that met the eligibility criteria and 32 articles from reference lists of the eligible articles. The earliest article was published in 2014, with most articles being published in 2022, indicating that the field of cryptocurrency pricing is still emerging. The overall upward trend in the number of published studies on cryptocurrency pricing reflects increasing interest and recognition of the importance of this research topic. Empirical cryptocurrency pricing studies focused on Bitcoin, suggesting that it remains the most popular and widely researched cryptocurrency in the market. As a pioneer and the first cryptocurrency, Bitcoin has received significant attention from researchers, investors and the public ( Wang & Vergne, 2017 ). Future studies could explore factors that influence other cryptocurrencies, such as Dogecoin or Litecoin, to offer a comprehensive overview of cryptocurrency pricing.

The peer-reviewed articles on the influential factors of cryptocurrency pricing were published in 54 different journals. The majority of articles (27) were published in Finance Research Letters. The remaining 47 articles were distributed across journals from various disciplines and highlight the wide-ranging interest and multi-faceted nature of cryptocurrencies. Finance Research Letters presents as the leading journal in cryptocurrency pricing research. Thus, future studies may consider other high-quality journals to allow investors or policymakers to obtain a more comprehensive understanding of cryptocurrency pricing. Future studies could also research the connections between traditional finance and the cryptocurrency market to improve the depth of research.

The geographic analysis conducted in this review offered another layer of insight into the research on cryptocurrency pricing. A total of 88 studies were conducted in 18 different regions, with Europe accounting for 29 studies. Cryptocurrency pricing research appears to be more active in Europe than in other locations, suggesting significant academic interest in the region. Extending the geographic coverage by encouraging research to focus on developing countries and perhaps exploring the development of financial technologies and their effect on the cryptocurrency market could be useful for the field.

A total of 48 different research methods were applied across the research to analyze the determinants of cryptocurrency pricing. The most used model was the vector autoregression model (9), followed by the autoregressive distributed lag model (6), generalized autoregressive conditional heteroskedasticity (5), three-factor model (4), the fixed-effect model (3) and the wavelet coherence analysis (3). Ordinary least squares regression, vector error correlation, the asset pricing model, the cost of production model, fixed-effect analysis and the text analytic approach were applied twice each. Future studies could apply other methods or combine existing research methods in the construction of cryptocurrency pricing models to improve their predictions.

This review has revealed the factors that influence cryptocurrency pricing and has been classified into six categories: (i) fundamental factors, (ii) technological factors, (iii) economic factors, (iv) market volatility, (v) investors’ attributes and (vi) social media. Although studies have mentioned that cryptocurrency pricing can be explained by many factors, Bitcoin continues to be the most studied. Future studies could examine the impact of other coins on cryptocurrency pricing. As cryptocurrency is the result of financial innovation, future research could also consider the technological dimensions of cryptocurrency. This exploration might include whether it is more explicit and dynamic than traditional currency. The rationale for this focus is that cryptocurrency needs to continually update its underlying software to maintain its technological advantage ( Wang & Vergne, 2017 ). Cryptocurrency could be an alternative way to reshape the existing financial system. Research could consider cryptocurrency connection with the existing financial market and examine the impact of economic policies on the cryptocurrency market. The role of financial technologies is evolving within existing financial systems. These technologies can improve efficiency and service quality but may also lead to new challenges for the financial market. Research that examines the potential challenges faced by cryptocurrency pricing or value would be of value. The research selected for this study has provided evidence to suggest that investors’ sentiment is a key factor influencing cryptocurrency pricing. Future studies could quantify these sentiment factors or examine the potential factors affecting investors’ sentiment towards cryptocurrency. Although many determinants have been identified in this review, several important factors continue to be neglected in the literature, such as cultural and political factors, and the development of financial technologies. These research gaps are areas of interest to the field.

Implications

This systematic literature review identified factors influencing cryptocurrency pricing and highlighted major gaps in the research. The findings generated from this research offer important contributions to the literature and practitioners.

Theoretical implications

This study contributes to the cryptocurrency literature in several ways. Firstly, this research provides a comprehensive overview of the existing literature and categorizes the significant factors that influence cryptocurrency pricing. Within this field, there has been a lack of systematic reviews that may guide future research by identifying factors that may affect the determinants of cryptocurrency pricing.

The review also highlights the varying research methods used to identify the determinants of cryptocurrency pricing. In total, 48 different research methods have been employed to analyze the determinants of cryptocurrency pricing. The most common research methods applied were the vector autoregression model and the autoregressive distributed lag model, with other types of models used in various studies. This study therefore informs future studies of the commonly used methods and theories that could be considered for theoretical frameworks to underpin cryptocurrency pricing research.

This review provides evidence that cryptocurrency can be considered an alternative currency that complements the existing financial industry. Prior studies have shown that cryptocurrency usage in transactions, its supply and price levels are consistent with monetary economics and the quantity theory of money ( Wang & Vergne, 2017 ). Moreover, cryptocurrency offers a low transaction cost, decentralization and a peer-to-peer system ( Kim et al. , 2021 ). This makes it possible for users to use a cost-effective remittance system in developing countries where banking systems are underdeveloped or unsecure ( Ciaian et al. , 2016 ). Therefore, cryptocurrency has the potential to serve as a medium of exchange for the global economy ( Ciaian et al. , 2016 ). In addition, Kristoufek (2015) has stated that although the Bitcoin price was mainly driven by speculative opportunities due to its high volatility and decentralization, its unique asset-possessing property is that it is both a standard financial asset and a speculative asset. Jareño et al. (2020) also revealed a positive connection between Bitcoin and gold price returns during times of economic turmoil. Bitcoin was found to have the properties similar to gold in that it could serve as a financial haven during periods of high economic uncertainty. Kjaerland et al. (2018) suggested that Bitcoin price volatility could be explained by investment theories such as the greater fool theory and momentum theory. Therefore, it can be concluded that cryptocurrencies have the potential to complement the existing financial industry, with this information having significance for practical applications.

Practical implications

This research has implications for multiple stakeholders. Firstly, this study brings together the literature and synthesizes multiple elements of the cryptocurrency market. The systematic review of this literature adds a depth of understanding through a discussion of the determinants of cryptocurrency prices. This information is useful for investors and investment managers when making trading decisions in relation to the cryptocurrency market. A large number of Bitcoin users are considered to be young and inexperienced ( Baur, Dimpfl, & Kuck, 2018 ) and are more likely to require potential indicators of cryptocurrency pricing to make appropriate investment decisions. Thus, investors will benefit from this review when seeking to diversify their portfolios with cryptocurrencies or by designing better trading strategies. The review may also benefit more experienced investors, such as investment managers. This study provides a consolidated discussion of the determinants of cryptocurrency prices and may assist investors to construct cryptocurrency price prediction models. Portfolio managers can effectively trace cryptocurrency price movements, thus avoiding large change events in cryptocurrency prices, which may have a significant effect on the risk and return of individual risky assets.

Secondly, the review has a series of policy implications. From the consolidated technological aspects, regulators may utilize cryptocurrency technologies to update their financial systems, thus being able to offer lower costs, higher efficiency and greater convenience for their consumers, as per their profiles and needs. Given the safe haven characteristics of cryptocurrencies, many investors are more likely to buy cryptocurrency to minimize financial risk during times of economic stress or crisis ( Jareño et al. , 2020 ). Thus, policymakers could monitor these financial activities or establish alternatives to avoid the depreciation of their currencies. The review also assists regulatory bodies in assessing the determinants of cryptocurrency returns as an alternative investment, thus enriching their knowledge ( Gurrib, Kweh, Nourani, & Ting, 2019 ). It is well known that the cryptocurrency market is unregulated and highly speculative ( Hameed & Farooq, 2017 ). If private cryptocurrencies widely enter the market as public forms of currency, this will likely encourage money laundering and financial crimes that will significantly affect monetary policy and financial stability ( Baldan & Zen, 2020 ). Therefore, regulators have a requirement to understand the potential factors that would induce economic crisis, expressed as the influential factors of cryptocurrency pricing. The understanding of these factors may assist regulators to effectively formulate monetary policy in response to these challenges.

Thirdly, this review also has important implications for companies that consider cryptocurrency as a means of payment in cross-border transactions. This may especially be the case between countries without a coherent and reliable payment infrastructure. Cryptocurrency offers characteristics such as low transaction costs and decentralization and offers a peer-to-peer payment system. In addition, the information from this review may allow individuals to access international business when there is a lack of access to traditional financial institutions or when they have less access to credit from within the banking system.

Limitations of the study and future research

Several limitations are acknowledged within this study. Firstly, this review only considered peer-reviewed articles. Future studies could consider other sources in the literature such as conference papers, government reports and theses to review a larger number of studies. Secondly, this review used only three databases to collect the selected articles. Studies not written in English and published in other databases may provide further insights. Future research that draws on more databases and other relevant search items may provide a more comprehensive review. Thirdly, some relevant articles may have been missed given the arbitrary nature of inclusion and exclusion criteria in the keywords, title and abstract. Future research could adjust the search strategies, the intervals and reading sources to collect relevant studies. Studies that included the design of a measurement scale of the influential factors with statistical validation would also improve insights into the literature.

Flow chart of systematic literature review

Number of articles published between 2014 and August 2022

Article distribution by journal and date of publication

Journal	Number of articles	2014	2015		2016	2017	2018	2019		2020		2021	Jan-Aug 2022
	4		1			1	1					1
	1							1
	1											1
	1											1
Mathematical Social Science	1												1
	1									1
	1											1
	1									1
	1	1
	1		1
	1		1
	1				1
	1				1
	2					1				1
	2						1			1
	1							1
	1							1
	1							1
	1						1
	1						1
	1									1
	1									1
	1									1
	1									1
	1									1
	1									1
	1							1
	1									1
	1							1
	1							1
	1									1
	1							1
	1									1
	1							1
	2									1		1
	1											1
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	2						1	1
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	2							1					1
	1							1
	1							1
	1								1
	1		1
	1										1

Table created by the authors

Location	Number of articles	2014	2015	2016	2017	2018	2019	2020	2021	Jan-Aug 2022
USA	9						1	2	2	4
Europe	29	1	2	1		4	1	6	2	11
UK	12		1			2	3	1	1	4
Canada	3				1			1	1
China	12			1	1			1	3	6
South Korea	2							1	1
Taiwan	1									1
UAE	4						1	1	1	1
Russia	3						1	1		1
Brazil	1							1
India	3							1	1	1
Philippines	1								1
Indonesia	1								1
Australia	3								1	2
Tunisia	1						1
Japan	1									1
Bangladesh	1									1
Lebanon	1								1

Table created by the authors

Theory/Model	Number of articles	2014	2015	2016	2017	2018	2019	2020	2021	2022
Vector autoregression analysis	9	*		*			*	***	*	**
Wavelet coherence analysis	3		*			*				*
Autoregressive distributed lag model	6		*	*		*	*			**
Ordinary least squares regression	2		*						*	*
Long short-term memory model	3					*		*		*
Vector error correlation	2				*				*
Text analytic approach	2							*	*
Tobit estimation approach	1		*
Modular Integrated Distributed Analysis System	1									*
Least Absolute Shrinkage and Selection Operator	2					*				*
Generalized AutoRegressive Conditional Heteroskedasticity	5								*	****
Dynamics Equi-correlation Model	2								**
Overlapping generations model	1									*
Axiomatic approach	1							*
Impossibility theorem	1							*
Machine learning approach	1									*
Dynamic Bayesian model	1									*
Smooth Transition Conditional Correlation Model	1									*
Quantile regression	1									*
Quantile-on-quantile regression	2									**
Rolling window estimations	1									*
Augmented version of Barro’s model	1		*
Comparative analysis	1						*
Artificial recurrent neural network model	1							*
Bayesian structural time series approach	1					*
Autoregressive integrated moving average model	2							*	*
Fourier KPSS unit root test	1							*
Asymmetric nonlinear cointegration approach	1							*
Negative coefficient of skewness analysis	1						*
Markov regime-switching model	1						*
Asset pricing model	2						*			*
Robust least squares (L.S.) method	1								*
Sentiment index model	1							*
Corpus linguistics approach	1							*
Value-at-risk analysis	1								*
Garman–Klass analysis	1								*
Systematic review	1					*
Quantile regression approach	1									*
Linear discriminant analysis	1								*
Autoregressive conditional jump intensity model	1									*
Structural break analysis	1									*
Heterogeneous autoregressive model	1									*
Random-effect analysis	2				*					*
Deep learning integration method	1									*
Portfolio analysis	2						*		*
Cost of production model	2							*	*
Fixed-effect analysis	3				*				*	*
Three-factor model	4							*	*	**

Table created by the authors

Source(s): Table created by the authors

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Corresponding author

About the authors.

Sanshao Peng is Ph.D. candidate in the Business School at the University of Southern Queensland. His research interests include financial technologies and cryptocurrency pricing. Sanshao has published few papers, including two conference papers and a paper under review in Q1 Journal, since she began her Ph.D.

Dr Catherine Prentice is Professor of Marketing at the University of Southern Queensland and Director of Asia Pacific Association for Gambling Studies. She currently serves as Associate Editor for three journals, including Service Industries Journal (Q1), Tourism Review (Q1) and Journal of Global Scholars of Marketing Science (B-ABDC). Catherine has published extensively in marketing and management journals as the first or solo author. She is one of the world-leading experts in emotional intelligence and artificial intelligence and has chaired several international conferences as well as delivering keynote speeches for various conferences. Her main research interests include artificial intelligence, emotional intelligence, service research, consumer psychology, consumer behavior, services and relationship marketing and gambling studies.

Syed Shams has more than 15 years of corporate and academic leadership in various finance and academic roles. He is former Associate Head of Research at the School of Business. He is the guest editor of the Sustainability (Q1) journal. Shams has extensively published articles in prestigious finance journals relating to climate finance, mergers and acquisitions, especially concentrating on corporate climate finance disclosures, such as Economics and Finance, Accounting and Finance Journal, Australian Journal of Management , Pacific Basin Finance Journal , Journal of Contemporary Accounting and Economics , International Journal of Managerial Finance , Journal of Behavioral and Experimental Finance and International Review of Financial Analysis .

Dr Tapan Sarker has more than 20 years of teaching and training, research, administrative and consulting experience. Within the industry, he has been Consultant for leading MNCs such as BHP Billiton, Rio Tinto and Mahindra and Mahindra Ltd, India. His interdisciplinary research expertise is in the areas of sustainable and green finance, public financial management, financing for the SDGs, business responses to climate change and circular economy. His research has been published in leading journals including Finance Research Letters , International Review of Economics and Finance , Australian Tax Forum , Global Finance Journal , Economic Modelling , among others.

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Article Contents

1. data and basic characteristics, 2. cryptocurrency-specific factors, 3. exposures to other assets, 4. additional results, 5. conclusion, acknowledgement, risks and returns of cryptocurrency.

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Yukun Liu, Aleh Tsyvinski, Risks and Returns of Cryptocurrency, The Review of Financial Studies , Volume 34, Issue 6, June 2021, Pages 2689–2727, https://doi.org/10.1093/rfs/hhaa113

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We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.

Cryptocurrency is a recent phenomenon that is receiving significant attention. On the one hand, it is based on a fundamentally new technology, the potential of which is not fully understood. On the other hand, at least in the current form, it fulfills similar functions as other, more traditional assets. Extensive academic attention has focused on developing theoretical models of cryptocurrencies. The theoretical literature on cryptocurrencies has suggested a number of factors that are potentially important in the valuation of cryptocurrencies. The first group of papers builds models stressing the network effect of cryptocurrency adoption (e.g., Pagnotta and Buraschi 2018 ; Biais et al. 2018 ; Cong, Li, and Wang 2019 ) and emphasizes the price dynamics induced by the positive externality of the network effect. The second group of papers focuses on the production side of the coins—the miners’ problem (e.g., Cong, He, and Li 2018 ; Sockin and Xiong 2019 )—and shows that the evolution of cryptocurrency prices is linked to the marginal cost of production. The third group of papers ties the movements of cryptocurrency prices to those of traditional asset classes such as fiat money (e.g., Athey et al. 2016 ; Schilling and Uhlig 2019 ; Jermann 2018 ). There is also a growing literature on the empirical regularities of cryptocurrencies. Borri (2019) shows that individual cryptocurrencies are exposed to cryptomarket tail-risks. Makarov and Schoar (2020) find that cryptocurrency markets exhibit periods of potential arbitrage opportunites across exchanges. Griffin and Shams (2020) study Bitcoin price manipulation. Our paper is the first comprehensive analysis of cryptocurrencies through the lens of empirical asset pricing. Its contribution is twofold. First, it tests the mechanisms and predictions of the existing theoretical models. Second, it establishes a set of basic asset pricing facts for this asset class, which provides a common benchmark that the current and future models of cryptocurrencies should take into consideration.

We start by constructing an index of cryptocurrency (or coin) market returns. This index is the value-weighted returns of all the coins with capitalizations of more than 1 million USD (1,707 coins in total) and covers the period of January 1, 2011, to December 31, 2018. We now describe some basic statistical properties of this index. During the sample period, the averages of the coin market returns at the daily, weekly, and monthly frequencies are 0.46%, 3.44%, and 20.44%, respectively. The daily, weekly, and monthly standard deviations of the coin market returns are 5.46%, 16.50%, and 70.80%, respectively. The coin market returns have positive skewness and kurtosis. We observe that the mean and standard deviation of the coin market returns are an order of magnitude higher than those of the stock returns during the same period. The Sharpe ratios at the daily and weekly levels are about 60% and 90% higher, and the Sharpe ratio at the monthly level is comparable to those of stocks. The returns have positive skewness increasing with the frequencies from daily to monthly. The returns experience high probabilities of extreme losses and gains. For example, an extreme loss of the daily 20% negative return on the coin market happens with a probability of 0.48%, while an extreme gain of the same size occurs with a probability of 0.89%.

We next turn to examine the relationship between the coin market returns and the main cryptocurrency-specific factors that are proposed in the theoretical literature. We formulate and investigate potential drivers and predictors for cryptocurrency returns. Specifically, we construct cryptocurrency network factors, cryptocurrency production factors, cryptocurrency momentum, proxies for average and negative investor attention, and proxies for cryptocurrency valuation ratios. For each of these factors, we aim to provide a number of possible empirical measures, as there are no canonical ways to define them in the cryptocurrency market.

We consider five measures to capture the cryptocurrency network effect. Consistent with the cryptocurrency models based on the network effect, 1 we find that the coin market returns are positively and significantly exposed to cryptocurrency network growth. Furthermore, we show that the evolution of cryptocurrency prices not only reflects current cryptocurrency adoption but also contains information about expected future network growth.

We then study the implications of the cryptocurrency models based on the miners’ production problem. 2 We construct production factors of cryptocurrency to proxy for the cost of mining and test the relationship between these production factors and cryptocurrency prices. To the first approximation, mining a cryptocurrency requires two inputs: electricity and computer power. We separately construct eight proxies for electricity costs and six proxies for computing costs. For electricity, we use time-varying and location-specific measures of the price, consumption, and generation of electricity in the United States and China (including Sichuan province, which hosts the largest mining farm in the world). For proxies of computing costs, we use the prices of Bitmain Antminer, one of the common Bitcoin mining equipments, as our primary measure. We also consider indirect measures—the stock returns of the companies that are major manufacturers of mining chips. Overall, we find that the coin market returns are not significantly exposed to the cryptocurrency production factors.

The existing theoretical models of cryptocurrencies have a number of implications for the predictability of cryptocurrency returns. Some papers argue that the evolution of cryptocurrency prices should follow a martingale, and thus cryptocurrency returns are not predictable (e.g., Schilling and Uhlig 2019 ). Other papers argue that, in dynamic cryptocurrency valuation models, cryptocurrency returns could potentially be predicted by momentum, investor attention, and cryptocurrency valuation ratios (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ). We show that momentum and investor attention strongly predict future cryptocurrency cumulative returns, but cryptocurrency valuation ratios do not.

First, we show that there is a significant time-series momentum phenomenon in the cryptocurrency market. We find that the current coin market returns predict cumulative future coin market returns from one week to eight weeks ahead. For example, a one-standard-deviation increase in the current coin market returns predicts a 3.30% increase in the weekly returns over the next week. Grouping weekly returns by terciles, we find that the top terciles outperform the bottom terciles over the one- to four-week horizons. For example, at the one-week horizon, the average return of the top tercile is 8.01% per week with a t -statistic of 4.30, while the average return of the bottom tercile is only 1.10% per week with a t -statistic of 0.92. The time-series momentum results are valid both in sample and out of sample.

Second, we construct proxies for investor attention with Google searches and show that high investor attention predicts high future returns over the one- to six-week horizons. For example, a one-standard-deviation increase in the investor attention measure yields a 3.0% increase in the 1-week-ahead future coin market returns. At the one-week horizon, the average return of the investor attention tercile is 6.53% per week with a t -statistic of 3.82, while the average return of the bottom tercile is only 0.43% per week with a t -statistic of 0.42. Another proxy for investor attention we construct is Twitter post counts, and we reach similar results with the Twitter measure. Additionally, we construct a proxy for negative investor attention and show that relatively high negative investor attention negatively predicts future cumulative coin market returns.

Research on the equity market (e.g., Hong, Lim, and Stein 2000 ; Hou, Xiong, and Peng 2009 ) shows that there is a strong interaction between momentum and investor attention. Sockin and Xiong (2019) also show that investor attention can generate momentum in the cryptocurrency market, and in their model, the momentum effect disappears controlling for investor attention. We investigate whether there is a similar interaction between momentum and investor attention in the cryptocurrency market. We find that investor attention is high during and after periods of high coin market returns. However, in a bivariate coin market predictability regression with both variables, we show that the two effects do not subsume each other. Finally, we test whether the magnitude of the momentum effect is different during periods of high investor attention and vice versa. In contrast to the equity market, we show that there is limited interaction between cryptocurrency momentum and investor attention.

Moreover, we test whether the cryptocurrency valuation ratios similar to those in the financial markets can predict future coin market returns. In the equity market, the fundamental-to-market ratios are commonly referred to as valuation ratios and are measured as the ratio of the book value of equity to the market value of equity or some other ratio of fundamental value to market value. It is more difficult to define a similar measure of the fundamental value for cryptocurrency. In their dynamic cryptocurrency asset pricing model, Cong, Li, and Wang (2019) show that the cryptocurrency fundamental-to-value ratio, defined as the number of user adoptions over market capitalization, negatively predicts future cryptocurrency returns. Motivated by the theoretical model and studies of other financial markets, we construct six cryptocurrency valuation ratios and test the return predictability of these valuation ratios. Although the coefficient estimates are consistently negative, none of the six cryptocurrency valuation ratios predict future cumulative coin market returns significantly.

Another approach to study what cryptocurrencies represent is to examine the exposures of cryptocurrency returns to other asset classes. In other words, we assess how investors and markets value current and future prospects of cryptocurrencies. The theoretical literature and the community of cryptocurrency have proposed various narratives for what cryptocurrencies represent. Schilling and Uhlig (2019) argue that, in an endowment economy where fiat money and cryptocurrency coexist and compete, the cryptocurrency returns comove with the price evolution of the fiat money. Athey et al. (2016) emphasize the importance of currency exchange rates on cryptocurrency prices. Another popular narrative is that cryptocurrency is “digital gold” and represents a new way to store value. Specifically, we study whether major cryptocurrencies comove with currencies, commodities, stocks, and macroeconomic factors. In contrast to these popular explanations, we find that the exposures of cryptocurrencies to these traditional assets are low. Overall, there is little evidence, in the view of the markets, behind the narrative that there are similarities between cryptocurrencies and these traditional assets.

We note several additional results. First, we acknowledge that we have a short time series and that there is much uncertainty and learning about cryptocurrencies during the sample period. We show that our main results are similar for the first half and the second half of the sample. Second, we discuss the relationship between the cryptocurrency time-series momentum and cross-sectional momentum. Third, we investigate the importance of regulative events in affecting cryptocurrency prices, and show that negative regulative events but not positive regulative events significantly affect cryptocurrency prices. Fourth, we examine the importance of speculative interests in driving cryptocurrency prices. We show that cryptocurrency returns are higher when speculative interests increase, but the coefficient estimates are only marginally significant. Fifth, we construct a direct measure of cryptocurrency investor sentiment and show that the expected coin market return is higher when investor sentiment is high. In the multivariate regressions with the sentiment, investor attention, and momentum measures, all three variables are statistically significant in predicting future cryptocurrency returns. Sixth, we test the role of beauty contests in the cryptocurrency market. Motivated by Biais and Bossaerts (1998) , we use the volume-volatility ratio to capture the degree of disagreement in the cryptocurrency market and show that cryptocurrency return is high when the current volume-volatility ratio is high. Seventh, we conduct a VAR analysis with the coin market returns and the different measures of coin network growth measures. Eighth, we test the effect of production factors with an alternative specification. Lastly, we examine the subsample results based on cryptocurrency characteristics.

Our paper uses standard textbook empirical asset pricing tools and methods, the discussion of which we mostly omit for conciseness. Our findings on momentum are related to a series of papers such as Jegadeesh and Titman (1993) , Moskowitz and Grinblatt (1999) , Moskowitz, Ooi, and Pedersen (2012) , Asness, Moskowitz, and Pedersen (2013) , and Daniel and Moskowitz (2016) . Da, Engelberg, and Gao (2011) use Google searches to proxy for investor attention.

Yermack (2015) is one of the first papers that brought academic attention to the field of cryptocurrency. Several recent articles document individual facts related to cryptocurrency investment. Stoffels (2017) studies cross-sectional cryptocurrency momentum. Hu, Parlour, and Rajan (2018) show that individual cryptocurrency returns correlate with Bitcoin returns. Borri (2019) shows that individual cryptocurrencies are exposed to cryptomarket tail-risks. Makarov and Schoar (2020) and Borri and Shakhnov (2018) find that cryptocurrency markets exhibit periods of potential arbitrage opportunites across exchanges. Griffin and Shams (2020) study Bitcoin price manipulation. Corbet et al. (2019) studies cryptocurrencies as a financial asset. Moreover, a number of recent papers develop models of cryptocurrencies (see, e.g., Weber 2016 ; Huberman, Leshno, and Moallemi 2017 ; Biais et al. 2018 ; Chiu and Koeppl 2017 ; Cong and He 2019 ; Cong, Li, and Wang 2019 ; Cong, He, and Li 2018 ; Sockin and Xiong 2019 ; Saleh 2018 ; Schilling and Uhlig 2019 ; Jermann 2018 ; Abadi and Brunnermeier 2018 ; Routledge and Zetlin-Jones 2018 ).

We collect trading data of all cryptocurrencies available from Coinmarketcap.com. Coinmarketcap.com is a leading source of cryptocurrency price and volume data. It aggregates information from over 200 major exchanges and provides daily data on opening, closing, high, and low prices, as well as volume and market capitalization (in dollars) for most of the cryptocurrencies. 3 For each cryptocurrency on the website, Coinmarketcap.com calculates its price by taking the volume-weighted average of all prices reported at each market. A cryptocurrency needs to meet a list of criteria to be listed, such as being traded on a public exchange with an application programming interface (API) that reports the last traded price and the last 24-hour trading volume, and having a nonzero trading volume on at least one supported exchange so that a price can be determined. Coinmarketcap.com lists both active and defunct cryptocurrencies, thus alleviating concerns about survivorship bias.

We first construct a coin market return as the value-weighted return of all the underlying coins. We use daily close prices to construct daily coin market returns. The weekly and monthly coin market returns are calculated from the daily coin market returns. We require the coins to have information on price, volume, and market capitalization. We further exclude coins with market capitalizations of less than 1,000,000 USD. For earlier years that are not covered by Coinmarketcap.com, we splice the coin market returns with Bitcoin returns from earlier years. The data of the earlier year Bitcoin returns are from CoinDeck and span from January 1, 2011, to April 29, 2013. We start from January 1, 2011, because there was not much liquidity and trading before that date. Altogether, the index of the coin market return covers the period from January 1, 2011, to December 31, 2018.

We use four primary measures to proxy for the network effect of user adoption: the number of wallet users, the number of active addresses, the number of transaction count, and the number of payment count. The data of wallet users are from Blockchain.info. We obtain data on active addresses, transaction count, and payment count from Coinmetrics.io. We use seven primary production factors to proxy for the cost of mining: the average price of electricity in the United States, the net generation of electricity of all sectors in the United States, the total electricity consumption of all sectors in the United States, the average price of electricity in China, and the average price of electricity in Sichuan province. We obtain data on the average price of electricity in the United States, the net generation of electricity of all sectors in the United States, and the total electricity consumption of all sectors in the United States from the U.S. Energy Information Administration. We obtain data on the average price of electricity in China and the average price of electricity in Sichuan province from the National Bureau of Statistics of China and the Price Monitoring Center, NDRC. Our primary computing cost data are the prices of Bitmain Antminer. We extract the Bitmain Antminer data from Keepa.com. The data for Bitmain Antminer start from September 2015.

Google search data series are downloaded from Google. Twitter post counts for the word “Bitcoin” are downloaded from Crimson Hexagon. 4 The spot exchange rates in units of U.S. dollars per foreign currency are from the Federal Reserve Bank of St. Louis. We focus on five major currencies: Australian dollar, Canadian dollar, euro, Singaporean dollar, and U.K. pound. The spot prices of precious metals are from several sources. The gold and silver prices are from the London Bullion Market Association (LBMA). Platinum prices are from the London Platinum and Palladium Market (LPPM).

Aggregate and individual stock returns are from CRSP. Detailed SIC three-digit industry return data series are constructed using individual stock returns. Chinese stock return data are from CSMAR. We build the value-weighted aggregate Chinese stock returns and detailed CIC (China Industry Classification) industry return data series from the individual stocks. The data series of Chinese stock returns last until December 2016. The return series of the 155 anomalies are downloaded from Andrew Chen’s website. 5

We obtain data on the Fama-French three-factor, Carhart four-factor, Fama-French five-factor, and Fama-French six-factor models from Kenneth French’s website. We also collect the return series of Fama-French 30 industries, Europe, Japan, AsiaExJapan, and North America from Kenneth French’s website.

The macroeconomic data series are from the website of the Federal Reserve Bank of St. Louis. Nondurable consumption is defined as the sum of personal consumption expenditures: nondurable goods, and personal consumption expenditures: services.

Stock market prices, dividends, and earnings, as well as the three-month Treasury bill rates, are from Robert Shiller’s website. Using these data series, we construct the stock market price-to-dividend ratio (pd), price-to-earnings ratio (pe), and the relative bill rate (tbill). The relative bill rate is defined as the three-month Treasury bill rate minus its 12 month backward moving average. Credit spread (credit) is defined as the yield spread between BAA corporate bonds and AAA corporate bonds. Term spread (term) is defined as the yield spread between the 10-year Treasury and 3-month Treasury. Data series on the BAA corporate yield, AAA corporate yield, 10-year Treasury yield, and 3-month Treasury yield are from the Federal Reserve Bank of St. Louis’s website.

We now document the main statistical properties of the time series for the coin market returns. Figure 1 shows the return distributions of coin market returns and coin market log returns at daily, weekly, and monthly frequencies. Figure 2 plots the price movements of the coin market compared with those of the three major cryptocurrencies. There are strong comovements across the three major cryptocurrencies. Table 1 compares the properties of the coin market returns with those of Bitcoin returns, Ethereum returns, Ripple returns, and stock market returns.

Coin market return distributions

This figure plots the distributions of daily, weekly, and monthly cryptocurrency returns and log returns.

Cryptocurrency market returns and major coins

This figure plots the cryptocurrency market returns against Bitcoin, Ethereum, and Ripple. The figures show the value of investment over time for one dollar of investment at the starting point of the graphs. The Bitcoin graph starts at April 29, 2013. The Ethereum graph starts at August 8, 2015. The Ripple graph starts at August 5, 2013.

Summary statistics

This table documents the summary statistics of the coin market returns (CMKT). Panel A reports the daily, weekly, and monthly summary statistics of the coin market index and compares them with returns for Bitcoin, Ethereum, Ripple, and the stock market. The mean, standard deviation, t -statistics, Sharpe ratio, skewness, kurtosis, and the percentage of obervations that are positive are reported. Panel B reports the percentage of extreme events based on the daily coin market index returns. The coin market returns, the Bitcoin returns, and the stock market returns are from January 1, 2011, to December 31, 2018. The Ethereum returns are from August 8, 2015, to December 31, 2018. The Ripple returns are from August 5, 2013 to December 31, 2018.

Table 1 shows the statistics of the coin market returns at the daily, weekly, and monthly frequencies compared with those of the stock market returns. Both the average and the standard deviation of the coin market returns are very high. At the daily frequency, the mean return is 0.46% and the standard deviation is 5.46%; at the weekly frequency, the mean return is 3.44% and the standard deviation is 16.50%; at the monthly frequency, the mean return is 20.44% and the standard deviation is 70.80%. Both the means and the standard deviations are an order of magnitude higher than those for the stock market returns. These facts are broadly known.

The Sharpe ratios of the coin market returns are 0.08 at the daily frequency, 0.21 at the weekly frequency, and 0.29 at the monthly frequency. At the daily and weekly frequencies, the Sharpe ratios of the coin market are about 60% and 90% higher than those of the stock market for the comparable time period. At the monthly frequency, the Sharpe ratio is similar to that of the stock market for the comparable time period.

We compare the characteristics of the coin market returns to those of the Bitcoin, Ripple, and Ethereum returns. Note that the Ripple return series starts on August 4, 2013, and the Ethereum return series starts on August 7, 2015. For the Bitcoin returns, the Sharpe ratios are 0.08 at the daily frequency, 0.21 at the weekly frequency, and 0.29 at the monthly frequency. For Ethereum, the Sharpe ratios are 0.08 at the daily frequency, 0.20 at the weekly frequency, and 0.36 at the monthly frequency. The Ethereum returns have a higher mean and standard deviation than the coin market returns. For the Ripple returns, the Sharpe ratios are 0.07 at the daily frequency, 0.13 at the weekly frequency, and 0.24 at the monthly frequency. The Ripple returns have a markedly higher mean and standard deviation compared with those of the coin market returns. The Sharpe ratios of Ripple returns are lower than those of the coin market returns at all three frequencies.

The coin market returns are positively skewed at all frequencies, in contrast to the stock returns, which are negatively skewed. The skewness increases from 0.74 at the daily frequency to 1.74 at the weekly frequency, and to 4.37 at the monthly frequency. The corresponding kurtosis is 15.52 at the daily frequency, 10.22 at the weekly frequency, and 26.54 at the monthly frequency. All three of the major cryptocurrencies have positive skewness and high kurtosis. The coin market returns have high probabilities of exceptional negative and positive daily returns. For example, the probability of a –20% daily return is almost 0.5%, and the probability of a 20% daily return is almost 0.9%.

In the Internet Appendix , we also show the mean, standard deviation, and Sharpe ratios of the returns on different days of the week. In contrast to the stocks, there is no pronounced Monday effect. However, the returns are lower on Saturdays: the average Sunday coin market return is 0.28% with a Sharpe ratio of 0.05, compared with a 0.46% daily average with a Sharpe ratio of 0.08; the average Sunday Bitcoin is 0.29% with a Sharpe ratio of 0.06, compared with a 0.46% daily average with a Sharpe ratio of 0.08; the average Sunday Ethereum is 0.25% with a Sharpe ratio of 0.03, compared with a 0.60% daily average with a Sharpe ratio of 0.08; and the average Sunday Ethereum is –0.15% with a Sharpe ratio of –0.02, compared with a 0.53% daily average with a Sharpe ratio of 0.07. While the coin market and Bitcoin returns are somewhat lower on Sundays, the returns on Saturday are consistently lower.

The theoretical literature has proposed a number of cryptocurrency-specific factors as drivers of cryptocurrency prices and as predictors of cryptocurrency returns. In this section, we develop and investigate the implications of cryptocurrency-specific factors. We first construct cryptocurrency network and production factors. We find that the coin market returns are strongly exposed to the network factors but not the production factors. Then, we test if cryptocurrency returns are predictable by studying whether different cryptocurrency-specific factors can predict future coin market returns. We consider momentum, proxies for investor attention, and proxies for cryptocurrency valuation ratios. All of these variables are specific to the cryptocurrency markets. We find that momentum and proxies for investor attention can account for future coin market returns, and thus strongly reject the notion that cryptocurrency prices are a martingale.

2.1 Network factors

The theoretical literature on cryptocurrency has emphasized the importance of network factors in the valuation of cryptocurrencies (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ; Pagnotta and Buraschi 2018 ; Biais et al. 2018 ). In particular, the network effect of user adoption can potentially play a central role in the valuation of cryptocurrencies. Because users’ adoption of cryptocurrencies generates positive network externality, cryptocurrency prices respond to user adoptions. Hence, variations in user adoptions of the cryptocurrency network could contribute to movements in cryptocurrency prices.

We construct network factors of cryptocurrency and test whether these factors can account for variations in cryptocurrency prices. We use four measures to proxy for the network effect: the number of wallet users, the number of active addresses, the number of transaction count, and the number of payment count. 6 Thus, we measure cryptocurrency network growth using the wallet user growth, active address growth, transaction count growth, and payment count growth. We also construct a composite measure by taking the first principal component of the four primary measures, which we denote as |$PC^{network}$|⁠ . Panel A of Table 2 reports the correlation across the network factors we consider. The four primary measures correlate with each other positively, with correlations ranging from 0.17 to 0.77. The first principal component of the four demand factors strongly correlates with all four of the primary measures. The first principal component has correlations of 0.45, 0.88, 0.88, and 0.90 with the wallet user growth measure, the active address growth measure, the transaction count growth measure, and the payment count growth measure, respectively.

Cryptocurrency return loadings to network factors

This table reports the factor loadings of the coin market returns on the network factors. The network factors include wallet user growth, active address growth, transaction count growth, payment count growth, and the first principal component of the four primary measures. Panel A shows the correlation matrix of the variables. Panel B reports the loadings of the coin market returns on the network factors. The standard t -statistic is reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

We regress the coin market returns on each of the four measures of changes in the cryptocurrency network and the composite measure. Panel B of Table 2 presents the results using the network factors. The coin market returns positively correlate with all four of the individual cryptocurrency network factors and the composite measure. The coefficient on the wallet user growth measure is significant at the 10% level, and the three other coefficients are significant at the 1% level. The |$R^2$| s range from 5% for the wallet user growth measure to 30% for the active address growth measure. The |$R^2$| s using the composite measure is 19%. Consistent with the theoretical models, these results suggest that the network factors that measure the network effect of user adoptions are important drivers of cryptocurrency prices.

Moreover, in a dynamic cryptocurrency pricing model with the network effect, cryptocurrency prices not only reflect current cryptocurrency adoption but also contain information about expected future network growth—a key mechanism of Cong, Li, and Wang (2019) . We test this model implication by examining whether current coin market returns contain information about future cryptocurrency network growth. In particular, we predict cumulative future cryptocurrency adoption growth over different horizons using current coin market returns. We investigate cumulative future cryptocurrency adoption growth from one-month to eight-month horizons. We use cumulative wallet user growth, active address growth, transaction count growth, and payment count growth to capture cryptocurrency adoption growth.

Consistent with the prediction that cryptocurrency returns reflect expected future cryptocurrency adoptions, we find that coin market returns positively predict future cryptocurrency adoption growth as shown in Table 3 . Specifically, coin market returns positively and statistically significantly predict cumulative wallet user growth at all the horizons. Coin market returns positively and statistically significantly predict cumulative active address growth and cumulative payment count growth for the first three periods and two periods, respectively, and cease to be significant afterward. The coin market returns positively predict cumulative transaction count growth for the first five periods, but the predictability is not statistically significant. The only exception is the transaction growth measure: there is an insignificant, negative effect on transaction growth over the long horizons. A possible explanation for the negative effect is congestion, as it becomes very expensive to transact in Bitcoin when there is congestion, which deters many of the smaller transactions that would have occurred otherwise (e.g., Easley, O’Hara, and Basu 2019 ).

Predicting future network growth

This table reports the results of predicting cumulative future coin network growth with coin market returns. The network factors include wallet user growth, active address growth, transaction count growth, and payment count growth. Data are monthly. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

2.2 Production factors

Several papers have argued that the costs of mining are essential for the infrastructure and security of cryptocurrencies (e.g., Sockin and Xiong 2019 ; Abadi and Brunnermeier 2018 ; Cong, He, and Li 2018 ). Notably, Sockin and Xiong (2019) show that, in a general equilibrium model with cryptocurrency production, the prices of the cryptocurrency are intimately linked to the marginal cost of mining.

We construct production factors of cryptocurrency to proxy for the cost of mining and test the relationship between these production factors and cryptocurrency prices. To the first approximation, mining a cryptocurrency requires two inputs: electricity and computer power. We separately construct proxies for electricity costs and computing costs. We first discuss our proxies for electricity costs. For electricity, we use seven primary measures. Three of the seven primary measures are U.S.-related: (i) average price of electricity in the United States, (ii) net generation of electricity of all sectors in the United States, and (iii) total electricity consumption of all sectors in the United States. The other four measures are China-related: (i) average price of electricity in China, (ii) electricity generation in China, (iii) average price of electricity in Sichuan province, and (iv) electricity generation in Sichuan province. We include the China proxies, because electricity supply is location specific and because China is considered to have the largest coin-mining operation among all countries. 7 We include Sichuan province proxies because Sichuan province hosts the largest mining farm in the world. Similarly, we also construct a composite measure as the first principal component of these seven primary measures. We denote the composite measure as |$PC^{elec}$|⁠ .

Panel A of Table 4 presents the correlation matrix of the electricity factors. Except for the two electricity price measures in China, the other five primary measures positively and strongly correlate with one another. Electricity prices in China are under strict government control. Unsurprisingly, they have low correlations with other electricity measures. The first principal component of the seven electricity factors strongly and positively correlates with most of the seven primary factors. The correlations are 0.76, 0.93, 0.88, 0.71, and 0.77 with the U.S. electricity price growth measure, the net U.S. generation growth measure, the U.S. electricity consumption growth measure, the China generation growth measure, and the Sichuan generation growth measure, respectively. The correlation between the first principal component and the China electricity price growth measure is –0.15, and the correlation between the first principal component and the Sichuan electricity price growth measure is 0.18. Panel B of Table 4 presents the electricity factor results for the coin market returns. Somewhat surprisingly, the coin market returns are not statistically significantly exposed to any of these production factor proxies. The |$R^2$| s of these regressions are low.

Cryptocurrency return loadings to electricity factors

This table reports the factor loadings of the coin market returns on the production factors that relate to electricity costs. Panel A shows the correlation matrix of the production factors. Panel B reports the factor loadings of the coin market returns on the production factors. Standard t -statistics are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

For proxies of computing costs, we use as our primary measure the prices of Bitmain Antminer, a major piece of Bitcoin mining equipment. We also consider the excess stock returns of the companies that are major manufacturers of either GPU mining chips (Nvidia Corporation and Advanced Micro Devices, Inc.) or ASIC mining chips (Taiwan Semiconductor Manufacturing Company, Limited, and Advanced Semiconductor Engineering, Inc.). 8 We construct a composite measure as the first principal component of these five primary computing factors. We denote the composite measure as |$PC^{comp}$|⁠ .

Panel A of Table 5 presents the correlation matrix of the computing factors. Most of the pairs are positively correlated. The correlation between Antminer price growth and Nvidia return is –0.03, and the correlation between Antminer price growth and AMD return is –0.15. The first principal component is positively correlated with the four return measures and has a low correlation with the Antminer price growth measure. Panel B of Table 5 presents the computing factor results for the coin market returns. The coin market returns have insignificant loadings on the four excess return measures. The coin market returns have some loadings on the Antminer price growth measure, but they are only significant at the 10% level. The coin market returns are not significantly exposed to the first principal component.

Cryptocurrency return loadings to computing factors

This table reports the factor loadings of the coin market returns on the production factors that relate to computing costs. Panel A shows the correlation matrix of the production factors. Panel B reports the factor loadings of the coin market returns on the production factors. Standard t -statistics are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

The model of Sockin and Xiong (2019) primarily concerns utility tokens. Therefore, we conduct our analyses on production factors on Bitcoin, Ethereum, and Ripple, respectively. Because Ethereum and Ripple are utility tokens, while Bitcoin is not, we expect to find that Ethereum and Ripple load significantly on the production factors. We show the results in the Internet Appendix . There is some evidence that Bitcoin returns are exposed to the Bitmain Antminer price growth, but Bitcoin and Ripple returns do not load significantly on these production factors. Overall, there is limited evidence that the computing factors are important drivers of cryptocurrency returns.

Lastly, we test the lead-lag effects between the changes in production factors and cryptocurrency returns to account for possible anticipation effects. We document the results in the Internet Appendix . We show that the one-month-ahead coin market returns are not significantly exposed to most of the production factors. The only exception is the changes in the average price of electricity in the United States, but the significant level is negative and only at the 10% level. However, we find that the current coin market returns positively predict some of the future production factors. In particular, the coin market returns positively and statistically significantly predict future changes in the average price of electricity in the United States, net generation of electricity of all sectors in the United States, total electricity consumption of all sectors in the United States, electricity generation in Sichuan province, and the first principal component of the production factors. Interestingly, we find that the results are stronger for the U.S.-based measures relative to the China-based measures. This is consistent with the fact that electricity prices and generation are heavily regulated in China. These results are consistent with a potential anticipation effect of production costs in the cryptocurrency market.

2.3 Are cryptocurrency returns predictable?

In this section, we test whether the coin market returns are predictable. The existing theoretical models of cryptocurrencies provide various predictions on the predictability of cryptocurrency returns. Schilling and Uhlig (2019) argue that the evolution of cryptocurrency prices should follow a martingale, and thus cryptocurrency returns are not predictable. Other papers predict that, in dynamic cryptocurrency valuation models, cryptocurrency returns could potentially be predicted by momentum, investor attention, and cryptocurrency valuation ratios (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ). Motivated by the existing theoretical development and empirical findings in the financial markets, we test whether the cryptocurrency returns are predictable by momentum, investor attention, and proxies for cryptocurrency valuation ratios.

2.3.1 Cryptocurrency momentum

One of the most studied asset pricing regularities is momentum (e.g., Jegadeesh and Titman 1993 ; Moskowitz and Grinblatt 1999 ). As discussed in Cong, Li, and Wang (2019) , the network effect of user adoption generates a positive externality that is not immediately incorporated into cryptocurrency prices. This channel can potentially lead to a momentum effect in cryptocurrency returns. In their model, Sockin and Xiong (2019) generate momentum in the cryptocurrency market through investor attention—a mechanism similar to De Long et al. (1990) .

In this section, we start by establishing that there is strong evidence of time-series momentum at various time horizons. Panel A in Table 6 documents the time-series momentum results in the regression setting. Specifically, we regress cumulative future coin market returns on current coin market returns from the one-week to eight-week horizons. The current coin market returns positively and statistically significantly predict cumulative future coin market returns at all eight horizons. The results are significant at the 5% level for the one-week to five-week horizons and are significant at the 10% level from the six-week to eight-week horizons. For example, a one-standard-deviation increase in the current coin market return leads to increases in cumulative future coin market returns of 3.30%, 9 8.09%, 13.37%, and 17.66% increases at the one-week, two-week, three-week, and four-week horizons, respectively. Specifically, the one-week-ahead weekly return is that of buying the underlying coin market index at 11:59:59 UTD Sunday and selling the underlying coin market index at 11:59:59 UTD one week later. In the Internet Appendix , we also report the results based on noncumulative returns. The current coin market returns positively and significantly predict one-week- to five-week-ahead returns. The current coin market returns positively but insignificantly predict six-week- and seven-week-ahead returns. The current coin market returns negatively but insignificantly predict eight-week-ahead returns, suggesting some potential long-term reversal effect.

Time-series momentum

This table reports the time-series momentum results. Panel A shows the regression results, and panel B shows the results based on grouping weekly coin market returns into terciles. The first part of panel B reports results for the whole sample. The second part of panel B uses the first two years of data to determine the tercile cutoffs and examine the out-of-sample time-series momentum performance. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the first part of panel B in Table 6 , we estimate the magnitude of the time-series momentum by grouping weekly returns into terciles and evaluating their performance going forward. We find that the top terciles outperform the bottom terciles at the one- to four-week horizons, consistent with the time-series regression results presented earlier. For example, at the one-week horizon, the average return of the top tercile is 8.01% per week with a t -statistic of 4.30, while the average return of the bottom tercile is 1.10% per week with a t -statistic of 0.92. The difference between the top and bottom terciles is 6.91% at the one-week horizon. At the two-week horizon, the average of the cumulative coin market returns of the top tercile is 16.22%, and that of the bottom tercile is only 3.59%. The difference between the top and bottom terciles is 12.63%. In the additional results section, we restrict our sample to 2014 onward. Again, we find a strong and significant momentum effect of somewhat smaller magnitude. 10

In the second part of panel B in Table 6 , we use the first two years of data to determine the tercile cutoffs and study the out-of-sample time-series momentum performance. We find a strong and significant momentum effect for the out-of-sample tests. For example, at the one-week horizon, the average return of the top tercile is 6.42%, and that of the bottom tercile is 0.80%. The difference between the top and bottom terciles is 5.62%, which is economically large and slightly smaller than the in-sample result of 6.91%.

Additionally, we test whether the time-series momentum effect is linked to network externalities, as suggested in Cong, Li, and Wang (2019) . In their dynamic cryptocurrency valuation model, the momentum effect is generated by the positive externality of the network effect that is not incorporated into cryptocurrency prices immediately. That is, their model implies that controlling for cryptocurrency adoption growth would subsume the time-series momentum effect. In Table 7 , we show that there is evidence that cryptocurrency adoption growth positively predicts future coin market returns. However, controlling for cryptocurrency adoption growth does not subsume the time-series momentum effect documented presented earlier.

Momentum and network effect

This table reports the results that compare coin market return predictability of momentum and network effect. The table reports the results of predicting cumulative future coin market returns with current coin market returns and each of the network factors. The Newey-West adjusted t -statistics with |$n-1$| lags are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

2.3.2 Cryptocurrency investor attention

The theoretical literature of cryptocurrencies has also suggested that investor attention could potentially be linked to future cryptocurrency returns (e.g., Sockin and Xiong 2019 ). In this section, we investigate the role of investor attention in predicting cryptocurrency returns. Specifically, we construct the deviation of Google searches for the word “Bitcoin” in a given week compared with the average of those in the preceding four weeks. We standardize the Google search measure to have a mean of zero and a standard deviation of one. We use Google searches for the word “Bitcoin” to proxy for investor attention of the cryptocurrency market because Bitcoin is by far the largest and most visible cryptocurrency available. In panel A of Table 8 , we report the results of regressing cumulative future coin market returns from one-week to eight-week horizons on the Google search measure. The Google search measure statistically significantly predicts the one-week to six-week ahead cumulative coin market returns at the 5% level. The coefficient estimates of the seven-week and eight-week horizons are positive but are no longer statistically significant. A one-standard-deviation increase in searches leads to increases in weekly returns of about 3% for the one-week ahead cumulative coin market returns and about 5% for the two-week-ahead cumulative coin market returns. 11 In the Internet Appendix , we also report results based on noncumulative returns. The current coin market returns positively and significantly predict one-week- to four-week-ahead returns. The current coin market returns positively but insignificantly predict five-week-ahead returns. The current coin market returns negatively but insignificantly predict six-, seven-, and eight-week-ahead returns.

Google searches

This table reports the time-series Google search results. Panel A shows the regression results, and panel B shows the results based on grouping weekly coin market returns into terciles. The first part of panel B reports results for the whole sample. The second part of panel B uses the first two years of data to determine the tercile cutoffs and examine the out-of-sample time-series performance. The Google search measure is constructed as the Google search data for the word “Bitcoin” minus its average of the previous four weeks, and then normalized to have a mean of zero and a standard deviation of one. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the first part of panel B in Table 8 , we investigate the return predictability of the Google search measures by grouping them into terciles and evaluating their performance going forward. Consistent with the regression results, we find that the top tercile outperforms the bottom tercile in terms of cumulative coin market returns at the one- to four-week-ahead horizons. For example, at the one-week horizon, the average return of the top tercile is 6.53% per week with a t -statistic of 3.82, while the average return of the bottom tercile is 0.43% per week with a t -statistic of 0.42. The difference between the top and bottom terciles is 6.09% at the one-week horizon. At the two-week horizon, the average of the cumulative coin market returns of the top tercile is 13.95% with a t -statistic of 4.89, and that of the bottom tercile is only 0.02% with a t -statistic of 0.01. The difference between the top and bottom terciles is 13.93%. In the additional results section, we restrict our sample to 2014 onward and find similar return predictive power of the Google search measures.

In the second part of panel B in Table 8 , we use the first two years of data to determine the tercile cutoffs and study the out-of-sample effect of investor attention, and we find a strong positive investor attention effect as well. For example, at the one-week horizon, the average return of the top tercile is 6.12%, and that of the bottom tercile is 0.70%. The difference between the top and the bottom terciles is 5.42%, which is economically large and slightly smaller than the in-sample estimate of 6.09%.

2.3.3 Negative investor attention

We have shown that unconditionally investor attention positively predicts cryptocurrency returns. However, not all investor attention is positive. For example, in their model, Sockin and Xiong (2019) differentiate positive investor attention and negative investor attention, and show that negative investor attention is followed by cryptocurrency price depreciation in the future.

In this section, we investigate whether negative investor attention predicts cryptocurrency returns. We construct a ratio between Google searches for the phrase “Bitcoin hack” and searches for the word “Bitcoin” to proxy for negative investor attention. We standardize the measure to have a mean of zero and a standard deviation of one. Panel A of Table 9 shows the results of the predictive regressions. The ratio negatively and significantly predicts one- to six-week-ahead cumulative coin market returns. For example, a one-standard-deviation increase in the ratio leads to a 2% decrease of coin market returns in the next week. Panel B of Table 9 reports the in-sample and out-of-sample return predictability of the negative investor attention measures by grouping them into terciles and evaluating their performance going forward. Consistent with the regression results, we find strong negative return predictability results of the negative investor attention measures.

Bitcoin hack

This table reports the time-series Bitcoin hack results. Panel A reports the regression results, and panel B reports the sorting results. The Bitcoin hack measure is constructed as the ratio between Google searches for the phrase “Bitcoin hack” and searches for the word “Bitcoin,” and then normalized to have a mean of zero and a standard deviation of one. Results are based on weekly data. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

Another way to see the results on the investor attention is that our measures of investor attentions proxy for speculative interest and sentiment in cryptocurrencies. Positive investor sentiment is followed by cryptocurrency price appreciation, and negative investor sentiment is followed by depreciation. We further investigate these issues in Section 4 .

2.3.4 Interaction between momentum and attention

We have shown that there are strong effects of time-series momentum and investor attention in the cryptocurrency market. The equity market research (e.g., Hong, Lim, and Stein 2000 ; Hou, Xiong, and Peng 2009 ) shows that there is a strong relationship between momentum and investor attention. It is possible that these two results capture the same underlying phenomenon. For example, Sockin and Xiong (2019) propose a potential channel to generate momentum. In their model, momentum arises because users have incorrect expectations about future prices—a mechanism similar to De Long et al. (1990) . Their model suggests that cryptocurrency momentum and investor attention could potentially arise from the same underlying mechanism. The cryptocurrency momentum and investor attention results could also interact with each other. For example, the cryptocurrency time-series momentum effect may be weaker at times of high investor attention, because there is little information leakage at times of high investor attention.

First, we show that the current investor attention of cryptocurrencies is indeed associated with current and past coin market performance. We regress the current deviation in the Google searches on the contemporaneous and the coin market returns of the previous four weeks. Table 10 documents the results. We find that the deviations in Google searches are positively and significantly associated with contemporaneous and the previous week’s coin market returns. The Google search measures do not significantly correlate with past coin market returns beyond one week. Intuitively, these results suggest that investor attention is elevated after superior cryptocurrency market performance.

Google searches and past returns

This table reports the relationships between the Google search measure and past coin market returns. The Google search measure is constructed as the Google search data for the word “Bitcoin” minus its average of the previous four weeks, and then normalized to have a mean of zero and a standard deviation of one. The standard t -statistic is reported in parentheses, and the bootstrapped t -statistic is reported in brackets. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is weekly.

We further test the interaction between the time-series momentum and the investor attention phenomena. The results are reported in Table 11 . In the first test of Table 11 , we regress cumulative future coin market returns on current coin market returns and Google search measures. We find that the coefficients to the current coin market returns are significant for all the horizons, and the coefficients to the Google search measures are significant from the one-week to the five-week horizons. The magnitudes of the coefficients are similar to the standalone estimates. For example, the one-week-ahead coefficients under the univariate regressions are 0.20 and 0.03 for the current coin market returns and the Google search measures, respectively, while they are 0.18 and 0.03 under the bivariate regressions. These results show that the time-series momentum and the investor attention results do not subsume each other.

Interaction between momentum and attention

This table reports the predictive regressions of future cumulative coin market returns on momentum, attention, and the interaction of the two. The indicator variable |$1_{\{Google>0\}}$| equals one if the current Google search measure is above the sample mean and zero otherwise. The indicator variable |$1_{\{R>0\}}$| equals one if the current coin market return is positive and zero otherwise. Results are based on weekly returns. The Newey-West adjusted t -statistics with |$n-1$| lags are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the second test of Table 11 , we test the performance of the time-series momentum result when investor attention is high. We construct an indicator variable, |$1_{\{Google>0\}}$|⁠ , that equals one if the current Google search measure is above the sample mean and zero otherwise. We regress the cumulative future coin market returns from one-week to eight-week horizons to the current coin market return, the indicator variable, and the interaction term. The interaction term is not significant at any of the eight horizons, suggesting that the magnitude of the time-series momentum effect is similar for high and low investor attention periods. In the third test of Table 11 , we test the performance of the investor attention result when the current coin market return is high. We construct an indicator variable, |$1_{\{R>0\}}$|⁠ , that equals one if the current coin market return is positive and zero otherwise. We regress the cumulative future coin market returns from one-week to eight-week horizons to current Google search measure, the indicator variable, and the interaction term. The interaction term is not significant at any of the eight horizons, suggesting that the magnitude of the investor attention effect is similar for high and low coin market return periods.

Furthermore, we study the cross-section of time-series momentum for high- and low-attention coins. We collect Google attention data for the 10 largest cryptocurrencies from the beginning of 2014 to the end of 2018. The sample period is shorter for this analysis, because before 2014, there are very few cryptocurrencies and the data for alternative coins are hard to get. The list of coins is Bitcoin, Ethereum, Ripple, Litecoin, Tether, Bitcoin-Cash, Tezos, Binance-coin, Monero, and Cardano. At a given point in time, we group the existing coins into two subsamples based on the Google attention data—a group of high-attention coins and a group of low-attention coins. We construct the value-weighted returns of the high-attention group and the low-attention group, separately, and test the time-series momentum strategy effect in each subgroup.

We regress the future cumulative returns on current returns for each of the subsamples and report the results in the Internet Appendix . We find that in this sample, the time-series momentum effect is stronger for the relatively low-attention coins. In particular, the coefficient estimates for both the high-attention and the low-attention subgroups are positive, suggesting that there are time-series momentum effects for both groups. However, the coefficient estimates for the high-attention subgroup is not statistically significant, while the coefficient estimates for the low-attention subgroup is statistically significant up to six weeks out. The magnitudes of the coefficient estimates are also much larger for the low-attention subgroup relative to the high-attention subgroup. The results are consistent with the “underreaction” mechanism of momentum.

2.3.5 Cryptocurrency valuation ratio

Additionally, we test whether the cryptocurrency valuation ratios similar to those in the financial markets can predict future coin market returns. In the equity market, the fundamental-to-market ratios are commonly referred to as valuation ratios and are measured as the ratio of the book value to the market value of equity or some other fundamental value to market value (e.g., dividend-to-price; earnings-to-price). Another value measure used in the literature that has been shown to correlate highly with fundamental-to-market value is the negative of the long-term cumulative past returns (e.g., De Bondt and Thaler 1985 , 1987 ; Fama and French 1996 ; and Moskowitz 2015 ). It is more difficult to define a similar measure of fundamental value for cryptocurrency. However, in their dynamic cryptocurrency asset pricing model, Cong, Li, and Wang (2019) argue that the cryptocurrency fundamental-to-value ratio can be defined as the number of user adoptions over market capitalization, which negatively predicts future cryptocurrency returns.

The market value of cryptocurrency is readily available. However, there is no direct measure of fundamental value for the cryptocurrencies. In its essence, value is a measure of the gap between the market value and the fundamental value of an asset. Because of the lack of a standard “book” value measure of the cryptocurrency market, we use an array of different proxies to capture the idea of fundamental value. We proxy the fundamental-to-market ratio by a number of value measures motivated by the finance literature. The first one is the long-term past performance measure: the negative of the past 100-week cumulative coin market return. The other four measures aim to proxy the cryptocurrency fundamental-to-market value directly: the user-to-market ratio, the address-to-market ratio, the transaction-to-market ratio, and the payment-to-market ratio. The idea of these four measures is to use some measures of the “book” value of the underlying cryptocurrency market and scale by the current market capitalization. The user base of the cryptocurrency market seems to capture the concept of “book” value in the financial markets. This is consistent with the theoretical literature of the cryptocurrency market that emphasizes the notion of the network effect, which can be proxied by the current user base of the cryptocurrencies. On the other hand, the market value of the cryptocurrency provides a market assessment of the current value of the complete cryptocurrency infrastructure. Therefore, the user base-to-market value measure can capture the notion of fundamental-to-market ratio in the financial markets. In panel A of Table 12 , we report the correlations across the different valuation ratios in the cryptocurrency market. The five primary measures are highly correlated with one another, with correlations ranging from 0.73 to 0.91. The first principal component measure for the five fundamental-to-market ratios has correlations of 0.91, 0.91, 0.96, 0.93, and 0.93 with the long-term past returns, the wallet user-to-market ratio, the active address-to-market ratio, the transaction-to-market ratio, and the payment-to-market ratio, respectively.