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Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns DISCUSSION PAPER SERIES DP13724 (v. 8) Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns Siddharth Bhambhwani, Stefanos Delikouras and George Korniotis FINANCIAL ECONOMICS ISSN 0265-8003 Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns Siddharth Bhambhwani, Stefanos Delikouras and George Korniotis Discussion Paper DP13724 First Published 09 May 2019 This Revision 23 July 2021 Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK Tel: +44 (0)20 7183 8801 www.cepr.org This Discussion Paper is issued under the auspices of the Centre’s research programmes: Financial Economics Any opinions expressed here are those of the author(s) and not those of the Centre for Economic Policy Research. Research disseminated by CEPR may include views on policy, but the Centre itself takes no institutional policy positions. The Centre for Economic Policy Research was established in 1983 as an educational charity, to promote independent analysis and public discussion of open economies and the relations among them. It is pluralist and non-partisan, bringing economic research to bear on the analysis of medium- and long-run policy questions. These Discussion Papers often represent preliminary or incomplete work, circulated to encourage discussion and comment. Citation and use of such a paper should take account of its provisional character. Copyright: Siddharth Bhambhwani, Stefanos Delikouras and George Korniotis Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns Abstract We show that cryptocurrency returns relate to asset pricing factors derived from two blockchain characteristics, growth in aggregate computing power and network size. Consistent with theoretical models, cryptocurrency returns have positive risk exposures to these blockchain-based factors, which carry positive risk prices. A stochastic discount factor with computing power and network explains the cross-sectional variation in expected cryptocurrency returns at least as well as models with cryptocurrency return-based factors like size and momentum. The explanatory power of the blockchain factors also increases as the cryptocurrency market matures. Overall, theoretically motivated factors are important sources of risk for cryptocurrency expected returns. JEL Classification: E4, G12, G15 Keywords: Hashrate, network, Factor Analysis, GMM, Rolling Estimation Siddharth Bhambhwani - [email protected] University of Miami Stefanos Delikouras - [email protected] University of Miami George Korniotis - [email protected] University of Miami and CEPR Acknowledgements We thank Nic Carter, Jacob Franek, Kevin Lu, and Tim Rice of Coinmetrics.io for their help with the data. We thank Ilona Babenka, Michael Barnett, Thomas Bates, Dirk Baur, Tyler Beason, Gennaro Bernile, Hendrik Bessembinder, Sreedhar Bharath, Dario Cestau, Indraneel Chakraborty, Andreas Charitou, Ilhan Demiralp, Evangelos Dioikitopoulos, Pedro Gete, Sara Holland, Allen Huang, Alok Kumar, Petri Jylha, George Kapetanios, Irene Karamanou, Matti Keloharju, Laura Lindsey, Scott Linn, Matthijs Lof, Andreas Milidonis, Dino Palazzo, George Nishiotis, Marios Panayides, Filippos Papakonstantinou, Simon Rottke, Yuri Tserlukevich, Vahid Saadi, Christoph Schiller, Mark Seasholes, Sean Seunghun Shin, Denis Sosyura, Jared Stanfield, Bryan Stanhouse, Luke Stein, Michael Ungeheuer, Jessie Wang, Tong Wang, Stavros Zenios, Zhuo Zhong as well as seminar participants at Aalto University, Arizona State University, IE Madrid, King's College London, 2019 Luxembourg Asset Management Summit, Miami Herbert Business School, University of Oklahoma, the 13th NUS Risk Management Institute Conference, the 2nd UWA Blockchain, Cryptocurrency, and FinTech Conference, 2019 FIRN Melbourne Asset Pricing Meeting, the NFA 2019 Conference, Tokyo Institute of Technology, Kyoto University, and University of Cyprus for helpful comments. Andrew Burch, Stanley Wai, and Irene Xiao provided excellent research assistance. All errors are our own. Powered by TCPDF (www.tcpdf.org) Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns Siddharth M. Bhambhwani∗ Stefanos Delikouras† George M. Korniotis‡ July 23, 2021 Abstract We show that cryptocurrency returns relate to asset pricing factors derived from two blockchain characteristics, growth in aggregate computing power and network size. Consistent with theoretical models, cryptocurrency returns have positive risk expo- sures to these blockchain-based factors, which carry positive risk prices. A stochastic discount factor with computing power and network explains the cross-sectional varia- tion in expected cryptocurrency returns at least as well as models with cryptocurrency return-based factors like size and momentum. The explanatory power of the blockchain factors also increases as the cryptocurrency market matures. Overall, theoretically mo- tivated factors are important sources of risk for cryptocurrency expected returns. Keywords: Hashrate, Network, Factor Analysis, GMM, Rolling Estimation. JEL Classification: E4, G12, G15 ∗Siddharth M. Bhambhwani, Hong Kong University of Science and Technology, email: [email protected] †Stefanos Delikouras, University of Miami, email: [email protected] ‡George M. Korniotis, University of Miami and CEPR, email: [email protected] We thank Nic Carter, Jacob Franek, Kevin Lu, and Tim Rice of Coinmetrics.io for their help with the data. We thank Ilona Babenka, Michael Barnett, Thomas Bates, Dirk Baur, Tyler Beason, Gennaro Bernile, Hendrik Bessembinder, Sreedhar Bharath, Dario Cestau, Indraneel Chakraborty, Andreas Charitou, Ilhan Demiralp, Evangelos Dioikitopoulos, Pedro Gete, Sara Holland, Allen Huang, Alok Kumar, Petri Jylha, George Kapetanios, Irene Karamanou, Matti Keloharju, Laura Lindsey, Scott Linn, Matthijs Lof, Andreas Milidonis, Dino Palazzo, George Nishiotis, Marios Panayides, Filippos Papakonstantinou, Simon Rottke, Yuri Tserlukevich, Vahid Saadi, Christoph Schiller, Mark Seasholes, Sean Seunghun Shin, Denis Sosyura, Jared Stanfield, Bryan Stanhouse, Luke Stein, Michael Ungeheuer, Jessie Wang, Tong Wang, Stavros Zenios, Zhuo Zhong as well as seminar participants at Aalto University, Arizona State University, IE Madrid, King's College London, 2019 Luxembourg Asset Management Summit, Miami Herbert Business School, University of Oklahoma, the 13th NUS Risk Management Institute Conference, the 2nd UWA Blockchain, Cryptocur- rency, and FinTech Conference, 2019 FIRN Melbourne Asset Pricing Meeting, the NFA 2019 Conference, Tokyo Institute of Technology, Kyoto University, and University of Cyprus for helpful comments. Andrew Burch, Stanley Wai, and Irene Xiao provided excellent research assistance. All errors are our own. Introduction Cryptocurrencies are a significant financial innovation. Yet, it is unclear which are the asset pricing factors that determine their expected returns. On the one hand, most empirical research focuses on the influence of investor sentiment and other non-fundamental factors on cryptocurrency prices and returns.1 On the other hand, theory suggests that blockchain characteristics such as computing power and network size are key determinants of cryptocur- rency prices.2 However, there is little empirical work on the importance of these blockchain fundamentals for the cross-section of cryptocurrency expected returns.3 To fill this gap, we focus on aggregate computing power and aggregate network size and examine whether they can price cryptocurrency expected returns. Our analysis centers around two conjectures. First, we posit that aggregate computing power and network size are fundamental indicators of the state of the cryptocurrency mar- ket. Aggregate computing power proxies for the cumulative resources expended on mining and relates to the overall reliability and security of cryptocurrency blockchains. Aggregate network size captures the general adoption levels of cryptocurrencies. Therefore, these two factors offer important insights about the state of the cryptocurrency market and should affect expected returns of cryptocurrencies. Second, we posit that the pricing power of these blockchain-based factors should increase over time as the cryptocurrency market becomes more established. This conjecture follows from P´astorand Veronesi(2003; 2006), who argue that valuation uncertainty is initially high for new assets leading to price run-ups at the time of their introduction. As the assets 1See Griffin and Shams(2020), Makarov and Schoar(2020), and Liu, Tsyvinski, and Wu(2021). 2See Pagnotta and Buraschi(2018), Biais, Bisiere, Bouvard, and Casamatta(2019), Biais, Bisiere, Bou- vard, Casamatta, and Menkveld(2020), and Cong, Li, and Wang(2020). 3Exceptions include Liu and Tsyvinski(2020) who show that over the 2011 to 2018 period, Bitcoin's network comoves with average cryptocurrency returns while variables related to Bitcoin's mining do not. Liu and Tsyvinski(2020) proxy for Bitcoin mining activity using the price of Bitmain's mining hardware and the cost of electricity in the U.S. and China. In contrast to their paper, we use hashrates because they are available for all Proof-of-Work cryptocurrencies and are measured at the daily level, thereby providing better information about aggregate mining activity at a very high-frequency level. Biais et al.(2020) focus on Bitcoin and highlight the importance of transaction benefits and network security. Pagnotta(2021) finds that the prices of Bitcoin, Ethereum, and Litecoin are positively related
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