Professor Andrew Urquhart is Professor of Finance and Financial Technology and Head of Finance at Birmingham Business School (BBS).
This is the third installment of the Coin Professor column, which provides Decrypt readers with important insights from the published academic literature on cryptocurrencies. This week, we’ll take a closer look at how cryptocurrencies are priced.
One of the most important questions in any market is, “What factors drive the expected return on an asset?” This age-old question has been studied thousands of times by academics, investment banks, traders, and stock market hedge funds.
One of the most popular research results, which helped Eugene Fama win the 2013 Nobel Prize in Economics, shows excess market returns, outperformance of small and large companies, and outperformance of high book value companies. This is the Fama France factor. Market companies and companies with low book values help explain stock returns.
They extend this to the Fama-France five-factor model, which includes profitability and investment factors, and is shown to be very successful in explaining stock returns.
But how can you price a cryptocurrency if you don’t have a balance sheet and have no information about book-to-book ratios or a company’s investment performance?
This is a well-studied area in the cryptocurrency literature, as cryptocurrency prices are notoriously volatile and difficult to predict.
One of the most important works, a 2022 paper by Yukun Liu, Aleh Tsyvinski, and Xi Wu, examines various pricing factors known in the stock market (such as size, momentum, volume, and volatility) that can be applied to cryptocurrencies. Researched and found the following: Excess market returns, size, and momentum are factored into over 1,800 cryptocurrencies and help explain returns. This result suggests that the pricing of cryptocurrencies is not that different from stocks.
A follow-up paper by Siddharth Bhambhwani, George M. Korniotis, and Stefanos Delikouras takes advantage of the information investors can glean from blockchain. This paper investigates whether two blockchain-based factors, namely computing power and network size, help explain the cross-section of cryptocurrency returns. The authors show that these two factors help explain returns, and the potential for blockchain to provide information that could help price cryptocurrencies above market data (price, volume, volatility, etc.). suggests that there is.
Finally, a very recent paper by Athanasios Sakkas and I goes beyond the aforementioned work to create 13 factors based on on-chain data from over 35 different blockchains.
These factors are based on whale holdings, coin movement, network value, and decentralization level of cryptocurrency holdings. By using advanced techniques that capture the performance of these factors together, this paper shows that a very simple model involving market excess returns and network distributions outperforms other factors found in the literature to date. also shows that it can explain the profits of virtual currencies. Specifically, investors require a premium, or what the authors call a “whale premium,” for holding cryptocurrencies with a low level of diversification.
Therefore, in determining the price of cryptocurrencies, cryptocurrencies may not be that different from the stock market. However, given the vast amount of data available on the blockchain, there is some value in using this information when predicting the future value of cryptocurrencies.
Edited by Stephen Graves
See below for more information.
Bhambhwani, S., Delikouras, S., and Korniotis, G. M. (2023). A cross-section of blockchain features and cryptocurrency profits. International Journal of Financial Markets, Institutions and Currency, 86, 101788.
Liu, Y. Czybinski A. (2021). Virtual currency risk and return. Review of Financial Research, 34, 2689-2727.
Liu, Y., Tsyvinksi, A., Wu, X. (2022). Common risk factors in cryptocurrencies. Journal of Finance, 77, 1133-1177.
Sakkas, A., Urquart, A. (2024). Blockchain factors. International Journal of Financial Markets, Institutions and Currency, 94, 103012.
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