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Valuing Thinly Traded Cryptocurrencies

Author: Eyal Beigman, Gerard Brennan, Sheng-Feng (Philip) Hsieh, and Alexander Sannella.1

The statements in this document reflect guidance issued as of May 4, 2020. 

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This article briefly discusses a methodology for fair value measurement of cryptocurrency pairs which are thinly traded or not directly traded on exchanges. The  translation, from Binance Coin (BNB) to U.S. dollars (BNB-USD), will be used as an  example to illustrate the outcome of the methodology.  

Lukka Prime2, a leading cryptocurrency pricing product that leverages a mark to  market methodology to provide reasonable fair value, measures for actively traded  cryptocurrency pairs. Following accounting guidance of fair value measurement from ASC  820 (FASB) and IFRS 13 (IASB), Lukka Prime dynamically determines the principal  market for each cryptocurrency pair by considering static characteristics, short-term and  long-term trade behaviors among exchanges worldwide, and treats the last quoted price of  the currency pair on the determined principal market as the fair value measure (level 1  input per the ASC 820 guidance) at that specific moment. To obtain reasonable fair value  measures for thinly traded currency pairs, however, a mark to model approach is needed  because of the limited number of observable transactions. As such, Lukka Prime may not  be the most appropriate valuation method for thinly traded pairs. 

The Valuation Methodology for Thinly Traded Cryptocurrencies Suppose that one needs to translate cryptocurrency ABC to the U.S. dollar for financial reporting purposes; it is intuitive to use the exchange rate of ABC-USD directly  as reference. However, prior literature has indicated and evidenced that the transaction  volume on financial markets, including cryptocurrency ones, is associated with the market  credibility (Nasiri, Bektas, and Jafari 20183) and works as a channel to incorporate private  and public market information (Bianchi and Dickerson 20204; Brandvold; Molnár, Vagstad, and Valstad 20155; Makarov and Schoar 20196; Park and Chai 20207; Sockin and Xiong  20208). In other words, the information embedded in the exchange with higher transaction  volume for each specific currency pair might be more credible.  

One could obtain two more alternative exchange rates for ABC-USD with the  intermediate currencies DEF and GHI, rather than the direct exchange rate ABC-USD, by  multiplying the exchange rate of ABC-DEF and DEF-USD or ABC-GHI and GHI-USD.  Supported by the prior literature, it is proposed that the translation using exchange rates  from cryptocurrency markets with higher transaction volume would be more representative  and reliable because of market credibility and the incorporation of public and private  market information. This is the theoretical foundation of the methodology. 

Figure 1: Different paths that translate cryptocurrency ABC to the U.S. dollar (USD)9 

The valuation methodology for thinly traded cryptocurrencies starts with listing all  possible paths from the target cryptocurrency (currency ABC) to USD. The number of pairs  comprising a path should be equal or higher than two. The exchange rate for each pair in a  path is estimated and obtained from Lukka Prime10. The determined optimal and representative path translating the target cryptocurrency to USD is the path with the highest  “bottleneck volume” from all path candidates.  

For both alternative paths from ABC to USD, ABC-> DEF->USD and  ABC->GHI->USD, the pairs with the minimum transaction volume, or the “bottleneck  volume,” in both paths are DEF-USD and ABC-GHI, respectively11. The higher the transaction volume, the more credible the market and its exchange rate would be. Hence,  the selected optimal path in the valuation methodology is the path with the higher bottleneck volume in both path candidates. In the example, because the higher bottleneck volume between DEF-USD and ABC-GHI happens on the DEF-USD leg, the determined  optimal path to translate ABC to USD is ABC->DEF->USD. 

Pilot Test Results 

Binance Coin (BNB) is one of the most popular cryptocurrencies worldwide with the  eighth highest market capitalization12. However, BNB could not be directly traded to USD  on major exchanges13. It is a need to dynamically obtain an objective and representative  fair value measure for BNB-USD for financial reporting if companies are holding BNB. 

In the pilot test, BTC, USDT, and ETH are selected as intermediate currencies  between BNB and USD to create possible paths for the illustration of the methodology.  Figure 2 presents four candidate paths to translate from BNB to USD, including BNB->BTC->USD (path 1), BNB->ETH->USD (path 2), BNB->USDT->BTC->USD 

(path 3), and BNB->USDT->ETH->USD (path 4). The period of cryptocurrency  transaction data covers from August 1 to December 31, 2018. The most optimal and  representative path is analyzed and determined on a minute-by-minute basis14.  

Figure 2: Different paths that translate Binance Coin (BNB) to U.S. dollars (USD) (Relative transaction volume is not shown in the figure.) 

Table 1 summarizes the distribution of each path candidate determined as the most  optimal and representative path from BNB to USD over the sample period. Path 1 (BNB->BTC->USD) dominates and occupies 83.29% of the sample period to be  determined as the optimal path under the methodology; 11.28% of the period identified the  optimal path as Path 2, BNB->ETH->USD; and 5.43% of the period identified the optimal  path as Path 3 (BNB->USDT->BTC->USD). Surprisingly, Path 4,  BNB->USDT->ETH->USD, is not selected to be the path. This means that the bottleneck volume in Path 4 is not the maximum bottleneck volume among four path candidates in  the sample period. 

Table 1: The ratio of the determination as the optimal path for the translation of BNB-USD (The sample period: August 1 to December 31, 2018)

Figure 3: The ratio of the difference between the maximum and the minimum of  BNB-USD exchange rates from the four candidate paths 

To prepare financial reporting, managers may determine the exchange rate to translate  from BNB to USD by subjectively selecting one of many alternative paths. However, using  a method that does not follow an objective approach, such as the one outlined in this article,  reduces the reliability of financial reporting for thinly traded cryptocurrencies. 

Figure 3 presents the ratio of the difference between the maximum and the minimum  of BNB-USD exchange rates from the four path candidates from August 1 to December  31, 2018. Although the average difference is only 0.005 (0.5%) for the whole sample period, it fluctuates in some specific time intervals. As shown, for instance, the peak  appeared in the five-day period, October 14 to 18, 2020, and the average difference reached  0.0157 (1.57%), with a peak of around 12%. The cryptocurrency being valued, the  temporary changes in market condition and investors’ trade behaviors, and/or the time  period in which the fair value is estimated are potential reasons contributing to the  relatively large fluctuation. 

Conclusion 

Built based on the theory that higher transaction volume is linked with higher market  credibility, the methodology considers transaction volume and aims to provide a more  representative and credible fair value measure for thinly traded cryptocurrencies. The  methodology specifically would be appropriate to value thinly traded cryptocurrencies as  well as ones not directly traded to U.S. dollars on exchanges. Aligning with current fair  value measurement guidance under ASC 820 and IFRS 13, the methodology could be  executed automatically and dynamically to provide valuation for financial reporting.

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1 The statements in this  document should not be treated as legal, tax, or accounting advice. The document is intended to provide general  information only. If a person would like such advice, they should seek professional advice with regard to their specific  facts. The statements in this document reflect guidance issued as of May 4, 2020. 

2 More details and information about the Lukka Prime could be reached from the Lukka Prime pricing webpage.  Available at: https://lukka.tech/lukka-prime-pricing/ 

3 Nasiri, S., E. Bektas, and G. R. Jafari. 2018. The impact of trading volume on the stock market credibility: Bohmian  quantum potential approach. Physica A: Statistical Mechanics and its Applications. 512: 1104-1112.

4 Bianchi, D., and A. Dickerson. 2020. Trading volume in cryptocurrency markets. Working Paper. Available at:  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3239670

5 Brandvold, M., P. Molnár, K. Vagstad, and O. C. A. Valstad. 2015, Price discovery on Bitcoin exchanges. Journal of  International Financial Markets, Institutions and Money, 36: 18-35. 

6 Makarov, I., and A. Schoar. 2019. Price discovery in cryptocurrency markets. AEA Papers and Proceedings 2019, 109:  97-99. 

7 Park, M., and S. Chai. 2020. The effect of information asymmetry on investment behavior in cryptocurrency market.  Proceedings of the 53rd Hawaii International Conference on System Sciences. Available at:  https://scholarspace.manoa.hawaii.edu/handle/10125/64236 

8 Sockin, M., and W. Xiong. 2020. A model of cryptocurrencies. Working Paper. Available at: https://www-nberorg.proxy.libraries.rutgers.edu/papers/w26816 

9 The thickness of arrows represents the relative transaction volume of each currency pair. 

10 Among exchanges could trade the specific currency pair, Lukka Prime is able to follow the accounting standards  (ASC 820 and IFRS 13) on the fair value measurement and dynamically determine the principal market and the fair  value of the specific currency pair at any given moment.

11 Observed from the thickness of arrows in Figure 1. 

12 The information about the market capitalization and the following transaction volume percentage for each  cryptocurrency pair on individual exchanges is derived from CoinMarketCap (https://coinmarketcap.com/) at 3:00 PM,  May 4, 2020. The percentage is dynamic and time-variant, depending on when you derived the information.

13 There are currently 10 reliable exchanges, mentioned in the Bitwise’s presentation to the SEC (Bitwise 2019). Available at: https://www.sec.gov/comments/sr-nysearca-2019-01/srnysearca201901-5164833-183434.pdf

14 Different time intervals, including 5-minute, 10-minute, and 60-minute windows, are also analyzed and indicated  similar results (untabulated).

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