Fair value estimates for illiquid cryptocurrency

Abstract

• Proposing a dynamic valuation framework for fair value estimates for illiquid cryptocurrency. • Discussing compliance with fair value accounting standards and cryptocurrency reporting requirements. • Factoring in comparable assets’ market information and news big data analytics that measure market participants’ attention and sentiment in the valuation framework. • Empirically testing the valuation framework with historical market data. • Developing a machine learning valuation model that achieved 87 % prediction accuracy. To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.