ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

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Cryptocurrencies and blockchain technology are disruptive innovations at the vanguard of a new wave of the digital revolution. The far-reaching appeal, global reach, unprecedented mobility of capital, and multitude of trading venues have created a marketplace like no other. The economic fundamentals underlying this market are yet to be fully comprehended, as evidenced by the often-contradicting guidelines recommended by accounting firms, government agencies, and standard setters. Many of the definitions and models used for classical markets cannot be applied directly to cryptocurrency. Basic concepts must be reinterpreted, and models must be modified to fit the mechanics of these markets. In this article, we focus on one such concept: that of fair value. We argue that in light of the fragmentation of cryptocurrency markets and the global dispersion of trading venues, a principal market may be difficult to identify. The primary objective of this article is to present a methodology to dynamically designate principal markets and derive fair value prices for financial reporting using this designation.

This article proposes a new dynamic method, the Principal Path Method (PPM), for pricing crypto asset against a primary or functional (fiat) currency in situations where these assets do not trade directly against the functional currency or trade at volumes that prevent resulting pricing information to qualify as Level 1 (ASC 820) for financial reporting. We base our method on the guidance provided in ASC 820, IFRS 13, and IAS 21. Our method is designed to extract prices from “compliant” markets that result in reliable inputs to the valuation process. We believe that our methodology improves the current techniques used to value thinly traded crypto assets such as using the last observable transaction price, creating a weighted-average price across multiple markets, or using data on comparable tokens, if available. Furthermore, we present empirical evidence that suggests pricing information generated by our method for non-exchangeable, thinly traded, or illiquid crypto assets better reflects the fundamental qualitative characteristics of useful information, relevance and faithful representation, and results in more reliable inputs used in the valuation process. Unlike methods currently used in practice, our method ensures the integrity of the valuation data employed by selecting prices from compliant markets.

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