Information Transparency and Market Efficiency in Blockchain-enabled Marketplaces : Role of Traders’ Analytical Ability

Classic economic theory asserts that full information transparency entails information symmetry and, thus, market efficiency in trading. We test if this theory still holds in a blockchain-enabled marketplace where full information transparency is accomplished via blockchain. We leverage the data from EnjinX, a non-fungible token (NFT) marketplace, where the entire historical NFT transactions are recorded on the blockchain and are symmetrically accessible to all buyers and sellers. We surprisingly observe substantial market inefficiencies, measured by the percentage of unexploited flipping opportunities. To explain this paradox that inefficiencies persist even in a fully information-transparent environment, we propose that not all traders are proficient at analyzing such transparent information for trading purposes. We argue that it is the limited analytical ability of traders, rather than information asymmetry, that ultimately drives market inefficiencies. We quantify traders’ analytical ability by examining whether traders’ performance can be augmented by machine learning algorithms. And we further show how traders’ analytical ability influences market efficiency and how the effect of blockchain amount of information on market efficiency is moderated by analytical ability. We find that having ten more historical transactions of an NFT increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders are incapable of effectively consuming the available information. Our findings contribute to the literature by quantifying analytical ability and highlighting the phenomenon of the analytical-ability divide: Due to this divide of ability, market efficiency may not be attained even when information is symmetrically transparent to all