Market Efficiency in Blockchain-enabled Marketplaces - A Perspective from Traders’ Analytical Ability

Abstract

Conventional wisdom holds that markets with transparent trading information accessible to all traders would exhibit a high level of market efficiency. We investigate whether this belief remains valid within the context of blockchain-enabled marketplaces, where complete information transparency for all participants is accomplished through the recording of historical trading data on the blockchain. We leverage the data from EnjinX, a blockchain-enabled non-fungible token (NFT) marketplace. We find that contrary to theoretical expectations, there are excessive market inefficiencies, indicated by an average of 73% unexploited flipping opportunities among all asset trades. To explain the persistence of excessive market inefficiencies in an environment characterized by symmetric (trading) information transparency, we propose that not all traders are proficient at analyzing the available market data for trading purposes: It is the limitation in traders’ analytical ability that underpins these inefficiencies. We quantify traders’ analytical ability by the extent to which their performance can be augmented by machine learning algorithms. We further show how traders’ analytical ability influences market efficiency and how the effect of the amount of blockchain trading history on market efficiency is moderated by analytical ability. We find that having ten more historical transactions available on the blockchain increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders are incapable of consuming the available market trading information effectively. Our findings contribute to the literature by rigorously quantifying analytical ability and highlighting the critical phenomenon of the analytical-ability divide in the blockchain era: Due to this divide of ability, anticipated market efficiency in trading may not be realized even when trading information is symmetrically available to all participants.

Keywords: transparent information structure, analytical-ability divide, market efficiency, blockchain-enabled marketplaces, machine learning augmentation