Blockchain, DLT, and Fintech

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Paper Number

1716

Paper Type

Completed

Description

Classic economic theory asserts that full information transparency entails information symmetry and, thus, market efficiency. We test if this theory still holds in a blockchain-enabled marketplace where full information transparency is accomplished. We leverage the data from EnjinX, a non-fungible-token (NFT) marketplace, where the entire historical NFT transactions are symmetrically accessible to all buyers and sellers. We surprisingly observe substantial market inefficiencies. To explain this paradox that inefficiencies persist even in a fully information-transparent environment, we propose that traders’ limited analytical ability, rather than information asymmetry, ultimately drives market inefficiencies. We quantify analytical ability by examining whether traders’ performance can be augmented by machine-learning algorithms. And we find that having ten more historical transactions increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders cannot effectively consume the available information. Our findings contribute to the literature by quantifying analytical ability and highlighting the analytical-ability divide phenomenon.

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07-Blockchain

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Dec 11th, 12:00 AM

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. We test if this theory still holds in a blockchain-enabled marketplace where full information transparency is accomplished. We leverage the data from EnjinX, a non-fungible-token (NFT) marketplace, where the entire historical NFT transactions are symmetrically accessible to all buyers and sellers. We surprisingly observe substantial market inefficiencies. To explain this paradox that inefficiencies persist even in a fully information-transparent environment, we propose that traders’ limited analytical ability, rather than information asymmetry, ultimately drives market inefficiencies. We quantify analytical ability by examining whether traders’ performance can be augmented by machine-learning algorithms. And we find that having ten more historical transactions increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders cannot effectively consume the available information. Our findings contribute to the literature by quantifying analytical ability and highlighting the analytical-ability divide phenomenon.

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