A theory of information overload applied to perfectly efficient financial markets

Purpose - In the era of big data investors deal every day with a huge flow of information. Given a model populated by economic agents with limited computational capacity, the paper shows how “too much” information could cause financial markets to depart from the assumption of informational efficiency. The purpose of the paper is to show that as information increases, at some point the efficient market hypothesis ceases to be true. In general, the hypothesis cannot be maintained if the use of the maximum amount of information is not optimal for investors. Design/methodology/approach - The authors use a model of cognitive heterogeneity to show the inadequacy of the notion of market efficiency in the modern society of big data. Findings - Theorem 1 proves that as information grows, agents' processing capacities do not, so at some point there will be an amount of information that no one can fully use. The introduction of computer-based processing techniques can restore efficiency, however, also machines are bounded. This means that as the amount of information increases, even in the presence of non-human techniques, at some point it will no longer be possible to process further information. Practical implications - This paper explains why investors very often prefer heuristics to complex strategies. Originality/value - This is, to the authors’ knowledge, the first model that uses information overload to prove informational inefficiency. This paper links big data to informational efficiency, whereas Theorem 1 proves that the old notion of efficiency is not well-founded because it relies on unlimited processing capacities of economic agents.