Market efficiency in the age of big data

Ian Martin and Stefan Nagel

Journal of Financial Economics, 2022, vol. 145, issue 1, 154-177

Abstract: Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our equilibrium model, N assets have cash flows that are linear in J characteristics, with unknown coefficients. Risk-neutral Bayesian investors learn these coefficients and determine market prices. If J and N are comparable in size, returns are cross-sectionally predictable ex post. In-sample tests of market efficiency reject the no-predictability null with high probability, even though investors use information optimally in real time. In contrast, out-of-sample tests retain their economic meaning.

Keywords: Bayesian learning; High-dimensional prediction problems; Return predictability; Out-of-sample tests (search for similar items in EconPapers)
JEL-codes: C11 G12 G14 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Related works:
Working Paper: Market efficiency in the age of big data (2022)
Working Paper: Market Efficiency in the Age of Big Data (2019)
Working Paper: Market Efficiency in the Age of Big Data (2019)
Working Paper: Market Efficiency in the Age of Big Data (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:145:y:2022:i:1:p:154-177

DOI: 10.1016/j.jfineco.2021.10.006

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