Market efficiency in the age of big data

Ian Martin and Stefan Nagel

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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)
Pages: 24 pages
Date: 2022-07-01
New Economics Papers: this item is included in nep-big, nep-cwa, nep-fmk, nep-ifn and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Published in Journal of Financial Economics, 1, July, 2022, 145(1), pp. 154 - 177. ISSN: 0304-405X

Downloads: (external link)
http://eprints.lse.ac.uk/112960/ Open access version. (application/pdf)

Related works:
Journal Article: 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:ehl:lserod:112960

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