Market Efficiency in the Age of Big Data

54 Pages Posted: 14 Jan 2020

See all articles by Ian Martin

Ian Martin

London School of Economics & Political Science (LSE) - Department of Finance

Stefan Nagel

University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research; CESifo (Center for Economic Studies and Ifo Institute)

Multiple version iconThere are 3 versions of this paper

Date Written: December 2019

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 model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning.

Keywords: Big Data, Machine Learning, Market Efficiency

JEL Classification: C11, C12, C58, G10, G12, G14

Suggested Citation

Martin, Ian W. R. and Nagel, Stefan, Market Efficiency in the Age of Big Data (December 2019). CEPR Discussion Paper No. DP14235, Available at SSRN: https://ssrn.com/abstract=3518573

Ian W. R. Martin (Contact Author)

London School of Economics & Political Science (LSE) - Department of Finance ( email )

United Kingdom

HOME PAGE: http://personal.lse.ac.uk/martiniw/

Stefan Nagel

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Centre for Economic Policy Research ( email )

London
United Kingdom

CESifo (Center for Economic Studies and Ifo Institute) ( email )

Poschinger Str. 5
Munich, DE-81679
Germany

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