Market Efficiency in the Age of Big Data
By Ian Martin, Stefan Nagel. Social Science Research Network, 2019
110 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/2a04610f48bb448953348c4b1087fc9c201a01f8
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Citations in the corpus
- Forest Through the Trees: Building Cross-Sections of Stock Returns (2020, background): “Martin and Nagel (2019) recently highlighted a stark difference between the insample predictability reflected by traditional statistical tests and its feasible, out-of-sample counterpart.”
- Deep Learning in Asset Pricing (2019, background): “32? among others show the time variation in risk premia. between different models as also emphasized among others by Martin and Nagel (2020) and Gu, Kelly, and Xiu (2020).”