Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/215017 
Year of Publication: 
2019
Series/Report no.: 
CESifo Working Paper No. 8015
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
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.
Subjects: 
Bayesian learning
high-dimensional prediction problems
return predictability
out-of-sample tests
JEL: 
G14
G12
C11
Document Type: 
Working Paper
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