Market Efficiency in the Age of Big Data

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 8015

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Information and Market Efficiency; Event Studies; Insider Trading ; Asset Pricing; Trading Volume; Bond Interest Rates ; Bayesian Analysis: General
• Thema: Bayesian learning ; high-dimensional prediction problems ; return predictability ; out-of-sample tests

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Martin, Ian ; Nagel, Stefan
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2019

Weitere Informationen

• Handle: http://hdl.handle.net/10419/215017
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ