Empirical Asset Pricing with Missing Data

41 Pages Posted: 10 Jan 2022 Last revised: 3 May 2024

See all articles by Heiner Beckmeyer

Heiner Beckmeyer

University of Münster

Timo Wiedemann

University of Münster - Finance Center Münster

Date Written: January 7, 2022

Abstract

What is the impact of missing information in empirical asset pricing? To seek an answer to this question, we first propose an overarching machine learning method that accurately imputes missing firm characteristics using the characteristic's own past, information about other characteristics and their temporal evolution. We then document the impact of adequately accounting for missing information on questions in asset pricing in three ways: first, we show that factor premia obtained from simple portfolio sorts are likely lower than previously thought; second, acknowledging that the information density differs between firms allows for a more accurate description of the risk-return trade-off across stocks; third, we confirm that simple imputation techniques work as well as sophisticated methods when used for machine learned return predictability. We argue that the complexity of the methods results in their ability to handle amounts of missing information that far exceed what we see empirically.

Keywords: Missing Data, Machine Learning, IPCA, Return Prediction, Big Data

JEL Classification: G10, G12, G14, C14, C55

Suggested Citation

Beckmeyer, Heiner and Wiedemann, Timo, Empirical Asset Pricing with Missing Data (January 7, 2022). Available at SSRN: https://ssrn.com/abstract=4003455 or http://dx.doi.org/10.2139/ssrn.4003455

Heiner Beckmeyer (Contact Author)

University of Münster ( email )

Schlossplatz 2
Muenster, D-48143
Germany

HOME PAGE: http://heinerbeckmeyer.com

Timo Wiedemann

University of Münster - Finance Center Münster ( email )

Universitätsstraße 14-16
Münster, 48143
Germany

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