Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/337469 
Year of Publication: 
2026
Series/Report no.: 
CFR Working Paper No. 26-05
Publisher: 
University of Cologne, Centre for Financial Research (CFR), Cologne
Abstract: 
Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns typical patterns that describe most firms, and our measure aggregates the deviations those patterns cannot explain. Unlike proxies based on disclosure or organizational complexity, this approach captures the processing difficulty of the characteristics themselves. Empirically, we document that atypicality strongly predicts future returns. A decile portfolio that sells high-ATYP firms and buys low-ATYP firms earns 1.47% per month (equal-weighted) and 0.82% (value-weighted). The effect strengthens where investor attention is low and arbi- trage is limited, suggesting mispricing as the explanation.
Subjects: 
atypical firms
processing difficulty
return predictability
mispricing
machine learning
JEL: 
G10
G11
G12
G14
C45
Document Type: 
Working Paper

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