Hard to process: Atypical firms and the cross-section of expected stock returns

Sebastian Weibels

No 26-05, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

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.

Keywords: atypical firms; processing difficulty; return predictability; mispricing; machine learning (search for similar items in EconPapers)
JEL-codes: C45 G10 G11 G12 G14 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:337469

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