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Autoencoder asset pricing models and economic restrictions — international evidence

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00639180" target="_blank" >RIV/67985556:_____/25:00639180 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216208:11230/25:10502887

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S105752192500729X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S105752192500729X?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.irfa.2025.104642" target="_blank" >10.1016/j.irfa.2025.104642</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Autoencoder asset pricing models and economic restrictions — international evidence

  • Original language description

    We evaluate the performance of the Conditional Autoencoder (CAE) model by Gu et al. (2021) across U.S. and international datasets, considering economic constraints such as the exclusion of microcap and illiquid firms and the inclusion of transaction costs. The CAE model captures nonlinear relationships between returns and firm characteristics by jointly estimating latent factors and conditional betas while enforcing the no-arbitrage condition. The original study demonstrated significant reductions in out-of-sample pricing errors from both statistical and economic perspectives in the U.S. context. We validate these findings on the original U.S. dataset and show that the model generalises well to a U.S. dataset with a broader set of firm characteristics and to international markets. When economic constraints are introduced, portfolio profitability declines substantially. Profitability drops by 60%–85% when shifting from the full sample to the liquid sample before trading costs. However, after costs, only the liquid strategies remain profitable. In particular, long-only strategies on the liquid sample are the only ones to consistently outperform market benchmarks across all datasets, achieving Sharpe ratios between 0.65 and 0.78 for both equal- and value-weighted portfolios. Overall, the findings underscore both the limitations and the practical potential of the CAE model under realistic market frictions.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50201 - Economic Theory

Result continuities

  • Project

    <a href="/en/project/GA24-11555S" target="_blank" >GA24-11555S: Taming the tail risks in financial markets with data-driven methods</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    International Review of Financial Analysis

  • ISSN

    1057-5219

  • e-ISSN

    1873-8079

  • Volume of the periodical

    107

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    9

  • Pages from-to

    104642

  • UT code for WoS article

    001579057000004

  • EID of the result in the Scopus database

    2-s2.0-105016479141