Kolmogorov-Arnold Networks in Finance: A Comparative Analysis for Derivative Pricing Models

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dc.contributor.MentorBasaluzzo, Gabriel
dc.creator.AutorKricun, Fernando
dc.date.accessioned2025-01-28T14:39:12Z
dc.date.available2025-01-28T14:39:12Z
dc.date.issued2024-09
dc.descriptionFil: Kricun, Fernando. Universidad de San Andrés. Escuela de Negocios; Argentina.
dc.description.abstractKolmogorov-Arnold Networks (KANs) have recently been introduced as an alternative to traditional Multilayer Perceptrons (MLPs) for neural network representation. In this study, we investigate the application of KANs in constructing physics-informed machine learning models to solve the Black-Scholes equation for pricing derivatives. We conduct a comparative analysis between KAN-based models and those using standard MLP architectures. Our findings reveal that KANs, despite having significantly fewer parameters, achieve comparable or superior accuracy to larger MLPs, with faster convergence across fewer training epochs. These results highlight the potential of KANs as a more efficient alternative to MLPs in the context of derivative pricing models.
dc.formatapplication/pdf
dc.identifier.urihttps://repositorio.udesa.edu.ar/handle/10908/24503
dc.languageeng
dc.publisherUniversidad de San Andrés. Escuela de Negocios
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleKolmogorov-Arnold Networks in Finance: A Comparative Analysis for Derivative Pricing Models
dc.typeTesis
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.typeinfo:ar-repo/semantics/tesis de grado
dc.typeinfo:eu-repo/semantics/updatedVersion
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