This paper examines the potential of deep learning techniques for the construction of tracking portfolios for the US Nasdaq 100 index. We use autoencoders (AE) and variational autoencoders (VAE) within deep neural networks, incorporating cardinality constraints to limit the number of assets in the portfolio. Our methodology entails training the models on historical stock returns from 2019 to 2022 and validating their performance in 2023. The results demonstrate that the tracking portfolios generated by both the autoencoder (AE) and variational autoencoder (VAE) models closely track the index in both the in-sample and out-of-sample periods, while reducing the number of assets. The tracking performance is evaluated in terms of cumulative returns and tracking error. Furthermore, we evaluate the performance of the AE and VAE methods across a range of asset selections, highlighting the strengths and limitations of each approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders


    Weitere Titelangaben:

    Communic.Comp.Inf.Science



    Kongress:

    Workshop on Engineering Applications ; 2024 ; Barranquilla, Colombia October 23, 2024 - October 25, 2024



    Erscheinungsdatum :

    18.10.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Variational Autoencoders

    Ghojogh, Benyamin / Crowley, Mark / Karray, Fakhri et al. | Springer Verlag | 2022


    Certifiably Robust Variational Autoencoders

    Barrett, Ben / Camuto, Alexander / Willetts, Matthew et al. | ArXiv | 2021

    Freier Zugriff

    Mixed-curvature Variational Autoencoders

    Skopek, Ondrej / Ganea, Octavian-Eugen / Bécigneul, Gary | ArXiv | 2019

    Freier Zugriff