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.


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    Title :

    Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders


    Additional title:

    Communic.Comp.Inf.Science



    Conference:

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



    Publication date :

    2024-10-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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