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  1. Complexity in Factor Pricing Models
    Author: Didisheim, Antoine; Ke, Shikun; Kelly, Bryan T.; Malamud, Semyon
    Published: 2023
    Publisher:  SSRN, [S.l.]

    We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or... more

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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best asset pricing models (in terms of expected out-of-sample performance) have an extremely large number of factors (more than the number of training observations or base assets). Our empirical findings verify the theoretically predicted “virtue of complexity” in the cross-section of stock returns and find that the best model combines tens of thousands of factors. We also derive the feasible Hansen- Jagannathan (HJ) bound: The maximal Sharpe ratio achievable by a feasible portfolio strategy. The infeasible HJ bound massively overstates the achievable maximal Sharpe ratio due to a complexity wedge that we characterize

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
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    Series: Swiss Finance Institute Research Paper ; No. 23-19
    Subjects: Portfolio choice; asset pricing tests; optimization; expected returns; predictability
    Other subjects: Array
    Scope: 1 Online-Ressource (148 p)
    Notes:

    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 13, 2023 erstellt
  2. The performance of time series forecasting based on classical and machine learning methods for S&P 500 index
    Author: Uzzal, Maudud Hassan; Ślepaczuk, Robert
    Published: 2023
    Publisher:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working papers / University of Warsaw, Faculty of Economic Sciences ; no. 2023, 5 = 412
    Subjects: deep learning; recurrent neural networks; ARIMA; algorithmic investment strategies; trading systems; LSTM; walk-forward process; optimization
    Scope: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  3. Optimal forecast combination with mean absolute error loss
    Author: Chan, Felix; Pauwels, Laurent
    Published: 2023
    Publisher:  Australian National University, Crawford School of Public Policy, Canberra

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VSP 1716
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: CAMA working paper series ; 2023, 59 (November 2023)
    Subjects: Forecasting; forecast combination; optimization; mean absolute error; optimal weights
    Scope: 1 Online-Ressource (circa 32 Seiten), Illustrationen