Direkt zur Hauptnavigation springen Direkt zum Inhalt springen
info(at)avldigital.remove-this.de
Deutsch English
Filtern nach
Aktive Filter
Letzte Suchanfragen

Ergebnisse für *

Zeige Ergebnisse 1 bis 2 von 2.

  1. A test on the location of tangency portfolio for small sample size and singular covariance matrix
    Autor*in: Drin, Svitlana; Mazur, Stepan; Muhinyuza, Stanislas
    Erschienen: [2023]
    Verlag:  Örebro University School of Business, Örebro, Sweden

    In this paper, we propose the test for the location of the tangency portfolio on the set of feasible portfolios when both the population and the sample covariance matrices of asset returns are singular. We derive the exact distribution of the test... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 776
    Fernleihe:
    keine Fernleihe

     

    In this paper, we propose the test for the location of the tangency portfolio on the set of feasible portfolios when both the population and the sample covariance matrices of asset returns are singular. We derive the exact distribution of the test statistic under both the null and alternative hypotheses. Furthermore, we establish the high-dimensional asymptotic distribution of that test statistic when both the portfolio dimension and the sample size increase to infinity. We complement our theoretical findings by comparing the high-dimensional asymptotic test with an exact finite sample test in the numerical study. A good performance of the obtained results is documented.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Array ; 2023, 11
    Schlagworte: Tangency portfolio; Hypothesis testing; Singular Wishart distribution; Singular covariance matrix; Moore-Penrose inverse; High-dimensional asymptotics
    Umfang: 1 Online-Ressource (circa 18 Seiten), Illustrationen
  2. Forecast model of the price of a product with a cold start
    Autor*in: Drin, Svitlana
    Erschienen: [2024]
    Verlag:  Örebro University School of Business, Örebro, Sweden

    This article presents a comprehensive study on developing a predictive product pricing model using LightGBM, a machine learning method optimized for regression challenges in situations with limited historical data. It begins by detailing the core... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 776
    Fernleihe:
    keine Fernleihe

     

    This article presents a comprehensive study on developing a predictive product pricing model using LightGBM, a machine learning method optimized for regression challenges in situations with limited historical data. It begins by detailing the core principles of LightGBM, including decision trees, boosting, and gradient descent, and then delves into the method's unique features like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). The model's efficacy is demonstrated through a comparative analysis with XGBoost, highlighting LightGBM's enhanced efficiency and slight improvement in prediction accuracy. This research offers valuable insights into the application of LightGBM in developing fast and accurate product pricing models, crucial for businesses in the rapidly evolving data landscape.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Array ; 2024, 2
    Schlagworte: GBM; GBDT; LightGBM; GOSS; EFB; predictive model
    Umfang: 1 Online-Ressource (circa 13 Seiten), Illustrationen