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  • 标题:Z-score vs minimum variance preselection methods for constructing small portfolios
  • 本地全文:下载
  • 作者:Francesco Cesarone ; Fabiomassimo Mango ; Gabriele Sabato
  • 期刊名称:Investment Management & Financial Innovations
  • 印刷版ISSN:1810-4967
  • 电子版ISSN:1812-9358
  • 出版年度:2020
  • 卷号:17
  • 期号:1
  • DOI:10.21511/imfi.17(1).2020.06
  • 语种:English
  • 出版社:LLC “Consulting Publishing Company “Business Perspectives”
  • 摘要:Several contributions in the literature argue that a significant in-sample risk reduction can be obtained by investing in a relatively small number of assets in an investment universe. Furthermore, selecting small portfolios seems to yield good out-of-sample performances in practice. This analysis provides further evidence that an appropriate preselection of the assets in a market can lead to an improvement in portfolio performance. For preselection, this paper investigates the effectiveness of a minimum variance approach and that of an innovative index (the new Altman Z-score) based on the creditworthiness of the companies. Different classes of portfolio models are examined on real-world data by applying both the minimum variance and the Z-score preselection methods. Preliminary results indicate that the new Altman Z-score preselection provides encouraging out-of-sample performances with respect to those obtained with the minimum variance approach.
  • 关键词:asset allocation;credit scoring;portfolio optimization;risk diversification;risk parity
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