文章基本信息

标题：Z-score vs minimum variance preselection methods for constructing small portfolios
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作者：Francesco Cesarone ; Fabiomassimo Mango ; Gabriele Sabato 等
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