Z-score vs minimum variance preselection methods for constructing small portfolios

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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.

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    • Figure 1. Ten preselected assets for each rebalancing date using Z-score preselection method
    • Figure 2. Ten preselected assets for each rebalancing date using the minimum variance preselection method
    • Figure 3. Out-of-sample compounded return for all models without preselection
    • Figure 4. Out-of-sample compounded return for all models using minimum variance preselection
    • Figure 5. Out-of-sample compounded return for all models using Z-score preselection
    • Table 1. List of portfolio strategies
    • Table 2. List of 31 assets belonging to the investment universe considered
    • Table 3. Out-of-sample results without preselection
    • Table 4. Out-of-sample results using the minimum variance preselection
    • Table 5. Out-of-sample results using Z-score preselection