The impact of sectorial and geographical segmentation on risk-based asset allocation techniques
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Author(s)Ignazio BasileLink to ORCID Index: https://orcid.org/0000-0001-8672-5026
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Pierpaolo FerrariLink to ORCID Index: https://orcid.org/0000-0001-5318-0592
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Guido AbateLink to ORCID Index: https://orcid.org/0000-0002-7406-0650
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DOIhttp://dx.doi.org/10.21511/imfi.16(3).2019.24
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Article InfoVolume 16 2019, Issue #3, pp. 260-274
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Funding dataFunder name: Universita degli studi Di Brescia, Departamento di Economia & Management, VAT#01773710171Funder identifier: –Award numbers: –
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In the last decades, risk-based portfolio construction techniques have enjoyed a widespread diffusion in the financial community. This study aims at evaluating how these portfolio construction techniques produce different results depending on whether the segmentation of the stock market investment universe is based on sectorial or geographical criteria. An empirical analysis, applied on the global equity market, is carried out by making use of the typical and most advanced statistical and financial evaluation measures. Geographical segmentation is carried out in relation to the listing market, while sectorial segmentation is made in relation to the productive sectors to which individual companies belong. Our comparative analysis provides substantially coherent results, demonstrating a significant preference for the sectorial criterion compared to the geographic one. In conclusion, this result can be attributed to the subdivision of the investment universe into sectorial indices characterized by greater internal coherence and better external differentiation, in addition to the lower concentration of sectorial segmentation compared to the geographical one.
- Keywords
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JEL Classification (Paper profile tab)G11
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References31
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Tables8
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Figures0
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- Table 1. Weights of the sectorial and geographic segmentation of the MSCI ACWI
- Table 2. Descriptive statistics of the benchmarks’ excess returns
- Table 3. The correlation matrix of the sectorial benchmarks’ excess returns
- Table 4. The correlation matrix of the geographic benchmarks’ excess returns
- Table 5. Tests of deviations from normality of the benchmarks’ excess returns
- Table 6. Descriptive statistics of the portfolios’ excess returns
- Table 7. Tests of deviations from normality for portfolios’ excess returns
- Table 8. The risk-adjusted performance of the risk-based portfolios
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Z-score vs minimum variance preselection methods for constructing small portfolios
Francesco Cesarone ,
Fabiomassimo Mango ,
Gabriele Sabato
doi: http://dx.doi.org/10.21511/imfi.17(1).2020.06
Investment Management and Financial Innovations Volume 17, 2020 Issue #1 pp. 64-76 Views: 1111 Downloads: 438 TO CITE АНОТАЦІЯSeveral 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.
