Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Clara Calvo, Carlos Ivorra, Vicente Liern and Blanca Pérez-Gladish
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Clara Calvo: Department of Mathematics for Economics and Business, University of Valencia, Avda. dels Tarongers s/n, 46022 Valencia, Spain
Carlos Ivorra: Department of Mathematics for Economics and Business, University of Valencia, Avda. dels Tarongers s/n, 46022 Valencia, Spain
Vicente Liern: Department of Mathematics for Economics and Business, University of Valencia, Avda. dels Tarongers s/n, 46022 Valencia, Spain
Blanca Pérez-Gladish: Department of Economy Quantitative, University of Oviedo, Campus del Cristo, 33006 Oviedo, Spain

Mathematics, 2021, vol. 9, issue 6, 1-11

Abstract: Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

Keywords: portfolio selection; value of information; diversification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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