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    Maximizing Positive Porfolio Diversification


    Maguire, Phil, Moser, Philippe, O'Reilly, Kieran, McMenamin, Conor, Kelly, Robert and Maguire, Rebecca (2014) Maximizing Positive Porfolio Diversification. In: 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, pp. 174-181. ISBN 9781479923809

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    Official URL: http://dx.doi.org/10.1109/CIFEr.2014.6924070

    Abstract

    In this article we introduce a new strategy for optimal diversification which combines elements of Diversified Risk Parity [1], [2] and Diversification Ratio [3], with emphasis on positive risk premiums. The Uncorrelated Positive Bets strategy involves the identification of reliable, independent sources of randomness and the quantification of their positive risk premium.We use principal component analysis to identify the most significant sources of randomness contributing to the market and then apply the Randomness Deficiency Coefficient metric [4] and principal portfolio positivity to identify a set of reliable uncorrelated positive bets. Portfolios are then optimized by maximizing their diversified positive risk premium. We contrast the performance of a range of diversification strategies for a portfolio held for a two-year out-of-sample period with a 30 stock constraint. In particular, we introduce the notion of diversification inefficiency to explain why diversification strategies might outperform the market.
    Item Type: Book Section
    Keywords: investment; principal component analysis; risk management;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 6526
    Identification Number: 10.1109/CIFEr.2014.6924070
    Depositing User: Philippe Moser
    Date Deposited: 04 Nov 2015 14:44
    Publisher: IEEE
    Refereed: Yes
    URI: https://mural.maynoothuniversity.ie/id/eprint/6526
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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