A MAXIMAL PREDICTABILITY PORTFOLIO MODEL: ALGORITHM AND PERFORMANCE EVALUATION

The purpose of this paper is to show that an algorithm recently proposed by authors can in fact solve a maximal predictability portfolio (MPP) optimization problem, which is a hard nonconvex fractional programming optimization. Also, we will compare MPP with standard mean-variance portfolio (MVP) and show that MPP outperforms MVP and index. We believe that this paper is of interest to researchers and practitioners in the field of portfolio optimization.

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Year of publication:	2007
Authors:	YAMAMOTO, REI ; ISHII, DAISUKE ; KONNO, HIROSHI
Published in:	International Journal of Theoretical and Applied Finance (IJTAF). - World Scientific Publishing Co. Pte. Ltd., ISSN 1793-6322. - Vol. 10.2007, 06, p. 1095-1109
Publisher:	World Scientific Publishing Co. Pte. Ltd.
Subject:	Portfolio optimization \| maximal predictability portfolio \| mean-variance portfolio \| global optimization \| fractional programming \| 0-1 integer programming

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Extent:	application/pdf text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10004971763