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Digital Portfolio Theory

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  • Jones, C Kenneth

Abstract

The Modern Portfolio Theory of Markowitz maximized portfolio expected return subject to holding total portfolio variance below a selected level. Digital Portfolio Theory is an extension of Modern Portfolio Theory, with the added dimension of memory. Digital Portfolio Theory decomposes the portfolio variance into independent components using the signal processing decomposition of variance. The risk or variance of each security's return process is represented by multiple periodic components. These periodic variance components are further decomposed into systematic and unsystematic parts relative to a reference index. The Digital Portfolio Theory model maximizes portfolio expected return subject to a set of linear constraints that control systematic, unsystematic, calendar and non-calendar variance. The paper formulates a single period, digital signal processing, portfolio selection model using cross-covariance constraints to describe covariance and autocorrelation characteristics. Expected calendar effects can be optimally arbitraged by controlling the memory or autocorrelation characteristics of the efficient portfolios. The Digital Portfolio Theory optimization model is compared to the Modern Portfolio Theory model and is used to find efficient portfolios with zero calendar risk for selected periods. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • Jones, C Kenneth, 2001. "Digital Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 18(3), pages 287-316, December.
    • Handle: RePEc:kap:compec:v:18:y:2001:i:3:p:287-316
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    Cited by:

    1. C. Quek & K. C. Yow & Philip Y. K. Cheng & C. C. Tan, 2009. "Investment portfolio balancing: application of a generic self‐organizing fuzzy neural network (GenSoFNN)," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 147-164, January.
    2. Erdemlioglu, Deniz & Joliet, Robert, 2019. "Long-term asset allocation, risk tolerance and market sentiment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 1-19.
    3. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.

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