Article

The time diversification monitoring of a stock portfolio: an approach based on the fractal dimension

Authors:

Mehmed Kantardzic,

Pedram Sadeghian,

Chun ShenAuthors Info & Claims

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

Pages 637 - 641

https://doi.org/10.1145/967900.968034

Published: 14 March 2004 Publication History

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Abstract

Diversification is a technique used to reduce the risk of investment and is accomplished by including uncorrelated and independent stocks in one's portfolio. By diversifying, the investor aims to reduce the risk of an entire portfolio depreciating in value, if a few of the assets within the portfolio are depreciated. In the past, the correlation coefficient has been used as a basis for diversification. However, the correlation coefficient is problematic since it can not capture nonlinear dependency, and analyzing pair-by-pair stocks in the portfolio does not always give the best estimation of diversification for the entire portfolio.In this paper we present a simple, but efficient methodology for monitoring portfolio diversification, which can capture most of the nonlinear phenomena in a portfolio. We propose a measurement of portfolio diversification through the fractal dimension parameter. Monitoring this parameter in a time domain represents the basis for automatic detection of significant changes in portfolio diversification. When the fractal dimension is significantly reduced, the algorithm eliminates stocks that are highly correlated and adds new uncorrelated stocks to the portfolio. We tested our method using real historical stock data and obtained significant improvements in the time diversification of selected stock portfolios.

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Cited By

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Zlatniczki ATelcs A(2024)Application of Portfolio Optimization to Achieve Persistent Time SeriesJournal of Optimization Theory and Applications10.1007/s10957-024-02426-1201:2(932-954)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10957-024-02426-1
Fior JCagliero LGarza PBurdick DPujara J(2020)Price series cross-correlation analysis to enhance the diversification of itemset-based stock portfoliosProceedings of the Sixth International Workshop on Data Science for Macro-Modeling10.1145/3401832.3402680(1-6)Online publication date: 14-Jun-2020
https://dl.acm.org/doi/10.1145/3401832.3402680
Bose I(2009)Financial Time Series Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch136(883-889)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch136
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The time diversification monitoring of a stock portfolio: an approach based on the fractal dimension
1. Applied computing
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Information & Contributors

Information

Published In

SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

March 2004

1733 pages

ISBN:1581138121

DOI:10.1145/967900

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Program Chairs:
Andrea Omicini
Università degli Studi di Bologna a Cesena
,
Roger L. Wainwright
University of Tulsa
,
Publications Chair:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2004

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SAC04: The 2004 ACM Symposium on Applied Computing

March 14 - 17, 2004

Nicosia, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all

Zlatniczki ATelcs A(2024)Application of Portfolio Optimization to Achieve Persistent Time SeriesJournal of Optimization Theory and Applications10.1007/s10957-024-02426-1201:2(932-954)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10957-024-02426-1
Fior JCagliero LGarza PBurdick DPujara J(2020)Price series cross-correlation analysis to enhance the diversification of itemset-based stock portfoliosProceedings of the Sixth International Workshop on Data Science for Macro-Modeling10.1145/3401832.3402680(1-6)Online publication date: 14-Jun-2020
https://dl.acm.org/doi/10.1145/3401832.3402680
Bose I(2009)Financial Time Series Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch136(883-889)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch136
de Sousa ETraina CTraina AWu LFaloutsos C(2007)A fast and effective method to find correlations among attributes in databasesData Mining and Knowledge Discovery10.1007/s10618-006-0056-414:3(367-407)Online publication date: 1-Jun-2007
https://dl.acm.org/doi/10.1007/s10618-006-0056-4
de Sousa ETraina ATraina CFaloutsos CHaddad H(2006)Evaluating the intrinsic dimension of evolving data streamsProceedings of the 2006 ACM symposium on Applied computing10.1145/1141277.1141426(643-648)Online publication date: 23-Apr-2006
https://dl.acm.org/doi/10.1145/1141277.1141426
de Sousa ETraina ATraina CFaloutsos C(2006)Measuring Evolving Data Streams’ Behavior through Their Intrinsic DimensionNew Generation Computing10.1007/s00354-006-0003-325:1(33-60)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.1007/s00354-006-0003-3
Bartkowiak ALipinski P(2005)Remarks on Evaluation of Correlation Dimension for 5 French Stock DataProceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing10.1109/SYNASC.2005.60Online publication date: 25-Sep-2005
https://dl.acm.org/doi/10.1109/SYNASC.2005.60

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