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The Journal of Financial Data Science

The Journal of Financial Data Science

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Article

Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization

Irene Aldridge
The Journal of Financial Data Science Winter 2019, jfds.2019.1.045; DOI: https://doi.org/10.3905/jfds.2019.1.045
Irene Aldridge
is managing director of research at AbleMarkets in New York, NY, and visiting professor at Cornell University in New York, NY
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Abstract

In the classic mean-variance portfolio theory as proposed by Harry Markowitz, the weights of the optimized portfolios are directly proportional to the inverse of the asset correlation matrix. However, most contemporary portfolio optimization research focuses on optimizing the correlation matrix itself, and not its inverse. In this article, the author demonstrates that this is a mistake. Specifically, from the Big Data perspective, she proves that the inverse of the correlation matrix is much more unstable and sensitive to random perturbations than is the correlation matrix itself. As such, optimization of the inverse of the correlation matrix adds more value to optimal portfolio selection than does optimization of the correlation matrix. The author further shows the empirical results of portfolio reallocation under different common portfolio composition scenarios. The technique outperforms traditional portfolio allocation techniques out of sample, delivering nearly 400% improvement over the equally weighted allocation over a 20-year investment period on the S&P 500 portfolio with monthly reallocation. In general, the author demonstrates that the correlation inverse optimization proposed in this article significantly outperforms the other core portfolio allocation strategies, such as equally weighted portfolios, vanilla mean-variance optimization, and techniques based on the spectral decomposition of the correlation matrix. The results presented in this article are novel in the data science space, extend far beyond financial data, and are applicable to any data correlation matrixes and their inverses, whether in advertising, healthcare, or genomics.

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The Journal of Financial Data Science: 3 (1)
The Journal of Financial Data Science
Vol. 3, Issue 1
Winter 2021
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Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization
Irene Aldridge
The Journal of Financial Data Science Jan 2019, jfds.2019.1.045; DOI: 10.3905/jfds.2019.1.045

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Big Data in Portfolio Allocation: A New Approach to Successful Portfolio Optimization
Irene Aldridge
The Journal of Financial Data Science Jan 2019, jfds.2019.1.045; DOI: 10.3905/jfds.2019.1.045
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  • Article
    • Abstract
    • BIG DATA OVERVIEW
    • TRADITIONAL PORTFOLIO OPTIMIZATION AND BIG DATA APPLICATIONS
    • BIG DATA WITH THE INVERSE OF THE CORRELATION MATRIX: A NOVEL APPROACH
    • CORRELATION MATRIXES VERSUS INVERSES: STABILITY AND SENSITIVITY TO PERTURBATIONS
    • SENSITIVITY OF CORRELATION MATRIXES VERSUS THEIR INVERSES: SIMULATION
    • OUT-OF-SAMPLE APPLICATIONS TO FINANCIAL DATA
    • CONCLUSIONS
    • ACKNOWLEDGMENTS
    • ENDNOTES
    • REFERENCES
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  • PDF (Subscribers Only)

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  • Managing Editor’s Letter
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  • Portfolio Construction Using First Principles Preference Theory and Machine Learning
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