Big Data in Portfolio Allocation

28 Pages Posted: 21 Mar 2018 Last revised: 17 Apr 2018

Irene Aldridge

AbleMarkets.com; Cornell University; BigDataFinance.org; ABLE Alpha Trading, LTD

Date Written: March 17, 2018

Abstract

In the classic portfolio management theory, the weights of the optimized portfolios are directly proportional to the inverse of the asset correlation matrix. We show that, from the Big Data perspective, the inverse of the correlation matrix adds more value to optimal portfolio selection than the correlation matrix itself. We further show the empirical results of portfolio reallocation under different common portfolio composition scenarios, and outperform traditional portfolio allocation techniques out-of-sample, delivering nearly 400% improvement over the equally-weighted allocation over a 20-year investment period.

Keywords: Portfolio optimization, big data, investment management, correlation

JEL Classification: C02, C60

Suggested Citation

Aldridge, Irene, Big Data in Portfolio Allocation (March 17, 2018). Available at SSRN: https://ssrn.com/abstract=3142880 or http://dx.doi.org/10.2139/ssrn.3142880

Irene Aldridge (Contact Author)

AbleMarkets.com ( email )

New York, NY 10128
United States

HOME PAGE: http://www.AbleMarkets.com

Cornell University ( email )

Ithaca, NY 14853
United States

BigDataFinance.org ( email )

United States

ABLE Alpha Trading, LTD ( email )

New York, NY 10004
United States

HOME PAGE: http://www.ablealpha.com

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