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Latest #BigData #research by Aldridge of @AbleMarkets hits Top 10 on @SSRN!

Irene Aldridge’s paper, “Big Data in Portfolio Allocation”, has garnered a record 150 downloads in the first day after publication on SSRN. The paper shows how applying Big Data techniques to classic Markowitz mean-variance optimization helps generate 400% excess return over 20 years in the S&P 500 stocks. Furthermore, the paper is the first to prove that, from the perspective of mean-variance portfolio optimization, it is the drivers of the inverse of the correlation matrix that carry the important information most relevant to successful portfolio composition.

To download the paper, please visit https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142880.

Here is the 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.