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PRINCIPAL COMPONENT ANALYSIS AND OPTIMAL PORTFOLIO

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Abstract

In this paper, we try to apply the advantages of principal component analysis (PCA) and online learning to the calculation of optimal portfolio investments. Each of these methods is widely used separately. This is done in order to make it possible to use online learning algorithms for high-dimension portfolios.

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This research received no external funding.

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Author notes

  1. N. Danilova and K. Yao both contributed equally to this work.

Authors and Affiliations

  1. Operations Research, Mathematics, Mechanics and Computer Sciences Institute of Southern Federal University, Milchakova St., 8a, Rostov-on-Don, 344090, Russia

    G. Beliavsky, N. Danilova & K. Yao

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  1. G. Beliavsky
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  2. N. Danilova
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  3. K. Yao
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Correspondence to G. Beliavsky.

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Beliavsky, G., Danilova, N. & Yao, K. PRINCIPAL COMPONENT ANALYSIS AND OPTIMAL PORTFOLIO. J Math Sci 271, 368–377 (2023). https://doi.org/10.1007/s10958-023-06526-7

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