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Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance

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  • Deshen Wang

    (Institute for Financial Services Analytics, University of Delaware, Newark, DE 19716, USA)

Abstract

The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the performance of SVD, because SVD is a least squares method that is sensitive to errors in the data matrix. This paper proposes a robust SVD algorithm by applying an adjustable robust estimator. Through adjusting the tuning parameter in the algorithm, the method can be both robust and efficient. Moreover, a sequential robust SVD algorithm is proposed in order to decrease the computation volume in sequential and streaming data. The advantages of the proposed algorithms are proved with a financial application.

Suggested Citation

  • Deshen Wang, 2017. "Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance," Data, MDPI, vol. 2(3), pages 1-15, August.
    • Handle: RePEc:gam:jdataj:v:2:y:2017:i:3:p:29-:d:110287
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    References listed on IDEAS

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    Cited by:

    1. Irene Aldridge & Payton Martin, 2022. "ESG In Corporate Filings: An AI Perspective," Papers 2212.00018, arXiv.org.
    2. Yuriy Zaporozhets & Artem Ivanov & Yuriy Kondratenko, 2019. "Geometrical Platform of Big Database Computing for Modeling of Complex Physical Phenomena in Electric Current Treatment of Liquid Metals," Data, MDPI, vol. 4(4), pages 1-18, October.

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