Document Type

Working Paper

Date of this Version

2021

Advisor

Winston Dou

Abstract

Through this paper we have been able to study several Low Rank Matrix Techniques, looking especially at their applications to the stock market space and how efficiently they can be used to predict stock indices. Several techniques have been used as well as key data used in previous studies to compare different approaches in this review paper. Some of these approaches include using Singular Value Decomposition techniques, analyzing its usage as per the different sources that can be used to make it more efficient. We also include the Low Rank Matrix by ASD (Alternating Steepest Descent) method to analyze how the efficient image inpainting algorithm may be applied to the stock markets. These approaches, after being analyzed on runtime, computational space and the availability of data amongst many factors, indicate to us that the Singular Value Decomposition using the Order Method is the most efficient method amongst all those methods analyzed.

Keywords

Low rank matrix completion, singular value decomposition

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Date Posted:04 November 2021

 

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