Date of this Version
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.
Low rank matrix completion, singular value decomposition
Agrawal, P. (2021). "Predicting Stock Market Indices Using LRMC," Summer Program for Undergraduate Research (SPUR). Available at https://repository.upenn.edu/spur/34
Date Posted: 09 November 2021