A Stock Market Prediction Model Based on Linear Regression and Matrix Method

Abstract

Stock market predictions, being one of the most important areas in financial studies, have for long been sought after by many researchers in these areas, with their applications being of utmost importance in studies on theory. Here, in an endeavor to bring about an efficient yet interpretative model for stock market predictions, there has been an impressive amalgamation of approaches based on linear regression models, matrix theory, and error correction mechanisms. Here, in particular, while model parameters have been identified with utmost precision based on least squares, there have been improvements with regard to computational speeds based on matrix calculation, with improved market versatility for resistance to dynamic markets based on error correction mechanisms. On testing applications, there has been an analysis based on historical Google stock market performances. It has been determined in these studies, based on their applications, that there has indeed been remarkable stability in performance levels in identifying stock performance based on its predictive nature, while particularly emphasizing their high flexibility levels in real-time applications based on its localization levels in its sliding window-based method. Though there have been limitations with regard to response levels based on short-term market changes, there have indeed been many advantageous levels in computational simplicity, interpretability, and versatility based on its applications in predictive conclusions for financial dealers. These studies have indeed paved new ways in achieving efficient fusion in financial predictive studies, with appropriate applications in their development in computational finance based on innovations brought about in finance theory based on modern computing innovations.