No abstract

Stock price prediction is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits for investors. Information Technology Sector of S&P 500 is one of the most sought after business segments in S&P 500 and is one of the most attracting areas for many investors due to high percentage annual returns on investment over the years. We used Microsoft Corp. (MSFT), one of the leading companies of the Information Technology Sector Index of S&P 500 information to build a non-linear real data-driven analytical model which accurately predicts the Weekly Closing Price (WCP) of the stock with predictive accuracy of 99.3% using six financial, four economic indicators and their two way interactions as the attributable entities that drive the stock returns. We rank the statistically significant indicators and their interactions based on the percentage of contribution to the WCP of the stock that provides significant information for the beneficiary of the proposed predictive model. We present a unique way for feature selection when multicollinearity presents in the multiple regression dataset using L1-regularization based on supervised machine learning algorithm.