  S&P 500 Index is one of the most sought after stock indices in the world. In
particular, Information Technology Sector of S&P 500 is the number one business
segment of the S&P 500 in terms of market capital, annual revenue and the
number of companies (75) associated with it, and is one of the most attracting
areas for many investors due to high percentage annual returns on investment
over the years. A non-linear real data-driven analytical model is built to
predict the Weekly Closing Price (WCP) of the Information Technology Sector
Index of S&P 500 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 Information Technology
Sector Index of the S&P 500 that provides significant information for the
beneficiary of the proposed predictive model. The model has the predictive
accuracy of 99.4%, and the paper presents some intriguing findings and the
model's usefulness.
