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A Real Data-Driven Analytical Model to Predict Information Technology Sector Index Price of S&P 500

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  • Jayanta K. Pokharel
  • Erasmus Tetteh-Bator
  • Chris P. Tsokos

Abstract

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.

Suggested Citation

  • Jayanta K. Pokharel & Erasmus Tetteh-Bator & Chris P. Tsokos, 2022. "A Real Data-Driven Analytical Model to Predict Information Technology Sector Index Price of S&P 500," Papers 2209.10720, arXiv.org.
    • Handle: RePEc:arx:papers:2209.10720
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    References listed on IDEAS

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