A Modern Optimization Approach with Data-Driven Analytical Modeling for the Healthcare Business Segment (HBS) from the S&P 500

Introduction: The S&P consists of eleven business segments, which are classified according to the type of industry. The current study focuses on developing a non-linear analytical model for the Healthcare Business Segment (HBS) of the S&P 500, as a function of different economic & financial indicators. Materials and Methods: The analytical model used six financial indicators together with four economic indicators to predict the weekly average closing price (WCP) of HBS stocks. Johnson’s SB transformation corrected skewness, while desirability-based optimization identified indicator values maximizing WCP. The model’s performance and generalizability were validated through repeated 10-fold cross-validation. Results: All attributable contributors were ranked in accordance with the percentage of contribution to the WCP. The cross-validated R2s and RMSEs were found to be consistent across each fold for the proposed analytical model. The R2 (96.74%) and adjusted R2 (96.03%) were found to be high and consistent for the test (unseen) dataset. Discussions: The analytical modeling produces vital information that helps investors and portfolio managers, and financial institutions evaluate healthcare industry investments in the S&P 500. The optimization strategy helps identify the best controllable factors, which leads to more precise and strategic decision-making Patents: This paper has also been submitted for a US provisional patent on 31 May 2023 (TTO ref. 23T220PR-CS).