A Stock Optimization Problem in Finance: Understanding Financial and Economic Indicators Through Analytical Predictive Modeling

Authors

Aditya Chakraborty, Macon & Joan Brock Virginia Health Sciences at Old Dominion UniversityFollow
Chris Tsokos, University of South Florida

ORCID

0000-0002-1476-113X (Chakraborty)

Document Type

Article

Publication Date

2024

DOI

10.3390/math12152407

Publication Title

Mathematics

Volume

12

Issue

15

Pages

2407 (1-17)

Abstract

Given the significant impact of healthcare stock changes on the global economy, including its GDP and other financial factors, we endeavored to create an analytical prediction model for forecasting the annual percentage change of these stocks. Our model, which is nonlinear, incorporates five key discoveries. We focused on predicting the average weekly closing price (pWCP) of AbbVie Inc. (North Chicago, IL, USA)'s healthcare stock (ABBV) from 1 August 2017 to 31 December 2019. The stock was chosen based on the low beta risk, high dividend yield, and high yearly percentage return criteria. Alongside predicting the weekly stock price, our model identifies the individual indicators and their interactions that notably influence the response. These indicators were ranked based on their contribution percentages to the response. The validity of the model was justified based on the root mean square error (RMSE) and R² value by performing 10-fold cross-validation. Furthermore, an optimization process using the desirability function was implemented to determine the optimal values of the indicators that maximize the response, along with the 95% confidence and 95% prediction interval. We also visually depicted the optimal ranges of any two indicators that affect the response AWCP. In our evaluation, we compared the original and predicted responses of AWCP using our analytical model. The results demonstrated a close alignment between the two sets of observations, highlighting the high accuracy of our model. Beyond these findings, our model provides additional valuable insights into the subject area. It has undergone thorough validation and testing, confirming its high quality and the precision of our weekly stock price predictions. The information derived from the modeling and analysis is important for constructive and accurate decision-making for individual investors, portfolio managers, and financial institutions concerning the financial and economic aspects of the healthcare industry. By identifying the optimum values of the controllable contributors through the optimization process, financial institutions can make the strategic changes needed for the company's long-term viability.

Rights

© 2024 The Authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data can be available from Yahoo Finance (https://finance.yahoo.com/), the U.S Bureau of Economic Analysis (https://www.bea.gov/), and the US Bureau of Labor Statistics (https://www.bls.gov/)."

Original Publication Citation

Chakraborty, A., & Tsokos, C. (2024). A stock optimization problem in finance: Understanding financial and economic indicators through analytical predictive modeling. Mathematics, 12(15), 1-17, Article 2407. https://doi.org/10.3390/math12152407

Repository Citation

Chakraborty, A., & Tsokos, C. (2024). A stock optimization problem in finance: Understanding financial and economic indicators through analytical predictive modeling. Mathematics, 12(15), 1-17, Article 2407. https://doi.org/10.3390/math12152407