Enhancing shareholder and market value through innovation capability using panel data analysis: an implementation of machine learning models as an additional predictor

Purpose The purpose of this paper is to examine the impact of innovation capability on shareholders and market value using panel data analysis, both static and dynamic, in addition to predicting return on equity (ROE) and Tobin’s Q using various machine learning algorithms to reach the most accurate model. Design/methodology/approach This study uses different econometric model and machine learning models. Both static and dynamic panel regression analyses have been considered. Findings This study finds that innovation capability significantly improves financial performance, with CatBoost outperforming other machine learning models in predictive accuracy. Key drivers include leverage, firm size and net income, highlighting the strategic importance of innovation in enhancing ROE and Tobin’s Q. These insights emphasize innovation’s role in achieving competitive advantage in emerging markets like Egypt. Originality/value This paper uniquely combines econometric and machine learning methods to analyze innovation capability’s impact on financial performance in Egypt, offering a novel framework and actionable insights for enhancing shareholder and market value in emerging markets.