Valuation Premium Analytics in Global Public Companies: A
Cross-Sectional Study Using 2024 Public Fundamentals
Saad Metawea1,∗, Maha Metawea2
1Faculty of Commerce, Mansoura University, Egypt
2Faculty of Business Administration, Delta University for Science and Technology, Egypt
Emails: s-Metawa@Yahoo.com; Maha.mtawea@Deltauniv.edu.eg
Abstract
This paper explores why there are listed companies that are valuing significantly higher in the market based
on their asset base compared to other companies. It analyses the relationship between valuation premiums
and profitability, asset efficiency, the combination of the two, the size of the firm and its loss status using a
cross-section of the largest publicly traded companies in the world in 2024. The empirical design integrates
the predictive analytics and hypothesis testing. During the explanatory phase, a strong ordinary least squares
specification is used to model the logarithm of the market value divided by the total assets. In the predictive
stage, logistic regression, random forest, and gradient boosting are used to identify firms in the top quartile
of the valuation-premium distribution. The findings show that profitability and asset efficiency interaction is
the most positive correlate of the valuation premium, and firm scale is the most negative correlate of relative
valuation after standardization by assets. The interaction-enriched specification enhances explanatory power
with significant material in comparison to an interaction-free model. The discriminatory performance of the
tree-based models tends to be high in the classification phase, with random forest performing out of sample
with an AUC of more than 0.93. The results of these studies indicate that valuation premium should be viewed
as a combined operating-quality indicator and not as a reward to margin performance in isolation and can serve
as a useful guide to screen a portfolio, benchmark a company and interpret market multiples.
Keywords: Business data analytics; Firm valuation; Finance analytics; Market value; Explainable analytics;
Classification; Public company fundamentals