2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 14 , Issue 2 , PP: 48–59, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Saad Metawea 1 * , Maha Metawea 2
Doi: https://doi.org/10.54216/AJBOR.140205
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.
Business data analytics , Firm valuation , Finance analytics , Market value , Explainable analytics , Classification , Public company fundamentals
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