Empirical Study of Integrated EVA Performance Measurement in China

Lin CHEN, Zhi-lin QIAO


Traditional performance measurement has some limitations. Economic Value Added (EVA) is a real method to measure the company’s true value. This paper discussed on how to improve traditional performance measurement with EVA. It presented the integrated EVA performance measurement (IEPM) model. The superiority of IEPM model to traditional performance measurement was empirically analyzed with BP neural network and the data from China’s listed companies. The results showed that the measurement ability of IEPM model was superior to that of traditional performance measurement. Its prediction ability was also proved to be better than that of traditional measurement. It suggests that introducing EVA to performance measurement well reflects the company’s real profit. So it is effective and reasonable to use IEPM model to evaluate and predict the company’s performance.
Key words: Economic value added, IEPM model, Neural network, Performance measurement
Résumé: Traditionnels de mesure du rendement a quelques limitations. Economic Value Added (EVA) est une vraie méthode pour mesurer la vraie valeur de l'entreprise. Ce document discuté sur la manière d'améliorer la mesure du rendement traditionnel avec EVA. Il a présenté les mesures de la performance intégrée EVA (IEPM) modèle. La supériorité de l'IEPM modèle traditionnel de la mesure du rendement a été empiriquement analysées avec BP de réseaux de neurones et les données provenant de la Chine sociétés cotées. Les résultats ont montré que la mesure de la capacité IEPM modèle a été supérieure à celle de la traditionnelle mesure de la performance. Sa capacité de prévision a également été révélée meilleure que celle de la mesure traditionnelle. Il suggère que l'introduction de l'EVA à la mesure du rendement reflète bien la société profit immobilier. Ainsi, il est efficace et raisonnable d'utiliser IEPM modèle pour évaluer et prévoir les résultats de la société.
Mots-Clés: Economic value added, IEPM modèle, Neural network, Mesure de la performance

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DOI: http://dx.doi.org/10.3968/j.css.1923669720080402.006


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