Manufacturing Enterprises' Digital Asset Value Evaluation Based on Multi-period Excess Return Method and Case Analysis

Data assets have become strategic resources for manufacturing enterprises due to digital transformation, but most remain off-balance-sheet, challenging stakeholders to evaluate their true value. Traditional methods like market and cost approaches fail to capture the dynamic nature of data assets, while the income approach still has limitations in predicting future earnings and discount rates. This work integrates the Multi-Period Excess Return Method (MPEEM) with the Analytic Hierarchy Process (AHP) to accurately assess the value of data assets. The MPEEM isolates excess returns from data assets, and AHP separates the contribution of off-balance-sheet assets. Empirical analysis of Midea Group, BYD and Xiaomi shows that data assets significantly contribute to enterprise value, with core drivers including smart manufacturing data, consumer behavior analyses, and supply chain optimization data. This paper provides a robust framework for valuing data assets, supporting strategic decision-making and regulatory compliance in the manufacturing sector.