The application of excess return method in the evaluation of logistics enterprise data assets

Data assets serve as a critical component in driving the innovation and development of intelligent logistics. By systematically reviewing the existing research on the value assessment of data assets in logistics enterprises, this study constructs a multi-period excess return evaluation model. An empirical analysis is conducted using SF Holding as a representative case of a logistics enterprise, which confirms that data assets generate excess returns and that the model possesses a certain degree of feasibility and reference value. This paper offers both a theoretical foundation and practical guidance for enhancing the data asset valuation system of logistics enterprises.