Data asset valuation model based on generative artificial intelligence - Yungang Tang; Yaoqian Liu; Daxin Liu (PLoS ONE, 2025)
In the digital economy era, the significance of data assets has increasingly become evident, particularly against the backdrop of the rapid development of Generative Artificial Intelligence. This paper constructed a data asset valuation model based on Generative AI, aimed at dynamically assessing the commercial value of data assets. The model integrates data feature extraction, value generation algorithms, and market adaptability evaluations to address the shortcomings of traditional valuation methods in dynamic market environments. The validity and applicability of the model were verified through an empirical analysis of data from Chinese A-share listed companies from 2015 to 2023. The results indicated that the integrated model exhibited a significant advantage over individual models in accuracy and stability, especially in data-intensive industries such as information technology and financial services. This research provided new perspectives and methodologies for enterprises in digital transformation and data asset management, thereby promoting the sustainable development of the data economy.