Zhang Wei, Gong Yan, Li Zhinan, Xu Yuefeng
Institute of Science and Technology Information, Beijing Academy of Science and Technology, Beijing, China.
Faculty of Arts and Social Science, The University of Sydney, Sydney, New South Wales, Australia.
PLoS One. 2025 Mar 18;20(3):e0316241. doi: 10.1371/journal.pone.0316241. eCollection 2025.
With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model's capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model's effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.
随着非定制数据资产的迅速扩张，开发可靠且客观的估值方法变得至关重要。然而，当前的评估技术常常面临诸如指标体系不完整以及过度依赖主观判断等挑战。为解决这些问题，本研究提出了一个由17个关键指标组成的结构化框架，用于评估数据资产价值。采用神经网络来计算指标权重，这降低了主观性并提高了评估的准确性。此外，利用知识图谱技术来组织和可视化指标之间的关系，提供全面的评估视角。所提出的模型结合信息熵和TOPSIS方法，通过整合指标权重和绩效指标来优化资产估值。为验证该模型，将其应用于两个数据集：过去七年的比特币市场数据和比亚迪股票数据。比特币数据集展示了该模型捕捉市场趋势和评估购买潜力的能力，而比亚迪股票数据集则凸显了其在不同金融资产中的适应性。这些案例的成功应用证实了该模型在支持数据驱动的资产管理和定价方面的有效性。此框架为数据资产估值提供了一种系统方法，对资产定价和管理具有重要的理论和实践意义。
