数字经济时代，数据被认为是一种新的资产，数据要素市场建立的前提是完成数据资产的确权、估值与定价。数据作为新兴资产，关键在于如何准确、有效的评估数据资产的价值。国内外学者将资产属性与数据特性结合，研究了如何使用传统资产评估方法及其衍生方法评估数据资产的价值。目前，以市场法、成本法和收益法为核心的传统资产评估方法仍然是数据资产价值评估的首要方法。本文从数据资产的定义入手，分析了数据成为资产的前提以及数据资产与其他资产的异同性，并且分析了现有数据资产价值评估的主要方法和前沿方法，剖析了各种方法的特点以及适用条件。 本文认为，数据资产的价值高于成本，所以在数据资产的价值评估上，收益法成为首选。利用收益法评估数据资产的价值目前需要克服的难点是如何准确预测数据资产的未来收益。本文以数据的相关商业模式和产品为研究对象，提出使用ARIMA模型对产品的收入历史数据进行拟合，并用拟合后的模型预测未来收益，最后结合收益法对数据资产价值进行评估。本文使用ARIMA模型以及蒙特卡洛模拟对东方财富金融数据资产价值进行了评估，结果表明方法具有适用性。最后，本文通过多元回归模型分析了市场环境以及宏观经济环境对于金融数据资产价值的影响。 综上，收益法是目前评估数据资产价值的首要方法。ARIMA模型发展成熟，在时间序列分析中占有重要地位，对历史数据序列拟合ARIMA模型对未来收益进行预测具有科学性和客观性。蒙特卡洛方法作为数值模拟的常用方法，运用到数据资产未来收益的模拟中是可行的。最后，本文总结了方法的不足之处以及分析了未来数据资产价值评估的方向。

In the era of digital economy, data is considered a new asset, and the premise of establishing a data element market is to complete the confirmation, valuation and pricing of data assets. As an emerging asset, the key lies in how to accurately and effectively evaluate the value of data assets. Scholars at home and abroad study how to use traditional asset evaluation methods and their derivatives to evaluate the value of data assets. At present, the traditional asset evaluation method with market-based method, cost-based method and income-based method as the core is still the primary method of data asset value evaluation. Starting from the definition of data assets, this thesis analyzes the premise of data becoming an asset and the similarities and differences between data assets and other assets, and analyzes the main methods and cutting-edge methods of existing data asset value assessment, and analyzes the characteristics and application of various methods. The value of data assets is higher than the cost, so in the evaluation of the value of data assets, the income method has become the first choice. The difficulty is how to accurately predict the future income of data assets. This paper takes the relevant business models and products of the data as the research objects, and proposes to use the ARIMA model to fit the historical data of the product's income, and use the fitted model to predict the future income, and finally combine the income-based method to evaluate the value of data assets. This thesis uses ARIMA model and Monte Carlo simulation to evaluate the asset value of Oriental Wealth Financial Data, and the results show that the method is applicable. Finally, this paper analyzes the impact of market environment and macroeconomic environment on the value of financial data assets through multiple regression analysis. To sum up, the income method is currently the primary method for evaluating the value of data assets. ARIMA model plays an important role in time series analysis. It is scientific and objective to fit the ARIMA model to the historical data series to predict future returns. And Monte Carlo method is feasible to apply to the simulation of future income of data assets. Finally, this thesis summarizes the shortcomings of the method and analyzes the direction of future data asset value assessment.