A Study On Data Asset Valuation Of Logistics Enterprises Based On Improved Multi-period Excess Earnings Approach

With the rapid development of technologies such as Internet,cloud computing and big data,China’s digital economy has shown unprecedented development momentum,and the digital economy has become an important engine to promote the high-quality development of China’s economy,and its role in promoting the development of enterprises has become more and more obvious.In the context of this major trend,the value of data assets,as the core and most important element of the digital economy,is also increasingly prominent.In the logistics industry,the integration and use of data assets has promoted the upgrading of the traditional logistics industry.Logistics enterprises are accelerating the construction of intelligent logistics networks and precisely controlling logistics and transportation links in an attempt to enhance logistics and distribution efficiency and improve corporate profitability.The growth of business value brought by the deep integration of logistics enterprises and their data assets has attracted extensive attention from scholars.Currently,a lot of research has been conducted on data assets at home and abroad,but most of the research is centered on technology mining and application,and is not well developed for its value realization in enterprises and the selection of evaluation methods.Therefore,this thesis focuses on data assets of logistics enterprises and adopts an improved multi-period excess return method to evaluate them.Firstly,this thesis compares the current background of data assets and the current status of domestic and international research,and then discusses the theories related to logistics enterprises and their data assets.Secondly,the limitations and applicability of traditional valuation methods in the valuation of data assets of logistics enterprises are analyzed.Finally,based on the above theoretical basis,an improved multi-period excess return method valuation model is constructed in this thesis.This appraisal model adopts the ARIMA model for the forecast of operating income,which is used to calculate the enterprise free cash flow;secondly,the difference method is used to divest the excess income generated by off-balance sheet intangible assets from the overall income,and the data asset income sharing ratio is determined by the hierarchical analysis method to obtain the contribution value of data assets.Based on the risk of data assets themselves,the previous discount rate is improved to finally obtain the value of data assets.In order to verify the reasonableness of the evaluation model,this thesis will select data assets in the logistics enterprise,YTO Express as the research object and apply them in the actual case.This thesis finds that: first,based on previous research,this thesis deeply analyzes the data assets of logistics enterprises and their related valuation theories,and applies the constructed improved multi-period excess return method to YTO Express,and finally obtains the value of data assets as of December 31,2021,to be RMB 762,106,400.This study completes the deficiencies of data asset theories related to the logistics industry and fills the theoretical gap in intangible asset valuation.Second,compared with previous valuation methods,the improved valuation model constructed in this thesis can improve the accuracy of predicting expected returns and discount rate parameters of data assets,which helps logistics enterprises to accurately assess the value of data assets and can provide certain reference for the valuation of data assets of logistics enterprises,thus promoting the benign development of logistics data trading market.