Research on Measuring the Value of Enterprise Data Assets Based on an Improved Excess Return Method Using XGBoost

Guanglin Cui ()
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Guanglin Cui: Wuhan University of Technology, School of Management

A chapter in Proceedings of the 2026 11th International Conference on Financial Innovation and Economic Development (ICFIED 2026), 2026, pp 257-268 from Springer

Abstract: Abstract In the context of the digital transformation of industries, data assets have become an important production factor in the development of the times. However, the measurement of their value is still in its infancy, and traditional valuation methods cannot assess their true worth. Based on the income approach, this paper improves the excess earnings method by proposing the use of the XGBoost model for prediction. The final value of a company’s data assets is determined using the excess earnings rate, data asset share rate, and market value adjustment coefficient. The study also compares the value of companies with and without revenue contributions from data assets, thereby providing a reference for the valuation of data assets within the industry.

Keywords: Excess returns; data assets; XGBoost; Shapley (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-642-5_27

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DOI: 10.2991/978-94-6239-642-5_27

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