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

DOI

10.2991/978-94-6239-642-5_27How to use a DOI?

Keywords

Excess returns; data assets; XGBoost; Shapley

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guanglin Cui
PY  - 2026
DA  - 2026/04/29
TI  - Research on Measuring the Value of Enterprise Data Assets Based on an Improved Excess Return Method Using XGBoost
BT  - Proceedings of the 2026 11th International Conference on Financial Innovation and Economic Development (ICFIED 2026)
PB  - Atlantis Press
SP  - 257
EP  - 268
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-642-5_27
DO  - 10.2991/978-94-6239-642-5_27
ID  - Cui2026
ER  -