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Assessment Model Improvement and Application of Economic Value Added Based on Scenario Prediction

Authors:

Huanhuan Cheng,

Shicai Wang,

GuoQiang Wu,

Bing LiuAuthors Info & Claims

ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics

Pages 383 - 387

https://doi.org/10.1145/3473714.3473781

Published: 13 August 2021 Publication History

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Abstract

This paper improves the traditional value assessment model of economic value added, puts forward the value assessment model of economic value added based on scenario prediction, and provides application cases. This model divides three scenarios of mutation, optimism and pessimism to predict enterprises with the help of scenario prediction, and the interval of enterprise valuation is obtained on this basis, which effectively makes up for the defects of the traditional model.

References

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Assessment Model Improvement and Application of Economic Value Added Based on Scenario Prediction

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ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics

June 2021

807 pages

ISBN:9781450390231

DOI:10.1145/3473714

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Chongqing Univ.: Chongqing University

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Association for Computing Machinery

New York, NY, United States

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Published: 13 August 2021

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ICCIR 2021

ICCIR 2021: 2021 International Conference on Control and Intelligent Robotics

June 18 - 20, 2021

Guangzhou, China

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ICCIR '21 Paper Acceptance Rate 131 of 239 submissions, 55%;

Overall Acceptance Rate 131 of 239 submissions, 55%

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A Big Data Drilling Method for Value Assessment of Leakage Data

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