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<p>In order to further promote listed companies to achieve high-quality development, this paper constructs an information disclosure quality assessment system in the context of the big data era. Through big data technology, information disclosure data of listed companies are collected, stored as well as exported to promote the structuring and standardization of massive data. The KNN algorithm is used to classify and organize information features and define hierarchical categories, thus obtaining a more low-dimensional subset of data features. In evaluating and deciding the indicator weights among the levels, the class weights and maximum feature values are calculated using a stratified sampling algorithm, and the evaluation system is formed according to their average values as quantitative and qualitative indicators. To verify the effectiveness of this assessment system, the analysis results show that the system divides the quality of information disclosure into five assessment criteria, and the precision and smoothness can reach about 85.6% and 83.24% on average, respectively. It can be seen that the information disclosure quality assessment system constructed based on big data technology improves the authenticity, usefulness and comparability of information disclosed by enterprises.</p>
</abstract>

In order to further promote listed companies to achieve high-quality development, this paper constructs an information disclosure quality assessment system in the context of the big data era. Through big data technology, information disclosure data of listed companies are collected, stored as well as exported to promote the structuring and standardization of massive data. The KNN algorithm is used to classify and organize information features and define hierarchical categories, thus obtaining a more low-dimensional subset of data features. In evaluating and deciding the indicator weights among the levels, the class weights and maximum feature values are calculated using a stratified sampling algorithm, and the evaluation system is formed according to their average values as quantitative and qualitative indicators. To verify the effectiveness of this assessment system, the analysis results show that the system divides the quality of information disclosure into five assessment criteria, and the precision and smoothness can reach about 85.6% and 83.24% on average, respectively. It can be seen that the information disclosure quality assessment system constructed based on big data technology improves the authenticity, usefulness and comparability of information disclosed by enterprises.

Research on the quality of information disclosure of listed companies in the era of big data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to further promote listed companies to achieve high-quality development, this paper constructs an information disclosure quality assessment system in...

Research on the quality of information disclosure of listed companies in the era of big data

Published Online: Sep 04, 2023

Page range: -

Received: Sep 21, 2022

Accepted: Mar 15, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00280

Keywords
Information disclosure quality, Indicator weights, KNN algorithm, Feature subsets, Stratified sampling, Quantitative indicators

© 2023 Chengrui Shen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the quality of information disclosure of listed companies in the era of big data

Published Online: Sep 04, 2023

Page range: -

Received: Sep 21, 2022

Accepted: Mar 15, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00280

KeywordsInformation disclosure quality, Indicator weights, KNN algorithm, Feature subsets, Stratified sampling, Quantitative indicators

© 2023 Chengrui Shen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Information disclosure quality, Indicator weights, KNN algorithm, Feature subsets, Stratified sampling, Quantitative indicators