The methods for Information Transparency Index Construction of Listed Companies in China in 2020
The Guanghua-Rotman Centre for Information and Capital Market Research in collaboration with New Fortune develops the Information Transparency Index of listed companies in China. The index was first published in the spring of 2019. We update the index for 2020 based on 3,461 companies listed in the Shanghai Stock and Shenzhen Stock Exchanges before December 31, 2017. The sample includes the firms listed on the Main board, the Small and Medium Enterprise (SME) board, and the Growth Enterprise Market (GEM) board.
The index covers both subjective indicators (50%) and objective indicators (50%). The former contains assessments of analysts, institutional investors, and board secretaries of listed companies (each accounting for 1/3 of the weight on the subjective component). The latter contains the analysis of financial statements (20%), the records of enforcement actions (20%) and media assessments based on machine learning (10%).
The methods for constructing the index are provided below. We also discuss the descriptive statistics of the top 500 ranked companies based on the constructed Transparency Index. The names of these companies are listed in the Appendix.
A. Analysts (weight 1/6, 16.67%)
Questions answered by analysts: For the industry you follow, which three companies do you believe have a) the most reliable financial information, and b) the most comprehensive information disclosure?
|
Percentage Rank |
Votes |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
6 or above |
14 |
6.7 |
5 |
|
6-15% |
4 or 5 |
34 |
16.3 |
4 |
|
16-40% |
2 or 3 |
74 |
35.4 |
3 |
|
41-100% |
1 |
87 |
41.6 |
2 |
|
0 |
3252 |
1 |
||
|
Total: 3461 |
B. Institutional investors (weight 1/6, 16.67%)
Questions answered by institutional investors: For the industry you follow and/or invest in, which three companies do you believe have a) the most reliable financial information, and b) the most comprehensive information disclosure?
|
Percentage Rank |
Votes |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
14 or above |
10 |
5.3 |
5 |
|
6-15% |
5-13 |
21 |
11.1 |
4 |
|
16-40% |
2-4 |
75 |
39.5 |
3 |
|
41-100% |
1 |
84 |
44.2 |
2 |
|
0 |
3271 |
1 |
||
|
Total: 3461 |
C. Board secretaries (weight 1/6, 16.67%)
Questions answered by board secretaries: For the industry to which your company belongs to, a) which three companies (excluding your company) do you believe have the most reliable financial information; b) which three companies (excluding your company) do you believe have the most comprehensive information disclosure; and c) which three companies (excluding your company) are most promising in terms of fundamental value?
|
Percentage Rank |
Votes |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
11 or above |
44 |
5.2 |
5 |
|
6-15% |
6-10 |
113 |
13.3 |
4 |
|
16-40% |
3-5 |
297 |
34.9 |
3 |
|
41-100% |
1-2 |
398 |
46.7 |
2 |
|
0 |
2609 |
1 |
||
|
Total: 3461 |
D. Regulatory penalties (weight 1/5, 20%)
Regulatory penalties include enforcement actions against manipulations (misleading statements) such as false and postponed disclosures, significant omissions, and overstating profits or assets in the past eight years (2011-2018). Regulators include the China Securities Regulatory Commission, local securities regulatory bureaus, the Shanghai and Shenzhen Stock Exchange. The data was obtained from the China Stock Market & Accounting Research (CSMAR) Database. Regulatory penalties are divided into non-monetary and monetary penalties which account for 25% and 50% of the total score, respectively. Comment letters account for the remaining 25% score. Due to the time lag between regulatory investigations and regulatory penalties, we continue to monitor the companies before the index is completed in 2019 and adjust the score to 1 only if a major fraudulent incident occurs.
|
D1. Non-monetary penalty (criticism, warning, condemnation) (25%) |
||||
|
Percentage Rank |
Number of Penalties |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
5 or above |
74 |
6.2 |
1 |
|
6-15% |
3 or 4 |
189 |
15.9 |
2 |
|
16-40% |
2 |
285 |
24.0 |
3 |
|
41-100% |
1 |
642 |
54.0 |
4 |
|
0 |
2271 |
5 |
||
|
Total: 3461 |
||||
|
D2. Monetary penalties (fines, confiscation of illegal gains) (50%) |
||||
|
Percentage Rank |
Number of Penalties |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
5 or above |
9 |
2.7 |
1 |
|
6-15% |
3 or 4 |
26 |
7.9 |
2 |
|
16-40% |
2 |
69 |
21.0 |
3 |
|
41-100% |
1 |
225 |
68.4 |
4 |
|
未被处罚 |
3132 |
5 |
||
|
Total: 3461 |
||||
|
D3. Comment letters (25%) |
||||
|
Percentage Rank |
Number of Penalties |
Number of Companies |
Proportion of Companies |
Standard Value |
|
0-5% |
8 or above |
113 |
6.1 |
1 |
|
6-15% |
5-7 |
183 |
9.9 |
2 |
|
16-40% |
2-4 |
740 |
40.0 |
3 |
|
41-100% |
1 |
814 |
44.0 |
4 |
|
0 |
1611 |
5 |
||
|
Total: 3461 |
||||
E. Financial statements analysis (weight 1/5, 20%)
We use earnings quality indicators that are used in academic research based on information from the company’s 2016-2018 financial statements. For manufacturing firms, we use the mean values of the absolute discretionary accrual estimated by Jones model (modified by Kothari) and the Dechow-Dichev-McNichols model in the past two years. We then rank every firm within its industry and assign scores based on the quintile cut point. Firms with lower absolute discretionary accruals are assigned higher score. For banks, we use the absolute value of discretionary loan loss provision estimated by model recommended by Betty-Liao. Financial and banking enterprises which do not have enough data to estimate their financial statement quality are assigned score of 3.
|
Percentage Rank |
Mean DAK Value |
Number of DAK Companies |
Mean DDM Value |
Number of DDM Companies |
Standard Value |
|
0-20% |
0.0157 |
674 |
0.0250 |
588 |
5 |
|
21-40% |
0.0329 |
674 |
0.0531 |
588 |
4 |
|
41-60% |
0.0514 |
675 |
0.0912 |
588 |
3 |
|
61-80% |
0.0748 |
674 |
0.1363 |
591 |
2 |
|
81-100% |
0.1325 |
675 |
0.2515 |
587 |
1 |
|
|
|
Total: 3372 |
|
Total: 2942 |
|
F. Media analysis (weight 1/10, 10%)
Big data intelligence analysis was carried out for all the Chinese newspaper articles of WiseNews released in 2018. The mean value was obtained through the following two indicators: Financial Media Attitude (FinSenti) and Artificial Intelligence Evaluation (AIE).
FinSenti: The articles released in 2018 are categorized into different types using machine learning techniques. Those about finance and economics were kept for assessment of public sentiment. A positive value represents a positive public sentiment while a negative one represents a negative public attitude. The FinSenti score was provided by the Chinese University of Hong Kong and Datago.
AIE: The frequency of being nominated by analysts, institutional investors, and board secretaries as a transparent and credible company was used as the machine learning sample. All media articles were analyzed using AI methods (BERT) to estimate the transparency of each company. The AIE score was calculated by the Machine Learning Research Group at the Guanghua-Rotman Centre for Information and Capital Market Research of the University of Toronto.
|
Percentage Rank |
AIE Value |
Number of AIE Companies |
Mean FinSenti Value |
Number of FinSenti Companies |
Standard Value |
|
0-20% |
0.83 or 1 |
374 |
0.5491 |
685 |
5 |
|
21-40% |
0.67 |
840 |
0.4204 |
693 |
4 |
|
41-60% |
0.5 |
346 |
0.3166 |
691 |
3 |
|
61-80% |
0.18 or 0.33 |
578 |
0.1924 |
693 |
2 |
|
81-100% |
0 |
1323 |
-0.0829 |
699 |
1 |
|
|
|
Total：3461 |
|
Total：3461 |
|
