The methods for Information Transparency Index Construction of Listed Companies in China in 2018

首页    The methods for Information Transparency Index Construction of Listed Companies in China in 2018

The Transparency 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). The latter contains the analysis of financial statements (20%), the records of enforcement actions (20%) and media assessments based on machine learning (10%).

 

Methods for index construction and the list and statistical description of the top 300 listed companies in the index are provided below.

 

1. The Methods for Index Construction

The construction and standardization of each indicator are as follows:

 

A. Analysts (weight 1/6, 16.67%)

Questions answered by analysts: For the industry you follow, which three companies do you think have a) the most reliable financial information, and b) the most comprehensive information disclosure?

Number of votes/Total number of votes for all companies

Number of companies

Proportion of companies (%)

Standard value

0-5%

10

5.6

5

6-15%

19

10.6

4

16-40%

53

29.6

3

41-100%

97

54.2

2

Not nominated

2,848

 

1

 

Total: 3,027

 

 

 

 

B. Institutional investors (weight 1/6, 16.67%)

Questions answered by institutional investors: For the industry you focus on, which three companies do you think have a) the most reliable financial information, and b) the most comprehensive information disclosure?

Number of votes/Total number of votes for all companies

Number of companies

Proportion of companies (%)

Standard value

0-5%

11

5.0

5

6-15%

25

11.4

4

16-40%

39

17.8

3

41-100%

144

65.8

2

Not nominated

2,808

 

1

 

Total: 3,027

 

 

 

 

 

C. Board secretaries (weight 1/6, 16.67%)

Questions answered by board secretaries: a) In your industry, which three companies (excluding your company) do you think have the most reliable financial information; b) in your industry, which three companies (excluding your company) do you think have the most comprehensive information disclosure; and c) in your industry, what are the three most promising listed companies?

Number of votes/Total number of votes for all companies

Number of companies

Proportion of companies (%)

Standard value

0-5%

15

3.9

5

6-15%

36

9.3

4

16-40%

139

35.9

3

41-100%

197

50.9

2

Not nominated

2,640

 

1

 

Total: 3,027

 

 

 

 

 

D. Regulatory penalties (weight 1/5, 20%)

The incidence of enforcement actions in the past eight years (2010-2017) include manipulation (misleading statements), false disclosures, postponed disclosures, significant omissions, and overstating profits or assets. The regulators include the China Securities Regulatory Commission, local securities regulatory bureaus, Shanghai Stock Exchange, and Shenzhen Stock Exchange. The data was obtained from the China Stock Market & Accounting Research (CSMAR) Database.

Number of enforcement actions / Number of enforcement actions that all companies have received in the past eight years

Number of penalties

Number of companies

Proportion of companies (%)

Standard value

0-5%

6 or above

87

6.0

1

6-15%

4-5

142

9.8

2

16-40%

2-3

517

35.5

3

41-100%

1

709

48.7

4

No penalties (After 2010)

 

1,572

 

5

 

 

Total: 3,027

 

 

 

 

 

E. Indicator from financial statements analysis (weight 1/5, 20%)

Big data statistical analysis was conducted using information from the company’s 2015-2017 financial statements. Earnings quality indicators widely used in academia were adopted. For manufacturing firms, we used mean values of the Jones model (modified by Kothari ROA) and the Dechow-Dichev-McNichols model in the past two years. According to the ranking within industries classified by CSRC, as an enterprise deviates from the mean value of the industry its ranking will be lower. For banks, the model recommended by Beatty-Liao was adopted. The 3 was taken as the missing value for the financial and banking enterprises.

 

 

DAK mean value

Number of DAK companies

DDM mean value

Number of DDM companies

Standard value

0-20%

0.015

541

0.010

480

5

21-40%

0.041

545

0.032

488

4

41-60%

0.071

547

0.059

488

3

61-80%

0.114

545

0.099

487

2

81-100%

0.285

547

0.234

493

1

 

 

Total2,725

 

Total2,436

 

 

 

 

F. Indicator from media analysis (weight 1/10, 10%)

Big data intelligence analysis was carried out for all the Chinese newspaper articles of WiseNews released in 2017. The mean value was obtained through the following two indicators: Financial Media Attitude (FinSenti) and Artificial Intelligence Evaluation (AIE).

FinSenti: The articles released in 2017 were 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 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.

 

CUHK mean value

Number of CUHK companies

UOFT mean value

Number of UOFT companies

Standard value

0-20%

0.679

612

0.523

612

5

21-40%

0.433

607

0.375

607

4

41-60%

0.277

604

0.326

604

3

61-80%

0.064

607

0.302

607

2

81-100%

-0.490

597

0.247

597

1

 

 

Total: 3,027

 

Total: 3,027

 

 

2019年8月20日 15:40
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