The Effect of Network Public Opinion Attention on Bank’s Risk Text
Information Disclosure Strategy
Abstract: Based on the risk text information data of bank’s annual reports from 2009
to 2017, and using machine learning method of content analysis, this paper investigate
the network public opinion attention effect on the risk text information disclosure
strategy in the bank’s annual reports, and explore which is its dominate hypothesis in
the new media era, "supply information" hypothesis or "information ambiguity"
hypothesis. Moreover, it analyses the joint effects of several factors such as the level ofbank’s risk taking, the bank’s network image maintenance ability, and the bank’s
regulatory perception. In general, the conclusion showed complexity effect. For the risk
text information tone management, the network public opinion attention has significant
transparency governance effect on information transmission, which mainly reflects the"information supply" hypothesis. For the risk information density strategy, the network
public opinion attention mainly reflects the "information ambiguity" hypothesis.
Keywords: risk text information tone; risk text information density; Network Public
Opinion Attention

1. Introduction
With the slowdown of economic growth and the upgrading of financial regulation,
in recent years, the bank has changed from "favorable public opinion" to "embarrassing
public opinion"1, and the risk information has become one of the most concerned topics.
For example, in 2017, the cheating incident of pudong development bank chengdu
branch was detected by CBRC after market rumors were fermented and reversed, andonline public opinions broke out. Among Baidu search results, 384,000 related public
opinions were found. According to the public opinion monitoring of sina public opinion
online in 2018, after the release of 2018 credit card industry report, among the TOP20
most frequently used negative words in the statistical period, the word "risk" wasmentioned nearly 1 million times. However, as an informal mechanism for investor
1

Huike information industry all media big data and 2017 China banking public opinion analysis annual reportprotection, compared with traditional public opinion, online public opinion lacks themechanism of "gatekeeper", which may reveal the truth or spread rumors, posing
unprecedented challenges to the disclosure of risk information of commercial Banks.
As a capital link, the risk information disclosure of commercial banks has thecharacteristics of public goods for social stability, fairness and effectiveness. According
to the information transmission economics theory of "signal supply", if the seller withprivate information can pass the information to the buyer without information in acertain way, or the buyer can make the seller disclose the information actively, themarket resource allocation will reach the optimal standard state, namely pareto
improvement (Spence,1974). However, according to the "information ambiguity"
hypothesis, when the company is in a bad situation, managers also have a strong
incentive to release vague information, so as to hide negative information frominvestors. As a financial institution strictly controlled by financial regulation rules, thenarrative text information of bank annual report has rich connotation, control space andfuzzy convenience that can't be reflected in structured financial data. In the era of new
media, we want to know whether and how the risk text information disclosure strategy
of banks will be affected by the attention of network public opinion. Is the "information
supply" hypothesis or the "information ambiguity" hypothesis dominant? For thisdebate, the existing literature is still lack of sufficient evidence.
Based on the above considerations, this paper takes 2009-2017 as the research
period to investigate the influence of network public opinion concern on the selectionof bank risk text information disclosure strategy, as well as the regulatory effect of bankrisk taking level and network image maintenance ability. Our analysis is based on
Chinese Emotional Vocabulary created from the Xiamen university professor You
Jiaxing. According to the context of bank annual report, we enlarge and perfect thelexicon of the banking industry. According to the context of bank annual report, we
enlarge and perfect the lexicon of the banking industry. Furthermore, we use Python's
text analysis technology to capture the risk text information of the annual reports ofChinese listed commercial Banks from two aspects of risk text information intonation
and risk information density through word segmentation and statistics of jieba

thesthesciple database, and use artificial intelligence algorithm to judge and identify therisk text information. At the same time, we represent the variables of network public
opinion by the netizens' comments and forwarding data in the stock post bar of
dongfang fortune, 2 the most visited and influential financial and securities portal in
China.3 In addition, z-value, which is based on ROA, CAR and ROA to measure thestability level of banks, is used to represent banks’ level of risk taking. WeChat andweibo are used to represent the network image maintenance ability of Banks.
This paper finds the research results as follows. (1) In terms of intonation strategy
of bank risk text information, network public opinion concern has significant
transparency governance effect on information transmission, which mainly reflects the"signal supply" hypothesis. The higher the attention of network public opinion, thelower the enthusiasms of bank risk text information tone. Meanwhile, the more stable
the bank operation, the lower the risk level, and the more positive the tone of the bankrisk text information. The level of bank risk taking has an incremental moderating effecton the network public opinion effect of text intonation. (2) In terms of the information
density strategy of bank risk text, network public opinion mainly reflects the"information ambiguity" hypothesis. The higher the attention of network public opinion,
and the lower the information density of bank risk text. At the same time, the lower therisk level of the bank, the higher the density of risk text information disclosed by thebank, but the risk level of the bank has no regulatory effect. (3) The stronger the network
image maintenance ability is, the less banks need to use positive text tone to induce
investors, which reflects the information transparency governance role of "signal
supply". However, the stronger the network image maintenance ability is, the lower thedisclosure density of bank risk text information is, which reflects the "information
ambiguity" hypothesis. At the same time, the bank network image maintenance ability
does not have the network public opinion attention effect adjustment function. (4)2The average daily page view is more than 100 million person-times, and the number of posts in stock bar is thelargest among similar stock post bar, such as sina stock bar, so it has strong reliability and representativeness.
3 We directly use the online comments from the investor group to study the impact of online public opinion
concerns on the risk information disclosure strategy of the annual reports of listed commercial Banks, so as to
avoid the possible disadvantages of repeated reports from different websites in the network media under the "baidu
news search engine", which has a strong logical correlation.

Further research found that the network public opinion attention has a significant
negative effect on the ambiguity of bank risk text information. In addition, citycommercial banks with little reputation are facing greater pressure from investors andoften have stronger motivation to use positive tone management in the text information
expression of annual financial reports.
This study contributes to the literature in the following ways. Firstly, which helps
strengthen the risk information disclosure ,establish a new generation of financial risk
reporting mechanism, improve the quality of the risk of commercial bank financial
report information disclosure, as well as help investors to better identify risk source andpotential opportunities, it is the post-crisis era change trend of international banking
regulation and international accounting standards convergence focus. In 2017, theministry of finance of China issued the revised version of accounting standards forenterprises No. 37 -- presentation of financial instruments, requiring enterprises to fully
disclose their risks in a qualitative and quantitative manner to help stakeholders
reasonably assess their financial impact. Compared with the quantitative "hardinformation" of risk data, the qualitative "soft information" of risk text has thecharacteristics of complexity, ambiguity, ambiguity, difficulty in investigation and noneed for audit, which endowing commercial banks with a large space of "information
shaping". The problem of the selection of information disclosure of risk text of banks
has attracted much attention. Based on existing literatures, this paper constructs thecausal chain between network public opinion and bank risk text information disclosure,and introduces the risk level of Banks and the influence of investor relationshipmanagement factors under the bank Internet, which helps further enrich the existing
theories, expand the text analysis research of existing literature in the field of risk
reporting, and improve the related accounting standards of text information disclosure
regulation provides a useful reference evidence.
Secondly, the existing literature on risk information disclosure mainly involves the
motivation of risk information disclosure and the information content of risk report.
However, due to the complexity and controversy of risk information report and theinfluence of standard provisions, the research conclusions are inconsistent (Elshandidy,

Shrives, Bamber, Abraham，2018). Compared with researches based on traditional
media, relevant researches from the perspectives of new media such as online
interactive platforms and we-media are still insufficient. As for the research on thecapital market utility of network public opinion, most existing literatures are conductedfrom the perspective of the impact on stock market asset pricing, financing efficiency
as well as corporate governance equity or efficiency. This paper helps expand theresearch to the impact on bank risk text information disclosure strategy field, whichuses machine learning content analysis method to test bank narrative risk information
disclosure characteristics, and to confirm that in the new media era, the role of theInternet in the selection of bank risk text information disclosure strategy is an important
theoretical topic worthy of in-depth study.
The following parts of this paper are arranged as follows. The second part is a
literature review, which reviews the main achievements and shortcomings of existing
researches; the third part is the theoretical derivation and research hypothesis. It
summarizes the existing research framework, modifies and deduces the theory basedon the special institutional background of China, and puts forward the research
hypothesis. The fourth part is the research design, which mainly introduces the data
source, variable selection and empirical model of this paper. The fourth part is theanalysis of empirical results, including descriptive statistical analysis and multiple
regression statistical analysis. The fifth part is research conclusion and enlightenment.
2. Literature Review and Hypothesis Presentation
2.1 The Effect of Network Public Opinion Attention: Bank’s Risk Text
Information Disclosure Strategy
The existing literature focuses on risk information disclosure, mainly covering twoareas: the first is to study the motivation of risk information disclosure (Solomon, 1999;
Solomon, Solomon, Norton and Joseph, 2000；Beretta and Bozzolan, 2004; Linsley
and Shrives, 2006; Abraham and Cox, 2007; Marshall and Weetman, 2007; Deumes
and Knechel, 2008; Hill and Short, 2009; Taylor, Tower, and Neilson, 2010; Miihkinen,
2012; Barakat and Hussainey, 2013; Elshandidy, Fraser and Hussainey, 2013; Ntim,
Lindop and Thomas, 2013; Elshandidy, Fraser and Hussainey, 2015; Malafronte,

Porzio and Starita, 2016; Al-Hadi, Hasan and Habib, 2016). The second type is theinformation content of risk report (Rajgopal, 1999; Roulstone, 1999; Hodder and
McAnally, 2001; Jorion, 2002; Linsley and Lawrence, 2007; Akhigbe and Martin, 2008;
Pérignon and Smith, 2010; Kravet and Muslu, 2013; Abraham and Shrives, 2014; Baoand Datta, 2014; Elbannan and Elbannan, 2015; Filzen, 2015). Some literatures havealso studied these two aspects namely risk reporting motivation and information content(Campbell, Chen, Dhaliwal, Lu, and Steele,2014;Elshandidy and Neri,2015 ；Elshandidy and Shrives,2016；Hope, Hu, and Lu,2016). Among them, regarding the
motivation of risk information disclosure, the existing literature's research conclusions
are mostly based on the developed market environment and do not exist consistency.
For example, some studies find that the improvement of accounting standards and theenhancement of regulatory environment are conducive to the improvement of thequantity and quality of enterprise risk disclosure (Miihkinen, 2012; Abraham, Shrives,
2014), mandatory disclosure requirements for risk information in annual financial
reports, will trigger additional discretionary risk reports (Tamer Elshandidy, SanthoshAbraham, Philip j. Shrives, 2018). However, some other researches find that thevoluntary disclosure exists more in the weak regulatory environment, and the excessive
regulation is not conducive to the increase of risk disclosure (Deumes, Knechel, 2008;
Marshall, Weetman, 2007).
Nowadays, with the increasing influence of network public opinion in the new
media era, the research on online public opinion as an informal mechanism for small
and medium investors’ protection is gradually carried out. Most of its research content
involves the influence of network public opinion on stock market asset pricing,financingefficiency

and

the

governance

role

of

onlinemedia（Wysocki,1998;Yin,Xiong,2017;Yang,Shen,Li,2017）. In this paper, I believe thatsince online media is an important place for information exchange and disseminationof retail investors, it is easy to quickly form a public opinion with strong inclination4.
4China Internet Network Information Center (CNNIC) released the 41st 《Statistical Report on China's
Internet Development Status》. According to the report, as of December 2017, the Internet users had climbed to
772 million, and the Internet penetration rate was 55.8, of which were 753 million mobile Internet users. The
proportion of mobile Internet users increased from 95.1 in 2016 to 97.5. In 2018, HCR Huichen Information'sTherefore, the network communication between small and medium-sized shareholders
has formed a network public opinion concern, which will strengthen the bank risk
information disclosure governance, and will also stimulate the bank's motivation to use
the text information disclosure strategy to maintain its reputation and public image.
Among them, intonation characteristics and risk information density characteristics are
two important options for bank risk text information disclosure. As the most basic
feature of text information disclosure, the tone is composed of optimistic or pessimistic,favorable or unfavorable, positive or negative. The existing literature research topics
involve information disclosure tone and corporate risk, media reports and CEO
compensation, managerial tone management, managerial characteristics and text tone,
the influence of tone on investor pricing, the relationship between tone and thecompany's future performance, etc. (Core et al,2008;Henry,2008;Feldman etal,2009;Kothari,2009;Loughran,McDonald,2011;Davisetal,2012;Larcker,Zakolyukina,2012;Price,2012;Jegadeesh,Wu,2013;De Franco et al,2014;Huang et al,2014;Davis, etal,2015), I expects that network public opinion concerns have significant external
governance effects on bank risk information tone management, and thus mainly reflectsthe "information supply" hypothesis. Based on this, the paper proposes the following
hypothesis:
H1-1: Network public opinion attention have a significant correlation with bankrisk information tone management. The bank risk information tone is less positive
subsequent to the higher degree of concerns.
With the rapid development of text analysis technology in financial accounting andfinancial research5, some research uses automatic content analysis to study the risk text
information disclosure. Existing literature studies show that risk text information
reports are prone to disclose historical information and good news. Since risk is thesensitive point and pain point of bank public opinion risk, I expect that the risk text

Wistron media monitoring platform data shows that the hottest topic "CCB sells 27 village bank shares, industry
development will enter the integration period" is mainly spread by the network, with the network accounting for
87.2%, the forum accounting for 6.4%, WeChat accounting for 5.5%, and the media accounting for 0.9%.
5 In 2018, Li Feng, the former professor of University of Michigan's Ross School of Business, was awarded
the “Outstanding Research Contribution of Accounting Literature” by the American Accounting Association for his
pioneering contribution to text analysis.

information density is obviously affected by the network public opinion concerns. I
state the second hypothesis as follows:
H1-2: Network public opinion attention have a significant correlation with bankrisk information density. The risk information density of banks is lower subsequent to
the higher degree of concerns.
2.2 The Joint Effect of the Bank’s Risk taking and the Bank’s Network Image
Maintenance Ability
According to the research on the needs of investor risk information, companies
with different risk levels have significant characteristics in risk disclosure. Those withpoor performance will undertake more pressure from investors to provide high-quality
risk information(Miihkinen,2012), while based on their company's risk level, high-risk
companies are more likely to disclose mandatory and voluntary risk information（Elshandidy et al,2013). This discussion leads to the hypothesis 2-1:
H2-1: Bank’s risk taking level has a moderating effect on the network public
opinion attention effect of risk text information disclosure strategy.
The bank's network image in the new media era refers to the public's
comprehensive evaluation and impression of the bank generated by the two-way
interaction between the bank and investors in the role of online media. Among them,on the official website setting of the bank, Weibo is the social information network
focusing on information reception, and WeChat is the interaction of social networking.
Due to the rapid nature of network communication, the information published on the
bank's online media platform can often be quickly reprinted by many websites andreceived by many investors. In some bank negative public opinion incidents, if banks
use online media platforms for more rigorous representation and guidance, it is possible
to prevent the development of the situation to seriously affect the bank's reputation.
Therefore, I believe that setting up of Weibo or WeChat on the online media platformof its online homepage is not only responsible for the positive guidance of lyrics, butalso a manifestation of brand maintenance and image maintenance. This paper expects
that banks with strong network image maintenance capabilities can effectively guideinvestors' public opinion through multi-channels with the help of online media

platforms, and improve the transparency of “information supply”. Based on this, thispaper proposes hypothesis 2-2:
H2-2: Bank network image maintenance capability has a moderating effect on the
network public opinion attention effect of risk text information disclosure strategy.
3 .Research Design
3.1 Sample and data
The sample consists of China's listed commercial banks, the research year covers
the 2009-2017 period. The paper uses the computer to capture the risk text information
data of the samples’ annual report, and then studies the network public opinion concern
effect of the commercial bank risk text information supply. After excluding commercial
banks without data, the final sample consists of 234. We winsorize all continuous
variables at the 1 percent and 99 percent levels to mitigate the influence of outliers.
The data source is as follows: Ι. Risk word frequency statistics, tone statistics,
which are obtained through annual language natural language processing by Python.
Ⅱ .Vocabulary tone judgement using dictionary method, based on the Chinese
Emotional Vocabulary of Professor You of Xiamen University, which is combined withthe text of bank’s annual report, expanded and improved by supplementing theprofessional vocabulary of the banking industry. The dictionary method is essentially aword frequency statistics method. Based on predefined dictionary and rules, it maps
words in the target document to each set one by one and obtains quantitative featuresof the text through statistical calculation（Li,2010）. Ⅲ.The data of network public
opinion concern comes from the comments of netizens in the Eastmoney Network,
which is China's most visited and most influential financial and securities portal, withmore than 100 million page views per day. According to Huikexun's all-media big data
platform, in the top ten online media in 2017, Eastmoney Network reported the most.
Its "Guba Network" is currently the largest stock post in China, with the highest numberof posts compared to other stock bars (such as Sina stocks, etc.). (4) The remaining
financial data and corporate characteristic data are from the CSMAR database.
3.2 Models and Variable Definitions
H1 regression equation: The effect of network public opinion attention

𝑇𝑂𝑁E ,

𝛽0

𝛽1 𝑇𝑂𝑁E ,𝛽6 𝑌𝑒𝑎𝑟 ,𝜀𝑖,𝑡𝑅𝑖𝑠𝑘𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ,𝛽0

𝛽5 𝐵𝑎𝑛𝑘𝑖,𝑡𝑌𝑒𝑎𝑟𝑖,𝑡𝛽2 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,𝛽3 𝐴𝑠𝑠𝑒𝑡 ,𝛽4 𝐸𝑃𝑆 ,𝛽5 𝐵𝑎𝑛𝑘 ,Model 1-1

𝛽1 𝑇𝑂𝑁Ei,t𝜀𝑖,𝑡𝛽2 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑖,𝑡𝛽3 𝐴𝑠𝑠𝑒𝑡𝑖,𝑡𝛽4 𝐸𝑃𝑆𝑖,𝑡Model 1-2

Dependent Variable Risk text information tone (𝑇𝑂𝑁E , :Taking the text of the bankannual report as the analysis object, referring to the dictionary method of Huang et al.
(2014), this paper uses the existing Chinese word segmentation module "jieba" toautomatically segment the text used in this article, and then perform word frequency.
The list of words in this paper refers to different categories words. Among them,“positive vocabulary, strong modal particle, positive word + positive adjective” areclassified as "Positive mood" tone; “negative vocabulary, weak modal particle, positive
word + negative adjective, Negative words + positive adjectives” are classified as"negative mood" tone. The annual report text information tone (TONE) is calculated as:(number of positive words - number of negative words) / Full text vocabulary. The
greater the TONE value, the more positive the tone of the company's annual report text
message.
Dependent Variable Risk Text Information Density ( 𝑅iskDensity , ):Following
prior literature Robert M. Bushman (2014), this paper use the ratio of the number ofwords in “risk, risk management” and the total number of words in the annual reporttext to measure the risk text information density of commercial banks.
The

main

explanatory

variables

Network

PublicOpinionAttention(𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 , ):this paper takes the Eastmoney Network's 2009-2017 sample
bank stocks as the data source, and counts the number of posts as annual data of samples.
Control variable: Bank size and profitability are important factors influencing thequality of risk reporting mechanism(Bischof, 2009; Ford, Sundmacher, Finch, & Carlin,
2009; Helbok & Wagner, 2006; Linsley, Shrives, & Crumpton, 2006; Oliveira,Rodrigues, & Craig, 2011a; Oliveira et al., 2011b; Woods, Dowd, & Humphrey, 2009;
Yong, Chalmers, & Faff, 2005；Elbannan,Elbannan,2015;Al-Hadi et al,2016).At thesame time, compared with high-profile banks, commercial banks with lower visibility

face higher investment pressure, so they have a stronger need to maintain theirreputation and social image. Therefore, this paper takes the bank size represented by
the logarithm of the total assets of the bank, the bank profitability represented by theearnings per share, and the bank type represented by whether the bank belongs to thecity commercial bank as the main control variable of the model.
H 2-1 regression equation: The joint effect of bank’s risk taking (zscore , )𝑇𝑂𝑁E ,

𝛽0

𝛽1 𝑇𝑂𝑁E ,𝛽5 𝐴𝑠𝑠𝑒𝑡 ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝑅iskDensity ,𝛽2 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,𝛽𝛽 𝑇𝑂𝑁E ,𝛽 𝐴𝑠𝑠𝑒𝑡 ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝛽6 𝐸𝑃𝑆 ,𝛽3 zscore ,𝛽4 zscore ,𝛽7 𝐵𝑎𝑛𝑘 ,𝛽8 𝑌𝑒𝑎𝑟 ,𝛽 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,𝛽 zscore ,𝛽 𝐸𝑃𝑆 ,

𝛽 𝐵𝑎𝑛𝑘 ,𝛽 𝑌𝑒𝑎𝑟 ,𝜀𝑖,𝑡Model 2-1-1

𝛽 zscore ,𝜀,Model 2-1-2

Bank’s risk taking level Variable: this paper chooses the Z index which measures
the bank's stability as the representative of bank risk-taking level. The Z value is equal
to the sum of the bank's return on assets (ROA) and capital adequacy ratio (CAR)
divided by the variance of ROA. The larger the Z value, the more stable the bank's
operations and the lower the risk.

H 2-2 regression equation: The joint effect of bank’s network image maintenanceability (𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝑇𝑂𝑁E ,

𝛽0

𝛽1 𝑇𝑂𝑁E ,𝛽4 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝛽7 𝐵𝑎𝑛𝑘 ,𝛽2 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝛽8 𝑌𝑒𝑎𝑟 ,𝛽3 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝛽5 𝐴𝑠𝑠𝑒𝑡 ,𝛽6 𝐸𝑃𝑆 ,𝜀𝑖,𝑡Model 2-2-1

𝑅iskDensity ,𝛽0

𝛽1 𝑇𝑂𝑁E ,𝛽2 𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,𝛽4 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝛽7 𝐵𝑎𝑛𝑘 ,𝜀𝑖,𝑡𝛽8 𝑌𝑒𝑎𝑟 ,𝛽3 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝛽5 𝐴𝑠𝑠𝑒𝑡 ,𝛽6 𝐸𝑃𝑆 ,Model 2-2-2

The variable of bank’s network image maintenance ability (𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 , ): We
use the Dumb variable, Namely, whether the bank set up Weibo or WeChat on its
official online media platform as the representative variable of the bank's ability tomaintain network image. When Weibo or WeChat is set on the bank's homepage,
𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 , variable takes 1, otherwise is 0.

Table 1
Variables
Dependent
variables

Independent
variables

Variables Definitions
Variable symbol

Deﬁnition

Risk Text
Information tone

𝑇𝑂𝑁E ,(number of positive words - number ofnegative words)/ Full text vocabulary

Risk Text
information density

𝑅𝑖𝑠𝑘𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ,the ratio of the number of words in “risk,
risk management” and the total number ofwords in the annual report text

Network Public
Opinion Attention

𝑁𝑒𝑡𝑤𝑜𝑝𝑖𝑛𝑖𝑜𝑛 ,the number of posts about the banks in the
Eastmoney Network

Bank’s Risk taking
level

zscore ,the sum of the bank's return on assets(ROA) and capital adequacy ratio (CAR)
divided by the variance of ROA
𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,Bank’s Network
Image Maintenance
Ability
Controlvariable

1 if Weibo or WeChat is set on the bank's
homepage, and 0 otherwise

Bank Size

𝐴𝑠𝑠𝑒𝑡 ,The natural logarithm of total assets

Bank Profitability

𝐸𝑃𝑆 ,Earnings per share

Bank Type𝐵𝑎𝑛𝑘 ,

1 if the firm is city commercial bank, and0 otherwiseYear

𝑌𝑒𝑎𝑟 ,

The annual values for the period 20092017 are 1 to 9 respectively

4. Empirical Results
4.1 Summary Statistics and Pearson Correlation Analysis
Table 2 provides the descriptive statistics for the variables used in this paper, andtable 3 shows the result of Pearson analysis. We find 𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 , is negatively
correlated with 𝑇𝑂𝑁𝐸 , and 𝑅iskDensity , , but it is positively correlated withzscore , .Furthermore, 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 , is negatively correlated with 𝑇𝑂𝑁𝐸 ,and 𝑅iskDensity , , ,this results preliminary supported our hypothesis.
Table 2

Summary Statistics

Variable

Mean

Std. Dev.

Min

𝑇𝑂𝑁𝐸 ,

2.279

1.4094

0

Max
12.31

𝑅𝑖𝑠𝑘𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ,0.73350.73350

1.92

𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,1.83432.087504.9729𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,0.42310.49511

0

1

EPS ,

1.12150.67190

3.28

𝐴𝑠𝑠𝑒𝑡 ,

12.0260.8087013.4164

𝐵𝑎𝑛𝑘 ,

0.54270.49920

1

zscore ,4.5726

0.799908.0789

Table 3

The Pearson Analysis

𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 , 𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 , 𝑅𝑖𝑠𝑘𝐷𝑒𝑛𝑠𝑖𝑡𝑦

𝑇𝑂𝑁𝐸 ,EPS ,𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,1.0000𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,0.07731.0000

𝑅𝑖𝑠𝑘𝐷𝑒𝑛𝑠𝑖𝑡𝑦 ,-0.1748-0.2915

1.0000𝑇𝑂𝑁𝐸 ,-0.2168-0.2182

0.70461.0000

EPS ,

0.176

-0.1793

0.1066-0.00311.0000𝐴𝑠𝑠𝑒𝑡 ,

0.13350.1404-0.1919-0.098-0.1259

1.0000𝐵𝑎𝑛𝑘 ,

0.15940.06440.30220.20130.0799-0.7151

1.0000zscore ,0.18160.01750.17910.268

0.10740.2849-0.0682

4.2 Regression Tests Results
The first is the regression result of the effect of network public opinion attention on
bank’s risk text information disclosure strategy. Table 4 shows 𝑇𝑂𝑁𝐸 , is negatively
correlated with 𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 , ，which clearly demonstrate that the bank’s risk text
information disclosure strategy have significant network public opinion attention
effect .Meanwhile, 𝑅𝐼𝑆𝐾 , is negatively correlated with 𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 , ，whichshows that the higher network public opinion attention is ,the lower bank’s risk text
information density be disclosed.
Table 4 Bank’s Risk Text Information Disclosure Strategy:
The Effect of Network Public Opinion Attention
𝑇𝑂𝑁𝐸 ,

𝑅iskDensity ,

zsco

1

C

-9.143***0.22(-3.2257)(0.4113)-0.1109*-0.0239**(-1.6847)(-1.9197)

0.12450.0539*(0.753)(1.8443)0.946***

0.0405(4.0491)(0.9287)

1.0339***0.186***(2.9871)(3.0711)-0.0614-0.0091(-1.339)(-1.1127)

𝑅

0.096778

0.131484

Adjusted-𝑅

0.0711

0.104

𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,EPS ,𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels.

Table 5 gives the regression result of the zscorei,t joint effect on the bank’s risk
text information disclosure strategy. It shows that zscore , is negatively
correlated with 𝑇𝑂𝑁𝐺 , , 𝑎𝑛𝑑 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑤𝑖𝑡ℎ 𝑅𝐼𝑆𝐾 , .The bank’s risktaking has an incremental joint effect on the tone of bank risk text information.

Table 5 The Joint Effects of Bank’s Risk Taking
𝑇𝑂𝑁𝐸 ,C𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝑅iskDensity ,0.8191

3.638

0.54140.5595（0.28）(0.9412)（0.91）（0.92）-0.0918*-0.9148*-0.0163*-0.0321（-1.98）(-1.9091)（-1.77）（-0.58）

zscore ,0.1077*0.7038*0.0715***

0.0617*（1.84）(-1.7306)（2.80）（1.71）

zscore ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,EPS ,0.1802*0.0034(1.7087)（0.29）-0.02423-0.02137

0.03070.0353(-0.17)（-0.15）（1.09）（1.27）

0.09740.1835-0.0149-0.0128(0.39)(0.6459)（-0.30）（-0.26）

0.37130.6706**.124

0.1231**(1.29)(2.005)（2.16）（2.13）

0.0038-0.0249-0.0025-0.0033(0.09)(-0.4947)（ -0.3）（-0.40）𝑅

0.05070.07940.14720.1498Adjusted-𝑅

0.01390.03095

0.11420.1112

𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels , respectively.

Table 6 demonstrates that the bank’s network image maintenance ability has ajoint effect on the bank’s risk text information tone strategy, but there is not in the risk
text information density strategy.

Table 6The Joint Effects of Bank’s Network Image Maintenance Ability
𝑅iskDensity ,𝑇𝑂𝑁𝐸 ,C𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,-11.8212-11.9064-0.9866*-1.0259*（ -4.54）（-4.60）（-1.84）（-1.91）-0.7341***-0.1539**-0.014*（-3.68）（-2.51）（-1.62）-0.0249**（-2.03）

𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,-0.7341*-1.0175***-0.1665***-0.2081***（-1.84）（-3.92）（ -4.24）（-4.00）𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁i,t

EPS ,0.145*

0.0209（1.69）（1.24）

0.07780.07330.041

0.0451*（0.58）（0.56）（1.59）（-2.03）

1.1728***1.1902***0.1378***0.1426***（5.46）（5.58）（3.14）（3.25）

1.1157***1.11170.2565***0.2568（3.75）（3.76）（4.50）（4.52）-0.0607-0.0597-0.0076-0.0084（-1.56）（-1.53）（ -1.02）（-1.11）𝑅

0.16370.17620.19620.2058Adjusted-𝑅

0.13690.14520.16830.1735

𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels, , respectively.

5. Robustness check
First, we use the variable Regulation , to proxy the bank’s risk supervisionstress perception, which is equity to the number of regulation words divided by the
number of full text vocabulary, and find it is negatively correlated with 𝑇𝑂𝑁𝐸 , ,buts𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑤𝑖𝑡ℎ 𝑅iskDensity , .
Table 7The Bank’s Regulatory Perception Incremental Effect

𝑅iskDensity ,𝑇𝑂𝑁𝐸 ,C𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,Regulation ,-1.3493

1.3437-5.6215**-0.9344*0.58687-5.6012**（-0.53）（0.45）（-2.14）(-1.74)（0.98）（-2.12）-0.0946**-0.0907*-0.1448**-0.2023***-0.016*-0.0911**（-2.15）（-1.97）（-2.41）(-3.90)（-1.73）（-2.13）0.2044*0.02790.1804*0.0393*0.0274*0.0215（1.80）（0.5391）（1.65）( 1.76)(1.75)（0.14）

zscore ,0.0412*

0.0635**（1.9）(2.27)

Regulation ,0.04460.0008

zscore ,（0.44）(0.04)-0.2023***-0.8286***(-3.90)（-4.19）𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,0.01823

0.3725*

Regulation ,(1.08)（1.67）𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,

EPS ,

0.005

-0.0302

0.039

0.0543**0.0333-0.032（0.04）（-0.21）(1.52)（2.06）(1.20)（-0.25）0.30970.07170.1337***0.0516-0.0171

0.6911***（1.52）（0.29）( 3.04)（1.24）(-0.34)（3.20）

0.6193**0.4039*0.2612***0.1794***0.131**

1.0166***（2.27）（1.70）( 4.61)（3.22）(2.27)（3.65）-0.02680.0145-0.0064-0.002-0.0016-0.0581（-0.70）（0.35）(-0.86)（-0.26）(-0.19)（-1.55）𝑅

0.07250.07060.21770.13290.15940.1597

Adjusted-𝑅

0.04030.022

0.18110.10290.11550.1202

𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels , respectively.

Secently, table 8 shows the robustness check result of explanatory variables and thestudy period, and the results also have proved the effect of network public opinion
attention on bank’s risk text information disclosure strategy.
Table 8 The Effect of Network Public Opinion Attention:
Explanatory variable RobustnessC

𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝑇𝑂𝑁𝐸 ,

𝑅iskDensity ,-2.26768-0.2258(-0.75)（-0.38）-0.0791*-0.0135*（-1.78）（-1.75）

-0.7145***-0.1699***（-3.57）（-4.32）0.0478*0.0608**（1.8）（2.47）

0.2*

0.0309（1.77）（1.39）-0.0983

0.0138（-0.72）（0.51）

0.39580.0581（1.55）（1.16）

0.768**

0.215***（ 2.61）（ 3.72）-0.0126-0.0072（-0.31）（-0.90）𝑅

0.14070.2492Adjusted-𝑅

0.09580.2099𝑂𝑛𝐿𝑖𝑛𝑒𝑃𝑟𝑒𝑠𝑒𝑛𝑐𝑒 ,zscore ,Regulation ,EPS ,𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels, respectively

Table 9 The Effect of Network Public Opinion Attention: narrow Study Period Robustness
2009-2014
C

𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,zscore ,zscore ,𝑁𝐸𝑇𝑂𝑃𝐼𝑁𝐼𝑂𝑁 ,𝑅iskDensity ,𝑇𝑂𝑁𝐸 ,-15.9307***-1.4552-0.82610.3251（-4.6295）（-0.3081）（-1.3963）（0.4138）-0.3753***-1.5927***-0.1127***-0.2861***（-3.6036）（-2.7703）（-6.4333）（-2.9928）

0.8518*0.0869*（1.7287）（1.65）

0.22*

0.0333*（1.80139）（1.6417）EPS ,

0.3826*0.22660.0834***（1.9714）（1.1718）（2.6828）

1.5661***0.8027***0.1432

0.0987*（5.42775）（2.1919）（2.9151）（1.6206）

1.0137**0.3646

0.15530.1045*（2.5726）（0.9576）（2.5018）（1.6543）-0.1256*-0.0826

0.0133-0.0124（-1.6353）（0.9507）（1.0537）（-0.871）𝑅

0.19150.2604

0.3962

0.4687Adj-𝑅

0.16190.20860.37090.4319𝐴𝑠𝑠𝑒𝑡 ,

𝐵𝑎𝑛𝑘 ,

𝑌𝑒𝑎𝑟 ,

Notes: *, **, ***denote signiﬁcance levels at 10%,5%,1% levels , respectively

7. Conclusions
Risk text information is an important content of the bank's annual financial reportunder the dual control of financial regulatory rules and accounting standards. It is alsoa convenient channel for the bank's risk information transmission with rich connotationand fuzzy manipulation compared with structured financial data. In general, theconclusion of this paper showed complexity effect. Specifically, for the risk text
information tone management, the network public opinion attention has significant
transparency governance effect on information transmission, which mainly reflects the"information supply" hypothesis. For the risk information density strategy, the network
public opinion attention mainly reflects the "information ambiguity" hypothesis.
Reference
Elshandidy,Shrives,Bamber,Abraham，2018, Risk Reporting: A Review of the Literature andImplications for Future Research, Social Science Electronic Publishing