Unit III

T h e J o u r n a l o f D e v e l o p i n g A r e a s Volume 59 No. 4 Fall 2025

A REEXAMINATION OF THE IMPACT OF

MANDATORY INFORMATION DISCLOSURE

ON MANAGERIAL LEARNING: EVIDENCE

FROM INDUSTRY-SPECIFIC DISCLOSURES IN

EMERGING MARKETS

Ying Lu

Xi’an Jiaotong University,China

Xingqiang Yin*

Xi’an Jiaotong University,China

Junrui Zhang

Xi’an Jiaotong University,China

ABSTRACT

While managerial learning from market signals is crucial for informed corporate decision-

making, the role of mandatory disclosure in shaping such learning remains contested.

Theoretical and empirical studies offer mixed evidence on whether mandatory disclosure

enhances or inhibits stock price informativeness, particularly in emerging markets

characterized by high information asymmetry. This study leverages China’s industry-

specific information disclosure guidelines(IIDGs) as a quasi-natural experiment to

investigate the causal impact of mandatory disclosure on managerial learning. The IIDGs,

initially issued in 2013, significantly reshaped firms’ information environments by

mandating disclosure of industry structures, performance drivers, and risk factors. Using

panel data from 2009 to 2019 comprising 12,384 firm-year observations of publicly listed

companies on the Shanghai and Shenzhen Stock Exchanges, we employ a staggered

difference-in-differences (DID) design complemented by robustness checks, including

parallel trends tests, propensity score matching, and placebo tests. To further validate our

findings, we utilize alternative measures and analyze forecast revisions as indicators of

managerial learning. Our results show that firms subject to the new disclosure requirements

experience a significant increase in investment sensitivity to stock prices compared to

unaffected firms, suggesting that enhanced disclosure enables managers to better interpret

market signals. We identify stock price informativeness as the underlying mechanism,

ruling out alternative explanations based on improved access to external financing. As

additional evidence consistent with managerial learning, we find that analyst forecast

accuracy improves for treated firms. Moreover, we observe heterogeneity in the learning

effect: firms facing greater macroeconomic uncertainty, stronger industry competition, and

lower analyst coverage exhibit more pronounced improvements. This effect is further

amplified among firms located in less developed regions. Together, the evidence indicates

that mandatory industry-specific disclosures enhance managerial learning by integrating

feedback from external market participants. This study highlights the implications of

transitioning from a region-based to an industry-based regulatory framework, offering

valuable guidance for policymakers in emerging markets aiming to design more effective

and targeted disclosure regimes. Policymakers should further institutionalize such reforms,

recognizing their positive spillover effects and heterogeneous impacts across firms and

regions. Firms, in turn, should be encouraged to view disclosure not as a regulatory burden,

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but as a strategic instrument for integrating market feedback and improving decision-

making quality.

JEL Classifications: D82, G1, G34 and O16

Keywords:Mandatory Industry-specific Disclosure, Managerial learning,

Emerging markets, Regulatory impact

Contact author’s email address: Email: xqy829@xjtu.edu.cn

INTRODUCTION

The managerial learning hypothesis suggests that managers often lack complete

information and need to acquire new insights from external sources (Barro, 1990; Dow

and Gorton, 1997; Luo, 2005; Bond et al., 2012). Consistent with this idea, it is widely

believed that increased stock price informativeness enhances the market feedback effect

(Fernandes and Ferreira, 2008; Foucault and Frésard, 2012; Kim et al., 2023). However,

despite these theoretical and empirical advancements, the impact of mandatory disclosure

on stock price informativeness remains a contentious issue in the literature.

On one hand, increased disclosure might reduce the incentives for market

participants to seek, analyze, and disseminate new information (Gao and Liang, 2013;

Jayaraman and Wu, 2019; Chen et al., 2021; Bird et al., 2021; Pinto, 2023), potentially

decreasing the information available for managers' investment decisions. Specifically, if

there is an overlap between what managers already know and what they seek to learn,

disclosing such information could diminish the informational advantage of investors (Gao

and Liang, 2013). This reduction in informational advantage could discourage investors

from engaging in costly information acquisition (Boot and Thakor, 2001), leading to a

crowding-out effect on informed trading and a decreased sensitivity of investment

decisions to stock price signals.

On the other hand, mandatory disclosure might enhance the informativeness of

stock prices through a crowding-in effect. Some studies argue that mandatory disclosure

reduces information asymmetry between firms and external investors, encouraging

investors to seek and trade on value-relevant information (Jiang et al., 2011; Drake et al.,

2015; Gao and Huang, 2020). This can lead to more undisclosed managerial information

being reflected in stock prices. Consequently, mandatory disclosure could also promote

increased managerial learning.

Literature based on developed markets, which examines various regulatory

reforms (such as EDGAR and electronic information systems), suggests that mandatory

disclosure crowds out informed traders' access to private information, reduces stock price

informativeness, and hampers managerial learning (Bird et al., 2021; McClure and

Zakolyukina, 2022). This predominantly supports the crowding-out effect over the

crowding-in effect (Jayaraman and Wu, 2019; Chen et al., 2021; Pinto, 2023; Binz et al.,

2023). However, Gao et al. (2024) find that after the implementation of China's industry-

specific information disclosure guidelines (IIDGs), companies disclosed more guideline-

related information and firm-level information, leading to a greater integration of firm-

level information into stock prices. This raises the question: How does mandatory

disclosure impact managerial learning in developing countries, particularly in China, with

its high prevalence of retail investors and greater information asymmetry? There is a

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pressing need for clear causal evidence on how mandatory disclosure influences

managerial learning in such contexts.

Utilizing China's IIDGs as the research framework, this study explores the

impact of mandatory disclosure on managerial learning. Initially introduced by the

Shenzhen Stock Exchange in 2013 and later adopted by both the Shenzhen and Shanghai

Stock Exchanges, these guidelines provide investors with detailed insights into industries'

operational structures, key performance drivers, and associated risks. They mandate listed

companies to disclose general industry characteristics, evolving trends, and sector-

specific operational details. Consequently, these guidelines significantly reshape the

information environment, influencing the information available for managerial decision-

making.

Using a dataset of 12,384 observations from the Shenzhen and Shanghai Stock

Exchanges between 2009 and 2019, we find that the implementation of IIDGs led to a

significant increase in managerial learning, as indicated by a stronger sensitivity of

investment to stock price information. To validate these findings, we conducted several

robustness tests, including placebo tests, parallel trends tests, and propensity score

matching, addressing potential endogeneity concerns. We also employed alternative

variables in our regression analysis to further ensure the stability and reliability of the

results. Drawing on the work of Zuo (2016) and Kim et al. (2023), we examined

managerial learning through the lens of management forecast revisions. Then we ruled

out alternative explanations related to financing channels.

To investigate whether industry-specific disclosure regulations enhance

managerial learning through increased stock price informativeness, we first examined

changes in stock price informativeness following the implementation of these regulations.

Using the volume-synchronized probability of informed trading (VPIN) metric, as

proposed by Chen, Goldstein and Jiang (2007), we divided the sample into high and low

VPIN groups to assess the differences in managerial learning across these subgroups. Our

findings indicate that the managerial learning effect is more pronounced in the high VPIN

group, suggesting that the increased probability of informed trading strengthens the

learning effect, thereby supporting our hypothesis about the underlying mechanism.

We conducted a series of cross-sectional tests to further explore the effects of

industry-specific disclosures on managerial learning. First, we examined the role of

macroeconomic uncertainty, which typically prompts managers to rely more on

stakeholder information (Chen, et al., 2021; Kim et al.,2023). Our findings reveal that the

positive effect of industry-specific disclosures on managerial learning is stronger in firms

facing higher macroeconomic uncertainty. Second, under the assumption that managers

place greater value on stock price information in highly competitive environments (Chen

et al., 2017; Kwan et al., 2022), we observed that the beneficial impact is more

pronounced in firms experiencing intense competition. Third, we considered the impact

of a company’s own information environment, hypothesizing that companies with greater

analyst coverage derive less incremental benefit from industry-specific disclosures. Our

empirical results support this hypothesis. Additionally, we assessed the influence of

regional economic development on the effect of disclosure regulations. The results

indicate that companies in less economically developed regions experience more

significant learning effects from the increased information provided by these disclosures.

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These cross-sectional tests provide further evidence of the positive influence of industry-

specific disclosures on managerial learning across varying firm and regional contexts.

Our study makes several important contributions to the literature. First, by

leveraging a quasi-natural experiment based on China's industry-specific information

disclosure guidelines, we address the endogeneity issues common in existing studies and

fill a critical gap in understanding how mandatory disclosure impacts managerial learning

in emerging markets. Our findings diverge from those observed in developed markets

(Gao and Liang, 2013; Jayaraman and Wu, 2019; Chen et al., 2021; Pinto, 2023;

Goldstein and Yang, 2019), providing new insights into the role of disclosure in these

contexts.

Second, in contrast to developed countries like the United States, where

companies often rely on market-based financing, Chinese listed firms primarily rely on

stakeholder financing, which weakens incentives for transparent disclosure (Lu et al.,

2023). Given that managers in China generally hold negative views toward mandatory

disclosure (Armitage and Marston, 2008), preferring to manipulate the timing and content

of information (Schrand and Walther, 2000; McVay, 2006; Bowen et al., 2005), our

findings suggest that mandatory public disclosure can, in fact, facilitate managerial

learning by incorporating feedback from external market participants. This contributes

empirical evidence supporting the argument for incentivizing better disclosure practices,

rather than viewing them solely as regulatory burdens. Furthermore, our study extends

the literature on factors influencing managerial learning (Baker et al., 2003; Foucault and

Frésard, 2014; Driss, 2023), offering novel insights into this area.

Finally, our results support the hypothesis by Boot and Thakor (2001) that

mandatory disclosure related to firm value can benefit companies in developing nations,

unlike the trends observed in developed economies. These findings provide valuable

guidance for policymakers in other emerging markets seeking to establish disclosure

standards and develop their financial systems.

The structure of this paper is organized as follows: Section 2 outlines the

institutional background and formulates the hypotheses. Section 3 details the sample,

model, variables used in the study, and descriptive statistics. Section 4 presents the

baseline regression results, robustness checks, and discusses the results of the channel

tests. Section 5 elaborates on the results of further analyses. Section 6 provides the

conclusion.

INSTITUTIONAL BACKGROUND AND HYPOTHESIS DEVELOPMENT

Mandatory Disclosure of Industry-Specific Information

In recent years, "new economy" enterprises—characterized by new technologies,

industries, business models, and operational modes—have expanded rapidly. These firms

differ significantly from traditional industries in terms of their business models, value

drivers, and risk profiles. As a result, conventional disclosure models, designed for

traditional enterprises, no longer meet the diverse needs of listed companies, particularly

in areas such as business models, valuation bases, profitability, and competitive

advantages. To address these challenges and account for increasing industry

diversification and segmentation, both the Shanghai and Shenzhen Stock Exchanges

began issuing industry-specific information disclosure guidelines (IIDGs) in 2013. These

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guidelines cover companies in both traditional and emerging sectors, offering tailored

reporting requirements to better reflect the unique characteristics of each industry.

Following the issuance of these guidelines, listed companies are now required to

provide more detailed industry-specific information disclosures. They must report on the

unique characteristics and evolving trends of their industries, including factors such as

macroeconomic conditions, industry norms, policy directions, and both national and local

tax regulations. Additionally, companies are expected to disclose operational information

that is specific to their organizations and pertinent to their sectors.

For example, Mingpai Jewelry (stock code 002574), listed on the Shenzhen

Stock Exchange in the jewelry sector, adapted to these guidelines in 2018. Appendix A

outlines the specific changes in their disclosure practices following the reform. The first

column shows the information disclosed before the reform, while the second column

details the enhanced disclosure post-reform. The company’s reports now include

additional insights into macroeconomic and industry developments, competitive

advantages, as well as key operational aspects such as business models, production

methods, procurement strategies, and the status of its physical stores.

In Appendix B, we summarize the disclosure requirements across the Shanghai

Stock Exchange, Shenzhen Stock Exchange Main Board, SME Board, and ChiNext

Board, following Shi (2022). Besides real estate, the guidelines cover industries such as

gold jewelry and accessories, solid mineral resources, food and beverage manufacturing,

and integrated circuits. This summary clarifies the industry-specific disclosure content

outlined in Shi (2022), and the timeline of guideline implementation can also be

referenced there.

As shown in the tables in Appendices A and B, these guidelines have

significantly increased the depth and breadth of the information that listed companies

must disclose, thereby improving the transparency and relevance of the information

available to investors.

Hypothesis Development

It is commonly understood that managers might not have a complete grasp of all aspects

of a company's operations and could rely on stock prices to gain new information that

informs corporate decisions (Bond et al., 2012), especially when the market holds a large

amount of unknown information (Luo, 2005; Bai et al., 2016; Edmans, 2017). The private

information reflected in stock prices assists managers in better understanding their

company's fundamentals and integrating this knowledge into their decision-making

processes (Chen et al., 2007; Bakke and Whited, 2010). We propose that disclosing

industry-specific information could modify the guiding role of stock prices in managerial

investment decisions by changing the quantity of private information they contain.

Mandatory disclosure could potentially induce a crowding-in effect of

previously unknown managerial information into stock prices. Industry-specific

information disclosure enables external investors to uncover information not known to

managers and integrate it into stock prices. Edmans et al. (2017) suggest that insiders and

informed outsiders have different private information—managers are better informed

about internal company matters, while external parties have more insight into

macroeconomic conditions, industry competition, and consumer demand. Additionally,

industry-specific disclosure requirements compel listed companies to publish more

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detailed operational information, which could encourage external investors to concentrate

on acquiring new information rather than relying on what managers already know

(Goldstein and Yang, 2019). This shift increases the amount of unknown information

reflected in stock prices, enhancing the potential for managerial learning.

Secondly, as individuals gain a deeper understanding of specific uncertainties,

they are more likely to use their information proactively in trading, leading to more

informed transactions (Goldstein and Yang, 2015). With the implementation of industry-

specific information disclosure guidelines, the enhanced disclosure of relevant

operational information reduces the uncertainty surrounding this data in stock prices. This

reduction in uncertainty motivates informed traders to exploit their informational

advantages and engage more actively in market transactions. Consequently, this active

participation increases the private information content reflected in stock prices, thereby

enriching the quality of information available for managerial decision-making.

Thirdly, industry-specific information disclosure can lower investors'

information processing costs, thereby aiding informed traders in acquiring and generating

new private information. Mandatory disclosure, as suggested by Blankespoor et al.

(2020), still involves acquisition costs for external investors, meaning it is not truly

public information but rather a subset of private information. Prior research shows that

industry-specific disclosures lighten the load of information acquisition and processing,

especially for retail investors who are prevalent in markets like China. By providing

detailed operational insights, these disclosures allow investors to better understand and

interpret other company information, enabling them to integrate their value judgments

into stock prices through trading (Fernandes and Ferreira, 2008). Consequently, the

implementation of IIDGs helps decrease external investors' information processing costs,

granting them easier access to private information and thus enhancing the

informativeness of stock prices. This improvement leads to a greater amount of firm-

specific information being reflected in the stock price. Based on these considerations, we

propose Hypothesis 1a:

Hypothesis1a: Industry-specific information disclosure will increase

the sensitivity of stock prices to investment decisions.

However, mandatory disclosure of industry-specific operational information

may lead to crowding-out effects, reducing the sensitivity of stock prices to investment

decisions. This occurs because increased mandatory disclosure can diminish the incentive

for informed traders to acquire private information, as it reduces their information

advantage (Jayaraman and Wu, 2018; Pinto, 2023). By making detailed industry

information publicly available, the amount of private information available to informed

traders decreases, leading to less informed trading and reduced stock price

informativeness. Bird et al. (2021) show that lowering the cost for outsiders to access

internal information crowds out external information gathering, causing stock prices to

reflect less managerial information and reducing the sensitivity of investment decisions to

stock prices. Similarly, McClure and Zakolyukina (2022), in a national-level study on the

impact of disclosure technology, found that the reduced cost of acquiring information due

to technology crowds out private information gathering prior to announcements, lowering

stock price informativeness.

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Moreover, mandatory disclosure reduces information acquisition and processing

costs, making it easier for investors to evaluate company performance without searching

for additional managerial information (Kim et al., 2013). However, this ease of access

disproportionately benefits retail investors, who are generally less capable than

institutional investors at analyzing new information. As a result, the implementation of

the guidelines may increase noise trading, which could impair firms' ability to extract

meaningful information from stock prices, thereby reducing investment efficiency (Han

et al., 2014).

Hypothesis1b: Industry-specific information disclosure will weaken the

sensitivity of stock prices to investment decisions in listed

companies.

RESEARCH DESIGN

Sample

We selected Chinese A-share companies listed on the Shanghai and Shenzhen stock

exchanges from 2009 to 2019 as our research sample. Our sample period ends in 2019

because the calculation of the control variable, RETURN, requires data from the

subsequent three years. Data related to investments and control variables were obtained

from the China Stock Market & Accounting Research (CSMAR) Database. We excluded

companies classified within the financial sectors and those with missing data or abnormal

trading statuses, indicated by labels such as ST, *ST, and PT.

Data on industry-specific information disclosure were manually collected. First,

we downloaded and organize relevant documents such as the "Guidance on Industry

Information Disclosure for Listed Companies" from the official websites of the Shanghai

Stock Exchange and the Shenzhen Stock Exchange. Next, we compared the contents of

these documents with the "Guidance on Industry Classification for Listed Companies"

issued by the China Securities Regulatory Commission to identify the sectors and

industries affected, as well as the start times of these regulations. For sub-industries that

did not directly correspond to existing industry codes, we manually compared them based

on the industry designation and main business scope of the listed companies to delineate

the policy treatment group and the control group samples.

Model Specifications

To analyze the impact of industry-specific disclosures on managerial learning, we

adopt the model developed by Chen et al. (2007), which measures managerial learning by

examining the sensitivity of managerial investment to stock prices through Tobin's Q. In

line with the approaches of Chen et al. (2007) ,Bird et al.(2021) , Driss (2023) and Kim et

al. (2023), we specify our model as follows:

In Model (1), the dependent variable, INVESTMENT, reflects a firm's

investment activity, operationalized through two measures: R&D expenditure divided by

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lagged total assets (R&D) and the sum of R&D expenditure and capital expenditure

divided by lagged total assets (CAPEXNRD). This measurement assumes that if managers

are to engage in "learning," they are likely to focus on challenging and uncertain types of

investment such as R&D, which may be more difficult to value and thus could

incentivize managerial learning (Bai et al., 2016; Kwan et al., 2022).

The variable Q represents the lagged value of Tobin's Q, calculated by dividing

the sum of market value of equity and book value of assets minus book value of equity

from the previous period by the lagged total assets (Ye et al., 2023). The variable POST

is a binary indicator set to one when the firm begins complying with the disclosure

guidelines in year t-1 and zero otherwise1.

The coefficient α1 on the interaction term Q×POST measures the sensitivity of

investment to stock price information, reflecting the extent of managerial learning from

the stock market. A positive and statistically significant α1 would indicate that

managerial learning has improved as a result of the industry-specific disclosures, thus

supporting Hypothesis 1a.

Incorporating insights from Chen et al. (2007) and Kwan et al. (2022), our

model includes current cash flow normalized by current total assets (CF) to control for

the influence of cash flow on investment decisions. We also account for the timing of

investments by including stock returns over the next three years (RETURN), reflecting

managers’ expectations about future profitability. To adjust for potential scale effects, we

include the inverse of lagged total assets (LNVAST), measured in billions of RMB, which

helps isolate correlations driven by commonly scaled variables. To address firm-specific

and time-specific variations that could influence the results, we control for time-invariant

heterogeneity across firms using firm fixed effects (Firm FE) and for annual economic or

policy changes using year fixed effects (Year FE). Following standard empirical practices

in finance and economics, all unbounded variables in the dataset are winsorized at the 1%

and 99% levels to reduce the influence of extreme outliers. We cluster standard errors at

the firm level to account for potential autocorrelation and heteroscedasticity within firms2.

This approach ensures the robustness of our regression estimates against underlying

industry-specific shocks that could otherwise skew the results.

Descriptive Statistics

Our final sample comprises 12,384 observations, and Panel A of Table 1 presents the

descriptive statistics for all variables used in analyzing the effects on managerial learning.

Within our dataset, the average value of CAPEXNRD is approximately 0.08, indicating

that, on average, firms allocate 8% of their assets from the previous year towards research

and development (R&D) expenditures and capital investments. More specifically, the

average values for R&D and CAPEX are 0.024 and 0.056, respectively. This translates to

2.4% of the prior year's total assets being invested in R&D and 5.6% in capital

expenditures. Additionally, CAPEX1, representing the difference between capital

expenditure and the net cash received from the disposal of fixed assets, intangible assets,

and other long-term assets divided by lagged total assets, has a mean value of 0.053,

accounting for 5.3% of the previous year's total assets. The mean value for CHGASSET is

0.151, indicating an average total asset growth rate of approximately 15%. The mean

value for Q, representing Tobin’s Q, is roughly 2.111, suggesting a significant market

valuation relative to the asset base. The mean value for the post-implementation indicator

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(POST) is 0.169, showing that only 16.9% of the observations fall into the treatment

group. Lastly, the distributions of the control variables are within expected ranges,

affirming the robustness of the dataset for our analysis.

Panel B of Table 1 provides insight into the annual compliance status of listed

companies with regulatory guidelines. It shows a noticeable increase in the number of

regulated listed companies over time, aligning with the introduction of various industry

guidelines. In 2013, only a negligible fraction of listed companies adhered to regulatory

norms. However, by 2019, over 30% of listed companies were subject to regulatory

oversight.

TABLE 1． DESCRIPTIVE STATISTICS

Panel A: Descriptive statistics for all variables

Variable Mean SD Min Median Max

CAPEXNRD 0.080 0.063 0.003 0.064 0.346

R&D 0.024 0.022 0.000 0.019 0.122

CAPEX 0.056 0.056 0.001 0.039 0.307

CAPEX1 0.053 0.056 -0.030 0.036 0.301

CHGASSET 0.151 0.294 -0.315 0.086 1.920

Q 2.111 1.277 0.886 1.702 8.002

POST 0.169 0.374 0.000 0.000 1.000

CF 0.056 0.077 -0.153 0.052 0.314

LNVAST 0.040 0.042 0.000 0.027 0.223

RETURN 0.380 0.858 -1.075 0.246 3.298

Panel B: The annual count of observations conforming to the guidelines

Year POST=0 POST=1

Observations Frequency % Frequency %

2009 193 100.00% 0 0.00% 193 2010 390 100.00% 0 0.00% 390 2011 459 100.00% 0 0.00% 459 2012 804 100.00% 0 0.00% 804 2013 1078 99.54% 5 0.46% 1,083 2014 1265 99.53% 6 0.47% 1,271 2015 1129 83.20% 228 16.80% 1,357 2016 1057 78.59% 288 21.41% 1,345 2017 1160 76.17% 363 23.83% 1,523 2018 1313 70.82% 541 29.18% 1,854 2019 1449 68.84% 656 31.16% 2,105 Total 10297 83.15% 2087 16.85% 12384

Note: Panel A delineates the descriptive statistics of variables in the analysis. Variable definitions

are presented in the main body of the text. Panel B represents the annual situation of listed

companies being guided by regulatory standards. The percentage (%) reflects the proportion of

samples regulated in that year out of the total sample size.

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MAIN RESULTS

Baseline Results

Table 2 delves into the impact of industry-specific information disclosure on the

responsiveness of investment to stock prices, with a particular emphasis on the variable

POST×Q. In Columns (1) and (2), R&D serves as the investment variable, while

Columns (3) and (4) employ CAPEXNRD as the measure. Results in Columns (1) and (3)

are presented without accounting for control variables.

The coefficients for POST×Q in Columns (2) and (4) are 0.0026 and 0.0085,

respectively, both statistically significant at the 1% level. These results suggest that

disclosing industry-specific information enhances managers' ability to make informed

investment decisions by leveraging insights from stock prices. In terms of economic

significance, after the implementation of industry-specific information disclosure

guidelines, a one standard deviation increase in Tobin’s Q leads to an increase in

corporate investment by 0.0195 (calculated as 1.277 × (0.0068 + 0.0085)). This increase

represents 24.42% (0.0195/0.080) of the average corporate investment, highlighting a

substantial impact of the enhanced disclosure on managerial learning and investment

responsiveness to stock price information.

Among the control variables considered, the coefficient for RETURN is notably

negative, while those for CF and INVAST are positively significant. These findings align

with the research conducted by Chen et al. (2007), providing further support for the

relationship between industry-specific disclosures and investment responsiveness.

TABLE 2: INDUSTRY-SPECIFIC INFORMATION DISCLOSURE AND

MANAGERIAL LEARNING

R&D CAPEXNRD

(1) (2) (3) (4)

Q 0.0026*** 0.0018*** 0.0094*** 0.0068***

(10.45) (8.32) (10.61) (7.79)

POST -0.0024** -0.0042*** -0.0050 -0.0089**

(-2.05) (-3.77) (-1.08) (-1.99)

POST×Q 0.0022*** 0.0026*** 0.0078*** 0.0085***

(3.35) (4.09) (3.29) (3.70)

CF 0.0204*** 0.0836***

(7.00) (7.39)

LNVAST 0.0951*** 0.2398***

(6.44) (5.00)

RETURN -0.0007*** -0.0058***

(-2.70) (-5.67)

Constant 0.0183*** 0.0153*** 0.0594*** 0.0533***

(34.76) (20.80) (31.12) (22.67)

Observations 12,384 12,384 12,384 12,384

Year FE YES YES YES YES

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm

Adjusted R-squared 0.790 0.799 0.479 0.492

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Note: This table presents the impact of industry-specific disclosures on the sensitivity of investment

to stock prices. CAPEXNRD denotes the investment metrics, while POST is a binary variable equal

to one for the year t-1 when firm i begins adhering to the disclosure guidelines, and zero otherwise.

Q represents the lagged value of Tobin's Q. Definitions of other variables are provided in Appendix

C. The sample includes A-share companies listed on the Shanghai and Shenzhen stock exchanges

from 2009 to 2019, comprising 12,384 firm-year observations. We employ OLS regressions with

controls for firm and year fixed effects. Standard errors are clustered at the firm level, with t-

statistics reported in parentheses. Significance levels are indicated by ∗ ∗ ∗ for 1%, ∗ ∗ for 5%,

and ∗ for 10% (two-tailed).

Robustness Tests

Parallel Trends Testing

In this section, we take steps to validate the robustness of our findings. Firstly, we

conducted a test of the "parallel trends" assumption to address potential issues of spurious

inference from the difference-in-differences estimator. This is detailed in Panel A of

Table 3. To accomplish this, we introduced dummy variables for the three periods before

and after the implementation of the guidance, along with their interaction terms with

Tobin's Q, into Model (1). The period immediately preceding the implementation served

as the baseline. Specifically, Current represents the year when a company commenced

disclosing industry-specific information, while Pre3 and Post3 denote dummy variables

for the three years before and after the disclosure, respectively. Pre3×Q represents the

interaction term between Pre3 and Tobin's Q, with other variables defined similarly.

Panel A of Table 3 illustrates that the coefficients for Pre2×Q and Pre3×Q are

not statistically significant in the regression. However, the coefficients for Post1×Q and

Post2×Q are significantly positive. This suggests that there are no significant differences

in the changes in management learning effects between treated and control companies

before the policy implementation, thus supporting the parallel trends assumption. This

strengthens the validity of our analysis and the interpretation of the results.

Propensity Score Matching

In our second step, we employed propensity score matching to address the impact of

selection bias. This method uses ownership nature (SOE), return on assets (ROA),

leverage ratio (LEV), operating cash flow to total assets ratio in the previous period (CF),

growth opportunities (Q), sum of stock returns for the next three years (RETURN), and

the inverse of total assets (LNVAST) as covariates between regulated and unregulated

enterprises.

We conducted a 1:1 nearest neighbor matching with a maximum distance of

0.05 with replacement and utilizing investment (CAPEXNRD) as the outcome variable for

the regulated enterprise sample.Observations without suitable matches were excluded,

resulting in a final sample of 3764 observations for propensity score matching regression

analysis. Figure 1 depicts the propensity score distributions (kernel density curves) of the

experimental and control groups before and after matching. Significant differences in

propensity scores between the experimental and control groups before matching

underscored the necessity of propensity score matching. However, the right panel

displays the propensity score distributions of the two groups after matching, showing a

close overlap between the propensity score distributions of the matched pairs, indicating

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a notable correction of score imbalances between the two groups. The matching effect is

considered satisfactory.

Regression results based on the matched sample are presented in Panel B of

Table 3, demonstrating consistency with previous findings even after mitigating selection

bias. This strengthens the robustness of our analysis and reinforces the reliability of our

conclusions.

FIGURE 1. PROBABILITY DISTRIBUTION PLOT OF PROPENSITY SCORES

Note: Figure 1 shows the propensity score distribution (kernel density curves) for the experimental

and control groups before and after matching. The left plot displays the score distribution before

matching, and the right plot presents it after matching, with the horizontal axis representing

propensity score values and the vertical axis representing probability density.

Alternative Measurements and Test

In our third step, we replaced the dependent variable with alternative measures,including

CAPEX, CAPEX1, and CHGASSET. Detailed definitions can be found in Appendix C.

The results are presented in columns 1-3 of Panel C in Table 3. The coefficients

of POST×Q ranged between 0.0047 and 0.0671, all of which were statistically significant

at least at the 5% level. These results suggest that even when employing alternative

dependent variables, the coefficient of POST×Q remains significant, thus confirming the

robustness of the benchmark results. Then, we utilize the sensitivity of changes in

management forecast accuracy to stock returns to measure managerial learning. If IIDGs

can increase the information in stocks that managers are unaware of, supplementing their

understanding of future earnings, then managers can use this information to improve their

forecasts. To test this prediction, we draw on the regression equation (2) used by Kim et

al. (2023):

Panel C of table 3 presents the estimation results. As expected, the coefficients

on |RETURN|×POST1 is significantly positive, suggesting that this sensitivity is greater

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among firms regulated by industry-specific information disclosure guidelines. This

greater sensitivity supports the idea that industry-specific information disclosure provides

new information about stock prices that managers incorporate into their forecast revisions,

thereby improving the accuracy of management's earnings forecasts.

Placebo Test

In our fourth step, we conducted a placebo test by randomly assigning observations to

treatment and control groups and examining the effect on the outcome variable. This

process was repeated 1000 times. Figure 2 depicts the kernel density plot of the 1000

regression coefficients.

The results indicate that most of the T-values from the random sampling are

near zero, rather than being concentrated in significant intervals (with absolute values

greater than 1.96). This suggests that the findings are not driven by omitted variables that

coincide with the information disclosure policy. The results of the placebo test alleviate

this concern, providing further confidence in the robustness and validity of the original

findings. This comprehensive approach strengthens the reliability of the analysis and

enhances the credibility of the conclusions drawn.

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FIGURE 2.P-VALUE DENSITY DISTRIBUTION PLOT

Note: This figure displays the outcomes of the placebo test, implemented using a method of

randomly generating experimental groups.The top and bottom panels show outcomes for CAPEX

and R&D, respectively. By randomly sampling POST×Q 1000 times, t-values are extracted and

plotted on the graph above.

Considering the impact of Financing Channel

In our final step, we aimed to rule out the influence of financing channels. While we

previously attributed the increased sensitivity of investment to stock prices to enhanced

managerial learning, an alternative explanation could be that increased industry

disclosures attract liquidity into the stock market. This, in turn, could reduce the cost of

capital and increase the available capital for managers, providing greater flexibility in

decision-making and potentially leading to heightened investment sensitivity to prices.

To address this possibility, we conduct a robustness check using Model (3).

Externalcapital represents external financing, measured by five indicators:

Debt1, Debt2, Equality, External, and WACC. Drawing on previous research(Zou and

Adams, 2008; Gao et al., 2020),the control variables mainly include: firm size (Size),

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leverage ratio (Lev), return on assets (Roa), market-to-book ratio (MB), cash flow (CF),

board independence (Independence), ownership structure (Soe), ownership concentration

(Top), CEO duality (Dual), board size (Board), institutional investor shareholding

(Institution), systematic risk (Beta), and asset growth rate (Growth).

The regression results in panel D show that the coefficient of POST is not

significant, indicating that industry-specific disclosures do not significantly affect the

scale of debt financing, equity financing, or financing costs for firms. The empirical

findings further reinforce the learning hypothesis.

TABLE 3. ROBUSTNESS TESTS

Panel A. Parallel trend test

R&D CAPEXNRD (1) (2)

Pre3×Q 0.0001 0.0014

(0.18) (0.82) Pre2×Q 0.0004 0.0024

(0.57) (1.27)

Current×Q 0.0007 0.0038* (1.34) (1.78)

Post1×Q 0.0020*** 0.0050*

(2.74) (1.75) Post2×Q 0.0016** 0.0046**

(2.35) (2.20)

Post3×Q 0.0024*** 0.0060** (2.92) (2.48)

Q 0.0018*** 0.0064***

(7.84) (7.06) Pre3 0.0003 -0.0035

(0.23) (-0.80)

Pre2 -0.0006 -0.0042

(-0.40) (-0.99)

Current -0.0004 -0.0024

(-0.37) (-0.52) Post1 -0.0031** -0.0022

(-2.40) (-0.41)

Post2 -0.0024* -0.0020 (-1.89) (-0.42)

Post3 -0.0040*** -0.0056

(-2.96) (-1.14) CF 0.0205*** 0.0843***

(7.00) (7.40)

LNVAST 0.0944*** 0.2332*** (6.44) (4.84)

RETURN -0.0007*** -0.0058*** (-2.65) (-5.61)

Constant 0.0153*** 0.0545***

(19.69) (21.09)

Observations 12,384 12,384

Year FE& Firm FE YES YES

Cluster by Firm Firm

Adjusted R-squared 0.799 0.491

Panel B. Using propensity scores matching method R&D CAPEXNRD

(1) (2)

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Q 0.0017*** 0.0084***

(4.49) (4.54) POST -0.0025 -0.0129

(-1.28) (-1.38)

POST×Q 0.0018** 0.0098** (1.97) (2.34)

CF 0.0139** 0.0998***

(2.48) (3.93) LNVAST 0.1219*** 0.3217***

(4.52) (3.58)

RETURN -0.0011** -0.0060*** (-2.26) (-2.94)

Constant 0.0150*** 0.0464***

(11.11) (9.42)

Observations 3,764 3,764

Year FE& Firm FE YES YES

Cluster by Firm Firm Adjusted R-squared 0.801 0.473

Panel C. Using alternative measures of investment and managerial learning CAPEX CAPEX1 CHGASSET △Accuracy

(1) (2) (3) (4) Q 0.0050*** 0.0044*** 0.0656***

(6.26) (5.49) (12.77)

POST -0.0040 -0.0023 -0.1185*** (-0.98) (-0.56) (-4.38)

POST×Q 0.0052*** 0.0047** 0.0671***

(2.62) (2.42) (4.19) POST1 -0.0985

(-0.48)

POST1×|Return| 2.0240* (1.75)

Controls YES YES YES YES

Observations 12,384 12,384 12,384 277

Year FE/Year-quarter Year Year Year Year-Quarter

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm Adjusted R-squared 0.438 0.432 0.245 0.496

Panel D. Excluding possible alternative explanation Debt1 Debt2 Equity External WACC

VARIABLES (1) (2) (4) (3) (5)

POST -0.0009 0.0044 0.0039 -0.0016 -0.0024 (-0.29) (1.60) (1.10) (-0.16) (-1.14)

Size 0.0177*** 0.0106*** 0.0425*** -0.0349** -0.0132***

(7.46) (4.55) (6.17) (-2.11) (-7.91) Lev 0.0700*** 0.0567*** -0.1972*** -0.0241 -0.0463***

(6.84) (6.01) (-4.26) (-0.40) (-6.81)

Roa 0.0512*** 0.0096 -0.0452 0.0015 -0.0388*** (2.90) (0.70) (-0.50) (0.00) (-3.69)

MB -0.0117* -0.0151*** -0.0220* 0.0556 0.0402***

(-1.72) (-2.63) (-1.86) (1.57) (7.86)

CF -0.0470*** -0.0340*** 0.0923*** -0.2023*** 0.0060

(-3.49) (-2.74) (4.19) (-4.90) (0.75)

Independence -0.0146 -0.0130 0.0133 0.0234 -0.0014 (-0.65) (-0.73) (0.42) (0.46) (-0.09)

Soe -0.0146*** -0.0058 -0.0103 -0.0299 -0.0043

(-2.87) (-1.24) (-1.20) (-1.46) (-0.95) Top 0.0001 0.0000 -0.0014*** -0.0010 -0.0000

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(0.99) (0.28) (-6.41) (-1.58) (-0.41)

Dual -0.0007 -0.0022 0.0026 -0.0000 0.0008 (-0.25) (-0.95) (0.75) (-0.00) (0.38)

Board -0.0007 -0.0016** -0.0027** 0.0005 -0.0005

(-0.82) (-2.02) (-2.22) (0.25) (-0.81) Insown -0.0000 0.0000 0.0013*** 0.0000 -0.0001

(-0.49) (0.17) (10.44) (0.05) (-1.58)

Growth 0.0003 0.0003* 0.0014** 0.0001 -0.0000 (1.48) (1.73) (2.15) (1.10) (-0.88)

Betaval -0.0003 0.0020 -0.0019 -0.0125* -0.0008

(-0.08) (0.70) (-0.33) (-1.72) (-0.39) Constant -0.4003*** -0.2334*** -0.8202*** 1.3502*** 0.3712***

(-7.77) (-4.62) (-6.17) (3.99) (10.08)

Observations 8,444 7,421 9,668 5,427 4,590

Year FE& Firm FE YES YES YES YES YES

Cluster by Firm Firm Firm Firm Firm

Adjusted R-squared -0.007 -0.046 0.140 0.829 0.434

Note: Panel A shows the results of the parallel trend test. Pre3 (Pre2, Pre1) is a binary variable

that takes the value of one if a firm adhered to an industry-specific information disclosure guideline

three years (two years, one year) ago and zero otherwise. "Current" equals 1 if the company

complies with the guidelines in the current year. Post1 (Post2, Post3) is a binary variable that

takes the value of one if a firm adhered to an industry-specific information disclosure guideline one

year (two years, three years) ago and zero otherwise.

Panel B presents the impact of industry-specific information disclosure on managerial learning

using the Propensity Score Matching (PSM) approach. Column 1 and Column 2 show the

regression results based on the matched sample, with R&D and CAPEXNRD as the outcome

variables, respectively.

Panel C presents results using alternative independent variables. CAPEX is defined as capital

expenditure divided by total assets in the previous period. CAPEX1 is calculated as the difference

between cash paid for the acquisition and construction of fixed assets, intangible assets, and other

long-term assets, and the net cash received from the disposal of these assets, divided by the total

assets of the previous year. CHGASSET represents the difference between total assets at the end of

the year and total assets at the beginning of the year, divided by total assets at the beginning of the

year. △Accuracy represents the change in forecast accuracy, while POST1×|Return| and

POST2×|Return| reflect the impact of IIDGs on the sensitivity of earnings forecast accuracy to

stock returns.

Panel D presents the results illustrating the impact of industry-specific information disclosure on

corporate financing. Debt1 and Debt2 represent corporate debt financing, while Equality reflects

equity financing. External and WACC represent the total external financing and the overall cost of

financing, respectively. POST is a dummy variable that equals 1 for the year t-1 when firm i starts

adhering to the disclosure guidelines, and zero otherwise. We use OLS regressions, controlling for

firm and year fixed effects. Standard errors are clustered at the firm level, and t-statistics are

reported in parentheses. Significance levels are indicated by ∗ ∗ ∗ for 1%, ∗ ∗ for 5%, and ∗ for

10% (two-tailed).

Channel Tests of Managerial Learning In light of the hypotheses and baseline regression results outlined in this paper, the

disclosure of industry-specific information is posited to enhance the informativeness of

stock prices, subsequently influencing managerial learning. Consequently, our

mechanism test aims to scrutinize the impact of stock price informativeness, measured by

the likelihood of informed trading (VPIN) as proposed by Chen et al. (2007). Put

differently, if the learning-based mechanism holds true, we expect that the

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implementation of industry-specific information disclosure will lead to an increase in

VPIN and learning effect is more pronounced in groups with higher VPIN.

Utilizing the standard Difference-in-Differences (DID) regression approach, as

suggested by Ye et al. (2023), we initially aimed to validate that the Probability of

Informed Trading (VPIN) increased following the implementation of industry-specific

information disclosure within our sample. To compute the Volume-synchronized

Probability of Informed Trading (VPIN) values, we employed the BVC algorithm

outlined in Easley et al. (2002). These VPIN values were extracted from the CSMAR

database, where higher VPIN values indicate a higher proportion of informed trading in

stock prices. Our analysis revealed a statistically significant increase in VPIN for treated

stocks compared to control stocks subsequent to the enactment of industry-specific

information disclosure guidelines, as demonstrated in Panel A of Table 4.

We then divided the sample into a high VPIN group and a low VPIN group and

examined whether there was a difference in findings between the two groups. We define

high and low VPIN based on the median level of VPIN across all firm-year observations.

The results for high and low VPIN are presented in panel B of Table 4. Consistent with

our conjecture, the coefficient on POST×Q is significantly positive only in the subsample

of the high VPIN group. This finding confirms our hypothesis that firms with higher

VPIN values would have a more significant increase in investment efficiency sensitivity

compared to firms with lower VPIN values. Through these analyses, we further validate

the mechanism by which industry-specific information disclosure enhances investment

efficiency sensitivity by amplifying VPIN.

TABLE 4. CHANNEL TEST ON THE IMPACT OF THE LIKELIHOOD OF

INFORMED TRADING (VPIN) Panel A: The effect of the industry-specific information disclosure on VPIN

VPIN VARIABLES (1) (2)

POST 0.0015** 0.0012* (2.33) (1.86)

CF -0.0031 (-1.52)

LNVAST 0.0162* (1.89)

RETURN 0.0017*** (7.68)

Constant 0.1758*** 0.1746*** (2,025.54) (510.16)

Observations 12,384 12,384 Year FE YES YES Firm FE YES YES

Cluster by Firm Firm Adjusted R-squared 0.644 0.649

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Panel B: The cross-sectional relationship between investment sensitivity and VPIN

R&D CAPEXNRD

VPIN

VARIABLES High Low High Low

Q 0.0016*** 0.0025*** 0.0048*** 0.0123***

(5.19) (7.70) (3.64) (9.32)

POST -0.0069*** -0.0020 -0.0186*** -0.0025

(-4.39) (-1.23) (-2.76) (-0.40)

POST×Q 0.0040*** 0.0014 0.0130*** 0.0034

(4.44) (1.56) (3.86) (1.00)

CF 0.0226*** 0.0201*** 0.0979*** 0.0649***

(5.76) (4.31) (6.12) (3.88)

LNVAST 0.0944*** 0.1044*** 0.2795*** 0.0725

(5.92) (2.99) (4.28) (0.73)

RETURN -0.0010** -0.0005 -0.0073*** -0.0034**

(-2.55) (-1.26) (-5.00) (-2.21)

Constant 0.0154*** 0.0142*** 0.0554*** 0.0473***

(14.89) (12.44) (15.11) (12.67)

Inter-group differences -0.003** -0.010***

Year FE YES YES YES YES

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm

Observations 7,373 5,011 7,373 5,011

Adjusted R-squared 0.780 0.837 0.468 0.558

Note: This table presents an analysis of the mechanism by which industry-specific information

disclosure enhances management's learning effect through an increase in informed trading. In

Panel A, we show the effect of industry-specific information disclosure on the VPIN (Volume-

synchronized Probability of Informed Trading). POST is a dummy variable that equals 1 for the

year t-1 when firm i starts adhering to the disclosure guidelines, and zero otherwise. In Panel B,

we classify the sample into high and low groups based on the median VPIN. The High group

consists of observations with a VPIN greater than the median, while the Low group consists of

observations with a VPIN less than the median. “Inter-group differences” present the p-value of a

1,000-repetition bootstrap analysis testing whether the coefficients in Columns (1) and (2), (3) and

(4) are statistically different. T-statistics in parentheses use firm-clustered standard errors.

Significance levels are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.

FURTHER ANALYSIS

The Impact of Macroeconomic Uncertainty

In this subsection, we explore the impact of macroeconomic uncertainty on the extent to

which managers learn from capital markets. As suggested by Edmans et al. (2017),

private information conveyed by informed traders through stock prices plays a crucial

role in driving management's market learning. In situations characterized by heightened

uncertainty, the significance of the information embedded in stock prices intensifies,

thereby strengthening managers' incentive to glean insights from the market (Chen et al.,

2021; Kim et al., 2023). Consequently, we hypothesize that in periods of high

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macroeconomic uncertainty, industry-specific information disclosure will exert a more

pronounced promotional effect on management learning.

To test our hypothesis, we use economic uncertainty indicators developed by

Nie and his team. For detailed data discussion, see Li et al. (2024). These indicators are

based on textual analysis of annual reports. In our study, we segment the sample into high

and low uncertainty groups, using the median value of the macroeconomic uncertainty

index as the benchmark. Subsequently, we conduct a group regression analysis. The

findings of this analysis are presented in Table 5. Remarkably, the coefficient of the

interaction term POST×Q demonstrates significant positivity exclusively within the high

economic uncertainty group. Furthermore, the inter-group coefficient test highlights a

significant disparity between the two groups. These empirical findings provide

compelling evidence in support of our hypothesis.

TABLE 5 :THE IMPACT OF MACROECONOMIC UNCERTAINTY

R&D CAPEXNRD

Macroeconomic uncertainty

VARIABLES High Low High Low

Q 0.0017*** 0.0019*** 0.0069*** 0.0077***

(5.81) (5.62) (5.30) (5.78)

POST -0.0049*** -0.0019 -0.0078 -0.0050

(-3.06) (-1.30) (-1.14) (-0.67)

POST×Q 0.0031*** 0.0012 0.0097*** 0.0047

(3.60) (1.48) (2.68) (1.45)

CF 0.0213*** 0.0205*** 0.0849*** 0.0916***

(5.68) (4.14) (5.30) (4.41)

LNVAST 0.1082*** 0.0741*** 0.3280*** 0.0727

(5.76) (2.94) (5.16) (0.75)

RETURN -0.0009*** -0.0010** -0.0076*** -0.0061***

(-2.79) (-2.46) (-5.27) (-3.59)

Constant 0.0147*** 0.0154*** 0.0510*** 0.0572***

(13.87) (12.89) (15.24) (14.49)

Inter-group differences -0.002** -0.005*

Year FE YES YES YES YES

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm

Observations 6,811 4,366 6,811 4,366

Adjusted R-squared 0.793 0.831 0.495 0.530

Note: This table investigates the influence of macroeconomic uncertainty on the effectiveness of

managerial learning. We use a measure of macroeconomic uncertainty derived from the textual

analysis of company annual reports by Nie and his team (Li, Nie, and Ruan, 2024). The dataset is

available for download (http://www.niehuihua.com/uploads/soft/230323/1-230323201458.rar). A

higher value of this indicator signifies a greater perception of macroeconomic uncertainty among

companies. “Inter-group differences” indicate the coefficient and significance differences between

columns (1) and (2), and (3) and (4). T-statistics in parentheses use firm-clustered standard errors.

Significance levels are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.

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The Impact of Industry Competition

In this section, we delve into the impact of competition on learning from capital markets.

According to Bond et al. (2012), managers typically possess a wealth of internal

information, including technology, production costs, and strategic insights, while external

investors are more likely to possess external information, such as industry status and

competitor analysis. Increased competition in industries heightens firms' susceptibility to

the influence of peer wealth and strategic changes (Chen et al.,2017), thereby enhancing

the value of external information and intensifying management's motivation to acquire it

(Chen et al., 2017; Kwan et al., 2022). Hence, industry-specific information disclosure's

potential to improve investment efficiency and guide managerial decisions is anticipated

to be particularly significant for firms in highly competitive product markets. Our study

validates this assertion. The empirical findings presented in Table 6 indicate that

industry-specific information disclosure has a greater impact on the sensitivity of stock

prices to investment in industries with lower Herfindahl indices.

TABLE 6: THE IMPACT OF INDUSTRY COMPETITION

R&D CAPEXNRD Degree of market competition

VARIABLES Low High Low High Q 0.0015*** 0.0017*** 0.0070*** 0.0057*** (5.71) (6.14) (5.34) (4.91)

POST -0.0022* -0.0046** -0.0037 -0.0185** (-1.84) (-2.37) (-0.64) (-2.34)

POST×Q 0.0010 0.0034*** 0.0059* 0.0121*** (1.44) (3.19) (1.91) (3.24)

CF 0.0155*** 0.0229*** 0.0743*** 0.0825*** (4.51) (5.36) (4.48) (5.18)

LNVAST 0.1119*** 0.0846*** 0.3351*** 0.1930*** (4.68) (4.05) (3.29) (3.15)

RETURN -0.0006* -0.0007* -0.0049*** -0.0068*** (-1.88) (-1.84) (-3.59) (-4.25)

Constant 0.0103*** 0.0209*** 0.0443*** 0.0633*** (10.28) (20.43) (12.92) (18.01)

Inter-group differences 0.002*** 0.006** Year FE YES YES YES YES Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm Observations 5,854 6,330 5,854 6,330

Adjusted R-squared 0.775 0.817 0.502 0.507

Note: This table illustrates the impact of industry competitiveness. Industry competitiveness

levels are classified based on the Herfindahl-Hirschman Index (HHI). Samples with an index

greater than the median are categorized as the Low group each year, while those with an index

lower than the median are classified into the High group. Columns (1) and (3) display

regression results for samples characterized by low industry competitiveness, while columns (2)

and (4) represent those with high industry competitiveness. “Inter-group differences” indicate

the coefficient and significance differences between columns (1) and (2), and (3) and (4). T-

statistics in parentheses use firm-clustered standard errors. Significance levels are denoted by

***, **, and * for 1%, 5%, and 10%, respectively.

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The Impact of Corporate Information Environment

In this section, we further explore the impact of a company's information environment on

managerial learning. Financial analysts play a key role in reducing information

asymmetry. Companies with higher analyst coverage tend to experience lower levels of

information asymmetry (Chang et al., 2006) and have more informative stock prices

(Hong et al., 2000). Consequently, the incremental effect of industry-specific information

disclosure may be diminished in these firms. Moreover, analysts often have better access

to macro-level information, such as industry data (Piotroski and Roulstone, 2004), which

could serve as a substitute for the industry-specific operational information disclosed in

annual reports. Therefore, we hypothesize that companies with higher analyst coverage

will experience smaller changes in the private information component of stock prices,

leading to a less pronounced managerial learning effect.

The empirical analysis supports this hypothesis. The results in Table 7 show

that for firms with lower analyst coverage, industry-specific information disclosure

significantly increase the sensitivity of stock prices to investment decisions. In contrast,

for firms with higher analyst coverage, the incremental effect of these disclosures is

smaller. This suggests that industry-specific information disclosure have a more

substantial impact on firms with weaker information environments, as indicated by the

number of analysts following the firm.

TABLE 7: THE IMPACT OF ANALYST FOLLOWING

RD CAPEXNRD

Analyst coverage

VARIABLES High Low High Low

Q 0.0020*** 0.0024*** 0.0075*** 0.0095***

(5.93) (4.70) (5.78) (5.95)

POST -0.0051*** -0.0068** -0.0094 -0.0294**

(-3.13) (-2.20) (-1.40) (-2.57)

POST×Q 0.0032*** 0.0046** 0.0100*** 0.0169**

(3.77) (2.15) (3.10) (2.33)

CF 0.0265*** 0.0179*** 0.1013*** 0.0516**

(6.48) (2.86) (6.27) (2.39)

LNVAST 0.0929*** 0.1128*** 0.2722*** 0.2013**

(4.19) (3.28) (3.57) (2.03)

RETURN -0.0009** -0.0003 -0.0074*** -0.0044**

(-2.32) (-0.62) (-4.47) (-2.22)

Constant 0.0179*** 0.0135*** 0.0635*** 0.0506***

(16.55) (8.68) (17.17) (11.04)

Inter-group differences 0.001* 0.007**

Year FE YES YES YES YES

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm

Observations 5,717 3,695 5,717 3,695

Adjusted R-squared 0.835 0.785 0.562 0.467

Note: This table illustrates the impact of analyst coverage. The analyst coverage is categorized

based on the number of analysts following the firm. Samples with an index greater than the

median are categorized as the High group, while those with an index lower than the median are

355

classified into the Low group for each industry on a yearly basis. Columns (1) and (3) display

the regression results for samples with high analyst coverage, while columns (2) and (4)

represent those with low analyst coverage. “Inter-group differences” indicate the coefficient

and significance differences between columns (1) and (2), and (3) and (4). T-statistics in

parentheses use firm-clustered standard errors. Significance levels are denoted by ***, **, and

* for 1%, 5%, and 10%, respectively.

The Impact of Transitioning From Regional Regulation

To Industry-Specific Regulation

Throughout the implementation of IIDGs, both publicly listed corporations and the

regulatory framework of exchanges undergo transformations. Beginning in 2014, the

China Securities Regulatory Commission (CSRC) embarked on a regulatory

transformation, shifting the oversight of information disclosure from "jurisdictional

regulation" to "industry-based regulation." The former jurisdiction-based regulatory

framework, characterized by its uniform standards, proved excessively stringent for

corporations endowed with varied attributes under jurisdictional supervision. Conversely,

under industrial regulation, enterprises operating within identical or similar industries are

overseen by corresponding regulatory bodies. Within a cohesive industrial framework,

regulators disseminate disclosure guidelines and showcase exemplary enterprises within

the industry, thereby nurturing uniformity in information disclosure standards among

listed corporations within the industry.

To adhere to this regulatory transition, listed corporations must adapt their

disclosure protocols and operational evaluations from comparing against local

counterparts to aligning with peers within their respective industries, as dictated by the

exchange's industry-specific regulatory framework. Enterprises situated in economically

developed regions typically operate under more robust external oversight and stringent

information disclosure protocols. Consequently, following the enforcement of sectoral

disclosure guidelines, the incremental information resulting from regulatory

standardization provides relatively fewer benefits for companies in developed regions,

thereby offering limited support for managerial learning. Conversely, enterprises situated

in less developed regions experience significant information enhancements when

transitioning to benchmarking against industry peers, resulting in a noticeable increase in

the private information embedded in stock prices and discernible learning effects.

To test this hypothesis, we segmented the sample based on the location of the

company's headquarters in either Beijing, Shanghai, Shenzhen, or Guangzhou – the four

major economically developed cities – into economically developed and less developed

regions. The empirical results, presented in Table 8, validate our hypothesis. Industry-

specific information disclosure significantly enhances the price-investment sensitivity of

listed companies initially situated in less developed regions, exhibiting noteworthy

differences between the two sample cohorts. These findings underscore the regional

discrepancies in the influence of industry-specific information disclosure on managerial

learning.

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TABLE 8: TRANSITION FROM REGIONAL REGULATION TO INDUSTRY-

SPECIFIC REGULATION

R&D CAPEXNRD

VARIABLES Developed

area

Underdeveloped

area

Developed area Underdeveloped

area Q 0.0020*** 0.0017*** 0.0060*** 0.0066***

(3.98) (7.17) (3.35) (6.74)

POST -0.0020 -0.0047*** -0.0048 -0.0109** (-0.73) (-3.83) (-0.53) (-2.13)

POST×Q 0.0015 0.0028*** 0.0071 0.0088***

(0.81) (4.18) (1.37) (3.43) CF 0.0319*** 0.0166*** 0.0862*** 0.0715***

(4.79) (5.54) (3.78) (6.07)

LNVAST 0.1024*** 0.1024*** 0.3875*** 0.2263*** (3.78) (6.16) (3.94) (4.06)

RETURN -0.0008 -0.0007*** -0.0031 -0.0062***

(-1.54) (-2.73) (-1.36) (-5.57) Constant 0.0167*** 0.0149*** 0.0449*** 0.0560***

(10.94) (18.29) (9.93) (20.45)

Inter-group differences 0.001*** 0.002* Year FE YES YES YES YES

Firm FE YES YES YES YES

Cluster by Firm Firm Firm Firm Observations 2,217 10,167 2,217 10,167

Adjusted R-squared 0.861 0.787 0.604 0.480

Note: The table illustrates the divergent impact of industry-specific information disclosure

among companies located in economically developed regions compared to those in other areas.

Columns (1) and (3) present regression outcomes for firms headquartered in Beijing, Shanghai,

Guangzhou, and Shenzhen—collectively referred to as the "developed area" in China—while

Columns (2) and (4) depict the regression results for companies located elsewhere. The "Inter-

group differences" section delineates the disparities and significance levels between the two

groups. T-statistics in parentheses use firm-clustered standard errors. Significance levels are

denoted by ***, **, and * for 1%, 5%, and 10% levels, respectively

CONCLUSION

Set against the backdrop of the Chinese market, this study leverages the industry-specific

information disclosure reform as an exogenous shock to investigate the impact of

mandatory disclosure on managerial learning in developing countries. Our findings reveal

that the adoption of IIDGs has significantly enhanced managerial learning. To ensure the

reliability of our conclusions, this research employs robust testing methods and

mechanism analysis, which uncover that the IIDGs exert their influence by elevating the

private information content in stock prices. Furthermore, cross-sectional analysis is

conducted to explore the influence of macroeconomic uncertainty, competitive dynamics

and information environment on managerial learning. This study not only contributes to

enriching the research on mandatory disclosure's impact on managerial learning in

developing countries but also provides empirical support for the influencing factors of

managerial learning.

This study provides three key policy implications: First, policymakers should

further advance institutional reforms to harness the positive effects of mandatory

357

disclosure in emerging markets. Our findings indicate that, unlike trends observed in

developed economies, mandatory industry-specific information disclosure in developing

countries exhibits a “crowding-in” effect rather than a crowding-out effect on stock price

information. In other words, value-relevant mandatory disclosure benefits firms in

emerging markets. Therefore, these results provide valuable guidance for policymakers to

foster a more resilient and dynamic regulatory environment that facilitates managerial

learning and market development. Moreover, this evidence may serve as a reference for

other emerging markets in establishing disclosure standards and advancing their financial

systems.

Second, policymakers should pay attention to the spillover effects and

heterogeneous impacts of disclosure reforms. In response to increasing industry

diversification and improving disclosure relevance, stock exchanges have introduced

industry-specific guidelines. While originally intended to enhance regulatory efficiency

and investor transparency, our study finds that these reforms also generate positive

spillover effects by promoting managerial learning. Furthermore, the effectiveness of

these reforms varies significantly across different contexts, influenced by factors such as

macroeconomic uncertainty, industry competition intensity, the quality of firms’

information environments, and regional economic development levels. Consequently,

future policy design should carefully consider the dynamic evolution of regulatory

impacts and potential unintended consequences, while exploring heterogeneous

mechanisms across industries and regions to improve the adaptability and incentive

effects of disclosure systems.

Third, firms should be guided to reshape their perceptions of mandatory

disclosure. Prior research documents that Chinese managers generally hold negative

views toward mandatory disclosure (Armitage and Marston, 2008) and tend to

manipulate the timing and content of disclosures (Schrand and Walther, 2000; McVay,

2006; Bowen et al., 2005). However, our findings reveal that mandatory disclosure

significantly promotes managerial learning by incorporating external market feedback.

This suggests that managers should reconsider the role of disclosure—not merely

viewing it as an external regulatory burden, but recognizing that standardized disclosure

can also generate unexpected benefits for the firm.

358

APPENDIX

APPENDIX A. EXAMPLE OF MINGPAI JEWELRY (STOCK CODE 002574)

Changes in

Disclosure

Extract from the

2017 annual report

(before the reform)

Extract from the 2018 annual report (After the reform)

Section 3:

Overview of

Company Business

I. Main Business

Activities During the

Reporting Period 1. Primary Business,

Products, and

Business Model 2. Current Industry

Development

I. Main Business Activities During the Reporting Period (Original)

1. Macroeconomic Overview (New) and Industry Development

2. Company's Industry Position and Competitive Advantages (New) 3. Primary Business, Products, and Business Model

4. Key Business Models (New)

- Main Sales Model - Main Production Model

- Main Procurement Model

Section 4: Discussion

and Analysis

of Operating Conditions

I. Overview Main Activities

Undertaken

I. Main Activities Undertaken (Original) II. Key Operating Conditions (New)

1. Physical Store Operations

- Performance of Directly Operated Stores - Changes in Store Numbers

2. Online Sales Performance

3. Inventory Status During the Reporting Period

APPENDIX B. DETAILS OF THE GUIDELINES

No. of Guidelines Industry Industry-specific information

SSE No. 2 Guidelines Real Estate Changes in external factors; industry development status and

company’s industry position; main business model and core competitiveness; real estate reserves; development investment

situation; real estate sales situation; building area and rental

income of leased real estate; financial data related to industry characteristics, etc.

SSE No. 25

Guidelines

Gold jewelry and

accessories

Changes in external factors; industry development status; market

competition status, company's market position, and competitive advantages and disadvantages; relevant information on main sales

models; relevant information on production models; relevant

information on procurement models; risk factors; addresses of the top ten direct and exclusive stores by revenue; product categories

on online sales platforms; information related to self-owned

brand operation models; research and development situation; inventory status, etc.

Main Board and SME

Board in SZSE No.2 Guidelines

Solid mineral

resources

Changes in external factors; industry development and industry

position information; financial data disclosed by product or mine; mineral exploration activities and expenditures; specific

accounting policies related to the industry; mining rights and

mineral resource reserves; qualifications and entry conditions for mineral exploration and development, etc.

Main Board and SME

Board in SZSE No.14 Guidelines

Food and

beverage manufacturing

Industry status and company operations; production,

procurement, and sales models; brand operation status; number of stores at the end and beginning of the period; product variety and

platform names for online sales; specific details related to

procurement and production; specific details on production and inventory levels; specific information on sales expenses, etc.

ChiNext Board in Integrated Industry development status; mainstream technology levels and

359

SZSE No. 12

Guidelines

circuit-related

business activities

changes in market demand; analysis of industry competition and

company's overall strengths and weaknesses based on core technology and cost control; business model; product categories;

cost structure; manufacturing business status; packaging and

testing business status; research and innovation capabilities; development strategy and business plans, etc.

APPENDIX C. VARIABLE DEFINITIONS

Model (1) CAPEXAND The sum of R&D expenditure and capital expenditure divided by lagged total assets.

R&D R&D expenditure divided by lagged total assets.

CAPEX Capital expenditure divided by total assets in the previous period.

CAPEX1 The difference between cash paid for the acquisition and construction of fixed assets,

intangible assets, and other long-term assets, and the net cash received from the disposal

of these assets, divided by the total assets of the previous year. CHGASSET The change in total assets over the year, expressed as a percentage of total assets at the

beginning of the year.

Q The lagged value of Tobin's Q, calculated by dividing the sum of market value of equity and book value of assets minus book value of equity from the previous period by the

lagged total assets.

POST Takes a value of one for the year t-1 when firm i starts adhering to the disclosure guidelines, and zero otherwise.

CF Current cash flow normalized by current total assets.

LNVAST The inverse of lagged total assets in billions of RMB. RETURN Stock return over the three subsequent years.

Model (2) ΔAccuracy -100*(|MF2 – Actual|-|MF1 – Actual|)/Stock Price, where MF2 is the management

forecast on Date 2, MF1 is the previous forecast for the same earnings, and Stock Price is

at the start of the quarter on Date 1. The net profit forecast is in billions of RMB. POST1 Dummy variable that equals one if the earnings forecast is issued after the implementation

of disclosure guidance.

|Return| The absolute value of the buy-and-hold return from one day after the issuance of MF1 to one day before the issuance of MF2.

Gap Number of days between MF1 and MF2

Days Number of days between the MF date and the forecasted period ending Size Natural log of total assets

MB The ratio of total assets to market value

Model（3）

Debt1 The ratio of long-term liabilities to total assets Debt2 The change in long-term debt to total assets

Equity Equity financing, measured by the ratio of changes in capital reserves and total equity to

total assets. External The sum of equity and debt financing, expressed as the ratio of total short-term

borrowings, long-term borrowings, bonds payable, capital reserves, and equity to total

assets.

WACC WACC = Debtcost × leverage + Equitycost × (1 - leverage), where Equitycost is based on

the PEG model, and Debt cost is the ratio of financial expenses to total liabilities.

Lev Total liabilities divided by total assets Roa Net profit divided by total assets

Independence The proportion of independent directors on the board of directors.

Soe Equals 1 if the firm is state-owned and 0 otherwise; Top The percentage shareholding of the largest shareholder

360

Dual Equals 1 if the CEO and chairman are the same person, and 0 otherwise;

Board The number of board members Institution The sum of institutional investors’ shareholdings

Beta Beta value estimated from the firm’s stock returns relative to market returns

Growth Current year’s asset increase divided by the previous year’s total assets

ENDNOTES

*We are grateful to Professor Gary Tian for his valuable suggestions and guidance throughout the

manuscript writing and revision process. 1 Following Driss (2023) and Bird et al. (2021), we use POST as an indicator variable for the post-

implementation period, simplifying the equation to focus on the primary variables: POST,

POST×Q, and Q. To enhance robustness, in line with Bird et al. (2021), we include CF×POST as a

control variable. Additionally, following Ye et al. (2023), we extend the model by incorporating

interaction terms treat×post, treat×Q, post×Q, and treat×post×Q, where treat represents the

treatment group and post indicates the post-treatment period. In both cases, the results remain

significant at the 1% level. 2 The significance and coefficients of the main results remain unchanged when industry clustering

is applied.

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