Economic Policy Uncertainty and Bank Stability: An Analysis Based on the Intermediary Effects of Opacity

Abstract

With the background of deepening uncertainty about global and Chinese economic policy, the stability of the banking industry is of great strategic significance for promoting the high-quality development of the real economy and maintaining the order of the financial market. This paper uses the panel data of 32 commercial banks in China during the period of 2007–2020 to test the impact of economic policy uncertainty on bank stability and the mediating role of opacity. The research results show that the economic policy uncertainty has a negative impact on bank stability. Opacity plays a partial intermediary role between economic policy uncertainty and bank stability: economic policy uncertainty indirectly affects bank stability by stimulating banks to reduce market exposure and improve earnings opacity.

1. Introduction

Economic policy uncertainty (EPU) is considered to be one of the driving forces of business fluctuations, and its economic effects have gained considerable attention in the last few years. Especially after the financial crisis in 2008, in order to develop the economy and stabilize society, the government changed their fiscal, monitoring, and other regulatory policy reforms, which relieved the downward pressure of the market to a certain extent but also brought about fluctuations in economic policies. Due to the impact of COVID-19 and the complex international situation, the economic policy uncertainty of China has gradually intensified, which brought great challenges to the stability of the bank operating environment. Macroeconomic recession [1], reduction in corporate investment and financing [2,3], and the rising cost of capital caused by the economic policy uncertainty [4] have led to the slowdown in bank credit growth as well as a decline in loan quality. In addition, in a period of high economic policy uncertainty, the herding behavior in bank decision making may further threaten the stability of the banking system [5]. As the core component of the financial system, the stability of the banking sector plays a crucial role in the development of the real economy and the stability of the financial market. At present, researchers focus mainly on the direct impact of economic policy uncertainty on the performance and risk taking of banks and the moderating role of external factors such as competition and supervision [6,7,8], due to the lack of in-depth research on the mechanism of economic policy uncertainty. Moreover, compared with financial institutions in developed countries, the banking industry of China is less mature and tends to have different responses in the face of economic policy uncertainty. Therefore, it is of great significance and value to clarify the impact of economic policy uncertainty on the bank stability of China and its mechanism.

Bank transparency has always been the focus of supervision. The 2003 Basel Accord proposed that attention should be paid to bank information disclosure and emphasized the importance of improving bank information transparency to maintain the stability of the banking industry as well as the financial market. At present, the lack of unified quantitative observation and supervision indicators allows for the autonomy of banks in information disclosure [9]. The existing literature suggests that banks may reduce transparency to maintain competitive advantages [10], suppress negative information [11], and manipulate financial statements to avoid regulatory requirements [12] during times of high economic policy uncertainty. However, high opacity is not conducive to bank stability and sustainable development. On the one hand, high opacity hinders outsiders’ perception and judgment of bank risks, such that banks are forced to choose high-risk businesses to meet the necessary rate of return required by investors [13]. On the other hand, the increased bank opacity will undermine market discipline, which is not conducive to government supervision [14], thus affecting bank stability. Banks are integral parts of economic growth, and correct decisions positively contribute to bank stability and sustainable development. When the uncertainty of external macroeconomic policy is high, a good disclosure decision helps banks to identify growing risks and take action that stakeholders are demanding. Therefore, how to effectively prevent the information disclosure errors caused by economic policy uncertainty is a key consideration for banks to maintain stability. However, researchers have barely considered the interrelation among economic policy uncertainty, opacity, and bank stability systematically, which is a topic that remains underexplored.

To examine how economic policy uncertainty affects bank stability, we try to use Stimulus–Organism–Response (SOR) theory to explain the transmission path of opacity between economic policy uncertainty and bank stability. SOR theory holds that external stimuli affect the final state and response of an organism by changing its emotional and cognitive processes [15]. In a complex and dynamic environment, an enterprise is similar to an organism [16], and external stimuli can indirectly affect the value and state of the enterprise by affecting the cognitive decision-making process of the enterprise [17]. In the face of fluctuations in economic policy uncertainty, banks lack confidence in the market and competitiveness decline, leading to changes in their attitude towards information disclosure [11,12]. For the reason of avoiding risks and covering up negative information, banks may adjust the degree of information disclosure and interpretation to increase opacity, which will indirectly affect bank stability.

This paper identified the impact of economic policy uncertainty on bank stability and the intermediary role of opacity from the perspective of information disclosure. The contributions of this paper mainly include the following three aspects. First of all, this paper systematically studies the interaction among economic policy uncertainty, opacity, and bank stability from the perspective of information disclosure and expands the literature on the mechanism of economic policy uncertainty and bank stability. Secondly, we apply SOR theory to the study of bank risk management, which enriches the application of SOR theory in organizational management and provides new ideas and insights for the management of the bank decision-making process. Thirdly, this paper discusses the impact of economic policy uncertainty on bank stability and its heterogeneous role in the Chinese context, which is useful for the government and regulators to guide and supervise various types of banks and also helps different types of banks to cope more effectively with the risks brought by policy changes.

The rest of the paper proceeds as follows. Section 2 presents the literature review and research hypotheses. Section 3 discusses the variable and methodology used in the empirical analysis. Section 4 highlights the detailed results analysis and interpretation. Section 5 covers discussion and policy implications. Finally, the last section gives the conclusions.

2. Literature Review and Research Hypotheses

2.1. Economic Policy Uncertainty and Bank Stability

Economic policy uncertainty refers to the uncertainty faced by microeconomic agents because it is difficult to accurately predict the direction, implementation timing, implementation intensity, and effect of economic policies in the future [18], mainly including monetary policy uncertainty, fiscal policy uncertainty, and regulatory policy uncertainty [19]. Various researchers have explored the impacts of economic policy uncertainty on macroeconomic development and the behavior of microeconomic agents. At the macro level, higher economic policy uncertainty is considered to be a strong predictor of economic downturns. The rise of economic policy uncertainty is not conducive to the development of the real economy, leading to the decline in the GDP, investment, consumption, and import and export [1,18]. The rise of economic policy uncertainty also has an important impact on the lending rate in the financial market [4] At the micro level, economic policy uncertainty brings about changes in corporate activities and decision making, leading to uncertainty at the firm level. Some studies have shown that the rise of economic policy uncertainty hinders the improvement of corporate total factor productivity and increases corporate financing costs. As a result, corporate may make the decisions to reduce the debt financing scale [3] and dividend distribution [20] and to delay investment behavior in the short term [2].

Compared with non-financial enterprises, banks are important intermediaries of the financial system and are more sensitive to the fluctuations of macroeconomic policies [6]. Over the past few years, studies have focused mainly on the impact of economic policy uncertainty on the activities and decisions of banks in different ways. Several researchers have discussed the relationship between EPU and bank credit behaviors, such as loan pricing decisions, loan growth, and the proportion of credit portfolio. Ashraf [4] pointed out that high uncertainty raised bank loan interest rates. During periods of higher uncertainty, the volatility and uncertainty of future cash flow and market return increases; thus, banks have to increase the cost of borrowing to earn a matching premium. Zhang and Xing [21] found that economic policy uncertainty led to a slowdown in the growth of bank credit. Using bank-level data from 40 developed and developing countries, Mohapatra and Purohit [22] reported that higher economic policy uncertainty encourages banks to increase the proportion of household loans and reduce the proportion of corporate loans in order to cope with greater risks during economic turbulence. In addition, some studies have also investigated the effects of economic policy uncertainty on bank management decisions. Zhou and Liu [23] used the quarterly panel data of 30 listed banks in China to find that economic policy uncertainty promoted the development of the income diversification strategy of commercial banks, and industry competition would intensify the positive impact of economic policy uncertainty on the level of bank income structure diversification. Danisman et al. [19] studied the influence of economic policy uncertainty on the earning management of banks with data from 6384 US banks. The empirical results show that banks tend to increase discretionary loan-loss provisions for capital management and income smoothing during periods of high economic policy uncertainty.

Furthermore, some studies have discussed the effect of economic policy uncertainty on the performance and risk taking of banks. It has been highlighted that economic policy uncertainty is not conducive to the stability of bank performance [5] and leads to the decline in asset quality [19] as well as the increase in credit risk and liquidity risk [4,7]. Bank stability means that commercial banks have the ability to resist risks for the sake of sustainable business and maintain a relatively stable and reasonable liquidity and profitability [6,7,8]. Recent studies have explored the relationship between economic policy uncertainty and bank stability, as well as the moderating effect of external factors such as market competition [7], institutional quality [8], and regulatory intensity [6], but there are still shortcomings. On the one hand, the existing research focuses mainly on the direct impact of economic policy uncertainty on decision-making behavior and bank stability, lacking an in-depth exploration of the mechanism of economic policy uncertainty. On the other hand, most of the existing literature uses the bank data from developed countries, and few studies pay attention to the impact of economic policy uncertainty on bank stability in the Chinese context.

As above, the uncertainty of economic policy has a significant impact on the macro operating environment, credit supply and demand, as well as the operational decisions of banks, and brings a great threat to the stability of the banking system. SOR theory highlights that under the action of external stimuli, a series of cognitive identity activities will occur inside the organism, and the final response presented externally is the change in the state of the organism [17]. The stimulus of economic policy uncertainty is not conducive to bank stability. The deepening of uncertainty inhibits enterprises’ operation decisions as well as investment and financing activities, which leads to the reduction in demand for loans and the solvency of enterprises. Stimulated by these external factors, banks lack confidence in the market and choose to increase opacity to reduce the disclosure of negative information and market risk exposure. On the one hand, the increased opacity from banks leads to information asymmetry among market participants. At this time, banks have to choose high-yield projects to satisfy investors’ requirements and expose themselves to high risks. On the other hand, increasing the opacity of banks is not conducive to the development of market and government supervision, undermines market discipline, and aggravates market panic, which have an impact on the stability of the banking system. Therefore, this paper proposes the following hypothesis.

Hypothesis 1.

Economic policy uncertainty has a negative effect on bank stability.

2.2. Role of Bank Opacity

The stimulus–organism–response (SOR) theory was first proposed by Mehrabian and Russell [15] as a theoretical framework for conceptualizing consumer or corporate behavior in various contexts. It is generally believed that SOR theory can better explain the influence of factors related to a specific organizational environment on organism behavior [16,24]. The SOR theoretical framework defines the enterprise as an organism in a dynamically changing environment, emphasizing that the generation of behavior is the reaction caused by certain emotional or cognitive changes caused by the stimulation of external factors [17]. The stimulus (S) in the model refers to an external factor that can affect the mental and cognitive state of the organism. After a series of cognitive identification activities inside the organism (O), the final result of the stimulus (R) manifests as the feedback and state presented by the organism externally. Previous studies have applied SOR theory to the organizational and enterprise levels, and believed that external stimuli will affect the cognitive decision-making process of enterprises [17]. According to SOR theory, Yang and Xie [25] propose that the intellectual property management mode (R) is determined by the intellectual property management objectives and management process (O) based on factors such as the position of the industrial chain they are in and the internal resource allocation power (S). Liang et al. [26] used SOR theory to explain the mechanism of organizational learning on enterprise innovation performance, believing that learning is a cognitive identification activity in which organisms actively acquire and process stimuli, and innovation performance is the result of the response to the stimuli. Klein et al. [17] explored the relationship among sustainability commitment (S), strategic orientation (O), and enterprise business model innovation (R) from the perspective of SOR and found that the stimulation of sustainability commitment indirectly affected enterprise business model innovation by changing the competitive mentality and strategic orientation of enterprises.

According to SOR theory, external factor stimulation acts on enterprise value and state by affecting the cognitive decision-making process of enterprises [16,17,24]. Opacity refers to the degree of difficulty for outsiders to perceive and assess the activities and resources of the bank [12], which includes mainly four components: poor information of internal and external personnel, market exposure, asset opacity, and income opacity [9,13]. Opacity can reflect the attitude of banks towards information disclosure [10,11,12,17,27]. The unpredictability and opacity of macroeconomic policies will affect the information disclosure attitude of banks. On the one hand, high uncertainty usually comes with economic turmoil, leading to a low mood among market participants and a wait-and-see attitude among investors. At this time, as the connection between the government and the real economy, banks have the responsibility to adopt conservative information disclosure policies vigilantly in order to reduce market risk exposure level, calm investors’ emotions, and stabilize the financial market. On the other hand, high economic policy uncertainty carries the negative aspect that bank performance decreases and the risk increases. In order to reduce the impact of the disclosure of negative information on their personal performance, bank managers may make the decision to increase opacity. High opacity can cover up the fact of declined bank performance, avoid supervision by regulators and investors, and maintain bank competitive advantages. However, high opacity increases information asymmetry and affects bank stability adversely. The reduction in information disclosure hinders external investors from perceiving and evaluating the activities of banks [10,11,12], a factor which pushes banks to choose projects with high risk to satisfy investor requirements. At the same time, the reduction in information disclosure is not conducive for the government to supervise and regulate market risks, resulting in the decline in the stability of the whole banking system. In summary, economic policy uncertainty (S) has an indirect impact on bank stability (R) by stimulating banks to adjust their opacity (O). This paper proposes the following hypothesis.

Hypothesis 2.

Opacity plays an intermediary role between economic policy uncertainty and bank stability.

External personnel can predict the future value of banks by integrating public information. The gap of internal and external information reflects the quality and quantity of bank information disclosure [9,27]. The more accurately outsiders can predict the future value of a bank using known information, the less opaque the bank will be [28]. In a period of high economic policy uncertainty, the market economy is sluggish, and enterprises reduce investment and financing, which leads to the slowdown of bank credit growth and the decline in asset quality. At this time, if outsiders can accurately predict the decline in bank performance, market investors will be alert, resulting in sharp fluctuations in the bank stock prices. Therefore, banks have a decreased willingness to disclose information, and they increase opacity, which results in the difficulty of supervision and exposes banks to higher systemic financial risks, which is not conducive to the stability of the banking system. This paper proposes the following hypothesis.

Hypothesis 3.

The information gap between internal and external personnel plays an intermediary role between economic policy uncertainty and bank stability.

The proportion of market financing (such as bonds, subordinated debt, and uninsured time deposits) on the liability side of balance sheets reflects the exposure of commercial banks to the market [29]. Increasing the proportion of market financing will attract more market participants to evaluate the fair value of banks and reduce information asymmetry [9,13]. In times of high economic policy uncertainty, banks want fewer market investors to directly participate in evaluation and choose to increase opacity to hide the fact that profitability has weakened and asset quality has declined. However, the lack of supervision and the intensification of information asymmetry adversely affect bank stability. This paper proposes the following hypothesis.

Hypothesis 4.

Market exposure plays an intermediary role between economic policy uncertainty and bank stability.

Bank asset opacity refers to the difficulty with which outsiders obtain important information such as bank asset risks from the financial statements, which will directly affect bank asset portfolio selection and risk level [9,30]. On the one hand, the lower the asset opacity is, the easier it is for depositors to determine the required interest rate on deposit based on the risk degree of bank assets [30]. With the deepening of economic policy uncertainty, the risk premium required by depositors increases, and banks expend more to absorb funds. At this time, banks are less willing to disclose asset information. By increasing asset opacity, banks can reduce the constraints on the choice of asset portfolios and the risk level of banks for depositors, which will affect bank stability. On the other hand, low asset opacity helps the government to carry out supervision and restrain the regulatory arbitrage behavior of banks, thus reducing the possibility of banks bearing greater systemic financial risks [13]. However, in a period of high economic policy uncertainty, banks tend to be conservative in information disclosure and improve asset opacity in order to reduce the disclosure of information such as asset quality decline and risk increase. It is difficult for regulators to effectively supervise the real assets of banks, which indirectly threatens bank stability. This paper proposes the following hypothesis.

Hypothesis 5.

Asset opacity plays an intermediary role between economic policy uncertainty and bank stability.

Earnings opacity measures the bank opacity from the perspective of earnings, which hinders investors from judging the real financial status, operating results, and market value of banks [11]. Bank earnings are linked to the personal performance of the management. Especially in times of high uncertainty, bank managers have more motivation to manipulate and whitewash financial statements with the discretionary power of loan-loss provisions in order to maximize their personal performance, which will result in an increase in earnings opacity [12]. In these conditions, accounting numbers no longer reflect the economic reality of the underlying risk profile, making it difficult for outsiders to accurately assess the fundamental value of banks [9]. Banks cannot reasonably control risks, and government supervision is blocked, which lead to the decline in the level of bank stability. This paper proposes the following hypothesis.

Hypothesis 6.

Earnings opacity plays an intermediary role between economic policy uncertainty and bank stability.

3. Data and Methodology

3.1. Measurements of Variables

3.1.1. Dependent Variable

The dependent variable in this paper is bank stability (Z). The Z-score has been widely used in relevant studies to measure the stability of commercial banks [6,7,8], and the calculation formula is shown in Equation (1). In order to obtain a smoother value, we follow previous studies and transform the Z-score into a natural logarithm to smooth out higher values [6,7].

$$Zscore=\frac{{\mu }_{ROA}+\frac{1}{K}}{{\sigma }_{ROA}}$$

(1)

where ${\mu }_{ROA}$ is the mean of return on total assets, K is the equity multiplier, and ${\sigma }_{ROA}$ is the standard deviation of return on total assets during the sample period. Z-score comprehensively measures bank stability from the two aspects of profitability and capital [7], which has been widely used in the study of bank risk management. The Z-score is negatively correlated with bank bankruptcy probability. The larger the Z-score is, the better the bank stability is [6].

3.1.2. Independent Variable

The explanatory variable of this paper is economic policy uncertainty (EPU), and the economic policy uncertainty index of China that was constructed by Baker et al. is used as the evaluation parameter [18]. To match with other yearly variables, following Shabir et al. [7], Jin et al. [11], and Desalegn and Zhu [12], we transformed the monthly EPU index into an annual index by using the arithmetic mean method and then divided the annual data by 100 to obtain the percentage value.

3.1.3. Intermediary Variables

The intermediary variable of this paper is bank opacity (Opacity). Referring to the method of Lepetit et al. [9], this paper constructs bank opacity from the four perspectives of internal and external personnel information gap, market exposure, asset opacity, and earning opacity.

The forecast error (FE) reflects the information asymmetry between internal and external personnel of the bank. Analysts have the ability to mine information and predict the future profitability of the target company from the outside [28]. Opacity is positively associated with analyst forecast errors, and the accuracy of analysts’ earnings forecasts for opaque companies will decline [27]. This paper adopts the method of Fosu et al. [27] to measure the forecast error by the ratio of the absolute value of the difference between the average analyst forecast and the actual earnings per share to the ending stock price. The calculation formula is shown in Equation (2).

$$Forecast erro{r}_{it}=\left|\frac{FEP{S}_{it}-AEP{S}_{it}}{Pric{e}_{it}}\right|$$

(2)

where $FEP{S}_{it}$ is the average of all earning forecasts of bank i in year t; $AEP{S}_{it}$ is the actual earning per share of bank i in year t; $Pric{e}_{it}$ means the stock price of bank i at the end of year t.

To capture the market exposure of banks, we follow the existing studies and use the ratio of short term and long term market funding to total assets (MFAR) to assess bank opacity [9,13]. In general, high opacity is associated with a higher degree of market exposure. When banks expand their exposure to the market, there will be more market participants joining in assessing the fair value of banks, which may reduce information asymmetry between market and banks [13,29].

To measure the degree of bank asset opacity, we use the ratio of loans to total assets (LAR) to estimate bank opacity from the perspective of bank asset structure according to Liu and Song [13], Petrella and Resti [14], and Chen et al. [30]. Compared with non-financial firms, banks have a higher proportion of opaque assets [14]. In order to maintain competitive advantage, banks have the right to select lending objects on their own as well as keep sensitive loan information confidential, making it difficult for external investors to evaluate the asset quality of banks [30]. Therefore, bank opacity is deeper with the ratio of loans to total assets increases.

Following the work of Jin et al. [11] and Desalegn and Zhu [12], we have utilized discretionary loan-loss provision (DLLP) as a measure of bank earnings opacity. Studies have shown that banks can manipulate and whitewash financial statements through earnings management to avoid regulatory requirements [19]. As an important accrual item in statements, loan-loss provision (LLP) includes two parts: non-discretionary loan-loss provision and discretionary loan-loss provision. DLLP represents the discretion of the bank managers to estimate loan losses, reflecting the degree of information asymmetry [11,12]. High DLLP will reduce the ability of outsiders to accurately evaluate corporate performance and valuation [27]. Many researchers construct an indicator of financial statement quality by predicting LLP and calculate the difference between the actual and expected value of LLP as DLLP. This paper uses the methods of Jin et al. [11] and Desalegn and Zhu [12] to estimate LLP.

$$\begin{array}{l}LL{P}_{it}={\alpha }_0+{\beta }_{1}CHNP{L}_{it+1}+{\beta }_{2}CHNP{L}_{it}+{\beta }_{3}CHNP{L}_{it-1}+{\beta }_{4}CHNP{L}_{it-2}+\\ {\beta }_{5}SIZ{E}_{it-1}+{\beta }_{6}CHLOA{N}_{it}+{\beta }_{7}GDP{G}_{it}+{\beta }_{8}CHU{R}_{it}+{\mu }_t\end{array}$$

(3)

where LLP is loan loss allowance divided by total assets at the beginning of the period; CHNPL is the change in nonperforming loans divided by total assets at the beginning of the period; SIZE is the natural logarithm of total assets; CHLOAN is measured by dividing the change in total loans by total assets at the beginning of the period; GDPG is the GDP growth rate; and CHUR represents the rate of change in the unemployment rate. The residual in Equation (3) is the discretionary loan-loss provision, which captures the degree of distortion in bank income. Negative residuals indicate earnings management with increasing returns, whereas positive residuals indicate earnings management with decreasing returns.

3.1.4. Control Variables

Referring to previous studies on bank stability, this paper uses indicators including bank size, loan quality, capital structure, income diversification, and profitability as bank-level control variables [7,13,21,23]. Asset size (SIZE) is an important indicator to measure bank comprehensive strength. Usually, banks with large assets have better stability and a stronger ability to resist the uncertainty of economic policy [13]. This paper uses the natural logarithm of assets to measure bank size. Loan quality (NPLR) reflects bank credit risk; credit business is the most important source of income for banks; and the fluctuation of loan quality will affect bank stability [21]. We use the nonperforming loan ratio to measure the loan quality of banks. Capital structure (CCAR) reflects bank liquidity risk, and we use the core capital adequacy ratio to measure bank capital structure [7]. Income diversification (NIIR) can reduce the ability of banks to withstand risks to some extent [23]. We use the proportion of non-interest income out of total income to measure the degree of bank income diversification. Profitability (ROA) reflects the ability of banks to resist operational risks, and banks with stronger profitability usually have better stability [7]. This paper uses return on assets to measure bank profitability.

At the macro level, this paper uses consumer price index (CPI), GDP growth rate (GDPg), and ${\mathrm{M}}_2$ growth rate (${\mathrm{M}}_2\mathrm{g}$) to control the impact of macroeconomic cyclical fluctuations on bank stability referring to relevant research [6,7,8]. The description and source of the variables mainly involved in this paper are shown in

Table 1. Variable description and sources.

Variable (Abbreviation)	Description	Source
Dependent Variable:
Banking stability (Z)	Natural logarithm of Z-score + 1.	CSMAR database
Independent Variable:
Economic policy uncertainty (EPU)	(Monthly index of economic policy uncertainty × 12)/100.	http://www.policyuncertainty.com/china_epu.html (accessed on 27 October 2022)
Intermediary Variables:
Opacity (Opacity)	Mean values of FE, MFAR, LAR, and DLLP normalized by 0–1 transformation.	Wind database
Internal and external personnel information gap (FE)	The absolute value of the difference between the average analyst forecast and the actual earnings per share to the ending stock price.
Market exposure (MFAR)	Short-term and long-term market funding to total assets.
Asset opacity (LAR)	Loans to total assets.
Earning opacity (DLLP)	Discretionary loan-loss provision.
Control Variables:
Size (SIZE)	Natural logarithm of bank assets.	CSMAR database
Loan quality (NPLR)	The nonperforming loans to total loans.
Income diversification (NIIR)	Non-interest income to total income.
Capital structure (CCAR)	Core capital adequacy ratio.
Profitability (ROA)	Return on asset.
Inflation (CPI)	Inflation based on the consumer price index.
Macroeconomic environment (GDPg)	GDP growth rate.
Monetary growth (${\mathrm{M}}_2\mathrm{g})$	${\mathrm{M}}_2$ growth rate.

.

3.2. Empirical Framework

In order to explore the impact of economic policy uncertainty on bank stability, Hypothesis 1 is tested, and a benchmark model is constructed:

$$\begin{array}{c}{\mathrm{Z}}_{it}={\alpha }_0+{\alpha }_{1}EP{U}_{it}+{\alpha }_{2}SIZ{E}_{it}+{\alpha }_{3}CCA{R}_{it}+{\alpha }_{4}NII{R}_{it}+{\alpha }_{5}NPL{R}_{it}+{\alpha }_{6}RO{A}_{it}+\\ {\alpha }_{7}CP{I}_{it}+{\alpha }_{8}GDP{g}_{it}+{\alpha }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(4)

where the explained variable $Z_{it}$ represents the stability level of the ith bank in period t; $EP{U}_{it}$ represents the economic policy uncertainty index faced by the ith bank in period t; and its coefficient ${\alpha }_1$ measures the degree of influence of economic policy uncertainty on bank stability. If it is significantly less than 0, it means that the higher the economic policy uncertainty index is, the more unstable the bank will be. Therefore, Hypothesis 1 is valid, and vice versa is not. $SIZ{E}_{it}$, $CCA{R}_{it}$, $NII{R}_{it}$, $NPL{R}_{it}$, $RO{A}_{it}$ represent the control variables at the bank level; $CP{I}_{it}$, $GDP{g}_{it}$, $M_2{g}_{it}$ represent the control variables at the macro level. Because the economic policy uncertainty index is a time series variable, if the time fixed effect is controlled in the regression, it will cause the problem of multicollinearity, resulting in the offset between the effect of economic policy uncertainty on bank stability and the time dummy variable, and the coefficient of the economic policy uncertainty index will not be estimated. Therefore, this paper refers to the practice of Zhang and Xing [21] and replaces the time effect by controlling the variables at the macro level.

Based on the previous literature review and research hypotheses, in order to explore the mediating effect of opacity between economic policy uncertainty and bank stability, this paper adopts the stepwise method of testing the mediating effect proposed by Baron and Kenny [31] and refers to the mediating effect analysis process of Wen and Ye [32]. This paper constructs a test model with bank opacity (Opacity) as the intermediary variable.

$$\begin{array}{ll}Opacit{y}_{it}=& {\beta }_0+{\beta }_{1}EP{U}_{it}+{\beta }_{2}SIZ{E}_{it}+{\beta }_{3}CCA{R}_{it}+{\beta }_{4}NII{R}_{it}+{\beta }_{5}NPL{R}_{it}+{\beta }_{6}RO{A}_{it}+\\ & {\beta }_{7}CP{I}_{it}+{\beta }_{8}GDP{g}_{it}+{\beta }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(5)

$$\begin{array}{ll}{\mathrm{Z}}_{it}=& {\gamma }_0+{\gamma }_{1}EP{U}_{it}+{\gamma }_{2}Opacit{y}_{it}+{\gamma }_{3}SIZ{E}_{it}+{\gamma }_{4}CCA{R}_{it}+{\gamma }_{5}NII{R}_{it}+{\gamma }_{6}NPL{R}_{it}+\\ & {\gamma }_{7}RO{A}_{it}+{\gamma }_{8}CP{I}_{it}+{\gamma }_{9}GDP{g}_{it}+{\gamma }_{10}{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(6)

where the mediated variable $Opacit{y}_{it}$ represents the opacity level of the ith bank in period t. If the estimated results ${\beta }_1$ and ${\gamma }_2$ are significant, the mediating effect exists. Then if ${\gamma }_1$ is not significant, then there is a complete mediating effect. Otherwise, there is a partial mediating effect, and the proportion of the indirect effect to the total effect is ${\beta }_1{\gamma }_2$/${\gamma }_1$.

In order to further explore the mediating role of various components of opacity between economic policy uncertainty and bank stability, this paper establishes a test model with internal and external personnel information difference (FE), market exposure (MFAR), asset opacity (LAR), and income opacity (DLLP) as mediating variables:

$$\begin{array}{ll}F{E}_{it}=& {\beta }_0+{\beta }_{1}EP{U}_{it}+{\beta }_{2}SIZ{E}_{it}+{\beta }_{3}CCA{R}_{it}+{\beta }_{4}NII{R}_{it}+{\beta }_{5}NPL{R}_{it}+{\beta }_{6}RO{A}_{it}+\\ & {\beta }_{7}CP{I}_{it}+{\beta }_{8}GDP{g}_{it}+{\beta }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(7)

$$\begin{array}{ll}MFA{R}_{it}=& {\beta }_0+{\beta }_{1}EP{U}_{it}+{\beta }_{2}SIZ{E}_{it}+{\beta }_{3}CCA{R}_{it}+{\beta }_{4}NII{R}_{it}+{\beta }_{5}NPL{R}_{it}+{\beta }_{6}RO{A}_{it}+\\ & {\beta }_{7}CP{I}_{it}+{\beta }_{8}GDP{g}_{it}+{\beta }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(8)

$$\begin{array}{ll}LA{R}_{it}=& {\beta }_0+{\beta }_{1}EP{U}_{it}+{\beta }_{2}SIZ{E}_{it}+{\beta }_{3}CCA{R}_{it}+{\beta }_{4}NII{R}_{it}+{\beta }_{5}NPL{R}_{it}+{\beta }_{6}RO{A}_{it}+\\ & {\beta }_{7}CP{I}_{it}+{\beta }_{8}GDP{g}_{it}+{\beta }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(9)

$$\begin{array}{ll}DLL{P}_{it}=& {\beta }_0+{\beta }_{1}EP{U}_{it}+{\beta }_{2}SIZ{E}_{it}+{\beta }_{3}CCA{R}_{it}+{\beta }_{4}NII{R}_{it}+{\beta }_{5}NPL{R}_{it}+{\beta }_{6}RO{A}_{it}+\\ & CP{I}_{it}+{\beta }_{8}GDP{g}_{it}+{\beta }_9{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(10)

$$\begin{array}{ll}{\mathrm{Z}}_{it}={\gamma }_0+& {\gamma }_{1}EP{U}_{it}+{\gamma }_{2}F{E}_{it}+{\gamma }_{3}MFA{R}_{it}+{\gamma }_{4}LA{R}_{it}+{\gamma }_{5}DLL{P}_{it}+{\gamma }_{6}SIZ{E}_{it}+{\gamma }_{7}CCA{R}_{it}+\\ & {\gamma }_{8}NII{R}_{it}+{\gamma }_{9}NPL{R}_{it}+{\gamma }_{10}RO{A}_{it}+{\gamma }_{11}CP{I}_{it}+{\gamma }_{12}GDP{g}_{it}+{\gamma }_{13}{M}_2{g}_{it}+{\epsilon }_{it}\end{array}$$

(11)

3.3. Case Study

The dataset of this study covers 32 listed commercial banks of China from 2007 to 2020, and the final sample contains 248 firm-year observations after dropping the inconsistent observations. Furthermore, we classified the sample banks as state-owned commercial banks, joint-stock commercial banks, and city commercial banks for the heterogeneity test according to the industry classification of China Securities Regulatory Commission. The Economic Policy uncertainty index is from the website of Economic Policy uncertainty (http://www.policyuncertainty.com/china_epu.html, accessed on 27 October 2022), and other data are from the China Stock Market & Accounting Research (CSMAR) Database (https://cn.gtadata.com/, accessed on 27 October 2022) and Wind database (https://www.wind.com.cn/, accessed on 27 October 2022).

4. Empirical Analysis

4.1. Descriptive Statistics

The results of descriptive statistics are shown in

Table 2. Descriptive statistics.

Variables	Mean	SD	Min	Max	Obs
Z	3.5931	0.1981	2.5518	4.2041	248
Opacity	0.4058	0.0600	0.2367	0.6940	248
FE	−0.0013	0.0155	−0.0919	0.1209	248
MFAR	0.6650	0.0918	0.4428	0.9123	248
LAR	0.4914	0.0764	0.2704	0.6312	248
DLLP	0.2206	0.3519	0.0031	3.3677	248
EPU	1.3959	0.1692	0.9160	1.6574	248
SIZE	28.3444	1.5195	25.0548	31.0359	248
NPLR	1.3119	0.5238	0.3800	5.6200	248
NIIR	0.2146	0.0899	−0.0159	0.4457	248
CCAR	0.0681	0.0140	0.0220	0.1307	248
ROA	0.0093	0.0021	0.0013	0.0155	248
CPI	102.6711	1.4888	99.3000	105.9000	248
M2g	0.1328	0.0506	0.0808	0.2842	248
GDPg	7.4346	2.7784	2.2000	14.2309	248

. The mean value of the stability level (Z) of the sample banks is 3.5931, the minimum value is 2.5518, the maximum value is 4.2041, and the standard deviation is 0.1981, indicating that the stability level of different commercial banks varies greatly. The mean value of bank Opacity is 0.4058, the minimum value is 0.2367, the maximum value is 0.6940, and the standard deviation is 0.0600, which means that there are differences in the degree of transparency among different types of sample banks, and the management has independent decision-making power on the degree of bank information disclosure. The mean value of the economic policy uncertainty index (EPU) is 1.3959, the standard deviation is 0.1692, the minimum value is 0.9160, and the maximum value is 1.6574, which shows that economic policy uncertainty of China has certain volatility.

4.2. Economic Policy Uncertainty and Bank Stability

In order to fully consider the appropriateness of the regression method, this paper first establishes a fixed effect model, rejects the use of mixed regression through the F test (p value = 0.00), and then establishes a random effect model, which accepts the null hypothesis through the Hausman test. Therefore, random effect is selected to estimate regression Equation (1), and the regression results are shown in

Table 3. Economic policy uncertainty and bank stability: empirical results and robustness test.

Variables	(1)		(2)		(3)
	FE	RE	FE	RE	T-1	T-2
EPU	−0.1078 *** (−5.59)	−0.1048 *** (−5.08)	−0.2486 *** (−5.66)	−0.2409 *** (−5.12)	−0.0677 *** (−2.77)	−0.0692 *** (−2.80)
SIZE	0.0258 *** (6.36)	0.0023 (1.08)	0.0258 *** (6.39)	0.0023 (1.09)	0.0021 (1.02)	0.0021 (0.99)
NPLR	−0.0098 * (−1.87)	−0.0165 *** (−3.07)	−0.0010 * (−1.91)	−0.0166 *** (−3.10)	−0.0159 *** (−3.00)	−0.0155 *** (−2.88)
NIIR	0.0250 (0.65)	0.0882 *** (2.61)	0.0257 (0.66)	0.0890 ** (2.64)	0.0840 ** (2.52)	0.0838 ** (2.51)
CCAR	12.6573 *** (54.69)	13.2158 *** (67.40)	12.6582 *** (54.81)	13.2178 *** (67.48)	13.1525 *** (67.53)	13.1487 *** (67.33)
ROA	20.6740 *** (11.34)	15.2892 *** (11.06)	20.6819 *** (11.37)	15.2798 *** (11.07)	15.3560 *** (11.26)	15.4143 *** (11.24)
CPI	0.0103 *** (4.68)	0.0093 *** (3.95)	0.0099 *** (4.61)	0.0088 *** (3.85)	0.0031 (0.98)	0.0033 (1.03)
M2g	0.3612 *** (5.13)	0.3094 *** (4.04)	0.3582 *** (5.13)	0.3052 *** (4.02)	0.2820 *** (3.70)	0.3087 *** (3.19)
GDPg	−0.0121 *** (−6.84)	−0.0117 *** (−6.68)	−0.1217 *** (−6.90)	−0.0117 *** (−6.71)	−0.0110 *** (−6.27)	−0.0112 *** (−6.02)
${\mathrm{EPU}}_{\mathrm{t}-1}$					−0.0512 *** (−2.76)	−0.0555 *** (−2.66)
${\mathrm{EPU}}_{\mathrm{t}-2}$						0.0068 (0.45)
_cons	0.9529 *** (3.99)	1.7272 *** (7.67)	1.0614 *** (4.61)	1.8367 *** (8.50)	2.3817 *** (7.33)	2.3589 *** (7.16)
Obs	248	248	248	248	248	248
R-squared	0.7225	0.7636	0.7224	0.7637	0.7647	0.7648

Note: *, ** and *** indicate the significance of coefficients at 10%, 5%, and 1%, respectively. The t statistic is reported in parentheses.

. Column (1) of Table 3 shows the regression results of the impact of economic policy uncertainty (EPU) on bank stability (Z). The coefficient of the economic policy uncertainty is significantly negative at the 1% level, which means bank stability (Z) decreases by 0.1048 units for every 1% increase in the economic policy uncertainty if other conditions are constant. Therefore, the increase in economic policy uncertainty reduces bank stability, which verifies Hypothesis 1.

Among the control variables, bank-level indicators such as income diversification (NIIR), core capital adequacy ratio (CCAR), and return on assets (ROA) have a significant role in promoting bank stability. However, the nonperforming loan ratio (NPLR) has a significantly negative impact on bank stability, indicating that reducing credit risk is an important channel for commercial banks to improve their stability.

4.3. Robustness Tests

4.3.1. Alternative EPU Measure

In order to reduce the measurement error of economic policy uncertainty, this paper uses the economic policy uncertainty index of China constructed by Davis et al. [33] for the robustness test, and the results are shown in Column (2) of Table 3. The Hausman test accepts the null hypothesis and uses the random effects model. The coefficient of the economic policy uncertainty is significantly negative, showing that the results remain robust.

4.3.2. Hysteresis Effect

The change in economic policy may have a continuous impact on banks, and banks also need some time to receive signals and react to the change in economic policy, so there may be a lag effect. We gradually add one-period-lagged and two-period-lagged data of economic policy uncertainty into Equation (1) for the robustness test, and the results are shown in Column (3) of Table 3. The results show that the current economic policy uncertainty and the one-period-lagged economic policy uncertainty have a significant negative impact on the current bank stability, which shows that the results remain robust.

4.3.3. Endogeneity Test

Potential reverse causality problems may lead to endogeneity problems in the model. Changes in bank stability will largely affect economic policy uncertainty in turn. Referring to the practice of Zhou and Liu [23], this paper uses the two-stage least square method (2SLS) to conduct the endogeneity test. We select the economic policy uncertainty indexes of four representative economies, namely the world (EPUG), the United States (EPUA), Japan (EPUJ), and Russia (EPUR), as the instrumental variables of the economic policy uncertainty of China. As the second largest trading country in the world, China is closely connected to foreign economic and trade, so foreign economic policy uncertainty is directly related to domestic economic policy uncertainty. At the same time, the bank stability of China has almost no correlation with foreign economic policy uncertainty, which meets the selection criteria of instrumental variables. Columns (1) and (3) of

Table 4. Economic policy uncertainty and bank stability: 2SLS.

Variables	First-Stage Regression Results		Second-Stage Regression Results
	(1)	(2)	(3)	(4)
EPU			−0.1158 *** (−5.05)	−0.2630 *** (−5.10)
EPUG	0.2196 *** (11.99)	0.0922 *** (12.01)
EPRA	0.1506 *** (13.62)	0.0629 *** (11.73)
EPUR	0.1554 *** (17.65)	0.0706 *** (19.45)
EPUJ	0.3511 *** (12.91)	0.1478 *** (12.85)
Control variables	Yes	Yes	Yes	Yes
Obs	248	248	248	248
R-squared	0.9100	0.9142	0.9636	0.9633

Note: *** indicate the significance of coefficients at 1%, respectively. The t statistic is reported in parentheses.

are the regression results using the economic policy uncertainty index constructed by Baker et al. [18], and Columns (2) and (4) of Table 4 are the regression results using the economic policy uncertainty index constructed by Davis et al. [34]. The empirical results are basically consistent with the above, so the impact of economic policy uncertainty on bank stability is still robust.

4.4. Heterogeneity Discussion

Considering that different types of commercial banks may be affected differently by economic policy uncertainty, this paper divides the sample banks into state-owned commercial banks, joint-stock commercial banks, and urban commercial banks for heterogeneity discussion, and the results are shown in

Table 5. Economic policy uncertainty and bank stability: heterogeneous analysis.

Variables	State-Owned Commercial Bank	Joint-Stock Commercial Banks	City Commercial Banks
EPU	−0.1264 *** (−5.80)	−0.0862 *** (−3.69)	−0.1393 *** (−2.99)
Control variables	Yes	Yes	Yes
Obs	64	107	77
R-squared	0.7864	0.7831	0.7734

Note: *** indicate the significance of coefficients at 1%, respectively. The t statistic is reported in parentheses.

. The rising economic policy uncertainty has an impact on the stability of all kinds of banks. For every 1% increase in the economic policy uncertainty index, the stability of state-owned commercial banks, joint-stock commercial banks, and city commercial banks will decrease by 0.1264, 0.0862, and 0.1393 percentage points, respectively.

State-owned commercial banks have a higher proportion of government holdings than other commercial banks, which means they have a stronger ability to perceive economic policy changes and obtain policy information and can predict economic policy changes to some extent. Therefore, compared with city commercial banks, fluctuations in economic policy uncertainty have less impact on the stability of state-owned commercial banks. However, at the same time, state-owned commercial banks are important bridges between the government and the public, and these banks undertake the important function of stabilizing the economy. Even in a period of high economic policy uncertainty, state-owned commercial banks still need to maintain consistent credit policies and release positive signals to the financial market to stabilize the mood of market investors. Even in a period of economic weakness, in order to stimulate economic recovery, the government needs state-owned commercial banks to take the initiative to expand the loan scale, relax the loan conditions, and even lower the loan interest rate to boost economic growth. Therefore, the fluctuation of economic policy uncertainty has a greater impact on the stability of state-owned commercial banks than on that of joint-stock commercial banks.

Compared with city commercial banks, joint-stock commercial banks have larger-scale, more sufficient funds; a more nearly perfect internal risk monitoring mechanism; and a stronger ability to predict and resist risks. At the same time, compared with state-owned commercial banks, joint-stock commercial banks are less subject to government intervention and have more independent management rights. In the face of fluctuations in economic policy uncertainty, they can make timely judgments and decisions to reduce the impact of the external macro environment stimulus on bank stability. Therefore, economic policy uncertainty has the least negative impact on joint-stock commercial banks.

4.5. Role of Opacity

This paper tests the role of Opacity in the relationship between economic policy uncertainty (EPU) and the stability of commercial banks (Z), according to the method proposed by Wen and Ye [32], and the regression results of mediating effect are shown in

Table 6. The role of opacity.

Variables	(1)	(2)	(3)
	Z	Opacity	Z
Opacity			−0.1881 *** (−2.67)
EPU	−0.1048 *** (−5.08)	0.2176 *** (3.38)	−0. 0639 * (−1.86)
Control variables	Yes	Yes	Yes
Obs	248	248	248
R-squared	0.7636	0.3334	0.6384

Note: * and *** indicate the significance of coefficients at 10% and 1%, respectively. The t statistic is reported in parentheses.

. The coefficient ${\alpha }_1$ of the independent variable (EPU) in Model 1 is significantly negative, and the coefficient ${\beta }_1$ of the independent variable (EPU) in Model 2 is significantly positive. Moreover, the coefficients ${\gamma }_1$ and ${\gamma }_2$ of the independent variable (EPU) and mediating variable (Opacity) in Model 3 are also significant. Therefore, the product of ${\beta }_1$ and ${\gamma }_2$ is the same sign as ${\gamma }_1$, indicating that opacity plays a partial intermediary role between economic policy uncertainty and bank stability, and the proportion of the mediating effect in the total effect is 39.07%, so Hypothesis 2 is verified. Economic policy uncertainty affects the cognition and decision making of banks. In a period of high economic policy uncertainty, the macro economy recedes, the capital cost of borrowing rises, and enterprise decision making is delayed, which lead to the performance decline in banks, the deterioration in asset quality, and an increase in risk. In order to maintain personal performance, avoid supervision, and reduce investors’ dissatisfaction, bank managers increase bank opacity to reduce the disclosure of negative information, which indirectly impacts bank stability.

In order to further explore the specific role of each component of opacity in the relationship between economic policy uncertainty and bank stability, this paper continues to test the intermediary role of internal and external personnel information difference (FE), market exposure (MFAR), asset opacity (LAR), and earning opacity (DLLP). The regression results of the mediating effect are shown in

Table 7. The role of each component of opacity.

Variables	(1)	(2)				(3)
	Z	FE	MFAR	LAR	DLLP	Z
FE						−0.0785 (−0.53)
MFAR						0.1114 *** (3.85)
LAR						−0.0885 ** (−2.53)
DLLP						−0.0364 *** (−5.45)
EPU	−0.1048 *** (−5.08)	0.0007 (0.08)	−0.0761 * (−1.73)	−0.0066 (−0.07)	0.3132 * (1.67)	−0.0872 *** (−4.53)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Obs	248	248	248	248	248	248
R-squared	0.7636	0.2353	0.3690	0.5911	0.4650	0.9695

Note: *, **, and *** indicate the significance of coefficients at 10%, 5%, and 1%, respectively. The t statistic is reported in parentheses.

.

Market exposure (MFAR) plays an intermediary role between economic policy uncertainty and bank stability, and the proportion of the mediating effect in the total effect is 8.09%, which verifies Hypothesis 4. In the face of rising economic policy uncertainty, banks may choose to reduce exposure to the market to reduce the evaluation of market investors, which increases the information asymmetry between banks and external stakeholders and is not conducive to the effective development of market regulation, which has a negative impact on bank stability. Earnings opacity (DLLP) plays an intermediary role between economic policy uncertainty and bank stability, and the proportion of the mediating effect in the total effect is 10.88%, which verifies Hypothesis 6. In a period of high economic policy uncertainty, bank managers whitewash and cover up the financial data in financial statements in order to reduce the impact of the decline in operating performance on their personal performance. The increase in earnings opacity prevents banks from objectively and reasonably evaluating their operating conditions and risk levels and also destroys the market order, which ultimately leads to the decline in bank stability.

However, the increase in economic policy uncertainty does not affect banks by changing the information difference between internal and external personnel, so Hypothesis 3 is not valid. Because the stock market of China has not reached the real weak efficiency yet, the future price of stocks will be affected by historical information [34]. Therefore, it is inaccurate for external personnel to predict future stock prices by public information, which may far exceed the impact of external macro environmental factors on stock prediction errors. What is more, there is no mediating effect of asset opacity between economic policy uncertainty and bank stability, so Hypothesis 5 is not valid. The reason may be that commercial banks have the independent choice of lending objects [30], and loan information that involves trade secrets and competitive advantages is difficult to obtain [9], which makes it difficult for external investors to evaluate the asset quality of banks. As a result, the stimulation from the external environment does not have a significant impact on the asset opacity of banks.

5. Discussion and Policy Implications

Based on the results of the empirical analysis, the following policy implications are forwarded from three levels. First, the government and regulatory authorities should formulate the industry standards and quantitative indicators of bank transparency and tighten regulations on activity restrictions of banks. As banks are inherently opaque and prone to uncertainty, weak policies could lead bank managers to engage in the behavior of inverse selection, which could jeopardize their stability. Therefore, policymakers should attach greater importance to reducing earnings opacity to protect the banks, the financial system, and the economy as a whole. What is more, the government and regulatory authorities should give more attention and support to city commercial banks, which are more vulnerable to the impact of uncertainty changes.

Second, banks need to improve risk management capabilities. The uncertainty of the external environment is not conducive to the stability and sustainable development of commercial banks. Therefore, banks should establish and improve the procedures for forecasting and identifying economic policy uncertainties and pay attention to the dynamic adjustment of risk management indicators. At the same time, internal governance should be enhanced to effectively alleviate agency conflicts, as managers may cover up negative information by adjusting opacity. In addition, expanding business channels and developing income diversification strategies help banks improve their stability. Moreover, due to the heterogeneous impact of economic policy uncertainty, different types of commercial banks should formulate risk management systems according to their own capabilities and needs, which will enhance their ability to respond to changes in the external environment.

Last but not least, stakeholders are encouraged to play an active monitoring role. In a period of high economic policy uncertainty, the participation of bank stakeholders such as enterprises and residents can expand the bank’s market exposure, which will probably guide banks to improve information disclosure system, form effective constraints on agents, and assist regulatory authorities in playing the role of market supervision.

6. Conclusions

China has been in the age of heightened economic policy uncertainty, which has resulted in various economic issues and threatened the stability of their financial systems. Some recent studies have examined the adverse influence of economic policy uncertainty on micro-economic development and enterprise decision making. However, the impact of economic policy uncertainty on banking stability is still overlooked. As one of the most important parts of the financial system, bank stability contributes to economic growth and financial market stability. Using the non-balanced panel data of listed commercial banks in China from 2007 to 2020, we conclude with the following findings. First, the increase in economic policy uncertainty is not conducive to the stability of banks. Second, the impact of economic policy uncertainty is heterogeneous, such that the negative impact on the stability of joint-stock commercial banks, state-owned commercial banks, and city commercial banks is increasing in turn. Finally, opacity plays a partial intermediary role between economic policy uncertainty and bank stability. The fluctuation of economic policy uncertainty affects the cognitive and decision-making process of banks, and banks conservatively reduce information disclosure to avoid supervision and lower investors’ dissatisfaction. Specifically, banks reduce their exposure to the market and increase earnings opacity, which indirectly has a negative impact on bank stability. The results are quite robust to various identification strategies that address endogeneity concerns, the lag effect of economic policy uncertainty, and the use of different economic policy uncertainty measures. Our study bridges the gap in the previous literature by analyzing the influence of economic policy uncertainty on the bank stability of China and its transmission path from the perspective of information disclosure.

However, there are still some limitations to the present work. On the one hand, the findings in this paper apply to the context of China and cannot be generalized to other countries. One the other hand, most city commercial banks were listed after 2018, resulting in most data from before 2018 missing, and the minimal sample data may have an impact on the research results. Therefore, future studies can extend the present study to other countries, especially to emerging economies that experience higher levels of economic policy uncertainties, and compare the results among different countries. Moreover, future research can consider the internal and external factors such as regulation, competition, and internal governance, which may moderate the mediating effect of opacity, which will help banks regulate opacity and maintain stability during periods of high uncertainty in economic policy.