The influence of digital finance based on the intermediary effect of investor confidence on organizations' financing constraints.

This study examines the impact of digital financing on the degree of financing constraints and discusses the mediating effect of investor confidence. The data are based on companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2010 to 2019. To investigate the impact of digital financing on the financing constraints of companies in different situations, the heterogeneity of internal control and equity characteristics of different organizations is analyzed. The results using fixed-effects models show that (i) the change in digital finance has a significant negative impact on the level of corporate financing constraints; (ii) investor confidence plays a mediating role between digital finance and financing constraints; and (iii) the level of internal control impacts the relationship between the digital finance and the corporate financing constraints. Specifically, for the organizations with better internal control, there is a significant negative relationship between digital finance and corporate financing constraints while for organizations with poor internal control, digital finance has no significant influence on the extent of financing constraints; and (iv) digital finance of private organizations is significantly negatively correlated with the extent of financing constraints, while for government organizations, a negative relationship is not evident.

Introduction

In a perfect capital market, there is no difference between the cost of raising funds from the internal market or the external market. Enterprises will not face financing constraints (Bakhtiari et al., 2020; Wu et al., 2021a). However, the actual capital market is not always perfect. Insufficient information generally creates differences in the internal and external financing costs of enterprises. The more obvious the information asymmetry, the greater the difference between internal and external financing costs. Enterprises fall into the dilemma of financing constraints, and the final investment level and efficiency are affected. Information asymmetry makes it difficult for investors to grasp the actual situation of enterprises, so they lack confidence in them and set a series of conditions and restrictions on enterprise financing, which, in turn, leads to financing constraints (Hao Y. et al., 2022; Hossain et al., 2022).

Financing constraints have long been a difficult problem for developing Chinese enterprises. As early as 2012, the World Bank conducted a survey of the investment and business environments of 58 countries. According to the report, the financing constraints faced by Chinese enterprises are relatively serious, and more than two-thirds of Chinese enterprise managers believe that the operations and growth of enterprises are limited by financing constraints (Tang et al., 2022a). The 2016 questionnaire survey report on Chinese enterprise managers issued by the Development Research Center of the State Council showed that more than one-third of the enterprise managers believe that financing is the primary issue enterprises face in the process of operation and growth. The 2017 survey results show that more than half of the Chinese entrepreneurs experienced obstacles in the process of enterprise development. According to the 2017 analysis report on the top 500 private enterprises in China issued by the All-China Federation of Industry and Commerce of the State Council, financing difficulties are one of the three factors restricting the growth of private enterprises. According to the 2019 survey results of the China Entrepreneur Development Confidence Index (for the first half of the year), the cost of raising funds for nearly half of enterprises is rising. Financing constraints restrict enterprise investment and development. Thus, easing financing constraints is of great significance for stimulating enterprise investment, supporting enterprise growth, and promoting macroeconomic growth.

Since China’s economy has entered a new normal, COVID-19 has severely impacted the market and has tested enterprises’ capacity to survive and develop (Cheval et al., 2020; Hao et al., 2020). Facing increasing uncertainty in the external environment, an increasing number of enterprises realize the necessity of maintaining financial flexibility, which cannot only improve the resilience of enterprises in the face of external shocks but also enable them to swiftly seize opportunities (Lasrado and Pereira, 2018). Nofsinger (2005) points out that financing, investment, and commercial activities are the three major actions in the daily operations of enterprises. Financing activities are the premise of investment and business activities. However, owing to market imperfections and information asymmetry, financing constraints have become an inevitable problem in all stages of enterprise operations (Ren et al., 2021). To ease the financing constraints, internal information should be actively transferred to the outside, thus reducing information asymmetry (Chai et al., 2021). Financial flexibility is one type of information. Enterprises with high financial flexibility send signals of good operations and strong anti-risk abilities to the market (Hao et al., 2021). In this process, and under the influence of information, investors’ perceptions shift and their investment preferences and behavior toward the enterprises change (Palepu et al., 2020). Therefore, what impact does financial flexibility have on the scale of financing constraints? From the perspective of investor psychology, what is the influence mechanism? This has become an urgent problem to address and is the focus of this study.

Based on the sample data of A-share listed companies in Shanghai and Shenzhen from 2010 to 2019, this study examines the relationship between financial flexibility and corporate financing constraints and discusses its intermediary mechanism from the perspective of investor confidence. In particular, the fixed-effects model is used to test the impact of financial flexibility on firms’ financing constraints (Hao Z. et al., 2022). A three-stage test method is employed to test the role of investor confidence in the relationship between financial flexibility and financing constraints (Arslan-Ayaydin et al., 2014). To analyze the heterogeneity of internal control and equity, the method of grouping regression was adopted. Finally, we provide suggestions for enterprises to improve their financial policies and alleviate their financing constraints.

The rest of this study is organized as follows. The first part reviews and combs the relevant literature involving three variables (financial flexibility, investor confidence, and financing constraints). Based on existing studies, we define the three concepts and analyze their measurement methods. By evaluating the relationship and influence mechanism between financial flexibility and financing constraints, financial flexibility and investor confidence, and investor confidence and financing constraints, this study sets the empirical research hypotheses of this study. The second part presents an empirical analysis of the impact of financial flexibility on enterprise financing constraints. Taking listed enterprises as samples, the data are screened to construct relevant indicators, three main variables and relevant control variables are defined, and the model is set to test the hypotheses. According to the regression results, this study’s research hypotheses are significantly supported. The third part summarizes the research results and then puts forward relevant suggestions for enterprises, regulators, and investors.

Literature review

Financial flexibility

Financial flexibility is the ability of enterprises to allocate their financial resources in real time, which enables them to better cope with the impact of future uncertainty to maximize their market value. During ordinary times, the enterprises retain a certain level of financial flexibility. Their financial policies are likewise flexible to some degree, which can enable enterprises to deal with uncertain events more calmly, resist external shocks, and prevent business reduction or loss caused by economic fluctuations (Graham and Harvey, 2001); maintaining financial flexibility can also enable enterprises to timely grasp investment opportunities, thus improving enterprise value and enhancing enterprise competitiveness (Myers and Majluf, 1984; Kwakwa et al., 2022). With the acceleration of marketization, promotion of global integration, rapid changes in technology, changes in the economic environment, and the adjustment of macro policies, enterprises will encounter unexpected shocks in the process of operations (Ren et al., 2022). An increasing number of enterprises have begun to realize the necessity of maintaining financial flexibility. At present, China’s economy has entered a new normal stage—the economic growth rate has slowed down, and the industrial structure urgently needs to be adjusted, optimized, and upgraded (Yang et al., 2021). This is a key period for the transformation of old and new kinetic energy. Meanwhile, the novel coronavirus pneumonia and Sino-US trade friction have caused the external market environment to be optimistic, the pressure to survive has increased, and the role of financial flexibility has become more prominent.

Investor confidence

However, this market is imperfect. The psychological factors of investors affect their decision-making behavior, which in turn affects financing behavior, investment activities, and the value of enterprises in the market. Luo et al. (2021) point out that changes in investor confidence can significantly impact stock prices. Meier (2018) believes that the market valuations of listed companies are positively related to investor confidence; if investor confidence improves, the company’s market valuation is correspondingly greater.

Investor confidence is divided into two categories: overall and individual confidence levels. The overall investor confidence in the market is affected by the overall situation of the market, and the influencing factors of individual investor confidence are also related to enterprise information disclosure, operation, and financial status. For example, an effective governance structure can improve the reliability of governance information (Appiah-Kubi et al., 2020), improve the quality of audit reports, appropriately disclose governance information (Holt and DeZoort, 2007; Lee and Shailer, 2008), and ensure timely disclosure of corporate environmental reports (García-Sánchez et al., 2019), which can enhance investor confidence. The internal cash flow, enterprise size, and capital structure also affect investor confidence.

To date, there have been few achievements in measuring individual investor confidence. The existing literature adopts the principal component analysis method to construct individual stock investor confidence, but the selected variables slightly differ. For example, Lei et al. (2001) selected three ratios—Price Earnings Ratio (P/E ratio), Price-to-Book Ratio (P/B ratio), and annual turnover rate of stocks—to construct investor confidence measurement indicators. Mills and Newberry (2001) selected three indicators to measure the growth rate of the main business income, price-to-book ratio, and shareholding percentage of institutional investors. Elyasiani and Jia (2010) selected the following three other indicators: The shareholding percentage of institutional investors, growth rate of main business income, and annual turnover rate of stocks.

Financing constraints

At the macro level, the higher the efficiency of the financial market, the fewer obstacles, and constraints enterprises face in financing (Beck et al., 2006). For example, changes in macro policies, such as the institutional credit environment and fiscal and monetary policies, reduce the financing obstacles and constraints of enterprises in the external market (De Paula et al., 2017).

At the micro level, Diamond and Verrecchia (1991) and others believe that enterprises can take the initiative to report more information to reduce information asymmetry and reduce the financing cost of enterprises in the external market. Gordon and Li (2003) found that companies with high political relevance face fewer obstacles in raising funds because they have information advantages over other companies. Deng et al. (2020) proposed that the difference in the nature of a company’s equity will also affect its financing status, and the obstacles faced by state-owned enterprises in raising funds are generally fewer than those faced by private enterprises. In addition, different business credits (Shi and Zhang, 2010), organizational characteristics, and governance structures of the group and enterprise life cycle will lead enterprises to face different financing conditions.

To measure financing constraints, Carpenter et al. (1998) believed that the greater the financing constraints enterprises face, the higher the external financing costs. Therefore, maintaining a low dividend distribution rate is often necessary. However, enterprises with low dividend distribution rates face high financing constraints. Duchin (2010) observed that compared with large-scale enterprises, it is more difficult for small-scale enterprises to finance from the external market at a low cost. As a result, small-scale enterprises face greater financing constraints. In addition, the nature of equity, years of listing, and bond grade are selected as criteria for measuring the degree of corporate financing constraints (Bodnaruk et al., 2015).

A range of indicators can be used to comprehensively measure the level of financing constraints. Whited and Wu (2006) selected the following six variables: Scale, cash flow, dividend distribution dummy variables, leverage ratio, enterprise sales growth rate, and industry sales growth rate, and established the WW index using the GMM model to estimate parameters. Kaplan and Zingales (1997) weighted several financial indicators according to the weights obtained from the regression to establish the KZ index. However, there is a problem with these following indexes: The financial indicators on which they rely are endogenous. To overcome this problem, Hadlock and Pierce (2010) constructed the Size age index (SA index), which includes two indicators: enterprise scale and enterprise listing years.

The relationship between financial flexibility, investor confidence, and enterprise financing constraints

The existing literature discusses the impact of financial flexibility on enterprise financing, the conclusions of which support the existence of a significant negative relationship between financial flexibility and financing constraints. Therefore, improving financial flexibility will help reduce the obstacles to financing. DeAngelo et al. (2010) pointed out in their research that enterprises with high financial flexibility can better deal with unexpected external factors to reduce the cost of financing in the capital market. Almeida and Campello (2007) proposed that enterprises’ internal capital markets can complement the external capital market. Enterprises can meet their investment needs through cash reserves to alleviate financing constraints. Chang and Ma (2018) believe that enterprises with high financial flexibility often have strong anti-risk abilities, so their credit ratings are good. When external financing is needed, they can obtain the required funds at a lower cost to alleviate their financing constraints. De Jong et al. (2012) think that enterprises can significantly improve their ability to deal with future environmental uncertainty and alleviate their financing constraints by retaining a certain borrowing capacity. Yu (2016) found that enterprise cash reserves can meet their temporary needs: because they are prepared in advance, there is no need to find ways to seek elsewhere. Having such reserves keeps costs low, which can enable enterprises to maintain a certain surplus financing capacity, thereby reducing the cost of external financing (Fliers, 2019). Therefore, enhancing the financial flexibility of enterprises can simultaneously reduce their internal and external financing costs, as well as lessen the financing constraints they face.

There are no studies that introduce investor confidence into the discussion of the relationship between financial flexibility and financing constraints or analyze the internal transmission mechanism of financial flexibility affecting financing constraints. The extant literature on investor confidence discusses the impact of internal corporate governance, information reporting, and transmission. In essence, corporate governance level and information disclosure are a type of information transmission that can reduce information asymmetry and improve investor confidence; the level of financial flexibility is also a form of information. Therefore, we can reasonably assume that improving financial flexibility can also enhance investor confidence. The existing literature has proved that an increase in investor confidence helps improve a company’s stock price and market valuation. The rise in the company’s stock price will reduce the cost of equity financing and help the company raise funds for investment more smoothly. Moreover, the increase in the company’s valuation is conducive to its financing in the capital market.

Financial flexibility arises from the response of enterprises to external uncertainty factors. It can help enterprises reduce business risks caused by unexpected environmental fluctuations and prevent crises caused by uncertainties (Shukor et al., 2020). It can also help enterprises timely capture investment opportunities and encourages them to strive to continuously improve the company’s value. Financial flexibility refers to the response of an enterprise’s management to external uncertainty. This is a forward-looking move with initiative; to some extent, financing constraints are the result of an incomplete market, which is passive (Bolton et al., 2020). At the macro level, the level of financial development, fiscal and monetary policies, and the institutional credit environment can all affect the financing constraints enterprises face (Tang et al., 2022b). At the micro level, the degree of political relevance, whether it is a state-owned enterprise, level of market attention, commercial credit, and the enterprise life cycle, all affect enterprises’ financing constraints. When the existing literature discusses the relationship between financial flexibility and financing constraints, the main conclusion is that a negative correlation exists between financial flexibility and the degree of financing constraints (Nikolov et al., 2021).

Generally speaking, there are three defects in previous studies. First, when constructing the financial flexibility measurement index, many refer to the method of Zeng et al. (2011). Although this method is relatively simple, it contains few measurement indicators and the evaluation content is not sufficiently comprehensive. Second, no scholars discuss the intermediary role of investor confidence between financial flexibility and financing constraints. As a subjective psychological idea of investors, investor confidence is disturbed by various types of information, including enterprise financial information, which ultimately affects the financing situation of enterprises in the market through changes in their investment behavior. Third, there was no group discussion or a more detailed exploration concerning enterprises.

Given this, this study refers to Ma’s (2010) multi-index comprehensive method. We simultaneously consider multiple financial indicators, introducing investor confidence into the research framework of the relationship between financial flexibility and enterprise financing constraints and clarifying the intermediary mechanism of investor confidence. Enterprises are divided according to the nature of equity and the level of internal control to study the potential impact of financial flexibility on enterprise financing constraints under the conditions of different natures of equity and levels of internal control.

Mechanism analysis and research hypotheses

The reverse relationship between financial flexibility and corporate financing constraints

To some extent, financing constraints arise from information asymmetry. In an actual financial market, widespread information asymmetry leads to financing constraints. In the absence of a complete understanding of the enterprise, investors act cautiously out of risk aversion, either demanding a higher return on investment or being unwilling to invest (Wu et al., 2020). In this case, enterprises’ financing obstacles and financing costs increase. The more serious the information asymmetry, the higher the degree of constraint on financing. To obtain external financing at a lower cost, enterprises need to transmit the signal of their operating conditions to the external market, distinguish their value compared to other enterprises, and reduce financing costs. Maintaining appropriate financial flexibility is a type of information disclosure and an external signal. Enterprises with higher financial flexibility can resist external risks and economic fluctuations, avoid difficulties, and enhance their competitiveness (Saeidi et al., 2019). That is to say that maintaining certain financial flexibility can transmit the signal of good business operation and financial status and strong anti-risk ability to external investors, differentiate enterprises from poor enterprises in the market, reduce the risk of adverse selection of investors, and alleviate the financing constraints caused by information friction. Based on this, this study puts forward Hypothesis 1:

Hypothesis 1: The change in the financial flexibility level has a significant negative impact on the financing constraints faced by enterprises. Enterprises with higher financial flexibility are less constrained by financing.

Positive relationship between financial flexibility and investor confidence

Maintaining a certain degree of financial flexibility is a response to external uncertainty. It can help enterprises solve sudden capital needs in production and operations, enable them to resist external adverse effects, reduce operational risks, and improve their viability (Bonaimé et al., 2016). During crises, asset prices fall sharply. Enterprises with good financial flexibility can take flexible measures to carry out investment and acquisition activities and use this opportunity to improve their competitiveness and value (Ma and Jin, 2016). Enterprises that lack financial flexibility are not only more vulnerable to external shocks but also find it more difficult to seize investment opportunities in a crisis (Georgiadis and Gräb, 2016; Wu et al., 2020). Therefore, maintaining appropriate financial flexibility can enable enterprises to seize opportunities during crises and improve their performance and market value. Based on this, this study puts forward Hypotheses 2:

Hypothesis 2: Financial flexibility is positively correlated with investor confidence. Enterprises with higher financial flexibility have stronger investor confidence.

Investor confidence plays an intermediary role in the relationship between financial flexibility and corporate financing constraints

Owing to the existence of adverse selection and moral hazards caused by information friction, external investors have less information. Owing to self-interest and a desire for risk compensation, borrowers increase restrictive treaties, set thresholds, increase loan interest, and increase the financing costs of the enterprise (Brown et al., 2012). However, maintaining a certain degree of financial flexibility can enhance investors’ confidence and persuade them that the enterprise bears fewer operational and financial risks. Consequently, investors will not ask for high-risk premiums (Huang et al., 2021). In addition, in the stock market, when investors believe that the enterprise has good prospects and the investment is profitable, driven by self-interests, they will have a strong desire to invest, actively invest, increase the purchase of enterprise securities, and increase the securities price (Antoniou et al., 2015). On the one hand, the rise of securities prices can improve the valuation of enterprises and make them more capable of debt financing; on the other, it will attract other investors in the market with a “catch-up’ mentality, making it easier for enterprises to raise funds required for projects in the market (Crick and Crick, 2020). In summary, changes in enterprises’ level of financial elasticity lead to changes in investors’ confidence in enterprises, thus affecting their preferences, decisions, and corporate financing. Based on this, this study puts forward Hypothesis 3:

Hypothesis 3: Investor confidence is negatively correlated with financing constraints, and enterprises with strong investor confidence are less constrained when raising funds. Investor confidence plays an intermediary role in the relationship between financial flexibility and financing constraints.

Internal control impacts the relationship between financial flexibility and enterprise financing constraints

Maintaining financial flexibility can improve enterprises’ ability to thwart or minimize risks. However, maintaining financial flexibility also generates potential expenses such as opportunity and agency costs (Martínez-Sola et al., 2018). Investors make different choices based on the difference between the internal control of the enterprise and the nature of equity. For enterprises with good internal controls, investors believe their decision to improve financial flexibility is correct and reasonable, and the benefits of maintaining financial flexibility are greater than the potential costs. Therefore, they are optimistic about the company’s prospects and increase their investment. For enterprises with poor internal controls, investors may consider the potential opportunity and agency costs brought about by maintaining financial flexibility and worry that management will damage the interests of investors for their own interests or conduct inefficient investment activities due to improper decision-making, resulting in investment losses (Broome et al., 2018). In addition, enterprises with poor internal controls are more likely to commit financial fraud and transmit wrong signals, thus causing investors to face greater risks and higher expected earnings volatility. Therefore, among enterprises with poor internal controls, improving the level of financial flexibility is not as important as improving internal controls. Based on this, this study puts forward Hypothesis 4:

Hypothesis 4: Enterprises with different levels of internal controls have different effects on financing constraints. Enterprises with better internal controls show a significant negative correlation between financial flexibility and enterprise financing constraints. The financial flexibility of enterprises with poor internal controls has no significant impact on the degree of financing constraints.

The nature of equity impacts the relationship between financial flexibility and corporate financing constraints

In China, there are significant differences between state-owned and non-state-owned enterprises (SOEs). The reform of China’s SOEs has not been completed, the governance system is not perfect, and even the situation of no separation between the government and enterprises has not completely disappeared. SOEs’ internal controls and governance capacities are generally lower than those of non-SOEs (Lin et al., 2020). Compared with non-SOEs, SOEs are more likely to make inefficient investments due to improper decision-making or the completion of assigned political tasks, thus reducing the confidence of external investors. However, due to the existence of a certain degree of “soft budget constraints” and implicit debt guarantees, SOEs are naturally favored by bank loan funds, meaning that they face fewer financing constraints (Dong et al., 2021). Non-SOEs have no implicit guarantees. In the face of fierce market competition, they must improve their governance level and make prudent decisions; otherwise, they face elimination. Therefore, compared to private enterprises, improving the financial flexibility of SOEs has no obvious effect on alleviating financing constraints. Based on this, this study puts forward Hypothesis 5:

Hypothesis 5: Equity plays a role between financial flexibility and enterprise financing constraints. The financial elasticity of private enterprises is significantly negatively correlated with the degree of financing constraint. However, in SOEs, this negative correlation is not obvious.

Methodology and data

Variable definition

Financial flexibility

This study adopts the coefficient of variation method when constructing the measurement indicators of financial flexibility and investor confidence. The logic is to weigh variables according to the value difference of each indicator (Arachchige et al., 2022). If the value of an indicator can distinguish the participating samples, it has a large amount of information and should be assigned a large weight. The specific calculation process is given by Eqs. (1) and (2).

The value range of indicator i is i = 1,2,,n, W is the weight of index i, V is the coefficient of variation of index i, σ is the standard deviation of index i, and is the average of all values of index i.

Referring to Ma and Zhang (2013), this study selects the multi-index weighted synthesis method to construct a measurement index of financial flexibility. This study selected six indicators: Cash production capacity, cash holdings, cash surplus, unused borrowing capacity, short-term debt proportion, and strong binding debt proportion. After positive and dimensionless treatment, first, two indicators of cash flexibility (CF) and debt flexibility (DF) are constructed through the coefficient of variation method (Table 1). Next, CF and DF are weighted, and the financial flexibility comprehensive index is constructed. Because they are of equal importance, they are assigned a weight of 50%.

TABLE 1

Calculation index system of financial flexibility.

	Primary index	Secondary index	Relationship with financial flexibility
Financial flexibility (FF)	Cash flexibility (CF)	Cash generating capacity	Positive
		Cash holdings	Positive
		Cash surplus	Positive
	Debt flexibility (DF)	Unused borrowing capacity	Positive
		Proportion of short-term debt	Negative
		Proportion of strong binding debt	Negative

(1) Cash flexibility

Cash generating capacity: Enterprises with good operating performance have good cash generating capacity and the ability to improve financial flexibility.

Cash generating capacity = Return on total assets (net profit/total assets)

Cash holdings: Enterprises with sufficient cash retention have greater flexibility.

Cash holdings = (Cash + Cash equivalents)/Total assets

Cash surplus: Considering the cash position after debt consumption, cash holdings can provide financial flexibility only after meeting the necessary payment.

Cash balance = Cash holdings – Debts due to 12 months/total assets

(2) Debt flexibility

Unused borrowing capacity: This reflects surplus borrowing capacity retained by the enterprise.

Unused borrowing capacity = 1 – Asset liability ratio

Proportion of short-term debt: Short-term debt is repaid within 1 year, and enterprises need to make arrangements for repayment funds.

Proportion of short-term debt = Current liabilities/total liabilities

Enterprises with a large proportion of short-term debt have low levels of financial flexibility.

Proportion of strong binding debt: Corporate debt can be divided into business, credit, individual (e.g., employee compensation payable), and debt under tax obligations (Holm-Hadulla and Thürwächter, 2021). Among them, credit debt and debt under tax obligations have the hard constraint of repaying the principal or interest on time, and there is room for negotiation between commercial and personal debts.

Proportion of strong binding debt = (Credit debt + Tax payable)/total liabilities.

Enterprises with a high proportion of strong binding debt have low financial flexibility.

Investor confidence

Referring to the methods of Lei et al. (2001), combined with the needs of this study, five indicators were selected to construct the investor confidence index: Institutional shareholding ratio, price-to-earnings (P/E) ratio, power-to-weight (P/W) ratio, and annual stock turnover rate. In the principal component analysis, the index did not pass the Keiser–Meyer–Olkin (KMO) test and the Bartlett sphericity tests. The correlation between variables was not high; therefore, it was not suitable to use the principal component analysis method. Accordingly, we adopted the coefficient of variation method to construct an investor confidence index.

Institutional shareholding ratio: After systematic learning and training, institutional investors have higher professional standards and more channels through which to obtain information. When institutional investors’ shareholding ratio increases, the market can reasonably expect the stock to have good information and increase its future income to enhance consumer confidence and increase investment (Drobetz et al., 2021).

P/E ratio: A high P/E ratio means that the company’s share price is higher than the earnings per share, which means that investors have great expectations for future profitability—under the belief that their future earnings will grow rapidly, they have high investment enthusiasm.

Market sales rate: The main business is the core competitiveness of a company and income is the basis of profit. A company with a high market sales rate implies that investors believe that the company has good business prospects, reliable income, and high confidence.

Price-to-book ratio: According to the fixed-growth dividend discount model, enterprises with high price-to-book ratios generally have high development potential and profitability. Investors are optimistic about growth prospects, believe that the company has growth potential, and are optimistic about its future.

Annual turnover rate of stocks: Turnover rate is a liquidity index. A high annual stock turnover rate means that they have strong liquidity and active market transactions, indicating that they are the object of investors’ attention and there is a strong desire to buy them (Carmona et al., 2021).

Financing constraints

Combined with the advantages and disadvantages of various indicators and the need for research, the SA index is used to measure financing constraints. The SA index includes two indicators: enterprise size and age (Wen et al., 2021).

Enterprise scale: Large-scale enterprises often disclose more operational and financial information, and make it easier to obtain. The degree of information asymmetry is small as is the degree of financing constraints caused by information asymmetry.

Enterprise age: The longer an enterprise is established, the more information it has about its past credit and operating conditions, which can be used as a reference for investors to alleviate the financing constraints caused by information asymmetry. The specific formula is as follows:

SA = (−0.737) × Enterprise scale + 0.043 × (Enterprise scale)2 − 0.040 × Enterprise age

The logarithm of the absolute value of the calculation results represents the enterprise financing constraints.

Other variables

(1) Grouping variables

Enterprise internal control: The Dibo internal control index is used to measure the enterprise’s internal control level (Gao, 2021).

Equity nature: Equity nature is divided according to whether it is an SOE, and the virtual variable is set. An SOE is assigned a value of 1, and a non-SOE is assigned a value of 0.

(2) Control variable

Tangible assets ratio: Tangible assets can be used as collateral when lending. It is easier for enterprises with a high proportion of tangible assets among all assets to raise funds through collateral (Yu et al., 2021). Therefore, the tangible assets ratio is negatively correlated with enterprise financing constraints.

Growth rate of operating income: Enterprises with rapid growth in operating income have good operating conditions and development prospects, guaranteed repayment, and easy access to financing; in other words, fewer financing constraints.

Dividend distribution rate: If the dividend distribution rate is high, creditors worry that the cash flow of the enterprise will be distributed to shareholders, resulting in insufficient funds for debt repayment and interest infringement. Therefore, the dividend distribution rate is positively related to an enterprise’s financing constraints.

Cash flow from operating activities: Cash inflow from operating activities is the main source of repayment. The greater the cash flow from operating activities, the better the operation of the enterprise, the guaranteed source of repayment, and the easier the access to financing.

Changes in current liabilities: The repayment period for current liabilities is short, which puts significant pressure on the capital of the enterprise. The change in current liabilities reflects the stability of enterprise operations. The greater the change in current liabilities, the greater the financing constraints.

Capital expenditure: Capital expenditure is used for long-term investment (Bazaluk et al., 2022). The increase in capital expenditure represents the improvement of enterprise operation, scale expansion, and possibly an increase in future income. Therefore, capital expenditure is negatively correlated with financing constraints.

Increase in non-cash working capital: This is about the increase in accounts receivable and prepaid expenses. Such increases reflect the changes in the operating conditions of the enterprise, so they also impact enterprise financing.

In addition, the dummy variables of province, industry, and year are controlled. Table 2 shows the definition of the above variables.

TABLE 2

Variable definition.

Variable type	Variable name	Variable symbol	Variable definition
Explained variable	Financing constraints	FC	Calculate the SA index according to the size and age of the enterprise and take the logarithm of its absolute value
Explanatory variable	Financial flexibility	FF	Weighted average of cash flexibility (CF) and debt flexibility (DF)
	Cash flexibility	CF	Weighted average of variation coefficient of cash production capacity, cash holdings, and cash surplus
	Debt flexibility	DF	Weighted average of variation coefficient of unused borrowing capacity, short-term debt proportion, and strong binding debt proportion
Mediating variable	Investor confidence	IC	Weighted average of variation coefficient of institutional shareholding ratio, P/E ratio, Price-to-sales, P/B ratio, and annual stock turnover rate
Other variables	Internal controls	CONT	Dibo internal control index
	Nature of equity	SOE	The value of SOE is 1 and that of non-SOE is 0
Control variable	Tangible assets ratio	TAN	(Total assets − Net intangible assets − net goodwill)/Total assets
	Growth rate of operating revenue	REV	(Current year’s operating revenue − Last year’s operating revenue)/Last year’s operating revenue
	Dividend distribution rate	DIV	Dividend per share/earnings per share
	Cash flow from operating activities	OCF	Net cash flow from operating activities in the current period/Total assets in the previous period
	Changes in current liabilities	ΔSTD	Changes in current liabilities/Total assets of the previous period
	Capital expenditure	EXP	Capital expenditure for purchasing fixed assets and intangible assets/Total assets of the previous period
	Increase in non-cash working capital	ΔNWC	(Current assets – Current liabilities)/Total assets of the previous period
	Province	PRO	Dummy variable. If it belongs to this province, use 1; otherwise, use 0
	Industry	IND	Dummy variable. If it belongs to this industry, use 1; otherwise, use 0
	Year	YEAR	Dummy variable. If it belongs to this year, use 1; otherwise, use 0

Model setting

The estimations are made using a multiple regression model of a two-way fixed-effects model that controls the individual and time effects simultaneously. Referring to Wen and Ye’s (2014) intermediary effect analysis method, we set up a model to test the intermediary effect of investor confidence. The inspection method was divided into three steps:

First, we test whether the change in the financial flexibility level affects the degree of constraints on enterprise financing. If there is no significant impact, the analysis will stop; if there is a significant impact, we can conduct an empirical analysis of the intermediary effect.

Second, we test whether a change in the financial flexibility level will result in a significant change in investor confidence.

Third, the first mock exam explains the impact of financial flexibility and investor confidence on enterprises’ financing constraints. If the change in financial flexibility level in the second step significantly affects investor confidence, the effect of investor confidence on enterprise financing constraints in the third step is also significant, and the change in financial flexibility level no longer has a significant impact on the degree of enterprise financing constraints, it is considered that there is a complete intermediary effect. If the change in financial flexibility level in the second step has a significant impact on investor confidence, the effect of investor confidence on enterprise financing constraints in the third step is also significant, and the change in financial flexibility level still has a significant impact on the degree of financing constraints, there is some intermediary effect.

To test the impact of financial flexibility on enterprise financing constraints (i.e., Hypothesis 1), this study sets the following multiple linear regression model:

To test the impact of financial flexibility on investor confidence (i.e., Hypothesis 2), the following multiple linear regression model is set:

To test the intermediary effect of investor confidence between financial flexibility and financing constraints (i.e., Hypothesis 3), the following multiple linear regression model is set:

where FC is the financing constraints of enterprise i in year t, and IC is the investor confidence of enterprise i in year t; α is the fixed effect of enterprise i; and i = 1,,n denotes the enterprise, Controls is the control variable, and ε is a random error term.

In addition, to test the relationship between financial flexibility and enterprise financing constraints under different internal control and equity properties, this study groups the sample enterprises according to internal control and equity properties and tests Eq. (3).

Data sources

We selected A-share listed companies in Shanghai and Shenzhen from 2010 to 2019 as the research sample. The data are from the CSMAR and Wind databases, excluding: (1) *ST enterprises (Listed companies in China whose shares have been specially treated due to losses for two consecutive years) in that year, such enterprises have abnormal financial conditions and serious liquidity constraints, so they have no research value; (2) at the beginning of the initial public offer (IPO), the financial decision-making behavior of IPO companies was not sufficiently mature (Zeng et al., 2011; Wu et al., 2021b); and (3) financial data were missing during the study period. The tails of the data are reduced. Finally, 18,814 data points from 3,345 sample companies over 9 years were obtained.

As shown in Table 3, the minimum value of financing constraints was 2.158, the maximum value was 5.709, the average value was 3.817, and the median value was 3.812, indicating that few enterprises have no constraints when raising funds, and some enterprises even face a high degree of financing constraints. The minimum value of financial flexibility was 0.137, the maximum value was 0.658, the mean value was 0.287, the median value was 0.276, and the standard deviation was small, indicating that the sample companies had an awareness of maintaining financial flexibility in general, but only some enterprises had a high level. The minimum value of investor confidence was 0, the average value was 0.004, and the median value was 0.003, indicating that investor confidence is generally low, while the maximum value is 0.273, which is far from the minimum value, indicating that investor confidence in different enterprises varies greatly. The maximum value of internal control (CONT) was 6.903, the mean value was 6.482, the median value was 6.51, and the standard deviation was 0.163, indicating that the internal control level of the sample enterprises is not very different, and the overall situation is good. However, the minimum value of 2.194 is very different from the maximum value, indicating that some enterprises still have poor internal controls.

TABLE 3

Variable definition.

Variable	Sample size	Average	Mid	SD	Maximum	Minimum
FC	18,814	3.817	3.812	0.257	2.158	5.709
FF	18,814	0.287	0.276	0.066	0.137	0.658
IC	18,814	0.004	0.003	0.003	0	0.273
CONT	18,814	6.482	6.51	0.163	2.194	6.903
DIV	18,814	0.326	0.225	1.283	0	107.4
SOE	18,814	0.377	0	0.485	0	1
REV	18,814	8.410	0.136	987.4	−0.953	134,607
TAN	18,814	0.923	0.955	0.1	0.105	1
OCF	18,814	0.057	0.051	0.261	−9.239	21.6
ΔSTD	18,814	0.125	0.043	1.38	−0.886	88.91
EXP	18,814	0.073	0.044	0.476	0	60.97
ΔNWC	18,814	0.055	0.058	0.995	−45.3	105.3

In the control variables, the average dividend distribution ratio (DIV) was 0.326, the distribution ratio was low, and the differences between the minimum value of 0 and the maximum value of 107.4 and the standard deviation of 1.283 were large, indicating that there are significant differences in dividend distribution among different enterprises. The median growth rate of operating income (REV) was 0.136, which is generally not high, but the standard deviation was 987.4, indicating that the growth in operating income of different enterprises varies greatly. The average tangible assets ratio (TAN) was 0.923 and the standard deviation was 0.1, indicating that the tangible assets ratio of the sample enterprises is high, and the difference is small. The mean value of cash flow from operating activities (OCF) was 0.057 and the standard deviation was 0.261. The operating conditions of each sample were quite different. The change in current liabilities (ΔSTD) mean value was 0.125, the standard deviation was 1.38, and the current liabilities generally show little change. However, there are great differences among different enterprises. Capital expenditure (EXP) is generally not large, with an average of only 0.073; however, the difference is large. The standard deviation of 0.476 was nearly seven times the average value. The increase in non-cash working capital (ΔNWC) has a maximum value of 105.3 and a minimum value of −45.3, thus showing great variation among the samples.

Results and discussion

Correlation analysis

Pearson’s correlation test was performed to determine the correlations between the main variables. The results show that the correlation between most variables is less than 0.5, indicating that multicollinearity is not serious and that the overall design of the model is reasonable. Among them, the correlation coefficients between financing constraints (FC), financial flexibility (FF), and investor confidence (IC) are −0.077 and −0.083, which are significant at the 1% level. It preliminarily verifies the hypothesis that improving financial flexibility and enhancing investor confidence can alleviate corporate financing constraints. However, the correlation between investor confidence and financial flexibility is not significant because Pearson’s correlation test only verifies the relationship between variables, and the relationships among them are affected by other factors (Yan et al., 2022). In the subsequent multiple linear regression analysis, it can be seen that there is a significant positive correlation between investor confidence and financial flexibility (Wu et al., 2021a).

Multiple regression analysis

Financial flexibility and financing constraints

Table 4 presents the regression results of Eq. (1). Columns (1–3) show the results of not adding control variables, adding control variables, and further controlling for industries and provinces. It can be seen from the data in the table that with the addition of control variables, the coefficient before FF changes from (−0.069) to (−0.042), and is negatively correlated with financing constraints (FC), significant at the 5% level. This shows that after considering other control variables, every increase in financial flexibility by 1 alleviates the financing constraint of enterprises by 4.2%. Thus, Hypothesis 1 of this study was supported.

TABLE 4

Multiple regression analysis of financial flexibility and financing constraints.

	(1)	(2)	(3)
Variable	FC	FC	FC
FF	−0.069***	−0.036***	−0.042***
	(0.015)	(0.014)	(0.014)
TAN		−0.214***	−0.206***
		(0.015)	(0.015)
DIV		0.022	0.069
		(0.125)	(0.120)
REV		–0.007	–0.006
		(0.008)	(0.007)
OCF		−0.005*	−0.005*
		(0.003)	(0.003)
STD		0.018	0.019
		(0.016)	(0.016)
EXP		–0.063	–0.066
		(0.304)	(0.292)
NWC		–3.370	0.384
		(9.052)	(9.021)
IND			Yes
PRO			Yes
Stkcd FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Constant	3.656***	3.847***	3.877***
	(0.005)	(0.014)	(0.022)
Observations	18,814	18,814	18,814
Adj. R2	0.815	0.828	0.832

Software: Stata 15.0. First column syntax: xtreg FC FF i.year, fe robust. Second column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.year, fe robust. Third column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year, fe robust. Clustering robust standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

An intermediary effect test of investor confidence

To further study the mechanism of investor confidence between FF and corporate financing constraints, this study tests the intermediary effect; the regression results are shown in Table 5. Column (2) shows the regression results for the impact of FF on investor confidence. It can be seen that at the 10% level, investor confidence will increase by 0.1% for every additional unit of financial flexibility. This change is considerably smaller than the impact of financial flexibility on financing constraints (4.2%), as shown in Column (1); however, it still shows that the improvement in financial flexibility sends a positive signal to investors. Thus, Hypothesis 2 of this study was supported.

TABLE 5

Test of intermediary effect of investor confidence.

	(1)	(2)	(3)
Variable	FC	IC	FC
IC			−2.102***
			(0.770)
FF	−0.042***	0.001*	−0.040***
	(0.014)	(0.000)	(0.013)
TAN	−0.206***	0.001***	−0.203***
	(0.015)	(0.000)	(0.015)
DIV	0.069	1.033*	2.241***
	(0.120)	(0.598)	(0.713)
REV	–0.006	−0.000**	–0.007
	(0.007)	(0.000)	(0.007)
OCF	−0.005*	0.000	−0.005*
	(0.003)	(0.000)	(0.003)
STD	0.019	–0.000	0.019
	(0.016)	(0.000)	(0.016)
EXP	–0.066	–0.003	–0.072
	(0.292)	(0.003)	(0.291)
NWC	0.384	–0.047	0.285
	(9.021)	(0.129)	(8.974)
IND	Yes	Yes	Yes
PRO	Yes	Yes	Yes
Stkcd FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Constant	3.877***	0.003***	3.883***
	(0.022)	(0.000)	(0.022)
Observations	18,814	18,814	18,814
Adj. R2	0.832	0.399	0.834

Software: Stata 15.0. First column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year, fe robust. Second column syntax: xtreg IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year, fe robust. Third column syntax: xtreg FC IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year, fe robust. Clustering robust standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Among the main variables, investor confidence has the greatest effect on financing constraints. Column (3) shows the regression results of the intermediary effect of investor confidence in which the investor confidence coefficient is (−2.102), which is significant at the 1% level. This shows that the financing constraints of enterprises are reduced by 210.2% for each unit increase in investor confidence. The coefficient of financial flexibility is (−0.040), which is still significant at the 1% level but is lower than (−0.042) in Column (1). This is because investor confidence plays an intermediary role between financial flexibility and financial constraints. Investor confidence partially transmits the impact of financial flexibility on financing constraints (Kumar and Vergara-Alert, 2020). Therefore, Hypothesis 3 of this study was supported. In other words, investor confidence is negatively correlated with corporate financing constraints and plays a partial intermediary role between financial flexibility and corporate financing constraints.

Analysis of internal control heterogeneity

To explore the relationship between financial flexibility and enterprise financing constraints under different conditions, this study groups the samples according to the nature of internal controls and equity and conducts multiple regression analysis on the grouped samples. Table 6 shows the regression results grouped by the level of internal controls, wherein the group with the first 50% of the internal control level is the group with good internal control, and the group with the last 50% is that with poor internal controls.

TABLE 6

Financial flexibility and financing constraints: grouped by internal control.

	Good internal control			Poor internal control
	(1)	(2)	(3)	(4)	(5)	(6)
Variable	FC	IC	FC	FC	IC	FC
IC			−6.012***			−0.572*
			(0.549)			(0.307)
FF	−0.063***	0.001**	−0.059***	0.024	0.001	0.025
	(0.016)	(0.000)	(0.016)	(0.020)	(0.001)	(0.020)
TAN	−0.218***	0.002***	−0.208***	−0.168***	0.002***	−0.166***
	(0.017)	(0.000)	(0.017)	(0.025)	(0.001)	(0.025)
DIV	−2.034**	–0.001	−2.042**	0.109	1.167*	0.776**
	(0.907)	(0.024)	(0.948)	(0.091)	(0.631)	(0.342)
REV	0.003	–0.000	0.002	0.608	–0.026	0.593
	(0.012)	(0.000)	(0.012)	(3.609)	(0.170)	(3.629)
OCF	−0.005**	0.000	−0.005**	–0.001	0.000	–0.000
	(0.002)	(0.000)	(0.002)	(0.010)	(0.000)	(0.010)
STD	0.011	–0.000	0.010	0.200***	0.001	0.201***
	(0.020)	(0.000)	(0.020)	(0.045)	(0.001)	(0.045)
EXP	0.145	–0.001	0.140	–2.038	–0.029	–2.055
	(0.313)	(0.003)	(0.313)	(2.202)	(0.059)	(2.199)
NWC	20.642	0.019	20.758	−86.273*	−2.500*	−87.702*
	(43.130)	(0.264)	(43.149)	(49.250)	(1.369)	(49.158)
IND	Yes	Yes	Yes	Yes	Yes	Yes
PRO	Yes	Yes	Yes	Yes	Yes	Yes
Stkcd FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Constant	3.892***	0.002***	3.905***	3.831***	0.002**	3.832***
	(0.024)	(0.001)	(0.024)	(0.040)	(0.001)	(0.040)
Observations	12,616	12,616	12,616	6,198	6,198	6,198
Adj.R-squared	0.809	0.297	0.812	0.887	0.501	0.888

Software: Stata 15.0. First column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT > 6.482, fe robust. Second column syntax: xtreg IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT > 6.482, fe robust. Third column syntax: xtreg FC IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT > 6.482, fe robust. Fourth column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT < 6.482, fe robust. Fifth column syntax: xtreg IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT < 6.482, fe robust. Sixth column syntax: xtreg FC IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if CONT < 6.482, fe robust. Clustering robust standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

In the group with good internal controls, there was a significant negative correlation between financial flexibility and financing constraints at the 1% level, with a correlation coefficient of −0.063. This shows that without considering the intermediary effect of investor confidence, enterprises with better internal controls can effectively alleviate the financing constraint of 6.3% for each unit of financial flexibility. Investor confidence plays an intermediary role. At the 5% level, financial flexibility increases by 0.1% for each unit of investor confidence. Column (3) shows the intermediary effect of investor confidence. At the 1% level, the financing constraints are reduced by 601.2 and 5.9%, respectively, for each unit increase in investor confidence and financial flexibility.

In the group with poor internal controls, there is no correlation between financial flexibility and financing constraints, and the investor confidence coefficient in Column (5) is not significant. This shows that even if enterprises with poor internal controls improve financial flexibility, it is difficult to alleviate financing constraints and enhance investor confidence.

In summary, the financial flexibility of enterprises with different levels of internal controls has varying effects on enterprise financing constraints. The financial flexibility of enterprises with better internal controls has a significant negative correlation with enterprise financing constraints, and the financial flexibility of enterprises with poor internal controls has no significant impact on the degree of financing constraints. Thus, Hypothesis 4 was supported.

Analysis of heterogeneity of ownership nature

Table 7 presents the results of the regression grouped by the nature of equity. There is no correlation between SOE financial flexibility and financing constraints. However, non-SOEs have a significantly negative correlation at the 5% level. Every 1 unit increase in financial flexibility will alleviate the financing constraints of non-SOEs by 3.1%. This shows that for SOEs, improving financial flexibility cannot significantly alleviate financing constraints. For non-SOEs, improving financial flexibility can significantly alleviate financing constraints. Thus, Hypothesis 5 was supported. Regardless of SOE status, improving financial flexibility has no significant effect on enhancing investor confidence. For SOEs, the reason may be the principal-agent cost speculated in this study, while non-SOEs may be affected by other factors, resulting in the coefficient not being significant.

TABLE 7

Financial flexibility and financing constraints: grouped by equity nature.

	State-owned enterprise			Non-state-owned enterprises
	(1)	(2)	(3)	(4)	(5)	(6)
Variable	FC	IC	FC	FC	IC	FC
IC			−2.956***			−1.595***
			(0.690)			(0.600)
FF	–0.006	–0.000	–0.006	−0.031**	0.001	−0.030*
	(0.023)	(0.000)	(0.022)	(0.016)	(0.001)	(0.015)
TAN	–0.053	0.000	–0.053	−0.196***	0.001**	−0.195***
	(0.038)	(0.000)	(0.038)	(0.015)	(0.000)	(0.015)
DIV	0.155	0.065***	0.348	0.021	1.166*	1.880***
	(0.425)	(0.024)	(0.468)	(0.117)	(0.638)	(0.699)
REV	0.005	0.000***	0.004	–0.008	0.002	–0.006
	(0.019)	(0.000)	(0.019)	(0.049)	(0.001)	(0.048)
OCF	–0.011	–0.000	–0.011	−0.007*	0.000	−0.007*
	(0.007)	(0.000)	(0.007)	(0.004)	(0.000)	(0.004)
STD	–0.015	–0.000	–0.016	0.039***	–0.000	0.039***
	(0.017)	(0.000)	(0.016)	(0.013)	(0.000)	(0.013)
EXP	2.271*	0.030	2.358*	–0.286	–0.001	–0.288
	(1.314)	(0.021)	(1.293)	(0.285)	(0.003)	(0.284)
NWC	15.249	−0.899**	12.590	1.481	–0.023	1.445
	(52.314)	(0.437)	(52.185)	(13.256)	(0.137)	(13.186)
IND	Yes	Yes	Yes	Yes	Yes	Yes
PRO	Yes	Yes	Yes	Yes	Yes	Yes
Stkcd FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Constant	3.749***	0.004***	3.761***	3.863***	0.003***	3.868***
	(0.044)	(0.000)	(0.044)	(0.025)	(0.001)	(0.026)
Observations	7,089	7,089	7,089	11,725	11,725	11,725
Adj.R-squared	0.834	0.374	0.834	0.859	0.447	0.860

Software: Stata 15.0. First column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 1, fe robust. Second column syntax: xtreg IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 1, fe robust. Third column syntax: xtreg FC IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 1, fe robust. Fourth column syntax: xtreg FC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 0, fe robust. Fifth column syntax: xtreg IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 0, fe robust. Sixth column syntax: xtreg FC IC FF TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year if SOE = = 0, fe robust. Clustering robust standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Robustness check

In the multiple regression analysis of financial flexibility and financing constraints, this study refers to the methods of Ma and Zhang (2013) to construct a financial flexibility index. However, since Zeng et al. (2011) used the double index combination method to measure the level of financial flexibility, this study employed this method to construct the financial flexibility index for a robustness test. FF_EQ is financial flexibility calculated using the method of Zeng et al. (2011) (Table 8). Financial flexibility consists of cash and debt financing flexibilities, which are equally important. Specifically,

TABLE 8

Robustness test: financial flexibility index.

	(1)	(2)	(3)
Variable	FC	FC	FC
FF_EQ	−0.058***	−0.034***	−0.032***
	(0.006)	(0.006)	(0.006)
TAN		−0.201***	−0.195***
		(0.015)	(0.015)
DIV		0.031	0.074
		(0.126)	(0.117)
REV		–0.007	–0.006
		(0.008)	(0.007)
OCF		−0.005*	–0.004
		(0.003)	(0.003)
STD		0.018	0.018
		(0.016)	(0.016)
EXP		–0.080	–0.084
		(0.300)	(0.289)
NWC		–3.304	0.249
		(9.003)	(8.973)
IND			Yes
PRO			Yes
Stkcd FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Constant	3.642***	3.829***	3.857***
	(0.002)	(0.014)	(0.023)
Observations	18,814	18,814	18,814
Adj.R-squared	0.818	0.829	0.833

Software: Stata 15.0. First column syntax: xtreg FC FF_EQ i.year, fe robust. Second column syntax: xtreg FC FF_EQ TAN DIV REV OCF STD EXP NWC i.year, fe robust. Third column syntax: xtreg FC FF_EQ TAN DIV REV OCF STD EXP NWC i.IND i.PRO i.year, fe robust. Clustering robust standard error in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Financial flexibility = 0.5 × Cash flexibility + 0.5 × Debt financing flexibility

Cash flexibility = Enterprise cash holding rate − Industry cash holding rate

Debt financing flexibility = Maximum (0, average debt ratio of the same industry, debt ratio of a company).

Table 8 shows that with the gradual addition of control variables, the financial flexibility coefficient changes from −0.058 to −0.03), which are significantly negatively correlated with financing constraints at the level of 1%, thus proving that the results of this study are robust.

Conclusion and policy implications

Conclusion

Based on the data on listed institutions in Shanghai and Shenzhen from 2010 to 2019, this study examines the impact of digital finance on the degree of financing constraints and discusses the intermediary effect of investor confidence. This study analyses the heterogeneity of internal control and ownership in different organizations to study the impact of digital finance on corporate financing constraints under different circumstances. The conclusions of this study are as follows.

The change in financial flexibility has a significantly negative impact on the financing constraints enterprises face. Enterprises that maintain high flexibility when formulating financial policies are less constrained in terms of financing. After changing the measurement indicators of financial flexibility and financing constraints, we can obtain this conclusion. A financial flexible reserve is a guaranteed method to deal with uncertainty in enterprise operations and a positive signal for external investors.

Investor confidence plays an intermediary role in the relationship between financial flexibility and financial constraints. There is a mitigation effect of financial flexibility on financing constraints. Part of this first affects investor confidence and then transmits it to financing constraints. Maintaining financial flexibility can enhance investors’ confidence, promote their investment in enterprises, and reduce constraints on enterprises in financing.

The level of internal controls affects the relationship between financial flexibility and enterprise financing constraints. Improving enterprises’ financial flexibility with better internal controls can make external investors believe that this financial decision is beneficial to the improvement of enterprise future value, thus effectively alleviating financing constraints. For enterprises with poor internal controls, the degree of financial flexibility has no significant impact on the degree of financing constraints.

The change in the financial flexibility level of non-SOEs significantly negatively affects the degree of financing constraints. However, this relationship is not significant in SOEs. Therefore, improving the financial flexibility of non-SOEs is more effective in alleviating their financing constraints.

Policy implications

This study shows that information asymmetry leads to financing constraints and that maintaining a certain level of FF can enhance investor confidence by reducing information friction, thereby reducing the financing constraints faced by enterprises. In this process, enterprises need to consciously reserve certain financial flexibility and actively transmit information about their operations and financial status to the outside world. Regulators should give full play to their positive role of acting as a “visible hand” that maintains market order and improves market infrastructure construction. Investors should also strengthen their learning, remain calm while receiving all aspects of information, rationally distinguish and judge, and avoid being misled and blindly following one form of investment advice. Enterprises, regulators, and investors should work together to form a virtuous circle that supports an orderly market, development space for enterprises, and protection of investors’ interests.

(1) Enterprise level

Facing the uncertainty of the external environment, enterprises should establish a firm awareness of risk prevention and controls, actively reserve appropriate financial flexibility, and form an evaluation and early warning mechanism for the same. In this way, enterprises can cope with unexpected needs, enhance resistance, and ensure their growth and survival. Furthermore, they can send positive signals to external investors, enhance their confidence in the enterprises’ growth prospects, stimulate investment enthusiasm, and reduce the constraints they face when raising funds.

Enterprises should actively improve internal control and governance levels, strive to establish, and improve incentive and supervision mechanisms, and reduce the risk of damage to enterprise value and investors’ interests caused by principal–agent problems. The quality of enterprise internal control directly affects the quality of enterprise financial information, the scientific rationality of enterprise decision-making, and the effect of financial flexibility signal transmission. Thus, improving internal controls can help enterprises fundamentally improve operation and management, avoid fraud and inefficient investment behavior, reduce investor doubts, improve the trust of external investors in enterprises, and raise their investment intention.

(2) Regulatory level

Regulators should further improve the capital market, information disclosure mechanism, and the framework construction of information disclosure. In addition, they should strengthen the supervision of enterprise information disclosure, minimize information friction, continuously boost the transparency of the financial market, maintain its integrity, and build a standardized and efficient financial market to increase investor convenience, enable them to obtain market and enterprise information in a timely manner, reduce the cost of information searches, help investors improve their investment decisions, and reduce the constraints enterprises face when raising funds.

Investor confidence is crucial for the capital market, the economy, and society. Only when investors have confidence in the market and enterprises can savings be transformed into investment and the social economy develops. If investors lose confidence, transactions will gradually shrink, enterprises will experience financing difficulties, investment activities will not be carried out, and social and economic development will stagnate. Investors are often in a weak position in terms of information and resources, especially ordinary individual investors. Therefore, regulators should establish and improve the protection mechanism for investors’ rights and interests, increase punishment for acts that undermine the integrity of the capital market and damage investors’ rights and interests, form a mechanism of “survival of the fittest,” and enhance investor confidence to promote the activity and prosperity of the entire financial market.

(3) Investor level

The capital market contains vast information and is characterized by a rapidly changing market situation. Investors should strengthen self-learning, enrich their financial knowledge, and distinguish valuable information from a large amount of complex information to avoid being misled and blindly following leads. At the same time, after paying attention to various information, we should have our own rational judgment and form a value investment concept so that good companies can have sufficient growth space, poor companies can be eliminated naturally, and achieve a win-win situation between ourselves and the enterprise.

Limitations and future directions

This study discusses the relationship between financial flexibility and financing constraints, examines the intermediary mechanism of investor confidence, and proposes policy suggestions to maintain financial flexibility, strengthen information disclosure, and maintain investor confidence. However, there are still some deficiencies in the research process that can be improved in the future.

First, the strategy to maintain financial flexibility is conditional. While it is necessary to maintain financial flexibility, doing so will also produce opportunity and principal-agent costs. The decision to maintain financial flexibility is rational and plays a positive role in improving the value of enterprises only when the benefits of maintaining financial flexibility are greater than the costs. Therefore, follow-up research can quantitatively analyze the value of financial flexibility and determine the best level of financial flexibility.

The second issue concerns the measurement of the financing constraints. Although financing constraints are a common phenomenon, their size is difficult to measure accurately. The existing measurement methods of financing constraints, whether grouping the degree of financing constraints based on a single index or building a comprehensive index combined with multiple indexes, estimate the financing constraints indirectly. There are certain limitations, and disputes remain concerning the advantages and disadvantages of various methods. Therefore, further research is needed to measure the financing constraints more accurately, especially in combination with China’s national conditions.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author contributions

CY: conceptualization, writing the original draft, and methodology. ZF: supervision. XS: formal analysis. YL: variable construction, funding acquisition, and data handling. All authors have read and agreed to the published version of the manuscript.