The role of accruals quality in the access to bank debt an empirical research of non-financial listed firms in vietnam

Nội dung text: The role of accruals quality in the access to bank debt an empirical research of non-financial listed firms in vietnam

1. THE ROLE OF ACCRUALS QUALITY IN THE ACCESS TO BANK DEBT AN EMPIRICAL RESEARCH OF NON-FINANCIAL LISTED FIRMS IN VIETNAM Nguyen Dinh Uong*1 ABSTRACT: This study, conducted with a sample of Vietnam non-financial listed firms during the period from 2008 to 2015, investigates the impact of accruals quality in the access to bank debt. The result illustrate a positive association between accruals quality and bank debt, even when controlling for other determinants of bank debt and for possible endogeneity between bank debt and accruals quality, which suggests that higher precision of earnings reduces information asymmetries with banks and favors the access of firms to bank loans. Keywords: Accruals quality, bank debt, information asymmetry. 1. INTRODUCTION Bank loans is an important factor and frequent activity in the business process of an enterprise. In the credit process, the bank check and evaluate the company’s financial statements will help the bank determine which assets the company can use as collateral, assess future cash flows, assess debt repayment capacity, and analyze risk of the company in redefining lending rates. Asymmetric information in credit operations is easy to happen because windowing pressing is become a popular activity in the recent years due to the economy is difficult and enterprise need more capital to operate and develop. The role of asymmetric information in bank debt contracting is an aspect of special interest in accounting and finance literature. In the presence of this market imperfection, financial institutions face adverse selection and moral hazard problems that make the assessment of the investment projects of their borrowers and the monitoring of their opportunistic behaviors difficult. Previous research has focused on the effect of asymmetric information as a determinant of bank debt from various points of view. The main findings of these studies are that bank debt is preferable to public debt when asymmetric information is present, due to the monitoring role that banks may play on the borrower (Johnson, 1997; Anderson and Makhija, 1999; Hooks, 2003; Denis and Mihov, 2003; among others); banking relationships are also valuable in obtaining bank financing, because of the information generated about the borrowers’ financial prospects (Petersen and Rajan, 1994; Berger and Udell, 1995; among others); finally, firm reputation may also reduce asymmetries (Diamond, 1991). On the other hand, precision of earnings has been shown to be a factor that, by reducing the information risk faced by lenders, improves debt contracting terms, such as the cost of debt financing (Francis et al., 2005), the debt maturity structure of firms, and the likelihood of providing collateral (Bharath et al., 2008). In order to test my hypothesis I consider several accruals quality proxies (Dechow and Dichev, 2002; McNichols, 2002; Ball and Shivakumar, 2006) and test their effect on bank debt in a sample of Vietnam * Department of Economic Mathematics, University of Economics and Law.
2. 448 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA non-financial listed firms. The results show a positive association between my proxies of accruals quality and bank debt, which suggests that the precision of earnings reduces information asymmetries between the firm and the bank in my institutional context. These findings provide valuable insights for managers since they suggest that by improving the quality of earnings firms can enhance their availability of debt financing. 2. THEORETICAL BACKGROUND 2.1 Asymmetric information and bank debt George Akerlof, Michael Spense and Joseph Stiglitz (1970) have explained many issues in the economy to argue that asymmetric information was existed. In the study of Myer and Majluf (1984), corporate executives have more information and therefore, they will make investment decisions that are beneficial to themselves without regard to shareholder interest. As a result, investment efficiency is significantly affected, leading to excessive or subdued investment and conflicts between shareholders and corporate creditors. In the creditors and borrowers relationship, the asymmetric information is the traditional financial literature to explain why capital does not always flow to firms with profitable investment opportunities (Stiglitz and Weiss, 1981). In this situation, creditors face adverse selection and moral hazard problems when granting credit. According to previous literature, banks are more effective in monitoring borrowers than other lenders, e.g., private debtholders, due to their closer relationship with the firms (Fama, 1985; Houston and James, 1996; Blackwell and Kidwell, 1988; Diamond, 1984, 1991) and their ability to design and redesign contracts according to the characteristics of the borrower (Bharath et al., 2008). I expect that in bank-based financial systems firms may improve their access to bank debt by reducing informational asymmetries. 2.2. The relationship between accruals and financial reporting quality According to Vietnam accounting standards, the Accrual basis is defined: All economic and financial operations of enterprises, which are related to assets, liabilities, owners’ equity, revenues, and costs must be recorded in accounting books at the time they arise, not at the time of the actual receipt or payment of cash or cash equivalents. Financial statements made on the basis of accrual shall reflect the financial status of enterprises in the past, at present and in the future. Meanwhile, cash flow statements are prepared on the cash basis and cash transaction were recorded when cash was actually collected or paid. Therefore, the difference between the profit in the income statement and cash flow in the cash flows statement creates the accounting variables that researchers commonly call accruals variables. In other words, “accruals” are non- cash profit accounting show in the statement of income. Financial report adjustment for better results or I can say “Window dressing” is the popular behavior of board of management to achieve their purpose. Window dressing through profit adjustment is the most popular behavior because profits is an important indicator and attracted reader of financial report. Cash flow from business operations on cash flow statements cannot be adjusted so that the board managements must recognize the accrued accounting variables and adjust them in order to adjust profit. Financial statements are, therefore, an important source of information in mitigating the problems associated with borrower risk and asymmetric information: the higher the quality of this information, i.e., the more accurate the precision of earnings to capture future cash flows, the lower the information risk of the firm, because the lender can better estimate the future cash flows of the firm with which the loans will be repaid.
3. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 449 Previous research has verified that accruals increase the ability to predict future cash flows (Dechow, 1994) and that the reduction of information risk due to higher accruals quality influences contract terms, such as interest cost, collateral and debt maturity (Francis et al., 2005; Bharath et al., 2008). 2.3 Accruals quality and access to bank debt Financial statement lending is a transaction technology based on the strength of the borrower’s financial statement. Banks use this accounting information in order to estimate the expected future cash flows of the borrowers, and then assess their repayment capacity (Berger and Udell, 2006). Financial statements are, therefore, an important source of information in mitigating the problems associated with borrower risk and asymmetric information: the higher the quality of this information, i.e., the more accurate the precision of earnings to capture future cash flows, the lower the information risk of the firm, because the lender can better estimate the future cash flows of the firm with which the loans will be repaid. Previous research has verified that accruals increase the ability to predict future cash flows (Dechow, 1994) and that the reduction of information risk due to higher accruals quality influences contract terms, such as interest cost, collateral and debt maturity (Francis et al., 2005; Bharath et al., 2008). Based on the results of these papers and on the negative association between information asymmetry and bank debt in private debt contexts, I establish the hypothesis that this reduction of information risk may influence not only the contract terms of the loans but also the access of the firm to these loans. 3. Sample and data I have used panel data from non-financial listed firms in Vietnam for my analysis. I selected listed firms (except the company operates in the financial sector such as bank, insurance ) during the period from 2008 to 2015 in HOSE and HNX. They should also present disaggregation of bank debt in their accounting statements. Subsequently, I refined the information, eliminating lost values, firms for which the information was not available for the three consecutive years and cases with errors in the accounting data. The data in the sample satisfies the requirements: First of all, companies operating in the financial sector (ie, banking, insurance, life insurance companies and investment trusts) and in the areas of utility (eg electricity ) are excluded. Because companies in this field have different accounting practices from manufacturing, trade and service enterprises. Secondly, to use the GLS regression model with latency requirements, only companies with 5 or more observation years are retained. Third, companies that have observations for variables that do not find the value will be discarded. Although this data is collected from 2008 to 2015, the calculation of the accruals must take the difference between the two years, so the 2008 observations will not appear in the data set processed. The final data set for this study was 3,179 observations taken from 495 listed firms omfr 2009 to 2015. 4. RESEARCH DESIGN 4.1 Model specification I analyze the relationship between bank debt and accruals quality by estimating the following regression: (1) where BANKDEBT represents the proportion of firm’s bank debt; AQ the accruals quality proxy; GROWP
4. 450 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA the growth opportunities; LEV the leverage; SIZE the size; FA is fixed assets over total assets as a proxy for col- lateral, ROA the return on assets; Altman Z-score, an indicator of firm’s financial strength, LAGE the age of the firm, and CFOIND the cash flow from operations relative to the industry average. The parameters are time dummy variables that change over time but are equal for all firms in each of the time periods considered, and , represents unobservable characteristics of the firms that have a significant impact on the firm’s bank debt. These vary across firms but are assumed to be constant for each firm. AQ is financial reporting quality. Financial statement lending is a transaction technology based on the strength of the borrower’s financial statement. Banks use this accounting information in order to estimate the expected future cash flows of the borrowers, and then assess their repayment capacity (Berger and Udell, 2006). Financial statements are, therefore, an important source of information in mitigating the problems associated with borrower risk and asymmetric information: the higher the quality of this information, i.e., the more accurate the precision of earnings to capture future cash flows, the lower the information risk of the firm, because the lender can better estimate the future cash flows of the firm with which the loans will be repaid. A positive association of size and age with bank debt is expected because the literature on bank debt shows that factors such as size and age are proxies of asymmetric information and firm’s reputation that influence the levels of bank debt because of the information they generate about the financial expectations of the borrowers (Diamond, 1991; Petersen and Rajan, 1994; Berger and Udell, 1995). Larger and older firms present lower levels of asymmetric information and have better reputations (Berger and Udell, 1995), so it is expected they use more public debt than companies with higher levels of asymmetric information (Denis and Mihov, 2003). Additionally, firms with higher growth opportunities are more likely to exhaust internal funds and consequently this would lead to use more debt. This suggests a positive relationship between growth opportunities and debt. However, De Andrộs Alonso et al., 2005 find a negative relationship between growth opportunities and bank debt for Spanish listed firms. Access to bank debt depends on solvency, and the relevance of collaterals in reducing moral hazard problems under asymmetric information (Boot et al., 1991; Boot and Thakor, 1994), so I would expect bank debt to present a positive association with firm solvency and its collaterals. On the other hand, since banks are the main providers of external funds for my sample, it is expected that more leveraged firms have a greater presence of bank debt. Based on the same argument (the choice between internal funds and private debt), more profitable that generate higher cash flows are more able to finance their projects with internal funds. Accordingly, I would expect a negative relationship of bank debt with profitability and the internal financing. I illustrate the expected sign in the table below Table 1 : Expected sign No Name of vari- Variables description Expected sign able 1 BANKDEBT I measure the financing received from banks using the variable BANKDEBT, which is calculated as total bank debt over total assets. It represents the propor- tion of firm’s bank debt.
5. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 451 2 AQ + AQ_DD, AQ_McN, AQ_BS are measured as the negative value of the according to the Dechow and Dichev model (2002), McNichols model (2002) and Ball and Shivakumar model (2006), respectively. AQ_sdDD, AQ_sdMcN, and AQ_sdBS report the negative value of the stan- dard deviation of firm i’s residuals from the industry-year regressions, for Dechow and Dichev model, Dechow and Dichev model modified by Mc- Nichols (2002), and Ball and Shivakumar model, respectively 3 GROWP The growth opportunities calculated as sales in year t over sales in years t - 1 +/- 4 LEV The leverage defined as total debt over total assets + 5 SIZE The size measured as the logarithm of assets + 6 FA FA is fixed assets over total assets as a proxy for collateral which is + defined as fixed assets over total assets 7 ROA ROA the return on assets which is measured as earnings before interests - and taxes over total assets 8 ALTMAN- Zit I employ the firm’s financial strength (Z), measured with Altman’s + Z-score (1968), where Z is defined as: Z = 0.012X1 + 0.014X2 + 0.033X3 + 0.066X4 + 0.999X5 where X1 is the working capital/total assets; X2 the retained earnings/ total assets; X3 the earnings before interest and taxes/total assets; X4 the market value equity/book value of total debt; X5 is the sales/total assets 9 LAGE Firm’s age (LAGE), defined as the logarithm of the number of years +/- since its inception 10 CFOIND Operating cash flow relative to the industry average (CFOIND) in order - to control for the ability of the firm to generate internal financing. 4.2. Variables description 4.2.1. Dependent variables I measure the financing received from banks using the variable BANKDEBT, which is calculated as total bank debt over total assets. 4.2.2 Accruals quality metrics As regards financial reporting quality metrics, I use proxies which have been used extensively in research (M Fuensanta Cutillas Gomariz, Juan Pedro Sỏnchez Ballesta, 2013; Nesrine Klai,Abdelwahed Omri,2011; Jennifer Martớnez-Ferrero,2014). Like these studies, I focus on the accuracy with which accruals convey information about cash flows in order to inform stakeholders, particularly investors and creditors. Dechow and Dichev model (estimated AQ_DD, AQ_sdDD) First, I use the model developed by Dechow and Dichev (2002). In this model, financial reporting quality is measured by the extent to which current working capital accruals map onto operating cash flows of the prior, current and future periods. Thus, Dechow and Dichev (2002) regress current working capital accruals (WCAt) on cash flow from operations in the previous fiscal year (CFOt-1), the current year (CFOt), and the subsequent year (CFOt+1), all deflated by average total assets.
6. 452 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA McNichols model (estimated AQ_McN, AQ_sdMcN) My second proxy for accruals quality, following Francis et al. (2005), is the Dechow and Dichev’s (2002) model, modified by McNichols (2002), which also includes the changes in revenues and property, plant and equipment (PPE) as explanatory variables: Ball and Shivakumar model (estimated AQ_BS, AQ_sdBS) The model includes three additional variables to those in the Dechow and Dichev (2002) model: I illustrate the variables in the table below: Table 2: Variables description Variable Name of variable Variables description Working capital accruals I calculated as the change in current assets ( CA), minus the change in cash and cash equivalents ( Cash), minus the change in current liabilities ( CL) plus the change in short term bank debt ( Debt). CFOit, CFOt-1, Cash flow from operations Obtain from cashflow statement and CFOt+1 of firm i in year t, t-1 and t + 1, respectively The change in revenues The property, plant and Obtain from balance sheet equipment D Dummy variable takes the value 1 if DCFO is negative and 0 otherwise Average total assets the mean of the firm’s total assets in years t -1 and t The model is estimated at two-digit level in its cross-sectional version for each industry-year combination of the Vietnam listed companies. The residual vector reflects the variation in working capital accruals unexplained by cash flows of the previous, current and subsequent periods. Therefore, the absolute value of the residual for each firm-year observation is an inverse measure of accruals quality. In order to facilitate the interpretation of this variable I use the negative value of , which I define as AQ include: AQ_DD from Dechow and Dichev model , AQ_McN from McNichols model , AQ_BS from Ball and Shivakumar model. The fourth, fifth and sixth proxies, I use are based on the standard deviation of the residuals from the industry-year estimations of previous models estimated in Eq. (2), Eq. (3), Eq. (4) includes: , , , respectively. Instead of the absolute value of the residuals for each firm, I compute an inverse measure of accruals quality for firm i in year t as the standard deviation of firm i’s residuals from the industry-year regressions, , calculated over periods t-4 to t. Larger standard deviations of residuals
7. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 453 indicate poorer accruals quality. I also use the negative values of , , , defined as AQ_sdDDit, AQ_sdMcNit, and AQ_sdBSit. 5. RESULTS 5.1. Descriptive statistics and preliminary analysis Table 3 summarizes the descriptive statistics for the variables used in my empirical research. In my sample, the average presence of bank debt over total assets (BANKDEBT) is 19.9%. The mean value of leverage is 54.01 %, whereas the mean value of fixed assets over total assets is 13.13 % and the average Altman Z-score is 0.38. On average, the firms in the sample are profitable (mean ROA 8.0%). The mean values of the accruals quality proxies are consistent with previous literature. The descriptive statistics highlight the importance of bank debt for listed firm in Vietnam, since it represents 19.9% of total assets, while for US, non-financial listed firmscommercial bank debt reaches 18.75% (Berger and Udell, 1998). Table 3: Descriptive statistics Mean Std.Dev Maximum Minimum Perc.25 Perc.50 Perc.75 BANKDEBT 0.1991 0.1842 0.7871 - 0.0146 0.1660 0.3276 AQ_DD (0.0902) 0.1186 (0.0001) (1.5060) (0.1051) (0.0544) (0.0244) AQ_McN (0.0891) 0.1176 (0.0001) (1.5023) (0.1063) (0.0536) (0.0248) AQ_BS (0.0903) 0.1174 (0.0001) (1.5060) (0.1073) (0.0545) (0.0249) AQ_sdDD (0.1067) 0.1112 (0.0000) (1.4621) (0.1306) (0.0723) (0.0405) AQ_sdMcN (0.1065) 0.1101 (0.0001) (1.4633) (0.1297) (0.0725) (0.0423) AQ_sdBS (0.1077) 0.1108 (0.0002) (1.4601) (0.1312) (0.0734) (0.0419) GROWP 1.2010 0.9464 24.1398 0.0184 0.9332 1.0912 1.2649 LEV 0.5107 0.2170 0.9706 0.0026 0.3410 0.5401 0.6801 SIZE 26.8716 1.3815 31.9056 23.2820 25.9818 26.7992 27.7906 FA 0.1953 0.1939 0.9764 - 0.0523 0.1313 0.2705 ROA 0.0926 0.0911 0.9970 (1.5681) 0.0436 0.0801 0.1274 ALTMAN-Z 1.0671 2.0876 23.5173 (0.0015) 0.1487 0.3859 1.0244 LAGE 2.8533 0.6534 4.4886 - 2.3979 2.8332 3.4012 CFOIND 1.0000 34.7483 557.6984 (596.4341) (0.1811) 0.2039 1.6029 Table 4 presents the Pearson correlation matrix between variables. As expected, accruals quality proxies show positive and significant correlations between them and with bank debt. Since higher values of accruals quality proxies represent higher accruals quality, these results present preliminary evidence of a positive association between accruals quality and bank debt. . Collinearity is a possible concern for these variables, which I will analyze in the robustness section, showing that it does not affect my results.
8. 454 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA Table 4: Correlation matrix BANKDEBT AQ_DD AQ_McN AQ_BS AQ_sdDD AQ_sdMcN AQ_sdBS GROWP LEV SIZE FA ROA ALTMAN-Z LAGE CFOIND BANKDEBT 1 AQ_DD 0.0774 1 AQ_McN 0.0813 0.9869 1 AQ_BS 0.0717 0.9905 0.9804 1 AQ_sdDD 0.0842 0.6592 0.654 0.653 1 AQ_sdMcN 0.0849 0.6604 0.6616 0.6549 0.9937 1 AQ_sdBS 0.0812 0.6527 0.6484 0.6533 0.9936 0.9884 1 GROWP -0.0393 -0.0164 -0.0073 -0.0239 -0.0411 * -0.037 * -0.042 1 LEV 0.5854 0.0112 0.0114 0.0031 0.0352 * 0.0331 0.0305 0.026 1 SIZE 0.3352 0.0313 0.0315 0.0247 0.0527 0.0612 0.041 0.0346 0.3522 1 FA 0.2338 0.102 0.106 0.1068 0.0974 0.1044 0.1015 -0.0425 -0.0376 0.0321* 1 ROA -0.1916 0.0739 0.0718 0.0701 0.0382* 0.0369* 0.0379* 0.0696 -0.3406 -0.0582* 0.0282 1 ALTMAN-Z 0.206 0.0001 -0.003 -0.0018 0.0193 0.0242 0.0107 0.0335* 0.1576 0.5366 0.0307* 0.0695 1 LAGE 0.0142 0.0657 0.0672 0.0713 0.0685 0.0699 0.072 -0.0871 -0.0088 0.0704 0.0933 0.1199 0.0036 1 CFOIND -0.0244 -0.0084 -0.0103 -0.0074 -0.0031 -0.0029 -0.0033 0.0171 -0.0156 0.0338 0.0156 0.0158 0.0421 -0.013 1 * Significance at the 10% level. Significance at the 5% level. Significance at the 1% level. 5.2. Regression results In Table 5, I present the results of the estimation of my model1. I present results for the six proxies of accruals quality defined above (columns 1 to column 6) using the fixed effects estimator. This result confirms my hypothesis that higher accruals quality reduces information asymmetries between firms and banks and allows firms to obtain more bank debt. For the control variables, I obtain that higher leverage, size, fixed assets, and Altman Z-score are significantly associated to higher bank debt, whereas more profitable firms, with more growth opportunities and with higher cash flow from operations relative to the industry average use less bank debt. The results show that firms with higher access to internal financing present lower levels of bank debt since both variables operating cash flow relative to industry average and ROA are negatively related to bank debt. Thus the firms of my sample rely on internal resources for carrying out investment projects when they are profitable and generate internal cash flow, whereas when they are not profitable or do not generate cash flows, they finance their projects with bank debt because this is the main source of external funds in the Vietnam market. The result is consistent with the pecking order theory of Myers and Majluf (1984). Table 5: Bank debt and accruals quality (1) (2) (3) (4) (5) (6) AQ_DD 0.0363 AQ_McN 0.0388 AQ_BS 0.0351 AQ_sdDD 0.0570 AQ_sdMcN 0.0562 AQ_sdBS 0.0561 GROWP -0.00510 -0.00523 -0.00500 -0.00773 -0.00779 -0.00781 LEV 0.464 0.465 0.465 0.472 0.472 0.472 SIZE 0.0132 0.0132 0.0132 0.0147 0.0147 0.0148 FA 0.214 0.213 0.214 0.215 0.214 0.214 ROA -0.0718 -0.0706 -0.0691 -0.0673 -0.0667 -0.0664 ALTMAN-Z 0.00481 0.00482 0.00479 0.00200 0.00198 0.00201 LAGE -0.00719 -0.00724 -0.00732 -0.00275 -0.00271 -0.00287 CFOIND -0.000226 -0.000226 -0.000226 -0.000107 -0.000108 -0.000107 CONST -0.403 -0.403 -0.403 -0.452 -0.451 -0.453
9. INTERNATIONAL CONFERENCE STARTUP AND INNOVATION NATION 455 * Significance at the 10% level. Significance at the 5% level. Significance at the 1% level. Table 6 presents the mean values of bank debt by quartiles of accruals quality, and the t test of difference between quartiles 1 and 4. Quartile 1 shows the mean value of bank debt for firms with lowest accruals quality, whereas quartile 4 shows the mean value of bank debt for firms with highest accruals quality. In the last column of Table 6, I include the t-test to determine whether the mean values of quartile 1 are significantly different from those of quartile 4. The findings show significant differences between quartile 1 and 4 for all accruals quality metrics, with higher presence of bank debt in those firms with higher accruals quality. Table 6: Bank debt by accruals quality quartiles Q1 Q2 Q3 Q4 t-test AQ_DD 0.1744 0.1922 0.2058 0.2317 -3.15 AQ_McN 0.1743 0.1931 0.2094 0.2273 -2.86 AQ_BS 0.1784 0.1958 0.2079 0.2220 -2.31* AQ_sdDD 0.1764 0.1946 0.2230 0.2139 -2.73 AQ_sdMcN 0.1757 0.1950 0.2258 0.2113 -2.67 AQ_sdBS 0.1814 0.1929 0.2189 0.2147 -3.84 * Significance at the 10% level. Significance at the 5% level. Significance at the 1% level. 5.3 Robustness results In this section, I consider the potential endogeneity between bank debt and accruals quality since there are theoretical arguments to expect that leverage, and in particular bank debt, which is the main source of debt in the Vietnam market, may also influence accruals quality. On the one hand, in high-leveraged firms, managers have incen- tives to manipulate earnings to avoid debt covenant violations (Watts and Zimmerman, 1986), so a negative effect of debt on accruals quality is expected. Although the debt covenant hypothesis is the traditional argument for the effect of debt on the manipulation of earnings, Feltham et al. (2007) develop a model that predicts that when the firm’s performance is average to good, and given that debt holders demand high quality information, managers will use their accounting discretion to provide more pre- cise information in order to obtain better contracting terms, such as interest costs. Accordingly, I address this possible endogeneity of bank debt using a two- stage least-squares model (2SLS). I model bank debt and accruals quality as simultaneously deter- mined. Accruals quality is estimated endogenously in the first stage regression and bank debt is the dependent variable in the second-stage regression. In the first stage, I estimate accruals quality according to the model4: = Intercept + + + + + + + + (5) where OPERCYCLE is the length of operating cycle, (SALES) the standard deviation of sales, (CFO) standard deviation of cash from operations, NEGEARN the percentage of years in which
10. 456 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA earnings are negative and FCOST, the ratio of financial expenses over total debt minus accounts payable. The rest of variables are defined as previously. In the second stage, I use the predicted value of accruals quality from the first stage regression. In Table 7, I show the 2SLS results, which are consistent with my main findings, i.e., accruals quality metrics are positively and significantly related to bank debt. Additional robustness tests have been applied. To avoid a possible specification error if I remove the control for one of these variables, I regress the Altman-Z on leverage and introduce the residuals from this regression instead of the Altman-Z. This renders the information of leverage orthogonal to Altman-Z, and residuals capture the portion of Altman-Z not explained by leverage. The conclusions are the same as those presented before. Finally, my results do not change if my estimate using t-statistics based on standard errors clustered at the firm and the year level (Petersen, 2009), which are robust to both heteroskedasticity and within-firm serial correlation, or if I use total bank debt over total debt as proxy for the dependent variable. Table 7: Bank debt and accruals quality: two stage regressions. Variable (1) (2) (3) (4) (5) (6) predict_AQ_DD 4.66537 predict_AQ_McN 3.86821 predict_AQ_BS 4.85456 predict_AQ_sdDD 2.38245 predict_AQ_sdMcN 2.91586 predict_AQ_sdBS 2.31351 GROWP -0.00506 -0.00226 -0.00520 -0.00255 -0.00203* -0.00223 LEV 0.36592 0.339111 0.36718 0.39694 0.38712 0.39578 SIZE 0.01989 0.01475 0.02061 0.01671 0.01505 0.01911 FA 0.08805 0.09826 0.08784 0.11590 0.11920 0.11829 ROA -0.09911 -0.05833 -0.10295 -0.06047 -0.05613 -0.05247 ALTMAN-Z 0.00185 0.001912 0.00107 0.00343 0.00285 0.00347 LAGE -0.00486 0.00087 -0.00525* 0.00096 0.00260843 0.00112113 CFOIND -0.00009 -0.00008 -0.00009 -0.00011 -0.00010 -0.00011 _cons -0.20500 -0.11912 -0.20673 -0.28059 -0.19248 -0.35159 * Significance at the 10% level. Significance at the 5% level. Significance at the 1% level. 6. CONCLUSIONS In this paper, I examine the effect of financial reporting quality on the access of firms to bank debt for a sample of Vietnam non-financial listed firms, and find that higher accruals quality, i.e., more precision of earnings in relation to cash flows, is associated with a greater presence of bank debt with respect to total assets. Since the quality of accounting information can be considered an inverse indicator of information asymmetry, this finding is consistent with the financial literature, which has shown that, in private debt markets, the use of bank debt is partially determined by information asymmetry. Moreover, this result also
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