Abstract
This study investigates whether the deterioration of banks’ financial health affects borrowers’ earnings quality. To examine this issue, we consider the European sovereign debt crisis a shock to the health of certain European banks; these banks were affected and tightened their lending conditions relatively more than nonexposed banks during the crisis. This setting represents an exogenous shock to German nonfinancial firms that were not directly affected by the crisis. We use a difference-in-differences research design, and German firms with lending relationships with nonexposed banks are used as controls. Using various discretionary accrual measures and a timely loss recognition concept to evaluate earnings quality, we find interesting evidence. Our investigation of discretionary accruals reveals that during the European sovereign debt crisis, exposed banks’ borrowers do not engage in less earnings management than other borrowers. However, we observe that exposed banks’ borrowers are more likely to report losses in a timely manner. Overall, our results provide new insight into the indirect consequences of the sovereign debt crisis on firms’ earnings quality.
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Notes
The terms “exposed” and “affected” are used synonymously.
In addition to investor responsiveness to earnings and external indicators of earnings misstatements, earnings properties represent one dimension of the earnings quality. In general, earnings quality proxies should not be used as substitutes (Dechow et al. 2010). Due to our sample, we can only analyze the dimension of earnings’ properties.
This finding is consistent with the previous literature analyzing other financial crises. The emerging markets financial crises in the late 1990s affected several economies. Chava and Purnanandam (2011) show that consistent with an adverse shock to the loan supply, crisis-affected banks decreased their lending quantity and increased the loan interest rates during the post-crisis period more significantly than banks that were unaffected by this crisis. The results suggest that the global integration of the financial sector contributed to the propagation of financial shocks from one economy to another through the banking channel. Similar evidence related to the global financial crisis exists; the resulting decline in bank health led to a significant reduction in bank lending. Additionally, banks that incurred larger losses reduced their loan supply more than banks that were less affected by the crisis (e.g., Ivashina and Scharfstein 2010 analyzing US banks). Furthermore, Santos (2011) and Bord and Santos (2014) find that loan spreads of credit to firms increased during the global financial crisis. Additionally, firms had to pay higher fees for guaranteed access to liquidity. Chodorow-Reich (2014) verifies that less healthy banks reduced lending more than healthy banks during the 2007–2009 period.
For a detailed discussion regarding the potential threats of this identification strategy, please refer to Bischof (2014).
Bottero et al. (2018) use Italian data to analyze the real effect on firms of the first Greek bailout’s shock to banks’ sovereign debt.
Compared to a bank-based financial system, a market-oriented financial system is characterized by high market capitalization and a low ratio of loans to nonfinancial firms to GDP (Kaufmann and Valderrama 2008).
For a literature overview of the firm–bank relationship and credit rationing, see, for example, Cenni et al. (2015).
Equity-issuing firms can increase their stock prices through either earnings management or voluntary disclosure. Jo and Kim (2007) find evidence that, in this context, earnings management is inversely associated with disclosure frequency.
For example, Bigus and Hillebrand (2017) focus solely on private German firms and analyze the effect of bank relationships on the financial reporting quality. The authors find evidence that firms increase the financial reporting quality to compensate for the loss of private information after a bank acquisition.
For a discussion regarding the importance of earnings management behavior in the credit risk assessment process, please refer to Ahn and Choi (2009).
We transfer the aspect proposed by Gormley et al. (2012) to our setting, although these authors focus on the entry of foreign banks into India during the 1990s given the regulatory change. This change led to a decline in the loan supply for certain firms.
Recent studies also focus on the link between conditional conservatism and earnings management (see García Lara et al. 2018). The prior literature argues that conditional conservatism restricts earnings management incentives and possibilities. For example, LaFond and Watts (2008) suggest that accounting conservatism is associated with decreasing earnings management because managers are less able to engage in manipulation and overstate firms’ financial performance.
Popov and van Horen (2015) also use exposure data as of March 2010 in a robustness test. Although two banks change groups from the December 2010 exposure, their results remain unchanged. Thus, we assume that using the exposure data from March 2010 also would not change our results.
In addition, Popov and van Horen (2015) use an alternative risk measure that is more consistent with the regulatory requirements because this measure assesses the holding of risky assets in relation to the equity of a bank. Based on this alternative measure, the evidence suggests that the results do not depend on how one scales the bank’s risky sovereign exposure.
A detailed description of the static composition of main banks in European non-GIIPS countries is presented in Popov and van Horen (2015, p. 1860).
First, Adelino and Ferreira (2016) identify 46 banks that have ratings equal to their sovereign before a downgrade during the sample years between 1989 and 2012. Analyzing the effect of bank credit rating downgrades on the bank lending supply reveals that, following a sovereign downgrade, these banks reduce their lending supply significantly more and increase their interest rate spreads more than banks with ratings that differ from their sovereign rating. By manually comparing banks treated by Adelino and Ferreira (2016) with the exposed banks in our sample, we conclude that the results of Adelino and Ferreira (2016) do not contradict our classification used in the analysis. Second, the results of Schmidt and Zwick (2018), who analyze the situation of only German banks, also support our exposure classification. For example, “[o]nly for the highly affected big private banks and Landesbanken … some evidence of loan supply restrictions during the … Euro area crisis” (p. 24) exists. Therefore, the classification of Popov and van Horen (2015) is consistent in this respect because certain crisis-affected German banks are classified as exposed. In addition, Schmidt and Zwick (2018) find no evidence for smaller banks of loan supply restrictions, which is also in line with our exposure classification.
The pre-crisis period used could be an inappropriate period for testing our hypotheses because the global financial crisis occurred during this time. Hence, we could change the definition of the pre-crisis period. This alternative pre-crisis period would begin in 2004 and last until 2006. On the one hand, this definition excludes the global financial crisis; however, on the other hand, we are faced with the problem that Amadeus provides few observations for this alternative pre-crisis period. Since this approach does not allow for a representative analysis of our hypothesis, we use the financial crisis period as our pre-crisis period. In a further sensitivity analysis, we exclude 2008 and 2009 from our sample, and 2007 is then the benchmark. The results remain the same (untabulated).
Because Amadeus does not distinguish between long-term and short-term provisions, we assume that all provisions are short term and include them in total accruals. In an unreported sensitivity analysis, we omit all provisions, and the results remain unchanged.
Following the recommendation of Petersen (2009), we use robust standard errors clustered by firm.
In their analysis of subsidiaries’ earnings quality behavior, Dutillieux et al. (2016) divide long-term debt into long-term financial debt and other debt.
In contrast to our growth measure, Reynolds et al. (2004) measure growth as the ratio of equity market value to equity book value.
For a detailed description of the related concepts of value relevance and conservatism, please refer to the explanations provided by Ball and Shivakumar (2005).
The original model by Ball and Shivakumar (2005) contains a dummy variable DCFO that equals one if the cash flow from operations is negative. To assess the difference in gain and loss recognition, these authors include an interaction term between the dummy variable and cash flow from operations. However, we follow the recent literature (Brown et al. 2014; Dutillieux et al. 2016) and estimate a separate coefficient of negative cash flow from operations. Using this approach, we avoid interaction terms with more than three variables, which eases the interpretation of our results. In addition, this approach uses historic financial statement data and, therefore, allows a calculation for both private and public firms.
Nevertheless, we discuss this assumption in detail in Sect. 4.1.
Another approach is an identification process that follows relationship banking studies. This process describes the firm’s main bank as the largest loan supplier and identifies the main bank using pre-crisis loan information from DealScan (e.g., Bharath et al. 2007; Lo 2014). DealScan provides information about large commercial loans; therefore, this process allows for the identification of an active banking relationship. The advantage of this approach is that in most cases, only one main bank exists. However, by employing the reclink algorithm, a matching approach for Amadeus and DealScan widely recognized in the literature, we obtain significantly fewer observations than that with our approach. In future research, we would use this identification approach as a sensitivity check.
Because the requirement of six firm year-observations for each industry, year and reporting practice would additionally reduce our sample size, we include firm-year observations with IFRS reporting from other European countries for estimation purposes.
We address this issue in our sensitivity analysis (Sect. 6.2).
In a sensitivity analysis, we winsorized the dependent variable |DA| at + 1 and − 1 as in Francis and Yu (2009). The results remain similar to those of the main analysis (untabulated results).
In addition, the untabulated analysis shows that the median and mean of the difference between the absolute values of the Jones model discretionary accruals and of return on assets per year are negative. This result indicates that the size of the reported effects is likely plausible. The overall ratio between these two figures is approximately 75%. Owens et al. (2017) claim that it is difficult to attribute this magnitude solely to management discretions. To assess the robustness of our primary results, we use alternative discretionary accrual measures in our sensitivity analyses.
In addition, we compute the variance inflation factors for both regression model estimations. This factor is 2 on average in both discretionary accruals models and 7.5 on average in the timely loss recognition model. Dutillieux et al. (2016) also address this concern of relatively high variance inflation factors in a timely loss recognition model and state that the higher values arise as a result of the multiple interaction terms.
To mitigate concerns that our findings are attributable to the smaller sample size in our propensity score matched sample, we use two additional matching approaches. First, we follow previous accounting research and use a caliper of 3% instead of 1% (Minutti-Meza 2013). Second, we apply a matching approach with replacement. Compared to our original matching approach, we observe no changes in the validity of our results (untabulated).
Notably, we use this propensity score matched sample to investigate discretionary accruals. Since we include signed operating cash flow in our investigation of timely loss recognition, we adapt Eq. (7) and use signed operating cash flow instead of absolute operating cash flow.
Signed discretionary accruals indicate the direction in which managers are expected to manage earnings (Jha 2013). Our results when using signed discretionary accrual measures do not indicate that firms with banking relationships with exposed banks manage earnings upwards or downwards since our sample reveals no significant coefficient of interest after matching (untabulated).
Therefore, firms with exposed main banks should have reduced the information asymmetry between them and potential lenders. As previous research has shown, information asymmetry is a very important determinant of interest payment agreements (Dell’Ariccia and Marquez 2004). Hence, we should be able to observe differences in loan rates (untabulated sensitivity analysis). To find a proxy based on the available data, we derive the average loan rate from the ratio of annual interest expenses to annual average of total liabilities. We use this proxy as an indicator in our analysis of whether firms with exposed main banks have a significantly higher or lower average loan rate than firms with nonexposed main banks after the peak-crisis period. Since we have no information regarding additional borrowing, we investigate the development of total liabilities to potentially identify new capital increases. Therefore, we assume that additional borrowing exists if a firm’s total liabilities increase by more than 50% in 1 year during the peak-crisis period or the post-peak-crisis period. Subsequently, we include all firms that meet this condition in our difference-in-differences comparison of loan rate development. Consistent with the current low-interest rate policy, the results reveal a decrease in the percentage of interest paid from total liabilities in the post-peak-crisis period among firms with unexposed main banks. Compared to the peak-crisis period, we also observe a decrease in approximated loan rates for borrowers of exposed main banks during the post-peak-crisis period. However, our difference-in-differences results are not statistically significant. Hence, our approximation of loan rates does not provide any evidence supporting the expected effect of firms’ effort in reducing information asymmetry.
However, drawing conclusions from this finding is difficult because we observe a similar pattern for borrowers’ listing status. Nevertheless, this pattern seems reasonable because most non-listed firms are also likely to report according to local GAAP, while most IFRS firms are listed. In our investigation of discretionary accruals, the results remain the same relative to our main analysis and firms’ reporting basis as the coefficients of interest are nonsignificant in the subsample of listed firms and subsample of non-listed firms. Therefore, we conclude that both subsamples do not differ regarding their discretionary accruals earnings management. Our investigation of potential differences in timely loss recognition between listed and non-listed firms during the European sovereign debt crisis reveals significant discrepancies. After propensity score matching, our coefficient of interest is significantly positive for non-listed firms, while the coefficient of interest is nonsignificantly positive for listed firms. These results are consistent with the prior literature as evidence shows that listed firms could have altered their funding sources during the sovereign debt crisis to substitute for bank financing. The results reported by Acharya et al. (2018) indicate that the negative impact of the loan supply reduction was more severe among firms that were unable to change their funding sources. Hence, changing funding sources is more difficult for private firms (Acharya et al. 2018); therefore, private firms should be more strongly affected by a decline in bank health than listed firms. Since listed firms have alternative sources of external funds, these firms potentially have fewer incentives to increase the earnings quality.
We have to address that this proxy might not fully capture the need for new external borrowing.
Kothari et al. (2005) suggest that the model can also be extended by including the lagged return on assets instead of the current return on assets. We conduct the regression again and obtain similar results.
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Kiy, F., Zick, T. Effects of declining bank health on borrowers’ earnings quality: evidence from the European sovereign debt crisis. J Bus Econ 90, 615–673 (2020). https://doi.org/10.1007/s11573-020-00968-0
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DOI: https://doi.org/10.1007/s11573-020-00968-0
Keywords
- Earnings quality
- Discretionary accruals
- Timely loss recognition
- Lending relationships
- European sovereign debt crisis