The Effect of IAS/IFRS Adoption on the Cost of Debt
5.8 Model Specification: Control Variables
Throughout the literature, the assessment of the cost of debt is connected to the default risk: a negative relationship is reported between the cost of debt capital and the measures of the default risk (Fisher, 1959; Fung and Rudd, 1986; Sengupta, 1998). The following sections explain the expected relationship between the cost of debt capital and the measures of default risk.
5.8.1 Size
According to Vermaelen (1981), the size of a company can be considered as a proxy for information asymmetry, because small companies provide less information than large companies. Additionally, according to Titman and Wessels (1988), the larger the company, the lower the possibility of bankruptcy, which in turn means that lenders and underwriters can ask for lower risk premiums, and therefore larger companies face a lower cost of debt because of the lower information asymmetry and the lower possibility of bankruptcy. Following the literature, the proxy used in the present study for measuring size was the natural logarithm of total assets (Sengupta, 1998; Francis et al., 2005; Nikolaev and Van Lent, 2005; Gray et al., 2009; Orens et al., 2010; Van Binsbergen et al., 2010).
5.8.2 Leverage
According to Modigliani and Miller (1958), companies that report high leverage ratios face a higher risk of uncertainty. This suggests that companies with high leverage ratios are considered to be more risky than companies with low leverage ratios. The inclusion of leverage is consistent with previous research (Leftwich, 1981; Francis et al., 2005)
that confirm that the lower the leverage, the lower the agency costs, which in turn lowers the cost of debt. The proxy used in the present study for measuring leverage was the ratio of total debt to total assets, to capture the ability of each company to repay its debt through its assets (Francis et al., 2005; Ashcraft and Santos, 2009; Gray et al., 2009).
5.8.3 Growth
Intangible assets are not secured, and their value can decrease quickly in the case of financial distress or bankruptcy (Titman and Wessels, 1988). This means that companies‟ ability to use debt financing will decline, which will lead them to equity financing. According to Titman and Wessels (1988), companies with high growth face higher agency costs because they can be more flexible when choosing future investments and when drawing capital from banks or bondholders. Consequently, if the company cannot collateralize the debt it raises, the debt will be more expensive, because a higher risk premium will be sought. Therefore, a negative relationship between cost of debt and growth is expected. The variable used in the present study for measuring growth was the market to book ratio (Francis et al., 2005; Nikolaev and Van Lent, 2005; Ashcraft and Santos, 2009; Van Binsbergen et al., 2010). The market to book ratio connects the market value of equity with the book value of equity. It has been argued that the market value of equity reflects the expectations of investors about the company‟s future profits, and the book value of equity represents past retained profits (McNichols et al., 2010). Therefore, the market to book ratio “is determined by both past and future investments, with the latter expected to be made optimally in light of anticipated future revenue opportunities” (McNichols et al., 2010, p. 2).
5.8.4 Earnings
Companies that report negative earnings have a tendency to manipulate their earnings in order to reassure potential investors about the companies‟ future performance (Brown, 2001). This means that, because of the increased uncertainty regarding the companies‟ future performance, lenders will ask for a higher risk premium, which in turn increases the cost of debt. Hence, it is expected that there will be a negative relationship between the cost of debt and negative earnings. The variable used in the present study for
measuring negative earnings was a dummy variable, coded 1 when companies reported negative earnings, and 0 otherwise (Nikolaev and Van Lent, 2005; Orens et al., 2010).
According to Bhaduri (2002), companies with highly volatile earnings face a higher risk of being unable to meet their debt commitments, which in turn leads to a higher probability of financial distress. Additionally, investors and lenders cannot assess with certainty the future earnings of companies that report high earnings volatility (Graham et al., 2005). Therefore, a positive relationship is expected between earnings volatility and the cost of debt. The variable used in the present study for measuring earnings volatility was the logarithm of the percentage change in earnings per share for year t versus earnings per share in year t−1 for each company (Orens et al., 2010).
5.8.5 Industry
Orens et al. (2010) found that the cost of debt capital is related to the industry in which each company operates. Specifically, they found that industry classification had a higher effect on the cost of debt in North America than in Continental Europe, but both coefficients reported were statistically significant. To test for a significant relationship between industry classification and cost of debt in the Greek market, the following classification of 14 industries was used based on the sector to which each company belonged according to the ASE:
1: Utilities (2 companies) 2: Oil & Gas (2 companies) 3: Retail (8 companies)
4: Food & Beverage (21 companies)
5: Personal & Household Goods (31 companies) 6: Technology (20 companies)
7: Chemicals (10 companies)
8: Industrial Goods & Services (22 companies) 9: Basic Resources (12 companies)
10: Telecommunications (2 companies) 11: Travel & Leisure (11 companies)
12: Construction & Materials (22 companies) 13: Health Care (9 companies)
5.8.6 Base rate
To control for changes in the base rate announced by the ECB, the base rate was also included as a control variable. The Baserate variable takes two values: the first value is the average base rate of all the observations whose the IAS variable takes the value zero (0) and is the same for all these observations (2.99) and the second value is the average base rate of all the observations whose the IAS variable takes the value one (1) and is the same for all the observations (3.00).
Table 5.1 summarizes the variables used as control variables for estimating the cost of debt, the proxies used for their measurement and the expected signs based on the justification provided above.
Variable Proxy References Expected sign
Size Natural logarithm of
total assets
Nikolaev and Van Lent (2005); Orens et al. (2010); Sengupta (1998); Van Binsbergen et al. (2010)
Negative (-)
Leverage Total debt to total assets Ashcraft and Santos (2009); Francis et al. (2005); Orens et al. (2010)
Positive (+)
Growth Market to book value Ashcraft and Santos (2009);
Francis et al. (2005); Nikolaev and Van Lent (2005); Van Binsbergen et al. (2010)
Negative (-)
VolatilityΔear Logarithm of percentage
change in earnings
Orens et al. (2010) Positive (+)
Loss Dummy variable coded 1
when a company report negative earnings and 0 otherwise
Nikolaev and Van Lent (2005); Orens et al. (2010)
Negative (-)
Industry Dummy variables Anderson et al. (2003); Pitman and
Fortin (2004)
Baserate 2.99 if the company
reports under Greek GAAP
3.00 if the company reports under IAS/IFRS
--- ---
Following the rationale of Orens et al. (2010), the effect of IAS/IFRS adoption on the cost of debt can be examined by using the following model:
where CoD is the cost of debt, IAS is a dummy variable coded 1 for all those numbers reported under IAS/IFRS and 0 otherwise, Size is measured by the natural logarithm of total assets, Leverage is the leverage measured by total debt to total assets, Growth is the growth measured by the market to book value ratio, Loss is a dummy variable coded 1 when companies reported negative earnings and 0 otherwise, VolatilityΔear is the
logarithm of the percentage change in earnings per share for year t versus earnings per share in year t−1, Industry includes dummies for the industry classification, Baserate is the variable used to capture the effect of changes in the base rate for the Euro area announced by the ECB, β are the coefficients to be estimated, i is an index for companies and t is an index for the year.
5.9 Results
5.9.1 Descriptive statistics
As explained in Section 1.6 and Section 4.6.1, the dataset comprised 181 Greek companies (Table 5.2) that were listed on the ASE between 2001 and 2008. The dependent variable required the use of the year end and the year beginning data (previous year‟s end data). For this reason, information for 2000 was also used for the calculation of total debt. As in Chapter 4, companies that belonged to the insurance, financial and banking services sector or that were not listed throughout the eight years investigated were excluded. Another 35 companies were excluded because of the non- availability of data. The database had some missing data in particular for the three years from 2001 to 2003; therefore, the annual reports of the companies were also used. It was checked whether there was an agreement between the data provided by the database and the annual reports. The total number of observations was 1448.
Number of companies listed on the Athens
Stock Exchange 264
Number of excluded companies belonging to the insurance, financial and banking
services sector 37
Number of excluded companies not listed
continuously over 2001-2008 11
Number of excluded companies with
missing data 35
Total No of companies used 181
Table 5.2: Number of Greek companies used
Table 5.3 provides descriptive statistics regarding the variables‟ minimum, maximum, mean and standard deviation. More descriptive statistics are provided in the Appendix (Table 9).
As can be seen from Table 5.3, the average Size of the companies increased after the adoption of IAS/IFRS, from 12.72 to 13.06. This implies that more assets were recognized under IAS/IFRS or that they were measured at a higher value, possibly because IAS/IFRS is fair-value oriented. This is consistent with the findings of Athianos et al. (2005), who found a statistically significant increase in the average size of Greek companies after the adoption of IAS/IFRS. It also agrees with the result of Barth et al. (2008), who investigated the effects of IAS/IFRS adoption on earnings management in 21 countries and found that the average size of companies increased significantly after the adoption of IAS/IFRS.
N=1448 Mean
Standard
deviation Min Max
Test for difference in mean Greek GAAP (N=676) IAS/ IFRS (N=772) Greek GAAP (N=676) IAS/ IFRS (N=772) Greek GAAP (N=676) IAS/ IFRS (N=772) Greek GAAP (N=676) IAS/ IFRS (N=772) Size 12.72 13.06 1.29 1.31 10.11 10.10 16.23 16.45 11.29*** Leverage 0.30 0.41 0.18 0.21 0.001 0.001 0.79 1.19 8.01*** Growth 3.73 2.48 10.73 3.88 -23.60 -13.01 106.22 27.68 4.73*** Loss 0.30 0.17 0.46 0.37 0 0 1 1 -6.08*** VolatilityΔear -0.35 0.10 4.57 2.17 -61.7 -7.67 29.4 12.90 -1.92* CoD 0.047 0.042 0.07 0.06 0.001 0.001 0.23 0.25 1.89* Baserate 2.99 3.00 0.00 0.00 2.99 3.00 2.99 3.00 -
Table 5.3: Descriptive statistics
Note: Greek GAAP includes all those numbers reported under Greek GAAP, IAS/IFRS includes all those values reported under IAS/IFRS, Size is the natural logarithm of total assets, Leverage is the total debt to total assets ratio, Growth is the market to book ratio, Loss is a dummy variable coded 1 when the reported earnings are negative and 0 otherwise, VolatilityΔear is the change in earnings measured as the logarithm
of the percentage change in earnings per share for year t versus earnings per share in year t-1 for firm each company, CoD is the cost of debt and Baserate is the variable used to capture the effect of changes in the base rate for the Euro area announced by the ECB.
1: the values were low and they were rounded to zero (0).
*, **, and *** represent the 10%, 5%, and 1% levels of significance, respectively, for a two-tailed test
Table 5.3 shows that Leverage increased when companies switched from Greek GAAP to IAS/IFRS. This implies that the debt financing of the Greek companies increased after the adoption of IAS/IFRS, in line with the findings of Paananen (2008), who found a higher average leverage for Swedish companies after the adoption of IAS/IFRS. The result is also consistent with the results of Athianos et al. (2005) and Iatridis and Rouvolis (2010), who found that the leverage of Greek companies increased when they adopted IAS/IFRS.
Table 5.3 also shows that the average value of the dummy variable for Greek companies reporting negative earnings (Loss) decreased from 0.30 to 0.17 after the adoption of IAS/IFRS, and this difference was statistically significant at the 1% significance level. This implies that after the adoption of IAS/IFRS only 17% of the Greek companies under investigation reported negative earnings. VolatilityΔear also increased in the years
after the adoption of IAS/IFRS, indicating that Greek companies tended to smooth their earnings less after their adoption. The increase in earnings volatility is consistent with the findings of Iatridis and Rouvolis (2010), who also found that Greek companies smoothed their earnings more under Greek GAAP than they did under IAS/IFRS.
Year Mean
Standard
deviation Min* Max
2001 0.050 0.081 0.00 0.336 2002 0.043 0.070 0.00 0.226 2003 0.047 0.071 0.00 0.220 2004 0.047 0.060 0.00 0.249 2005 0.037 0.070 0.00 0.218 2006 0.042 0.067 0.00 0.224 2007 0.045 0.067 0.00 0.221 2008 0.044 0.063 0.00 0.217
Table 5.4: Descriptive statistics of cost of debt on a year by year basis
*: the numbers reported are low and therefore rounded to zero
The average value of the cost of debt (CoD) also decreased after the adoption of IAS/IFRS (from 0.047 to 0.042), indicating that the borrowing costs of the Greek companies were lower after the adoption of IAS/IFRS, possibly because of the increased transparency of the accounting information that resulted from their adoption. Table 5.4 is a breakdown of the cost of debt on a year by year basis.
Table 5.5 shows the average base rate per year for the Euro area announced by the ECB from 2001 to 2008. As can be seen in the table, the lowest base rate was reported in 2005, and the highest base rate was reported in 2001.
Year Average base rate
2001 4.2500 2002 3.2083 2003 2.2500 2004 2.0000 2005 1.9375 2006 2.6250 2007 3.8542 2008 3.8542
Table 5.5: Average base rate per year for the Euro area
Source: European Central Bank (2012)
According to Guajarati (2003), in the presence of multicollinearity, the estimated beta coefficients may be biased. Additionally, some of the independent variables may be
dropped from the model because it cannot isolate the effect that each independent variable may have on the dependent variable. Therefore, a test was run for multicollinearity.
The presence of multicollinearity can be tested through the use of the VIF or through the correlation matrix. The VIF reveals the degree to which each independent variable is able to explain the other independent variables used in the model. According to Guajarati (2003), an acceptable level of VIF is under 10. The correlation matrix reveals whether or not any two independent variables are correlated.
IAS Size Leverage Growth VolatilityΔear Loss first second third fourth fifth sixth seventh IAS 1.000 Size 0.757*** 1.000 Leverage 0.131*** 0.126*** 1.000 Growth -0.069*** -0.211*** -0.036 1.000 VolatilityΔear 0.062** 0.025 -0.036 -0.012 1.000 Loss -0.035 -0.029 0.134*** -0.008 -0.008 1.000 first 0.04* 0.02 0.297 0.02 0.015 -0.122*** 1.000 second 0.06** 0.02 -0.283 0.05** 0.014 -0.117*** -0.02 1.000 third 0.02 -0.03 -0.07 0.01 -0.003 -0.028 -0.02 -0.01 1.000 fourth 0.04 -0.02 0.027*** 0.08*** 0.005 -0.006 -0.05* -0.02 -0.02 1.000 fifth 0.06** -0.04 0.027*** 0.13*** 0.004 0.042 -0.08*** -0.04 -0.70 -0.08*** 1.000 sixth -0.01 -0.02 0.036*** 0.01*** -0.092*** -0.005 -0.05** -0.02 -0.02 -0.05** -0.08*** 1.000 seventh -0.02 -0.03 0.05** 0.13*** 0.004 0.027 -0.08*** -0.03 -0.04 -0.07*** -0.13*** -0.08*** 1.000 eighth -0.01 -0.01 0.01 0.05** 0.009 -0.101*** -0.06** -0.02 -0.03 -0.05** -0.09*** -0.05** -0.08*** ninth -0.02 -0.04 0.02 0.13*** 0.003 -0.038 -0.08*** -0.04 -0.04 -0.08*** -0.13*** -0.08*** -0.13*** tenth -0.01 -0.03 0.041 0.10*** 0.002 0.039 -0.06** -0.03 -0.03 -0.06** -0.10*** -0.06** -0.09*** eleventh -0.01 -0.01 0.131*** 0.04 0.015 0.005 -0.02 -0.01 -0.01 -0.02 -0.04 -0.02 -0.04 twelfth -0.01 -0.03 0.077*** 0.09*** -0.003 0.012 -0.06** -0.03 -0.03 -0.05** -0.09*** -0.06** -0.09*** thirteenth -0.02 -0.16*** -0.015 0.08*** 0.005 0.051* -0.08*** -0.04 -0.04 -0.08*** -0.13*** -0.08*** -0.13*** Baserate 0.13 0.05 0.051* 0.36 -0.02 -0.04 -0.03 -0.24 0.01 0.04 0.01 -0.01 -0.03
Table 5.6: Correlation matrix and Variance Inflation Factor (VIF)
Note: first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth are dummy variables used for the different industries and VIF is the Variance Inflation Factor. The rest of the variables have been defined in table 5.3
eighth ninth tenth eleventh twelfth thirteenth Baserate VIF IAS 2.40 Size 2.68 Leverage 1.79 Growth 1.09 VolatilityΔear 1.02 Loss 1.05 first 1.45 second 1.32 third 1.18 fourth 1.18 fifth 1.27 sixth 1.13 seventh 1.27 eighth 1.000 2.23 ninth -0.09*** 1.000 1.24 tenth -0.06** -0.10*** 1.000 1.11 eleventh -0.02 -0.04 -0.03 1.000 1.08 twelfth -0.06** -0.09*** -0.07*** -0.03 1.000 1.11 thirteenth -0.09*** -0.14*** -0.14*** -0.04 -0.09*** 1.000 1.16 Baserate 0.05 -0.02 -0.01 -0.01 0.10 -0.04 1.000 1.48
Table 5.6: Correlation matrix and Variance Inflation Factor (VIF), continued
Note: first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth are dummy variables used for the different industries and VIF is the Variance Inflation Factor. The rest of the variables have been defined in table 5.3
As can be seen from Table 5.6, the VIF for all the variables was under 10, which indicates that there was no multicollinearity in the model. As can also be seen from the table, size and leverage were positively correlated with the dummy variable used for the adoption of IAS/IFRS, indicating that after their adoption the size of the Greek companies increased, along with their leverage level.
5.9.2 Estimation results
Panel and pooled data analysis techniques were used to determine the most appropriate technique that best explains the model. Panel data analysis required the use of fixed and random effects techniques.
In the pooled data analysis, which was estimated by OLS, all the observations were placed together in the regression. This meant that the estimated beta coefficients would capture no time or company effects but would capture the overall influence of the independent variables on the dependent variable. In the pooled data technique, the time and company effects are captured by the error term. This method is appropriate only when there is no time or company effect.
When there are time and company effects, these require the use of panel data techniques (Gujarati, 2003). The use of panel data analysis captures company and time effects and helps researchers to minimize problems that may occur when using pooled data analysis, and the coefficient estimations are robust. As mentioned above, panel data analysis requires the use of fixed and random effects techniques.
Fixed effects techniques enable researchers to capture effects that cannot be captured when using pooled data analysis. Only company effects are captured by the intercept of the model, meaning that the intercept may differ for each company but remain constant over time. The method used for estimating fixed effects was OLS. On the other hand, the random effects technique captures both company and time effects in the error term. The method used for estimating random effects was generalized least squares (GLS).
After running the regression using the fixed and random effects techniques, a Hausman test was run, which tested the null hypothesis that the coefficients estimated by the use
of fixed and random effects were similar. A statistically significant Hausman test would indicate that the best technique to use would be the fixed effects technique.
CoD CoD IAS -0.3705 (-8.28)*** fourth -0.0129 (-1.92)* Size -0.02714 (-1.76)* fifth -0.0085 (-1.83)* Leverage 0.25873 (2.53)** sixth -0.0071 (-0.09) Growth -0.0108 (-0.92) seventh -0.00799 (-1.61) Volatility 0.01915 (2.49)** eighth 0.0561 (2.14)** Loss -0.00048 (-1.38) ninth -0.0054 (-1.20) first -0.0255 (-0.96) tenth -0.0074 (-1.23) second 0.0062 (0.90) eleventh -0.0011 (-0.06) third -0.0155 (-0.92) twelfth -0.0034 (-0.87) Constant 0.05859 (4.25)*** thirteenth 0.00059 (0.21) Baserate 0. 2358 (10.58)*** R2 47.14 P-value 0.000 Hausman Test 3.18 N 1448
Table 5.7: Estimation results
Note: All the variables have been defined in tables 5.3 and 5.4. *, **, and *** represent the 10%, 5%, and 1% levels of significance
The model developed in Sections 5.6-5.8 is linear. The assumptions of a linear regression are that each of the independent variables has a linear relationship with the dependent variable, the residuals of the regression are normally distributed, and there is no heteroscedasticity and no serial correlation of the residuals. To detect non-normally distributed residuals and heteroscedasticity, robust regressions were run in STATA. Table 5.7 includes a summary of the results produced by STATA, and the complete table is provided in the Appendix (Table 10).
Table 5.7 shows the results obtained from the regressions for 1448 observations. The random effects technique was found to explain the data better than the fixed effects technique, because the result of the Hausman test was insignificant (Table 10, Panel D, Appendix).
As can be seen from Table 5.7, the GLS model was characterized by an R2 of 47.14% and reported a significant and negative relationship between the cost of debt and the binary variable (IAS) used to measure IAS/IFRS adoption. Specifically, the reported coefficient of the dummy variable was −0.3705 and was statistically significant at the