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Corresponding author: Julia van Vuuren ( juliavvuuren@gmail.com ) Academic editor: Peter Roosenboom
© 2023 Julia van Vuuren.
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Citation:
van Vuuren J (2023) The effect of IFRS 16 on the attitude of sophisticated and unsophisticated lenders towards loan contracting. Maandblad voor Accountancy en Bedrijfseconomie 97(11/12): 375-382. https://doi.org/10.5117/mab.97.109390
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This research describes the effect of IFRS 16 on the attitude of sophisticated lenders towards loan contracting. The goal of IFRS 16 was to enhance reporting quality and provide a more faithful representation of the financial statements. Data from 3,955 firms using IFRS and 1,433 using US GAAP were analyzed. Firms reporting under IFRS obtained larger loan sizes, although the difference with firms reporting under US GAAP is insignificant. They also received lower borrowing rates, and shorter maturities. IFRS 16 has a reinforcing effect towards loan contracting for sophisticated lenders, defined as banks, for the borrowing rate and maturities. For loan size, IFRS 16 has a reinforcing effect for unsophisticated lenders, defined as trade creditors.
IFRS 16, loan contacting, borrowing rate, loan size, maturity
Companies can use the outcomes of this research to better understand the rule, but also when applying for or extending a loan to understand what lenders consider in their decisions. For lenders, this can be helpful to better understand how they can interpret this change in the financial statements.
In 2016 the International Accounting Standards Board (IASB) introduced a new standard about leases (IFRS 16), and as of the 1st of January 2019, companies using IFRS are obliged to apply this new standard. This new lease standard significantly changed how companies reported their P&L and balance sheet leases. Before adopting IFRS 16, companies had a lot of off-balance sheet leases. For companies making extensive use of leases, for example airlines, this new rule made their financial statements utterly different since they have to capitalise their leases on the balance sheet as Right-of-Use assets and simultaneously provide for a Right-of-Use lease liability. The IASB introduced this new standard to improve reporting quality and research has shown that this has improved the quality and gives a more faithful representation of financial statements (
The change in representation can have various economic consequences. For example,
Traditionally lenders look at the financial ratios of a company. With many capitalised leases on the balance sheet, these ratios have changed significantly, especially for companies with many leases. For sophisticated lenders, companies tend to adjust their financial statements to pre-IFRS 16 statements to make it comparable for lenders to previous years. They generally have more information to base their lending decisions on and are therefore expected to respond less to IFRS 16. Besides that, IFRS 16 might even have a reinforcing effect on the loan contracting of sophisticated lenders because it provides more transparency and reliability. For unsophisticated lenders, IFRS 16 also provides more transparency in the numbers reported by companies. Still, they must base their decisions on what is publicly available and therefore have a disadvantage compared to sophisticated lenders.
To research if and how the attitude of lenders changed, a difference-in-difference analysis has been conducted, where the intervention group is IFRS users and the control group is US GAAP users. The intervention in this research is the new IFRS 16 standard on leases in 2019. Three years have passed since the implementation of IFRS 16, so the time horizon of this research is six years (three years post and pre). The difference in difference analysis looks at a group that is treated, in this case, the group where the new standard for leases was introduced and a group that is untreated, in this case, US GAAP users. The eventual goal of a difference in difference analysis is to look at the change in the treatment group compared to the control group (i.e., how much change there would have been expected in the IFRS group if no new lease standard was introduced). The additional change in the IFRS group can then be interpreted as the effect of the new lease standard (
Companies reporting under IFRS 16 are given larger loan sizes, but there is no statistically significant difference between companies reporting under IFRS 16 and those that do not. Companies reporting under IFRS were given shorter maturities after the implementation of IFRS 16. This might affect companies cashflow planning but most of all their financial flexibility. Companies must adapt their financial strategies by considering the potential implications this has on their operations. I also found that companies were given lower borrowing rates when applying IFRS 16. The lenders risk perception has changed, resulting in lower borrowing rates and, thus more favourable contract terms for companies reporting under IFRS. Companies subject to lower borrowing rates can potentially reduce their borrowing costs, thereby improving their financial performance and access to capital in the future.
Furthermore, I looked at the effect IFRS 16 has on the loan contracting of sophisticated lenders. The results show that IFRS 16 has a reinforcing effect on loan contracting for sophisticated lenders. When sophisticated lenders gave out loans, companies reporting under IFRS 16 were given longer maturities and even lower borrowing rates.
Some limitations to this research include the impossibility of using the Dealscan database, which entails detailed information on loan contracting, such as lender types and restrictive contract covenants. Another limitation is the COVID-19 pandemic which might have biased the results.
The International Accounting Standard 17 (IAS 17) was introduced in 1892. It required both the legal owner of the asset (lessor) and the user of the asset (lessee) to make a distinguishment between an operating or a finance lease (
In 2016 the International Accounting Standards Board (IASB) issued a new standard for leasing, and as of the financial year 2019, companies were obliged to report under International Financial Reporting Standards (IFRS) 16. To ensure that leases were no longer off-balance sheet, IFRS 16 requires the companies to treat all leases as finance leases. Companies are now required to recognise a Right of Use (ROU) asset with a corresponding liability on their balance sheet for all their leases (
Bank loans include, besides a price term, also a non-price term. Items like loan size, maturity or restrictive covenants are terms that lenders use to consider whether or not to give out a loan and what the terms of the loan are. Lenders also use these terms in loan contracts to extenuate potential agency conflicts and information problems. Lenders control their exposure to risk by reducing loan size and shortening loan maturity (
Another expectation that is derived from
H1: Companies reporting under IFRS 16 are given larger loan sizes
H2: Companies reporting under IFRS 16 are given longer loan maturities
H3: Companies reporting under IFRS 16 are given lower borrowing rates
People with diverse access to financial resources have various capacities for acquiring and processing information when knowledge about financial assets is costly to analyse. Sophisticated lenders generally have more information available and are expected not to experience differences from the new accounting standard for IFRS 16. Determining when lenders are classified as sophisticated or unsophisticated is also essential. Research from
The research from
Sophisticated lenders can request additional information from the lessee, such as a separate P&L of the balance sheet, which eliminates the differences that occurred due to a change in accounting standards. This could mean that they can base their decisions regarding the loans on this additional, usually voluntarily, disclosed information. Unsophisticated lenders have less information and base their decisions on what is available publicly (
H4: IFRS 16 has a reinforcing effect on loan contracting for sophisticated lenders
Y 1 = β0 + β1 IFRSi × AFTERt + β2 IFRSi + β3 AFTERt + β4 ROAi,t + β5 LEVi,t + β6 SIZEi,t + β7 Curr_ratioi,t + β8 Big4i,t + ƒi + δi,t + εi,t
Y 2 = β0 + β1 IFRSi × AFTERt × Sop_lenderi + β2 IFRSi × AFTERt + β3 IFRSi × Sop_lenderi + β4 Sop_lenderi × AFTERt + β5 IFRSi + β6 AFTERt + β7 Sop_lenderi + β8 ROAi,t + β9 LEVi,t + β10 SIZEi,t + β11 Curr_ratioi,t + β12 Big4i,t + ƒi + δi,t + εi,t
In Y1, hypotheses 1, 2 and 3 are tested using the difference-in-difference method. Y is equal to the dependent variables Maturity, LoanSize and Borr_rate tested in the first three hypotheses. The coefficient on IFRSi measures the effect of the dependent variable (Y) being a company reporting under IFRS. The coefficient on AFTERt measures whether there are changes in Y before 2019 and after. The most important effect to measure is the IFRSi × AFTERt, which measures the effect of the change in 2019 for companies reporting under IFRS on Y.
In Y2, hypothesis 4 is tested using a difference-in-difference method with a triple interaction. Y is equal to the dependent variables Maturity, LoanSize and Borr_rate which are also tested in the first three hypotheses. However, now the variable Sop_lenderi is added as a moderating variable, creating the triple interaction of IFRSi × AFTERt × Sop_lenderi. With this triple interaction, the effect of the change in lease standard in 2019 for companies reporting under IFRS on Y is tested, and the effect the Sop_lender has on that is added as a moderating variable.
Both regression equations include firm-fixed effects (ƒi,t) , which account for unidentified firm-level time-invariant heterogeneity (
To test H1 and H2, two nonprice terms of loan contracts will be used based on the research of
To test H3, the variable Borr_rate is used. The Borr_rate is calculated as the average interest rate lenders charge throughout a year with multiple loans (
To test H4, all variables from H1, H2 and H3 are used, but the type of lender is added as a moderating variable. When looking at the type of lender, you can look at sophisticated and unsophisticated lenders.
Besides these hypothesis-specific variables, some control variables will be used. These control variables were established similarly to
Borr_rate and Maturity can be calculated and are available in the CAPITAL IQ – Capital structure debt database. LoanSize and the distinction between sophisticated lenders and unsophisticated lenders can be calculated using the Compustat Global – Fundamental annual database. The databases will be merged using the GVKEY, a company-specific code available in both databases. The control group will be divided from the treated group by a variable available in the Compustat Global database and states the accounting standard used by that company. A dummy variable can then be created where one is for companies using IFRS and zero for companies using US GAAP. All variables are displayed in USD for convenience since most variables are already in USD when extracted from the database.
Variables | Definition | Database |
---|---|---|
LoanSize | Logarithm of loan amount calculated as short-term + long-term debt | Compustat Global |
Borr_rate | Interest rate charged by lenders (in %) | Capital IQ |
Maturity | ∆ in months between the original loan date and the maturity date | Capital IQ |
Sop_lender | Ratio of long-term debt to total debt | Compustat Global |
Unsop_lender | Ratio of short-term debt to total debt | Compustat Global |
ROA | Net income divided by total assets | Compustat Global |
LEV | Total debt divided by total assets | Compustat Global |
SIZE | Logarithm of total assets | Compustat Global |
Curr_ratio | Current assets divided by current liabilities | Compustat Global |
Big4 | 1 if audited by Big4 firm, zero otherwise | Compustat Global |
IFRS | 1 for companies using IFRS, zero otherwise | Compustat Global |
AFTER | 1 if observation is after 2019, zero otherwise | Compustat Global |
A Difference-in-Difference analysis will be conducted to test the hypothesis outlined in this research. The difference-in-difference analysis can be used to research if and how lenders’ attitudes have changed after the introduction of IFRS 16. With a difference-in-difference analysis, it is easy to compare a treated group with an untreated group and get the effect of an event on the groups (
The untreated group, or control group, are US GAAP users. Since US GAAP users did not change their lease accounting, they mitigate potential confounding factors, such as economic conditions, and are an excellent control group in this research. The eventual result from the difference-in-difference analysis will tell the effect of the new lease standard by looking at the change in the control group and, thus, how much change is expected in the IFRS group if there had not been a new standard. The additional change post-implementation is then contributed to IFRS 16 and will tell us something about the effect of IFRS 16 on the attitude of sophisticated and unsophisticated lenders towards loan contracting (
In line with the research of
To distinguish between IFRS and US GAAP, all rows that do not report using either US GAAP or IFRS are removed. Firms listed on the US stock exchange must report under US GAAP (Financial Accounting Foundation, n.d.), so I looked at which stock exchange the firms were listed for the Compustat databases and added them to the control group if they were listed on the US stock exchange. This left 23.733 firm-year observations of IFRS users and 8.600 firm-year observations of US GAAP users. Lastly, I winsorize all variables at 1 and 99% to normalize the sample.
After the data is collected and cleaned, the data is analysed. Table
DESCRIPTIVE STATISTICS | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables | Full sample | IFRS users | US GAAP users | |||||||||
N: 32.333 | N: 23.733 | N: 8.600 | ||||||||||
Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | |
LoanSize | 7.12 | 2.73 | 1.75 | 12.53 | 7.42 | 2.77 | 1.75 | 12.53 | 6.30 | 2.43 | 1.75 | 12.53 |
Borr_rate | 4.31 | 2.07 | 0 | 8.22 | 4.12 | 2.17 | 0 | 8.22 | 4.82 | 1.68 | 0 | 8.22 |
Maturity | 93.84 | 60.19 | 0 | 207.94 | 84.30 | 57.05 | 0 | 207.94 | 120.18 | 60.80 | 0.19 | 207.94 |
Sop_lender | 0.61 | 0.33 | 0 | 1 | 0.52 | 0.32 | 0 | 1 | 0.85 | 0.22 | 0 | 1 |
Unsop_lender | 0.39 | 0.33 | 0 | 1 | 0.48 | 0.32 | 0 | 1 | 0.15 | 0.22 | 0 | 1 |
ROA | 0.02 | 0.05 | -0.07 | 0.12 | 0.02 | 0.05 | -0.07 | 0.12 | 0.02 | 0.06 | -0.07 | 0.12 |
LEV | 0.32 | 0.17 | 0 | 0.67 | 0.3 | 0.17 | 0 | 0.67 | 0.35 | 0.19 | 0 | 0.67 |
SIZE | 8.47 | 2.54 | 3.44 | 13.32 | 8.81 | 2.59 | 3.44 | 13.32 | 7.53 | 2.14 | 3.44 | 13.32 |
Curr_ratio | 1.56 | 0.73 | 0 | 2.86 | 1.51 | 0.69 | 0.02 | 2.86 | 1.7 | 0.80 | 0 | 2.68 |
Big4 | 0.63 | 0.48 | 0 | 1 | 0.58 | 0.49 | 0 | 1 | 0.76 | 0.42 | 0 | 1 |
IFRS | 0.73 | 0.44 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
AFTER | 0.5 | 0.5 | 0 | 1 | 0.5 | 0.5 | 0 | 1 | 0.5 | 0.5 | 0 | 1 |
To test hypothesis 1, I looked at whether there was a significant increase/decrease before and after the implementation of IFRS 16 for a loan’s loan size. This was done using a Difference-in-difference analysis; the results can be found in Table
Difference-in-Difference analysis | |||
---|---|---|---|
Model | LoanSize | Maturity | Borrowing rate |
IFRS × AFTER | 0.009 | -15.824*** | -0.137*** |
(0.007) | (0.788) | (0.017) | |
IFRS | -0.014 | 23.223+ | 0.635* |
(0.108) | -12,923 | (0.287) | |
AFTER | |||
Big 4 | 0.009 | 5.279*** | -0.082** |
(0.007) | -1,251 | (0.028) | |
Size | 0.997*** | 6.731*** | -0.109*** |
(0.006) | (0.724) | (0.016) | |
Current ratio | -0.030*** | 6.155*** | -0.070*** |
(0.005) | (0.542) | (0.012) | |
Leverage | 3.759*** | 13.105*** | 0.122* |
(0.021) | -2,580 | (0.057) | |
Return on Assets | -0.147** | 8,655 | -0.313* |
(0.046) | -5,500 | (0.122) | |
Industry FE | No | No | No |
Year FE | Yes | Yes | Yes |
Firm FE | Yes | Yes | Yes |
Observations | 32,329 | 32,329 | 32,329 |
R-squared | 0.992 | 0.776 | 0.907 |
Adjusted R-squared | 0.991 | 0.731 | 0.889 |
RMSE | 0.24 | 28.48 | 0.63 |
For hypothesis 2 I looked at the Maturity that is given by lenders. The second hypothesis expected that there would be longer maturities because company reports would become more transparent and there would be less information asymmetry. As found by the regression reported in Table
To test the third hypothesis, the dependent variable Borrowing rate is used. As discussed in the methodology section, firms reporting under IFRS are expected to be given lower borrowing rates. When you look at the results as reported in Table
In hypothesis 4, all variables from the first three hypotheses are tested. However, the sophisticated lender is added as a moderating variable to determine the effect of lender type towards the loan contracting after IFRS 16. This is done by adding a triple interaction to the Difference-in-Difference analysis. Compared to the regressions presented in Table
Difference-in-Difference analysis | |||
---|---|---|---|
Model | LoanSize | Maturity | Borrowing rate |
IFRS × AFTER × Sop_lender | -0.026 | 9.545** | -0.208** |
(0.029) | -3,483 | (0.078) | |
IFRS × Sop_lender | -0.103*** | -4,739 | 0.021 |
(0.026) | -3,111 | (0.070) | |
AFTER × Sop_lender | -0.012 | 18.533*** | -0.120+ |
(0.027) | -3,221 | (0.072) | |
IFRS × AFTER | 0.013 | -15.437*** | -0.064 |
(0.025) | -2,927 | (0.066) | |
IFRS | 0.074 | 26.178* | 0.635* |
(0.109) | -12,984 | (0.291) | |
AFTER | |||
Sophisticated lender | 0.265*** | 10.034*** | 0.073 |
(0.024) | -2,837 | (0.064) | |
Big 4 | 0.010 | 5.237*** | -0.080** |
(0.010) | -1,236 | (0.028) | |
Size | 0.992*** | 5.748*** | -0.103*** |
(0.006) | (0.717) | (0.016) | |
Current ratio | -0.064*** | 2.283*** | -0.059*** |
(0.005) | (0.593) | (0.013) | |
Leverage | 3.684*** | 1,878 | 0.171** |
(0.022) | -2,621 | (0.059) | |
Return on Assets | -0.166*** | 9.062+ | -0.332** |
(0.046) | -5,438 | (0.122) | |
Industry FE | No | No | No |
Year FE | Yes | Yes | Yes |
Firm FE | Yes | Yes | Yes |
Observations | 32,329 | 23,849 | 32,329 |
R-squared | 0.993 | 0.781 | 0.908 |
Adjusted R-squared | 0.991 | 0.738 | 0.889 |
RMSE | 0.24 | 28.15 | 0.63 |
When you look at the triple interaction term of the second dependent variable, borrowing rate, the outcome is still negative and significant when control variables are added. Generally, borrowing rates are lower for companies reporting under IFRS after 2019, and this effect is strengthened when looking at sophisticated lenders, which means that sophisticated lenders charge an even lower borrowing rate. This is in line with the expectations that the effect of IFRS 16 is reinforcing for sophisticated lenders.
The last dependent variable tested with the triple interaction term is Maturity, presented in Table
The results of the regressions show some interesting findings. As predicted in hypothesis 1, implementing IFRS 16 could lead to larger loan sizes. However, these results were insignificant and not sufficient to support the first hypothesis and therefore the null hypothesis is accepted. In the second hypothesis I looked at the maturity which was significant but not in the expected direction, implying that IFRS users would have lower maturities after 2019. This is different from what has previously been found by
The third hypothesis expects that firms are subject to a lower borrowing rate when reporting under IFRS after the implementation of IFRS 16.
For the fourth hypothesis, the moderating variable Sop_lender is added. The sophisticated lender is determined as banks since they have more information available and can base their decisions on additionally disclosed information (
One of the main objectives of the IASB when IFRS 16 was drawn up, was that it would give a more faithful representation of a company’s financials. With that in mind, it was expected that there would be larger loan sizes, longer maturities and lower borrowing rates. The borrowing rate was lower after the implementation of IFRS 16 for firms reporting under IFRS. Companies subject to lower borrowing rates can potentially reduce their borrowing costs, thereby improving their financial performance and access to capital in the future. The loan size was larger but insignificant, so no conclusion can be drawn on that aspect. Lastly, for maturity, it turned out that they became shorter after the adoption of IFRS 16. This might affect companies cash flow planning but most of all their financial flexibility. Companies must adapt their financial strategies by considering the potential implications this has on their operations. This was not expected based on the literature research and is different from the thought that a more faithful and transparent reporting rule would give more certainty to lenders and, therefore, would lengthen the term of a loan to spread risks.
What must be taken into account is that all dependent variables are related to each other and decisions about these loan terms are taken simultaneously at the time of loan origination. Therefore you could look at all these components separate, but in the end one overall decision must be made. In this research the conclusion for loan contracting as a whole would be that IFRS 16 has a positive effect on loan contracting. This means that companies got more favourable contracts with lower borrowing rates, larger loan sizes and shorter maturities. Overall, it can be said that companies are given better contract terms then before the implementation of IFRS 16.
When the moderating variable sophisticated lender is added, the expectation is that IFRS 16 would have a reinforcing effect for sophisticated lenders, meaning even longer loan sizes, longer maturities and lower borrowing rates. What was immediately apparent was that in terms of loan size, the sophisticated lender did not give larger loan sizes but even lower loan amounts than unsophisticated lenders. For the borrowing rate, this was not the case. As expected, the borrowing rate that sophisticated lenders charged was lower than that of unsophisticated lenders; therefore, IFRS 16 has a reinforcing effect on sophisticated lenders. Lastly, I looked at the attitude the sophisticated lender has towards the maturity of a loan. With the regular regression, it turned out that IFRS 16 would lead to shorter maturities. However, when you look at the attitude of the sophisticated lenders, maturities were longer after the implementation of IFRS 16 for companies reporting under IFRS. Therefore it can also be concluded that IFRS 16 has a reinforcing effect on the attitude of sophisticated lenders when you look at borrowing rates and maturities.
The expectation was that lenders would have more trust in companies since their financial reports are showing their lease liabilities completely and therefore give a better overview of a company’s financial situation which gives lenders a more faithful and complete picture. Unfortunately this was not directly shown from the results of this research, but even though the results did not indicate directly that IFRS 16 gives a more faithful representation resulting in more favourable loan contracting, there is still reason enough to believe that IFRS 16 has improved the reporting quality, since the financial statements now give a more complete overview of all the liabilities a company might have, and thus gives a more faithful representation of a company’s debt position.
This research also has some limitations. The main limitation was the reorganisation of the Thomson Reuters Dealscan database. This database generally contains more detailed information on loan contracting, such as lender types and restrictive contract covenants. However, this database was no longer helpful since many company-specific identifiers were mixed up or unavailable in the other databases used in this research. Another limitation of this research is that some firms are early adopters of IFRS 16. An early adopter reports under IFRS 16 before it is mandatory to use the standard. Unfortunately, this is not specified in the Compustat database; therefore, I could not make that distinction. The third limitation of this research is that COVID-19 might bias the results. Since COVID-19 only happened in 2020, it only affects the period after it was mandatory to report using IFRS 16. Besides that, COVID-19 is a worldwide pandemic. Therefore there is no control group available that did not undergo the effects of this pandemic, and therefore, I cannot control for this effect.
J. van Vuuren MSc – Julia, Accounting and Financial Management, Rotterdam School of Management.
Dit artikel van Julia van Vuuren is gebaseerd op haar afstudeerscriptie. Daarmee is zij een van de winnaars van de MAB-scriptieprijs 2023.