Luciana Maia Campos Machado
FIPECAFI and FGV/EAESP, Brazil
E-mail: luciana.machado@fipecafi.org
William Eid
FGV/EAESP, Brazil
E-mail: william.eid@fgv.br
Submission: 19/07/2018
Revision: 22/08/2018
Accept: 24/09/2018
ABSTRACT
After
inflation rates’ stabilization in Brazil, Monetary Correction for financial
data was extinguished. Since then, the distinct reflections of price variations
on monetary and non-monetary variables presented on financial statements are no
longer considered. From 1995 to 2013, however, the cumulative inflation reaches
226%. Considering this situation, we may ask ourselves about the robustness of
empirical analysis in the financial universe, not because of research design,
but because of the variables that are present in them: companies’ financial
data. We compared financial indicators and empirical models built with data
adjusted for inflation and original data issued by firms. A database of 143
Brazilian companies traded on Bovespa were adjusted for inflation, from 2004 to
2013, under the precepts of the extinct Monetary Correction. We obtained two
different samples: the first one containing financial data adjusted for
inflation and the second one corporate data originally released by companies.
Statistical tests showed that financial indicators such as ROI, Assets
Turnover, Debt and Market-to-book are significantly higher when we do not
consider inflation effects. In addition, the panel regression models, when
adjusted, had higher predictable power (greater R²) and representative changes
of significance on variables and regression coefficients. Results suggest that
analysts and investors need to consider the inflation effect when making
financial decisions.
Keywords: Inflation;
Monetary Correction; Brazilian data
1. INTRODUCTION
Financial statements
are one of the main tools we use to evaluate companies and make empirical
tests. Bell (1961) states that the underlying capital can only be determined
when we measure the current value of the company with perfection, but
historical data are not sufficient for this issue. Analyzing a firm at two
different times or comparing it to another firm, whose operation began earlier
or later, would only be possible with financial values adjusted accordingly.
For Martins (2000)
there are only two factors potentially responsible for variations in the
measurement of companies’ financial results: inflation and cost of opportunity.
For these factors, we usually apply simplistic corrections from the
methodological point of view, characteristic indicated as an “inexcusable
failure” of accounting professionals, according to author’s words.
After the The Real Plan
stabilization in 1994, Brazilian inflation rates reached stable levels and the
law 9.249/95 vetoed the use of any monetary adjustment for financial statements
(DOUPNIK; MARTINS; BARBIERI, 1995). After this milestone, studies as the ones
made by Bonizio and Vicente (2001), Gabriel, Neto and Corrar (2005), Ambrozini
(2006), Oliveira, Marques and Canan (2007) and Moribe, Panosso and Marroni
(2008) analyzed some impacts that the end of Monetary correction had on
accounting and profitability indicators.
Although some public
databases used in several studies offer an adjustment option for inflation,
they do not differ “market” variables that are partially adjusted for inflation
(such as net income, debt and market value) of non-monetary variables (such as
permanent assets). One possible problem may arise exactly from the use of
indicators composed by two of these variables, being one “market” adjusted and
the other not.
1.1.
Study
Problem
Since the end of
Monetary Correction, inflation rates in Brazil were controlled, but reached a
substantial cumulative value of 226% between 1995 and 2004. Under these
conditions, it is important to reflect about how financial indicators -
composed by financial statements’ variables - are impacted, since we no longer
have the accounting corrections idealized for significant inflation times.
More than that leads us
to a question: are financial studies
[really] reliable in Brazil?
This study does not
intend to discuss any accounting approach, nor will it address in detail the
techniques of monetary adjustment. Our main objective is to examine whether the
recognition of inflationary effects cause significant differences in financial
indicators commonly used in financial studies. Moreover, we aim to analyze if
this difference also leads to changes in estimators and significance of
statistical models present in empirical studies.
Applying the precepts
of the Monetary Correction, accounting data of 143 Brazilian companies traded
on BOVESPA were adjusted for inflation, from 2004 to 2013, resulting in two
different samples: the first containing adjusted financial data (considering
inflation) and the second containing financial statements’ data originally
released.
For the two samples we
estimated financial indicators frequently used in empirical researches, for comparative
purposes. Statistical tests suggest financial indicators such as ROI, Assets
Turnover, Debt and Market-to-book are significantly higher when we do not
consider the effects of inflation.
2. LITERATURE REVIEW
2.1.
Inflation
and Financial Results
Before the inflation rates stabilization in Brazil, inflation was a recurring theme in financial papers and also in decision-making business process. The dramatic effect on the economy was an evidence that the role of financial manager had become increasingly important (PUGGINA, 1981).
Grazziotin (1980)
conducted a detailed study about the inflation impact in the financial
management of working capital, cash, accounts receivable, inventories, cost of
capital and dividends. The author proposed a reflection on the necessary
adjustments in an inflationary context, mainly based on the distinction between
non-monetary and monetary variables. The need to maintain equity consistency
proved to be one of the biggest challenges when maximizing the company's value
in uncertain scenarios.
After 1996, Brazil had a less peculiar inflation scenario, where price changes were no longer achieving three digits or more, and Monetary Correction for financial data was extinguished. In this environment, Eid Jr. (1997) developed a work that brought to the Brazilian market theories that were applicable only in stable countries. The work complemented studies developed by Grazziotin (1980) and Puggina (1981). Since that time, studies begin to relate financial management to an inflation environment, no longer conditioned to hyperinflation.
Figure 1: Annual Inflation vs. Accumulated inflation
Source: own elaboration, with IBGE data (2014).
Some years later authors as Bonizio and Vicente (2001), Gabriel and Corrar Neto (2005), Ambrozini (2006), Oliveira, Marques and Canan (2007) and Moribe, Panosso and Marroni (2008) studied the impact of the end of Monetary Correction on accounting data and profitability indicators in Brazil. There is consensus among all the authors that when we do not consider inflation effects, results can lead us to an overestimation of performance indicators: a significant impact on financial analysis. In Israel, Barniv (1999) examined the value relevance of unexpected inflation-adjusted earnings and historical-cost earnings in the Israel hyperinflationary environment. Results showed that unexpected inflation-adjusted earnings are value-relevant to investors in their economy.
Kirkulak and Balsari (2009) analyzed the role of incremental information content of inflation-adjusted data in explaining the market value of equity and stock returns in Istanbul. Author’s findings showed that inflation adjustment affects financial ratios significantly, which may create different risk assessments for firms.
Examined the value relevance of inflation-adjusted and historical cost amounts in a hyperinflationary economy (Zimbabwe). Authors concluded that inflation gains and losses can provide incremental information content beyond that provided by historical cost amounts.
In addition to previous work, this study aims to analyze the impact of not considering the effects of inflation on financial statement indicators, using a greater number of financial indicators and empirical models built with these financial variables.
2.2.
Monetary
Correction: Income or Expense?
When Monetary
Correction was still in force, inflation effects were indirectly considered by
registering them in a single account named Monetary
Correction, which can be creditor or debtor. The technique consists in the
restatement of fixed assets and shareholders' equity, both non-monetary items.
If a company had more
value on equity than fixed assets, will have an inflationary loss, while
another that has higher fixed assets in relation to equity will have
inflationary a gain. These so called “gains” and “losses” do not represent an
income or an expense, but rather an indirect setting to consider inflationary
effects on monetary accounts.
Imagine, for example,
two fictitious Brazilian companies that had the same total equity in 2004, but
different levels of immobilization compared to its equity. Let's consider the
accumulated inflation between 2004 and 2014, approximately 62% (IPCA):
Table 1: two fictitious Brazilian companies
Company
01 |
|
Monetary Assets |
Monetary Liabilities |
$ 20.000 |
$ 15.000 |
Fixed Assets |
Fixed Assets |
$ 25.000 |
$ 30.000 |
Company
02 |
|
Monetary Assets |
Monetary Liabilities |
$ 5.000 |
$ 18.000 |
Fixed Assets |
Fixed Assets |
$ 40.000 |
$ 27.000 |
Applying the Monetary Correction
principles not on the exercise, but on the period (2004 – 2014), we then would
have:
Table 2: Applying the Monetary Correction
Company 01: Fixed Assets = $ 25.000 x 62% = $ 15.500 Equity = $ 30.000 x 62% = $ 18.600 Monetary Correction = -$ 3.100 (Debt) |
Company 02: Fixed Assets = $ 40.000 x 62% = $ 24.800 Equity = $ 27.000 x 62% = $ 16.740 Monetary Correction = $ 8.060 (Credit) |
Note that companies that maintain
liquid resources in larger quantities has more monetary loss when facing a high
inflation environment. This characteristic was also studied by Blejer (1979).
The author examined the relationship between changes in inflation rates and the
demand from companies for cash (high liquidity assets). Contrary to the Falls
and Natke (1996) findings, his research suggests that financial risk due to
high inflation has a negative effect on the demand for money. The positive
effect exerted on the same variable, which happens when companies increase the
liquidity of its assets in order to hedge risk, was not strongly significant.
3. METHODOLOGY
3.1.
Sample
Construction
This work aims to
analyze the importance of inflation on indicators extracted from financial
statements, often used to analyze the financial situation of companies and in
academic research.
Therefore, we’ve
obtained a sample of non-financial Brazilian companies, from 2004 to 2014.
Economática database provided, at the time of data collection, quotations from
a total of 2611 companies, of which 677 were Brazilian companies traded on
BOVESPA.
We’ve selected a sample
of 150 non-financial Brazilian companies based on market liquidity during the
period. From this sample were excluded companies that had, in some moment,
negative shareholders' equity and companies that for some reason did not
present data available throughout some time of the sample horizon.
This sample allows us
to analyze a period of high inflation, which was at lower levels from the year
following the end of the sample (2014). Brazil, as pointed by Lopes (2006),
provides an excellent laboratory for testing accounting information because of
idiosyncrasies caused by a contracting environment, being one of them
arrangements related to inflation adjustment.
As our main objective
is to evaluate the impacts of accumulated inflation in a stable environment,
the period from 2004 to 2014 is considered a sample cut that reflects the
interest of research particularly well.
For comparison
purposes, we adjusted the original base according to the principles of Monetary
Correction, in force until the year 1995. We applied the general price index to
correct non-monetary items, which are, fixed assets and equity:
Where:
correspond respectively to
shareholders' equity, net income and fixed assets not adjusted for inflation
(originally issued) for each company (i) at
each observed year (t);
correspond to shareholders' equity, net income and fixed
assets adjusted for inflation, under the precepts of Monetary Correction.
is our correction factor. IPEAD (Federal University of Minas Gerais) offers monthly a table containing a factor based on IPCA that update assets quoted in currencies from previous periods, translated into the corporate values for the same time analysis (end of 2014).
With these settings, we
started working with two financial statements’ samples, the first of corporate
data officially released by companies, and the another one based on the
assumptions of monetary correction.
3.2.
“Market
adjusted” and monetary variables
We define as real
assets and liabilities, or non-monetary accounts, those whose current value
also changes when price level floats, that is, financial accounts that keeps
its purchasing power under an inflationary environment (GRAZZIOTIN, 1980).
After the year of 1995,
considering the inflation
stabilization in Brazil, Monetary Correction, which allowed the accounting
recognition of inflationary effects, was extinguished. Since that time,
financial statement variables are no longer adjusted and the balance sheets of
companies to dot account for inflation.
However, monetary
accounts can be viewed as partially adjusted by the “market”, since they
incorporate in their value the effects of inflation every year. Note, before
any further analysis, we may have a problem combining variables of these two
groups together in any analysis.
Even when we look at a
stable environment in terms of inflation in Brazil, between the years of 2004
and 2014, the cumulative inflation in Brazil reaches a representative value of
226%. Market adjusted variables reflect it in a sensitive way. The non-monetary
indicators - that since the end of Monetary Correction do not incorporate the
effects of inflation on their value – do not incorporate the variation in the
same intensity.
4. RESULTS
The next table shows
the results of paired mean tests for financial indicators relating accounts
considered as "market adjusted" with non-monetary accounts. We found
that p-values has α less than zero for all indicators, allowing the rejection
of the null hypothesis (H0), that the medium does not differ
significantly:
Table 3: shows the results of paired mean tests for financial indicators
Mean Diff. Test |
Normality Test P-Value |
||||
Indicator |
Original
Mean |
Adjusted
Mean |
P-Value |
Original Indicator |
Adjusted Indicator |
ASSETS TURN |
0,4288 |
0,3646 |
0,000*** |
0,9452 |
0,2610 |
ROA |
0,0570 |
0,0392 |
0,005*** |
0,0553 |
0,5346 |
MKT BOOK |
0,9930 |
0,8582 |
0,000*** |
0,8459 |
0,1675 |
DEBT |
0,2844 |
0,2461 |
0,000*** |
0,2461 |
0,5436 |
*Significant at 10%;
**Significant at 5%; ***Significant at 1%.
Even though
inflationary variations in Brazil have been stable during our sample period,
accumulated inflation presents significant differences in financial indicators
commonly used in financial research.
The following graphs
demonstrate the behavior of financial indicators (Assets Turnover,
Market-to-book, ROA, Debt/ Total Assets), comparatively for adjusted and
non-adjusted indicators, based on the monetary correction principles. All
indicators combine variables that are automatically “market” adjusted with
variables that would be impacted by monetary correction adjustments:
Figure 2: graphs demonstrate the behavior of financial indicators
Source:
own elaboration, with Economática data.
The graphs represent the behavior of
four financial indicators, being Assets Turnover, Market-to-book, ROA and Debt,
on average, for the sample. The behavior of inflation-adjusted financial
variables is compared to original indicators, originally issued by companies
traded on BOVESPA.
Overall, it is clear
that all indicators estimated based on original values (not-adjusted since
the end of monetary correction) are significantly higher than those adjusted by
inflation.
·
Assets Turnover: The Assets
Turnover corresponds to revenue from sales divided by total assets of each
company for each period. The assets turnover estimated with original data
indicates that the sample companies had higher efficiency using its assets to
generate revenue than when we analyze the same indicator adjusted.
Our
objective is not to draw any conclusions about capital structure or market
value of companies, but rather to use variables that are consensually
influential in both to test the effect of the accumulated inflation on
empirical models containing financial variables extracted from the balance
sheets, that no longer adjust the accounts by inflation since the extinction of
monetary correction in Brazil.
·
ROA: Return on assets demonstrates if a firm
is being effective in implementing its assets. The also called ROA is one of
the most common indicators on fundamental analysis of investment. As in the
work of Gabriel, Neto and Corrar (2005), original values indicated a higher ROA
than when we look at adjusted data. The effects of inflation are not
considered; the analysis signals the company’s operation as more profitable
than actually is.
·
Market-to-book: The market-to-book indicator is the
ratio between market and book value of a company and is also used as a growth
opportunity proxy. The non-adjusted market-to-book suggests that the sample
companies are priced as having many growth opportunities (more than one) in the
majority of the sample. However, after the indicator was adjusted, only during
the year of 2007 the indicator modestly exceeds the value of one. The effect of
inflation, when not considered, also causes the false impression that the
market is evaluating companies as prone to growth, or above their book value.
·
Debt: Debt corresponds to the total gross debt
divided by total assets, for each firm in each period. The original indicator
suggests that companies have higher debt than when we analyze the same
indicator, but adjusted.
The four indicators mentioned are frequently used not only to study the
economic and financial performance of companies, but as part of finance
empirical models.
On
the next session, we estimated some statistical models that contains these
indicators, based on previous academic papers. Our objective is to find out if
not considering the effects of inflation on financial statements can cause
impacts that go beyond individual indicators analysis, affecting coefficients
magnitude and significance of variables in our models. The regressions were
estimated with original financial statements’ data and then with inflation
adjusted data, for comparative purposes.
4.1.
Capital
Structure
Six factors are common
in studies that address capital structure decisions of companies over some
perspective: profitability, risk, size, composition of assets and growth. Based
on previous works as Toy et al. (1974), Ferri and Jones (1979), Titman and
Wessels (1988) and Klock Thies (1992), recent studies such as Perobelli and
Fama (2003), Nakamura et al. (2007), Brito, Corrar and Batistella (2007) and
Gonçalves and Bispo (2012) use regression models that contain these variables
in their definition.
Based on these studies,
we estimated the following panel data model:
Where: DEBT is the ratio between total debt and
total assets; Profit is the total
revenue divided by total assets; FixedAssets
is the total amount of fixed assets related by total assets and Growth is a proxy for growth
opportunities, represented by the market-to-book value of the companies.
The panel data
regression has been estimated with the two samples: first with original data,
and then with adjusted data, considering the precepts of the Monetary
Correction.
The results are
presented in the table below:
Table 4: shows the results of paired mean tests for financial indicators
Coeff. (Std. Error) |
|||
Variables |
Original |
Adjusted |
Effect |
Profitability |
-0,0529*** (0,0136784) |
-0,0473*** (0,0120281) |
- 12% Remains significant |
Risk |
0,0113* (0,0059856) |
0,0083 (0,0059856) |
- 36% Loses significance |
Size |
0,0145** (0,0072379) |
0,0211*** (0,0068808) |
+ 46 % Increases
significance |
Fixed Assets |
-0,0410* (0,0296546) |
-0,1767*** (0,0231159) |
+ 331 % Increases
significance |
Growth |
-0,0183** (0,0081444) |
-0,0204*** (0,007162) |
+ 11% Increases
significance |
R² |
0,0987 |
0,2612 |
+ 165% Increases the
predictive power |
*Significant at 10%;
**Significant at 5%; ***Significant at 1%.
When the model is
estimated with adjusted data, there is a substantial increase on its predictive
power: R² increases 165%. The coefficient magnitude also changes: the variables
Size, Fixed Assets and Growth
explained Debt variation more
intensively, as they have a higher statistical significance. Profitability variable was already
significant at 1% level, but lost a little intensity on its coefficient.
Finally, the Risk variable lost
significance in determining the debt level of companies in our sample.
4.2.
Market
Value of Shares
In order to analyze now
the impacts considering a model focus on market values, we estimated the market
value of companies, based on the following model:
Where: MARKET VALUE is the natural logarithm
(ln) of the total market value of companies; Income is the net income divided by total assets; Debt is the ratio between total debt and
total assets; Risk is the standard
deviation of stock returns; and Profitability
is the net income divided by total assets.
Table 5: results generated by original and adjusted data
Coeff. (Std. Error) |
|||
Variables |
Original |
Adjusted |
Effect |
Income |
-1,0003 (0,6468516) |
1,6534** (0,7444751) |
Change in the signal Becomes significant |
Debt |
1,7845*** (0,4202005) |
3,5978*** (0,4202005) |
+ 107% Remains significant |
Risk |
-0,1260*** (0,0393952) |
-0,0195 (0,0328931) |
- 556% Loses significance |
Size |
0,3897*** (0,0416187) |
0,4715*** (0,0384305) |
+ 21% Remains significant |
Profitability |
0,5340 (0,4611) |
2,3249*** (2,324869 ) |
+ 335% Becomes significant |
R² |
0,2172 |
0,4778 |
+ 120% Increases the predictive power |
*Significant at 10%;
**Significant at 5%; ***Significant at 1%.
Comparing the results
generated by original and adjusted data we can notice an improve on the model
predictive power: 120% increase in the R². It is expected that profitability
indicators (as Income and Profitability) to be positively related
with market value: companies that are more profitable should have significantly
higher market value. The regression containing original data presents the two
variables as not significant when explaining the market value changes of
companies. Profitability showed a
sign contrary to expectations. When we analyze adjusted data instead, the two
variables become significant in explaining the market value of companies and
reveal the expected relationship with the dependent variable of our regression,
that is, positive.
The Risk variable lost significance, while Debt and Size remains significant at 1% level, but presents substantial
increase in the magnitude of their coefficients.
The
recognition of inflationary effects on the variables that composed our models
suggests that considering inflation, separately for i) variables that
automatically adjust price increases, which are market variables, and ii)
variables that were manually adjusted when the Monetary Correction were still
in course in Brazil, lead us to more reliable models, of greater economic and
statistical significance.
5. CONCLUSION
After the stabilization
of the Real Plan, the Law 9,249/ 95 vetoed the use of any monetary adjustment
for financial statements in Brazil. Since then, data from financial statements
do not have a formal adjust that consider the impacts of price changes in
monetary and non-monetary assets or liabilities. Although controlled annually,
in the years that follow the extinction of monetary correction, inflation
reaches a representative cumulative value: 226%.
Researches and
financial analysis have as object of study the financial statements issued by
companies, often comparing the evolution of indicators constructed with
financial data from different companies during large time horizons. It is
necessary to have values measured accurately in order to generate robust and
reliable results.
We
construct two different samples: one composed by financial statements’ data
originally issued by companies and the other one by financial indicators
adjusted based on the precepts of the extinct Monetary Correction.
First, our study compared financial indicators frequently used on research and financial evaluations, made with the two different databases (containing original and adjusted variables). Second, we compared panel regression models estimated with original and adjusted variables, looking for changes in coefficients and predictive power of the models.
As in Kirkulak and Balsari (2009), our results suggest that it is essential to consider inflation effects on accounting variables, even in a stable inflation scenario, because accumulated inflation acts differently on variables that automatically incorporate price changes in their value (market adjusted accounts).
Original financial indicators were significantly higher when compared to adjusted ones, which can lead to wrong conclusions about Assets Turnover, ROA, Growth Opportunities (Market-to-Book) and Debt Level, especially when we analyze samples that cover long periods.
Even more highlighting, panel data regression indicated substantial changes on the coefficients and predictive power of models frequently used on financial studies. The adjusted data led us to results of greater economic significance and reliability.
When analysts or investors analyze accounting data to make decisions, it is essential that inflation-adjusted data is also considered, so that financial indicators are not distorted.
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