Impact of Debt on Profitability
Most companies are interested in knowing how their current or future
debt will impact their profitability or if it would impact profitability
at all. Some ways of doing this is by conducting some regression
analysis on historical data. Regression analysis can determine if there
is a relationship between different variables and can also help to
forecast what the future might bring if the pattern continues. Although
regression analysis is a valuable tool it is not the only tool that is
used and is often used in conjunction with other types of analysis. If
we use the following data in Exhibits A and B we can see how debt can
potentially impact profitability. Exhibit A is based on the industrial
sector and B is based on the food sector.
Analysis
At first glance, it is clear that as current liabilities increased so did gross profit. This could be an indication that current liabilities were being used to buy more products or find ways to decrease the cost of producing a product. Current liabilities are liabilities that need to be paid off within one year of occurrence. This information can also be an indication that liabilities are being obtained effectively. The same can be said for non-current liabilities. A cursory glance shows that as non-current liabilities increase so does gross profit. Non-current liabilities can be loans such as business loans and mortgages that are due to be paid more than a year from occurrence. The increase of this as well as the increase in gross profit can indicate a direct correlation. This is indicated in the following excel results and scatter graph.
Correlation Current Liabilities vs. Gross Profit 0.847380003
Correlation Non-Current Liabilities vs. Gross Profit 0.829432866
The correlations of non-current liabilities and current liabilities to gross profit have a strong positive relationship. In correlation analysis it is understood that the closer to 1.0 a relationship is the stronger the relationship. In this case .847 and .829 respectively indicate very strong positive relationships meaning that as one increases it is likely that the other would increase as well. The results can tell a company’s management that they should increase their liabilities to increase gross profits however it can also be shown that analyzing only one or two variables in the data can be too simple of an analysis. Sometimes more than one variable in data needs to be analyzed to provide more accurate results. In the graphs below we see a simple correlation analysis of return on assets and equity to non-current liabilities and current liabilities. The two graphs are but a sample of the results and similar results would be seen if comparing the two types of liabilities with return on equity.
It is clear that these correlations are more volatile and unpredictable therefore it can be said that there is a negative correlation between them but there is not a strong correlation between liabilities and return on assets or equity. If we did a formula in excel for these we will get the following results:
Correlation Return on Assets vs. Non Current -0.088362265
Correlation Return on Assets vs. Current -0.026411956
Correlation Return on Equity vs. Non Current 0.078610395
Correlation Return on Equity vs. Current 0.135380361
The results show that there is a weak negative correlation between return on assets and liabilities and a weak positive correlation between the return on equity and the liabilities. The return on assets is greatly dependent upon the profit of the company and according to the correlation as the non-current and current liabilities increase so does the profit. Therefore it can be deduced that liabilities have an impact on the return on assets and on equity. However due to the volatility of the information it can be said that there is a weak relationship between them and management should not put much faith in this analysis.
If we move on to the Food sector we can also notice some correlations between the current and non-current liabilities and the gross profit, return on assets, and return on equity.
As we can see here there is a strong positive correlation between liabilities and gross profit but as we move into some other information such as Return on Equity and Assets we get into weak correlations. The graphs would look similar to the ones in the Industrial sector and the same conclusions could be drawn. The simple regression analysis showed a strong correlation between two variables. Multiple regression analysis can show us if there is a relationship between multiple variables in this case gross profit as it related to non-current and current liabilities.
A quick analysis in excel yields the following results:
In these results we can see many different things going on. For both examples the gross profit was the dependent variable and non-current and current liabilities were the independent variables. The analysis is to determine if there is any relationship between the liabilities and the gross profit and it will show us if the analysis is reliable or not. If we take a look at Significance F we will know if the model we used is significant enough or not based on the value. In these cases the values are .0786 and .000629 and a significance level of .05 or less is considered significant and from .05 to .10 it is marginal meaning it may or may not be significant. As we can see the .0786 from the industrial sector indicates marginal significance but the .000629 from the food sector indicates a high level of significance.
Next we would take a look at the coefficients and if they are positive than we have a positive relationship but if they are negative than the relationship is negative. We can also see that sometimes outliers can move the information towards one way or another. Outliers are the information that is not normal and usually only happen on occasion. In this case those outliers would be a significant revenue loss due to a natural disaster or a lawsuit or even a significant revenue increase due to a natural disaster or lawsuit and this is something that would not be expected to occur during the normal course of business.
Companies like to know what is driving their profits, let’s face it
if a company did not make a profit they would not be in business very
long. If a company can know what makes their profits go up and down
then they can make adjustments as needed and in a timely manner.
Regression analysis gives them one way of doing this. Simple
regression analysis can tell you if there is a correlation between two
variables and in our examples we used gross profit and liabilities both
current and non-current. A simple analysis showed that there was a
strong relationship between gross profit and liabilities however there
was a weak relationship between liabilities and return on assets and
equity. We compared a dependent variable to one independent variable
but in multiple regression analysis you can compare a dependent variable
to multiple independent variables. If we had the necessary data we
could have also compared certain expenses such as advertising along with
the liabilities to see if they had any type of relationship with gross
profit. We need to also take into consideration the impact of various
variables on the income statement or balance sheet from an accounting
viewpoint. In accounting, the assets and liabilities affect the balance
sheet. When a liability increases normally cash increases due to
obtaining a loan or the inventory can increase due to the purchase of
inventory on account or by using cash from a loan. If we consider these
factors then we might not expect a strong relationship between
liabilities and gross profit.
Another thing to consider would be the over analyzing of historical data. Sometimes the information is black and white meaning that there is not an underlying reason for why the results are as such however in an effort to understand the data and predict the future, management can find themselves running into the problem of subjecting the data to analysis that is not necessary. Regression analysis requires the use of a series of data and not just one or two years since it holds true that the more data that you have the more accurate the results. If this had been done in this case we would have seen a weaker correlation between the gross profit and liabilities. The data can also show that liabilities were being increased and used to directly impact the revenue of the company. Say for instance a loan was taken out to upgrade machinery that allowed a company to produce products faster and more efficiently. This could mean that more products were being sold, increasing revenue, lowering costs, and thereby increasing the profit. The same can be said if the company used the increase in liability to purchase land, a building, or some other asset that would take time to begin to impact the profit margins. For example, a company takes out a loan to purchase a building for a new division they wish to set up that will generate more revenue however the first few years they will not see much revenue coming from the new division because they would be in the planning and setup stages. This could lead to an up and down trend of revenue and profit that would change the data based on the actual information.
Another factor that comes into play when using regression or any type of analysis is the misinterpretation of the results. If 1.0 was considered to be the threshold of significance this would change the interpretation of the data. Also there could be a misinterpretation of whether or not the analysis is significant or not in trying to determine a relationship. If we were to assume that the data indicates a low level of significance then we might discount the results and not give them any merit. Regression analysis can be used to tell us many different things but with any analysis the significance comes from the interpretation of the results.
Analysis
At first glance, it is clear that as current liabilities increased so did gross profit. This could be an indication that current liabilities were being used to buy more products or find ways to decrease the cost of producing a product. Current liabilities are liabilities that need to be paid off within one year of occurrence. This information can also be an indication that liabilities are being obtained effectively. The same can be said for non-current liabilities. A cursory glance shows that as non-current liabilities increase so does gross profit. Non-current liabilities can be loans such as business loans and mortgages that are due to be paid more than a year from occurrence. The increase of this as well as the increase in gross profit can indicate a direct correlation. This is indicated in the following excel results and scatter graph.
Correlation Current Liabilities vs. Gross Profit 0.847380003
Correlation Non-Current Liabilities vs. Gross Profit 0.829432866
The correlations of non-current liabilities and current liabilities to gross profit have a strong positive relationship. In correlation analysis it is understood that the closer to 1.0 a relationship is the stronger the relationship. In this case .847 and .829 respectively indicate very strong positive relationships meaning that as one increases it is likely that the other would increase as well. The results can tell a company’s management that they should increase their liabilities to increase gross profits however it can also be shown that analyzing only one or two variables in the data can be too simple of an analysis. Sometimes more than one variable in data needs to be analyzed to provide more accurate results. In the graphs below we see a simple correlation analysis of return on assets and equity to non-current liabilities and current liabilities. The two graphs are but a sample of the results and similar results would be seen if comparing the two types of liabilities with return on equity.
It is clear that these correlations are more volatile and unpredictable therefore it can be said that there is a negative correlation between them but there is not a strong correlation between liabilities and return on assets or equity. If we did a formula in excel for these we will get the following results:
Correlation Return on Assets vs. Non Current -0.088362265
Correlation Return on Assets vs. Current -0.026411956
Correlation Return on Equity vs. Non Current 0.078610395
Correlation Return on Equity vs. Current 0.135380361
The results show that there is a weak negative correlation between return on assets and liabilities and a weak positive correlation between the return on equity and the liabilities. The return on assets is greatly dependent upon the profit of the company and according to the correlation as the non-current and current liabilities increase so does the profit. Therefore it can be deduced that liabilities have an impact on the return on assets and on equity. However due to the volatility of the information it can be said that there is a weak relationship between them and management should not put much faith in this analysis.
If we move on to the Food sector we can also notice some correlations between the current and non-current liabilities and the gross profit, return on assets, and return on equity.
As we can see here there is a strong positive correlation between liabilities and gross profit but as we move into some other information such as Return on Equity and Assets we get into weak correlations. The graphs would look similar to the ones in the Industrial sector and the same conclusions could be drawn. The simple regression analysis showed a strong correlation between two variables. Multiple regression analysis can show us if there is a relationship between multiple variables in this case gross profit as it related to non-current and current liabilities.
A quick analysis in excel yields the following results:
In these results we can see many different things going on. For both examples the gross profit was the dependent variable and non-current and current liabilities were the independent variables. The analysis is to determine if there is any relationship between the liabilities and the gross profit and it will show us if the analysis is reliable or not. If we take a look at Significance F we will know if the model we used is significant enough or not based on the value. In these cases the values are .0786 and .000629 and a significance level of .05 or less is considered significant and from .05 to .10 it is marginal meaning it may or may not be significant. As we can see the .0786 from the industrial sector indicates marginal significance but the .000629 from the food sector indicates a high level of significance.
Next we would take a look at the coefficients and if they are positive than we have a positive relationship but if they are negative than the relationship is negative. We can also see that sometimes outliers can move the information towards one way or another. Outliers are the information that is not normal and usually only happen on occasion. In this case those outliers would be a significant revenue loss due to a natural disaster or a lawsuit or even a significant revenue increase due to a natural disaster or lawsuit and this is something that would not be expected to occur during the normal course of business.
Another thing to consider would be the over analyzing of historical data. Sometimes the information is black and white meaning that there is not an underlying reason for why the results are as such however in an effort to understand the data and predict the future, management can find themselves running into the problem of subjecting the data to analysis that is not necessary. Regression analysis requires the use of a series of data and not just one or two years since it holds true that the more data that you have the more accurate the results. If this had been done in this case we would have seen a weaker correlation between the gross profit and liabilities. The data can also show that liabilities were being increased and used to directly impact the revenue of the company. Say for instance a loan was taken out to upgrade machinery that allowed a company to produce products faster and more efficiently. This could mean that more products were being sold, increasing revenue, lowering costs, and thereby increasing the profit. The same can be said if the company used the increase in liability to purchase land, a building, or some other asset that would take time to begin to impact the profit margins. For example, a company takes out a loan to purchase a building for a new division they wish to set up that will generate more revenue however the first few years they will not see much revenue coming from the new division because they would be in the planning and setup stages. This could lead to an up and down trend of revenue and profit that would change the data based on the actual information.
Another factor that comes into play when using regression or any type of analysis is the misinterpretation of the results. If 1.0 was considered to be the threshold of significance this would change the interpretation of the data. Also there could be a misinterpretation of whether or not the analysis is significant or not in trying to determine a relationship. If we were to assume that the data indicates a low level of significance then we might discount the results and not give them any merit. Regression analysis can be used to tell us many different things but with any analysis the significance comes from the interpretation of the results.
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