AnalysisoftheCompanysFinancialStrategy

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2.3 Regression analysis of dairy companies

Within the external environment analysis for a certain company such method as regression may be used41. For that I set up regression equation which consisted of financial coefficients. For that reason, I collected data from financial results of 571 companies in 2015 with the use of Kontur.Focus. Among the analyzed factors there were total revenue, net profit, ROS, ROA and ROE. In 2014, after embargo was imposed, a lot of companies specialized in imported from EU products’ distribution were on the brink of financial collapse. Others, who were able to stay afloat, were incredibly damaged. Among the most attackable sides were profitability ratios and for that reason they are primarily analyzed as explanatory variables.

The tentative regression equation is as follows:

NP = a + bTR + cROA + dROE + eROS, where (1) NP – net profit (dependent variable)

a, b, c – constants, TR – total revenue,

ROA – return on assets,

ROE – return on equity,

ROS – return on sales.

41 48.Piasecki, B. (2000). Describing the Full Spectrum of Corporate Environmental Strategy. Corporate Environmental Strategy, 7(1), 1-3. doi:10.1016/S1066-7938(00)80110-4

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Figure 2.3.1 Box plot for net profit, rub

According to 2015 result, net profit (loss) values varies from -257 million rubles to 124 million rubles (Figure 2.3.1). As the outlying cases are probably significant, they were not excluded from the analysis. Before setting up regression equation, I checked dependent variable for normal distribution.

Figure 2.3.2 Density plot for net profit, thousands of rub

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Graphic analysis showed that the variable has abnormal distribution and other distribution tests were omitted. In order to get the distribution to the normal, I transferred it into logarithm form. It also made in order to measure the impact of explanatory variables in percentage. After the logarithm creation, I got the following distribution.

Figure 2.3.3 Density plot for net profit after logarithm transformation, thousands of rub

The graph shows that the distribution stayed a little abnormal, yet it is extremely closed to the normal one, so the transformation brought positive results and it would be used. The Skewness/Kurtosis test witness the hypothesis of normal distribution cannot be rejected at least within 8% significance. There was no multicollinearity, heteroscedasticity, Ramsey test showed model do not suffer from omitted variables, model had homoscedasticity. I did the coefficients capability test transformed in logarithm all the numerous coefficients. After all the tests and model checks, the final regression equation was as follows:

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lNP = 1.472 + 0.862lTR – 0.087lROA + 0.351lROE + 0.932lROS

(2)

This equation (Table 2.3.1) showed that:

•Each percent of revenue increase brings 86.2% of net profit growth;

•Each percent of ROA decrease brings 8.7% of net profit increase;

•Each percent of ROE increase brings 35.1% of net profit increase;

•Each percent of ROS increase brings 93.2% of net profit increase;

Table 2.3.1

Stata output with the final regression model

----------------------------

	(1)
	lnp
----------------------------
lros	0.932***
	(0.0359)
lroa	-0.0874**
	(0.0294)
lroe	0.351***
	(0.0491)
ltr	0.862***
	(0.0238)
_cons	1.472***
	(0.373)
----------------------------
N	444
adj. R-sq	0.832
AIC	1195.1
BIC	1215.6

----------------------------

Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

ROA increase in line with net profit drop demonstrates the critical fall of assets, faster than profit. This paradox is more likely to be exception to the rules than logic tendency. All in all, this equation may be considered as a recommended

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for Russian dairy companies corrected to the fact that assets amount should not be decreased on purpose.

Besides the regression analysis conducted within the whole dairy branch the regression equations showing the dependence between several coefficients can be also set up for forecast financial performance of a certain company.

In order to conduct an equation, I collected data from 2008 up to 2015. The interval was 1 month and was 96 observations. For conducting an experiment, I collected data for the following factors:

•Total revenue (TR, dependable variable);

•Net profit (PR, supposed dependable variable);

•Number of debtors (ND);

•Amount of retro-bonuses (RB);

•Amount of trade terms (TT);

•Number of retailers who conducted listing of new products (LIST);

•Number of retailers who conducted «yellow discounts» (YD);

•Number of retailers who provided catalogue with products’ image (CB);

•Number of retailers who provided indoor/outdoor products’ image (BADV);

•Products’ number (PN);

•Staff bonuses (SB);

•Commercial staff bonuses (CB).

Firstly, it was decided to evaluate the impact of explanatory variables on net profit. However, it failed as the variable occurred to be insignificant, explanatory variables had no influence on the variable and the model in whole was statistically insignificant. The fact that variable could be changed was obvious at the step of

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multicollinearity test when during pair correlation matrix setting up no explanatory variables had a dependent with net profit.

For that reason, the dependable variable was changed to total revenue. I advocated the change driven by the fact that the revenue increase would anyway lead to net profit increase (in case the company is experienced and has correct costs policy) The tentative regression equation is as follows:

TR = a + bND + cRB + dTT + eLIST + fYD + gCB + hBADV + iPN + jSB + kCB, (1)

where a, b, c, d, e, f, g, h, i, j, k – constants.

As similar as branch regression, the dependable variable must be checked for normal distribution.

Figure 2.3.4 Density plot for revenue, thousands of rub

Graphic analysis showed that the variable has abnormal distribution and other distribution tests were omitted. In order to get the distribution to the normal, I transferred it into logarithm form. It also made in order to measure the impact of

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explanatory variables in percentage. After the logarithm creation, I got the following

distribution (Figure 2.3.5).

Figure 2.3.5 Density plot for net profit after logarithm transformation, thousands of rub

The graph shows that the distribution stayed a little abnormal, yet it is extremely closed to the normal one, so the transformation brought positive results and it would be used. The Skewness/Kurtosis test witness the hypothesis of normal distribution cannot be rejected at least within 5% significance. There was no multicollinearity, heteroscedasticity, Ramsey test showed model do not suffer from omitted variables, model had homoscedasticity. I did the coefficients capability test transformed in logarithm all the numerous coefficients.

After all the tests and model checks, the final regression equation was as follows:

lTR = 17.174 + 0.0688lRB + 0.136lTT – 0.0017ND + 0.0027PN – 0.0498lCB

(2)

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This equation shows that:

•The 1% increase in retro-bonuses paid to a debtor leads to 6.9% revenue increase;

•The 1% increase in trade terms paid to a debtor leads to 13.6% revenue increase;

•The 1 client decrease leads to 0.17% revenue increase;

•The 1 item increase of products leads to 0.27% revenue increase;

•The 1% decrease of commercial staff bonuses and rewards leads to 4.98% revenue increase.

Table 2.3.2

Stata output with the final regression model

----------------------------

	(1)
	ltr
----------------------------
lrb	0.0688*
	(0.0271)
ltt	0.136***
	(0.0369)
lcb	-0.0498**
	(0.0179)
number_of_d~t	-0.00171***
	(0.000403)
number_of~pts	0.00272***
	(0.000703)
_cons	17.17***
	(0.647)
----------------------------
N	73
adj. R-sq	0.798
AIC	173.8
BIC	197.8

----------------------------

Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.00

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The final results are presented in Table 2.3.2 can be interpreted as follows. First off, the retro-bonuses paid to debtors are discussed within contact. Generally, it is a fixed percentage from the entire commodity turnover. Due to this, counteragents are aimed at increasing their turnover in order to get as high payback as possible at the end of the month or a quarter. Trade terms are formed on the same basis. The inverse dependence with revenue and number of debtors can be explained with company size as managers need more free time to work with key clients. Product line expenditure always lead to revenue increase in long-term perspective as it leads to sales amount growth. However, the inverse dependence between the commercial staff bonuses and revenue is not so obvious, yet it is explanative. The data for revenue collected from each month are actual while the bonuses are retrospective based on previous month work results. This equation can be used by each wholesale company while regulating their costs and the influence estimation of their reduction on revenue.

This chapter consist of two main regression analysis, first one belongs to dairy branch companies financial results, I set up regression equation which consisted of financial coefficients. Among the analyzed factors there were total revenue, net profit, ROS, ROA and ROE. The most attackable sides were profitability ratios and for that reason they are primarily analyzed as explanatory variables, the second equation belongs to FrieslandCampina and the dependable variable was total revenue.

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CHAPTER 3. RECOMMENDATIONS FOR PERFORMANCE

IMPROVEMENTS

3.1 Assets liquidity increasing and costs improvement

Assets liquidity increasing strategy is related to net assets value growth as a crucial requirement for company’s financial improvement. It must start with financial reorganization, then increasing of net assets value in line with costs decrease and profit growth, then ends with best financial coefficients characterizing profitable and strong business42.

To develop several steps for this stage, I conducted financial analysis and calculated liquidity ratio. Through these formulas, I provide recommendations directly by numerator and denominator analysis.

First, current assets must be increased significantly. This means that the company must hit all the elements that are important for ongoing business activities. To develop this financial aspect, the company must:

1)Accelerate the process of inventories realization;

2)Run marketing campaigns;

3)Reduce the time of restitution of accounts receivable;

4)Decrease the return time of accounts receivable;

5)Set the limit for overdue receivables (<5% from total receivables);

6)Decrease accounts payable;

7)Change raw materials supplier without quality loss;

8)Tight loop production duration and reduce its time;

9)Cancellation of Commercial lending.

To achieve these conditions the company can take these steps:

42 44.Pagano, M. (1989, May). Trading Volume and Asset Liquidity. The Quarterly Journal of Economics, 104(2), 255274. doi:10.2307/2937847

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