TOP INTERNATIONAL BRANDS VALUATION
LOMONOSOV MOSCOW STATE UNIVERSITY, ECONOMICS FACULTY
Abstract: The present paper compares Forbes’ The World’s Most Valuable Brands rating
with the results of the author’s own method of evaluating brands’ yield.
Keywords: brand, yield, stocks, depositary receipt.
JEL Classification: F31, G15, G3, M3
PAVLOV, SERGEY (2014) "TOP INTERNATIONAL BRANDS VALUATION". Journal of Russian Review (ISSN 2313-1578), VOL. (0), 32-36.
An integral part of many companies, brand, is an intangible asset that impacts directly on profitability of a business.
The present paper compares Forbes’ 2012 The World’s Most Valuable Brands rating with the results of its own evaluation based on the analyses of price and price to earnings ratio of ordinary stocks and depositary receipts issued by companies during the same period.
Since the method employed by Forbes differs from the one used in this paper, two questions are bound to arise:
2. Prior Studies
Paving the way for the calculations presented below was the article The Company’s Brand Evaluation on the Base of the Financial Markets’ Instruments (Yandiev, 2007), a study introducing a novel approach to evaluating brand value growth, based on the following premises:
To better appreciate the last premise, take a BMW car as an example. On the one hand, it is a complex mechanical device made of metal and other materials, a vehicle. However, a car by any other manufacturer can satisfy a consumer’s demand for a personal vehicle just as efficiently. Nevertheless, in choosing a BMW car, the consumer does not merely buy an automobile, but also acquires certain emotional benefits linked to the ownership of this particular car: the sense of one’s status, pride, even daring. Satisfying a person’s need to experience these emotions is often more important than satisfying their need for a vehicle itself.
In other words, the car turns out to be merely a package containing the real product: an emotional experience. Consequently, an ordinary stock issued by a company is, in fact, a ‘portfolio’ of two notional financial assets, one issued to produce a ‘package product’, the other to produce an ‘emotion product’.
Proceeding from these premises, the study introduced the following model of evaluating the brand yield:
The pivotal question of the study was how to calculate w. Another article, Yield of Brand (Mishin, 2011), suggested a correlation coefficient of common share to depositary receipt yield:
Therefore brand yield could be presented as
The method may be critized for the use of same weighing coefficient for all of the companies analyzed, while individual cases might have required adjustments to the value. This results in a marked variation among the resulting figures, as will be shown below. However, individual adjustments can be omitted to keep the technique universally applicable.
This study analysed 28 companies from Forbes’ The World’s Most Valuable Brands list. We selected the companies that had depositary receipts on the foreign market at the beginning of 2012.
The data for the calculations were obtained from common share quotations on the domestic market as of January 2012 and 2013, provided by Bloomberg’s online service, and also depositary receipts quotations for the same dates, provided by J.P. Morgan’s adr.com online service.
The resulting equation of linear regression will help to determine how significant the relationship between the dependent and the explanatory variables is. It will also be possible to plot real values and estimates and see how accurate the model is in predicting dependent variable values.
The analysis was made using the Gretl econometrics package.
The first method had placed the correlation coefficient at
Evaluating brand yield is:
The resulting equation enabled the author to calculate the estimated brand yield values and compare them with Forbes’ figures:
|Brand||Brand value growth (FORBES)||Estimated growth|
This table shows that the results of the method employed in the present paper (Estimated growth) and the estimates given by Forbes differ considerabbly, which is indicated by the coefficient of the correlation between the two sets:
Let’s now turn to the analysis of the regression relationship between the variables FORBES и Estimated_growth.
The first model to run: FORBES is dependent on Estimated_growth
Null hypothesis: β=0; Gretl gives the following analysis report:
It is clear that Estimated_growth’s coefficient evaluation is only significant at the 10% level of significance, and the constant’s evaluation is almost totally insigificant. This shows that the quality of the model is not very high, which makes the plotting of observable values and the estimates hardly necessary.
The second (inverse) model to run; Estimated_growth is dependent on Forbes.
Forbes’ coefficient evaluation is significant at the 10% level of significance, and the constant’s evaluation is significant even at the 1% level. This model is of higher quality than the previous one, therefore it makes sense to check the graph.
The dual linear regression equation for this relationship is as follows: